November 6, 2013 board packet

Our Mission is to protect, manage and improve the water resources of Capitol Region Watershed District

Regular Meeting of the Capitol Region Watershed District (CRWD) Board Of Managers, for Wednesday,

November 6, 2013 6:00 p.m. at the office of the CRWD, 1410 Energy Park Drive, Suite 4, St. Paul, Minnesota.

REGULAR MEETING AGENDA

I. Call to Order of Regular Meeting (President Joe Collins)

A) Attendance

C) Review, Amendments and Approval of the Agenda

II. Public Comment – For Items not on the Agenda (Please observe a limit of three minutes per person.)

III. Permit Applications and Program Updates (Permit Process: 1) Staff Review/Recommendation, 2) Applicant Response, 3) Public Comment, and 4)

Board Discussion and Action.)

A) Permit # 13-019 Hamline Station (Kelley)

B) Permit # 13-028 Loomis Armored Transport (Kelley)

C) Permit # 13-030 Western U Plaza (Kelley)

D) Permit # 13-031 US Bank Demolition (Kelley)

E) Permit Program/Rules Update (Kelley)

IV. Special Reports A) Summary and Analysis of Water Quality Data from the Capitol Region Watershed District’s

Stormwater Monitoring Program, 2005-2012, Benjamin D.Janke, Ph.D, University of Minnesota

B) Statistical Analysis of Lake Data in the Capitol Region Watershed District, Joe Bischoff, Wenck

Associates, Inc.

V. Action Items

A) AR: Approve Minutes of the October 16, 2013 Regular Meeting (Sylvander)

B) AR: Approve Contract Amendment #4 with Wenck Associates Inc. for the Highland Ravine

Project (Eleria)

C) AR: Establish the Monitoring, Research and Maintenance Division (Doneux)

D) AR: Approve Program Manager III Position (Doneux)

E) AR: Approve 2014 Employee Health Insurance Program (Doneux)

VI. Unfinished Business

A. FI: Inspiring Communities Program Update (Eleria and Castro)

VII. General Information

A) Administrator’s Report

VIII. Next Meeting

A) Wednesday, November 20, 2013 Meeting Agenda Review

IX. Adjournment W:\04 Board of Managers\Agendas\2013\November 6, 2013 Agenda Regular Mtg.docx

Materials Enclosed

Capitol Region Watershed District Permit 13-019 Hamline Station

Permit 13-019 Board Meeting: 11/06/13

Aerial Photo

Applicant: Chris Dettling Consultant: David Bade PPL, Inc. Westwood Professional Services 1035 East Franklin Avenue 7699 Anagram Drive Minneapolis, MN 55404 Eden Prairie, Minnesota 55344 Description: Construction of a new Commercial/Residential redevelopment at the former Midway Chevrolet Property at Hamline and University Stormwater Management: Underground infiltration gallery District Rule: C, D, F, Disturbed Area: 2.0 Acres Impervious Area: 1.84 Acres Recommendation: Approve with 5 Conditions

Permit Location

University Avenue

Ham

line Avenue

1. Receipt of $9,200 surety and signed maintenance agreement. 2. Submit a copy of NPDES permit. 3. Remove geotextile fabric from bottom of infiltration system detail on Sheet C6. Geotextile shall be placed on top and sides only. 4. Specify non-limestone rock for the clean washed angular aggregate surrounding the StormTech system 5. Revise detail GRD-13 on Sheet C6. The metal posts installed 36” deep at the paver interface have potential to punct ure the impermeable liner beneath the structural soil, and dislodge pavers. Provide alternative tree protection fence anchoring device such as sand bags on cross members or other alternative approved by City of St. Paul and CRWD.

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Capitol Region Watershed District Permit Report

CRWD Permit #: 13-019 Review date: October 29, 2013 Project Name: Hamline Station Applicant: Chris Dettling PPL, Inc. 1035 East Franklin Ave. Minneapolis, MN 55404 Purpose: Redevelopment of the former Midway Chevrolet. Location: North side of University Avenue between Hamline Ave and

Syndicate St. Applicable Rules: C, D, and F Recommendation: Approve with 5 Conditions EXHIBITS:

1. Stormwater Management Report, by RLK, dated 10/28/13, recd. 10/28/13. 2. CRWD Volume Control Worksheet, recd. 6/19/13. 3. Declaration for Maintenance of Stormwater Facilities (unsigned), not dated, recd.

6/19/13. 4. Project plans (C1-C6, L1, L2), by ESG and Westwood, dated 10/3/13, recd.

10/28/13. HISTORY & CONSIDERATIONS: None. RULE C: STORMWATER MANAGEMENT

Standards Proposed discharge rates for the 2-, 10-, and 100-year events shall not exceed

existing rates. Developments and redevelopments must reduce runoff volumes in the amount

equivalent to an inch of runoff from the impervious areas of the site. Stormwater must be pretreated before discharging to infiltration areas to

maintain the long-term viability of the infiltration area.


Developments and redevelopments must incorporate effective non-point source pollution reduction BMPs to achieve 90% total suspended solid removal.

Findings 1. A hydrograph method based on sound hydrologic theory is used to analyze

runoff for the design or analysis of flows and water levels. 2. Runoff rates for the proposed activity do not exceed existing runoff rates for

the 2-, 10-, and 100-year critical storm events. Stormwater leaving the project area is discharged into a well-defined receiving channel or pipe and routed to a public drainage system.

3. Stormwater runoff volume retention is achieved onsite in the amount equivalent to the runoff generated from one inch of rainfall over the impervious surfaces of the development.

a. The amount of proposed impervious onsite is 80,355 square feet. b. Volume retention: Volume Retention Required (cu. ft.) Volume Retention Provided (cu. ft.)

6,027 Underground Storage 6,477

c. Banking of excess volume retention is not proposed. d. Infiltration volume and facility size has been calculated using the

appropriate hydrological soil group classification and design infiltration rate.

e. The infiltration area is capable of infiltrating the required volume within 48 hours.

f. Stormwater runoff is pretreated to remove solids before discharging to filtration areas.

4. Alternative compliance sequencing has not been requested. 5. The proposed underground storage system achieves 90% total suspended

solids removal from the runoff generated on an annual basis. 6. A recordable executed maintenance agreement has not been submitted.

RULE D: FLOOD CONTROL

Standards Compensatory storage shall be provided for fill placed within the 100-year

floodplain. All habitable buildings, roads, and parking structures on or adjacent to a

project site shall comply with District freeboard requirements. Findings 1. There is no floodplain on the property according to FEMA. 2. All habitable buildings, roads, and parking structures on or adjacent to the

project site comply with CRWD freeboard requirements.


RULE E: WETLAND MANAGEMENT Standard

Wetlands shall not be drained, filled (wholly or in part), excavated, or have sustaining hydrology impacted such that there will be a decrease in the inherent (existing) functions and values of the wetland.

A minimum buffer of 25 feet of permanent nonimpacted vegetative ground cover abutting and surrounding a wetland is required.

Findings 1. There are no known wetlands located on the property.

RULE F: EROSION AND SEDIMENT CONTROL

Standards A plan shall demonstrate that appropriate erosion and sediment control

measures protect downstream water bodies from the effects of a land-disturbing activity.

Erosion Control Plans must adhere to the MPCA Protecting Water Quality in Urban Areas Manual.

Findings 1. Erosion and sediment control measures are consistent with best management

practices, as demonstrated in the MPCA manual Protecting Water Quality in Urban Areas.

2. Adjacent properties are protected from sediment transport/deposition. 3. Wetlands, waterbodies and water conveyance systems are protected from

erosion/sediment transport/deposition. 4. Project site is greater than 1 acre; an NPDES permit is required.

RULE G: ILLICIT DISCHARGE AND CONNECTION

Standard Stormwater management and utility plans shall indicate all existing and

proposed connections from developed and undeveloped lands for all water that drains to the District MS4.

Findings 1. New direct connections or replacement of existing connections are not

proposed. 2. Prohibited discharges are not proposed.

RECOMMENDATION: Approve with 5 Conditions Conditions:

1. Receipt of $9,200 surety and documentation of recorded maintenance agreement. 2. Submit a copy of NPDES permit.


3. Remove geotextile fabric from bottom of infiltration system detail on Sheet C6. Geotextile shall be placed on top and sides only.

4. Specify non-limestone rock for the clean washed angular aggregate surrounding the StormTech system

5. Revise detail GRD-13 on Sheet C6. The metal posts installed 36” deep at the paver interface have potential to puncture the impermeable liner beneath the structural soil, and dislodge pavers. Provide alternative tree protection fence anchoring device such as sand bags on cross members or other alternative approved by City of St. Paul and CRWD.

PROPOSED WEST BUILDINGFFE=925.0-924.0 (SLOPED FLOOR)

PROPOSED EAST BUILDINGFFE=924.0

1309 HOUSING1311-1337 RETAIL

1305 HOUSING

UTILITY NOTES

LEGEND EXISTINGPROPOSED

Capitol Region Watershed District Permit Application 13-028 Loomis Armored Transport

Permit Report 13-028 November 6, 2013 Board Meeting

Aerial Photo

Applicant: Herbert Tousley Consultant: Mike Kettler Ironton Management Sunde Engineering 332 Minnesota Street, Suite W2300 10830 Nesbitt Avenue St. Paul, MN 55101 Bloomington, MN 55437 Description: Construction of a new building and parking lot within the Beacon Bluff redevelopment. Stormwater Management: Stormwater pretreatment pond and filtration bench District Rule: C, D, and F Disturbed Area: 3.2 Acres Impervious Area: 3.52 Acres RECOMMENDATION: Approve with 3 Conditions

Permit Location

East Seventh Street

For

est S

t

1. Receipt of $17,600 surety and documentation of recorded maintenance agreement. 2. Provide a copy of the NPDES permit. 3. Revise pond outlet #4 in the HydroCAD model. Currently, the filtration draintile is modeled as a 6-inch orifice which results in a flow rate of 1.73 cfs during the 100-year event. Assuming a 1.0 in/hr filtration rate, however, results in a peak “filter flow” of 0.1 cfs based on a filter area of 4,546 square feet. Adjust high water and overflow elevations as necessary.

W:\07 Programs\Permitting\2013\13-028 Loomis Amored Transport\13-028 Permit_Review3.doc Page 1 of 4


CRWD Permit #: 13-028 Review date: October 28, 2013 Project Name: Loomis Armored Transport Applicant: Mr. Herbert Tousley Ironton Management 332 Minnesota St, Suite W2300 St. Paul, MN 55401 Purpose: Construction of new building, parking lot, and filtration pond Location: North of the intersection of East Seventh Street and Cypress Street. Applicable Rules: C, D, and F Recommendation: Approve with 3 Conditions EXHIBITS:

1. Stormwater Management Calculations, by Sunde Engineering, PLLC., dated 10/28/13, recd. 10/28/13.

2. Schematic Design Plans (sheets C1, C2, C3, C4, C5, C6, and C7), by Sunde Engineering, dated 10/28/13, recd. 10/28/13.

HISTORY & CONSIDERATIONS: None. RULE C: STORMWATER MANAGEMENT




maintain the long-term viability of the infiltration area. Developments and redevelopments must incorporate effective non-point

source pollution reduction BMPs to achieve 90% total suspended solid removal.





3. Stormwater runoff volume retention is not achieved onsite in the amount equivalent to the runoff generated from one inch of rainfall over the impervious surfaces of the development.


13,625 None, filtration is proposed.

c. Filtration is proposed due to contaminated soils: Volume Retention Required (cu. ft.) Volume Retention Provided (cu. ft.)

17,713 18,984

d. Banking of excess volume retention is not proposed. e. Filtration volume and facility size has been calculated using the

appropriate hydrological soil group classification and design filtration rate.

f. The filtration areas are capable of filtering the required volume within 48 hours.

g. Stormwater runoff is pretreated to remove solids before discharging to filtration areas.

4. Alternative compliance sequencing has not been requested. 5. Best management practices achieve 90% total suspended solids removal on an

annual basis. 6. A recordable executed maintenance agreement has not been submitted.

RULE D: FLOOD CONTROL

Standards Compensatory storage shall be provided for fill placed within the 100-year



project site comply with CRWD freeboard requirements.


RULE E: WETLAND MANAGEMENT Standard


















1. Receipt of $17,600 surety and documentation of recorded maintenance agreement.

2. Provide a copy of the NPDES permit. 3. Revise pond outlet #4 in the HydroCAD model. Currently, the filtration draintile

is modeled as a 6-inch orifice which results in a flow rate of 1.73 cfs during the


100-year event. Assuming a 1.0 in/hr filtration rate, however, results in a peak “filter flow” of 0.1 cfs based on a filter area of 4,546 square feet. Adjust high water and overflow elevations as necessary.

Capitol Region Watershed District Permit Application 13-030 Western U Plaza


Aerial Photo

Applicant: St. Paul Old Home Plaza, LLC Consultant: Robert Wiegert PO Box 727, 366 South Tenth Avenue Paramount Engineering Waite Park, MN 556387 1440 Arcade Street North St. Paul, MN 55106 Description: Redevelopment and reuse of former Old Home property at Western and University Stormwater Management: Underground infiltration District Rule: C,D, and F Disturbed Area: 1.6 Acres Impervious Area: 1.03 Acres

Permit Location

1. Provide documentation that the maintenance agreement has been recorded with Ramsey County. 2. Remove geotextile fabric from bottom of infiltration system detail on Sheets C6 and C7. Geotextile shall be placed on top and sides only. 3. Specify non-limestone rock for the clean washed angular aggregate surrounding the StormTech system. 4. Revise HydroCAD model to include the portion of Area 4 (new building) that is draining to the intersection of University Ave and Virginia Street. 5. Remove the 6” drain tile from the underground infiltration system. VOLUME BANK RECOMMENDATION: Approve creation of a volume bank for the Sand Companies and deposit of 4,844 cubic feet of volume reduction credits.

University Avenue

Wes

tern

Ave

nue

W:\07 Programs\Permitting\2013\13-030 Western U Plaza\13-030 Permit_Review_02.doc Page 1 of 4


CRWD Permit #: 13-030 Review date: October 28, 2013 Project Name: Western U Plaza Applicant: St. Paul Old Home Plaza, LLC PO Box 727, 366 South Tenth Avenue Waite Park, MN 56387-0727 Purpose: Demolition of a portion of existing building and addition of

parking structure, apartment complex, and underground infiltration system.

Location: Southeast corn of the intersection of University Avenue West and

Western Avenue. Applicable Rules: C, D, E, and F Recommendation: Approve with 5 Conditions Volume Bank Recommendation: Approve creation of a volume bank for the Sand Companies and deposit of 4,844 cubic feet of volume reduction credits. EXHIBITS:

1. Western U Plaza Storm Water Management Plan (includes Narrative, Figure 1.1, HydroCAD model, volume control worksheet, and Geotechnical Evaluation Report by Sand Companies), by MSA Professional Services, dated 9/24/13, recd. 9/25/13.

2. Western U Plaza Storm Water Management Plan, by MSA Professional Services, dated 10/21/13, recd. 10/24/13.

3. Schematic Design Plans (sheets C1, C2, C.3, C4, C5, C6, C7), by Paramount Engineering & Design, dated 10/9/13, recd. 10/24/13.

HISTORY & CONSIDERATIONS: Storage in excess of the required volume control is proposed, but no additional phases of development are suggested. If further development on-site is proposed, CRWD will view


it as “common scheme of development” and new impervious area that is subject to Capitol Region Watershed District regulation even though the specific development may be less than one (1) acre of disturbed area. RULE C: STORMWATER MANAGEMENT




maintain the long-term viability of the infiltration area. Developments and redevelopments must incorporate effective non-point

source pollution reduction BMPs to achieve 90% total suspended solid removal.




3. Stormwater runoff volume retention is achieved onsite in the amount equivalent to the runoff generated from one inch of rainfall over the impervious surfaces of the development.


3,378 BMP Volume Below Underground 8,222 cf

c. Banking of 4,884 cubic feet of excess volume retention has been

requested. d. Infiltration volume and facility size has been calculated using the

appropriate hydrological soil group classification and design infiltration rate.

e. The infiltration area is capable of infiltrating the required volume within 48 hours.

f. Stormwater runoff is pretreated to remove solids before discharging to infiltration areas.

4. Alternative compliance sequencing has not been requested. 5. Best management practices achieve 90% total suspended solids removal from

the runoff on an annual basis. 6. A recordable executed maintenance agreement has not been submitted.


RULE D: FLOOD CONTROL Standards Compensatory storage shall be provided for fill placed within the 100-year



project site comply with CRWD freeboard requirements. RULE E: WETLAND MANAGEMENT Standard



















1. Provide documentation that the maintenance agreement has been recorded with Ramsey County.

2. Remove geotextile fabric from bottom of infiltration system detail on Sheets C6 and C7. Geotextile shall be placed on top and sides only.

3. Specify non-limestone rock for the clean washed angular aggregate surrounding the StormTech system.

4. Revise HydroCAD model to include the portion of Area 4 (new building) that is draining to the intersection of University Ave and Virginia Street.

5. Remove the 6” drain tile from the underground infiltration system. Note: Storage in excess of the required volume control is proposed, but no additional phases of development are suggested. If further development on site is proposed, CRWD will view it as “common scheme of development” and new impervious area that is subject to Capitol Region Watershed District regulation even though the specific development may be less than one (1) acre of disturbed area. VOLUME BANK RECOMMENDATION Approve creation of a volume bank for the Sand Companies and deposit of 4,844 cubic feet of volume reduction credits.

Capitol Region Watershed District Permit Application 13-031 US Bank Demolition


Aerial Photo

Applicant: Scott Belsaas Consultant: Barry Jaeger Shepard Development, LLC Jaeger Construction 1999 Shepard Road 2317 Waters Drive St. Paul, MN 55116 Mendota Heights, MN 55120 Description: Demolition of the US Bank building at 2751 Shepard Road Stormwater Management: None, Erosion Control Permit Only District Rule: F Disturbed Area: 8.73 Acres Impervious Area: None Proposed RECOMMENDATION: Approve with 3 Conditions

Permit Location

1. Receipt of $17,460 surety. 2. Provide a copy of the NPDES permit. 3. Identify locations of material stockpiles and required perimeter controls to contain stockpiled materials.

University Avenue

Wes

tern

Ave

nue

W:\07 Programs\Permitting\2013\13-031 US Bank Demolition\13-031 Permit_Review_01.doc Page 1 of 2


CRWD Permit #: 13-031 Review date: October 31, 2013 Project Name: US Bank Demolition Applicant: Scott Belsaas Shepard Development, LLC 1999 Shepard Road St. Paul, MN Purpose: Demolition of US Bank Building and restoration to vegetation Location: 2751 Shepard Road, west of Davern Street. Applicable Rules: F Recommendation: Approve with 3 Conditions EXHIBITS:

1. Erosion and Sediment Control Plan by BKBM, dated 10/15/13, recd 10/16/13 HISTORY & CONSIDERATIONS: None. RULE F: EROSION AND SEDIMENT CONTROL






2. Adjacent properties are protected from sediment transport/deposition.

W:\07 Programs\Permitting\2013\13-031 US Bank Demolition\13-031 Permit_Review_01.doc Page 2 of 2

3. Wetlands, waterbodies and water conveyance systems are protected from erosion/sediment transport/deposition.

4. Project site is greater than 1 acre; an NPDES permit is required. RECOMMENDATION: Approve with 3 Conditions Conditions:

1. Receipt of $17,460 surety. 2. Provide a copy of the NPDES permit. 3. Identify locations of materials stockpiles and required perimeter controls to

contain stockpiled materials.

“

“ ”

DATE

PROJECT #

PROJECT STATUS

DRAWN BY

CHECKED BY

2013 BKBM Professional Engineers, Inc.All rights reserved.This document is an instrument of service and is the property of BKBMProfessional Engineers, Inc. and may not be used or copied withoutprior written consent.

C

I hereby certify that this plan, specification orreport was prepared by me or under mydirect supervision and that I am a dulyLicensed Professional Engineer under thelaws of the state of Minnesota.

Date Lic. No.

NOT FORCONSTRUCTION

ST. PAUL, Minnesota

14117CD

U.S. BANKBUILDINGDEMOLITION

50475

Revisions

No.

Description Date

Eric T. Luth

10-15-2013

ETL

KAM

10-15-2013

EROSIONCONTROL PLAN

C1.0


DATE: October 31, 2013

TO: CRWD Board of Managers

FROM: Britta Suppes, Monitoring Coordinator

RE: Summary and Analysis of Water Quality Data from the Capitol Region Watershed District’s

Stormwater Monitoring Program, 2005-2012, Benjamin D.Janke, Ph.D, University of Minnesota

Background

Since 2005, CRWD has been collecting and analyzing water quality data through the District Monitoring

Program. With over eight years of data, CRWD determined that additional analysis of the robust data set was

needed to identify long-term trends. In January 2013, CRWD contracted with Dr. Jacques Finlay and Dr. Ben

Janke at the University of Minnesota to perform additional analyses of CRWD monitoring data. The final

deliverable by Dr. Janke is a comprehensive report titled “Summary and Analysis of Water Quality Data from

CRWD’s Stormwater Monitoring Program, 2005-2012”. The objectives of the analyses and report were to:

(1) Investigate spatial and seasonal patterns in water yields and concentrations of nutrient and metals

(2) Understand the impact of storm even characteristics (e.g. rainfall depth, antecedent dry days or rainfall)

on loading of water and nutrients

(3) Investigate the impact on water and nutrient loading of differences in land cover and drainage

characteristics among monitored sub-watersheds

(4) Determine exceedence probabilities of water yields and nutrient loads

(5) Quantify probabilities and seasonality of metal toxicity exceedences

The primary goals of these analyses were to better understand water and nutrient loading patterns in the

watershed, inform the design of future stormwater BMPs, and aid in the development of TMDLs or water

quality goals for the CRWD’s lakes and streams. The analyses provide methods that may be used to compare

future monitoring seasons to data from the summary period (2005-2012). A summary has also been included of

major patterns in spatial and seasonal variability among monitored sub-watersheds, which may help identify

seasons and/or specific sub-watersheds or BMPs that may be crucial for managing water quality in the CRWD.

Issues

Dr. Janke has analyzed the 2005-2012 monitoring data and has completed a final draft report summarizing

water quality data and will present and review the report with the Board of Managers.

Requested Action

None, information only.

enc: Final Draft— Summary and Analysis of Water Quality Data from CRWD’s Stormwater Monitoring

Program, 2005-2012 (w/o appendices)

W:\07 Programs\Monitoring & Data Acquisition\2012 Monitoring\2012 Annual Report\UMN-Ben Janke Report 2012\Brd Memo Ben Janke Report 11-6-13.docx

November 6, 2013 Board Meeting

IV. Special Reports—A) Summary

of 2005-2012 Monitoring Data by

Dr. Ben Janke (Janke)

FINAL DRAFT

-

Prepared for the Capitol Region Watershed District by:

Benjamin D. Janke, Ph.D

Department of Ecology, Evolution, and Behavior

University of Minnesota

Saint Paul, MN, USA

Oct 27, 2013

2 CRWD Stormwater Monitoring Data Analysis Report

Table of Contents

1. Introduction ........................................................................................................................... 4

List of Analyses .................................................................................................................................... 5

2. Data Collection and Methods ........................................................................................... 7

2.1. Data Collection ............................................................................................................................ 7 2.2. Methods .......................................................................................................................................... 8

2.2.1. Land Cover and Drainage Characteristics ................................................................................ 8 2.2.2. Stormflow and Baseflow Water Yields ................................................................................... 12 2.2.3. Statistical Methods for Yield and Concentration Data ..................................................... 12 2.2.4. Cumulative Loading and Cumulative Rainfall Frequency .............................................. 14 2.2.5. Metals Toxicity ................................................................................................................................. 15

3. Results ................................................................................................................................... 12

3.1. Characterization of CRWD Sub-watersheds ................................................................... 17 3.1.1. Land Cover and Drainage Characteristics ............................................................................. 17 3.1.2. Sub-watershed Stormflow and Baseflow Water Yields................................................... 17 3.1.3. Stormflow Response ...................................................................................................................... 20

3.2. Seasonal and Spatial Patterns in Water, Nutrients, and Metals ............................. 22 3.2.1. Stormflow Concentrations of Nutrients, TSS, Chloride, and Metals ........................... 22 3.2.2. Baseflow Concentrations of Nutrients, TSS, Chloride, and Metals ............................. 23 3.2.3. Seasonal Differences in Nutrient and Metal Concentrations ........................................ 23 Stormflow ........................................................................................................................................................ 24 Baseflow .......................................................................................................................................................... 25 3.2.4. Cumulative Water Volume and Nutrient Loading -- Stormflow .................................. 30 3.2.1. Cumulative Water Volume and Nutrient Loading -- Baseflow ..................................... 32 Cumulative Baseflow Loading – Annual .............................................................................................. 36

3.3. Impact of Storm Event Characteristics on Water and Nutrient Loading ............. 37 3.3.1. Cumulative Rainfall Frequency and Runoff Volume ......................................................... 37 3.3.2. Cumulative Rainfall Frequency and Nutrient, TSS, and Cl- Loading .......................... 40 3.3.3. Effect of Antecedent Rainfall on Stormwater and Nutrient Loading ......................... 40

3.4. Impact of Land Cover and Drainage Characteristics on Water and Nutrients in

Stormflow ........................................................................................................................................... 42 3.5. Exceedence Probabilities of Water Yields and Nutrient Loads............................... 43

3.5.1. Stormflow ........................................................................................................................................... 43 3.5.2. Baseflow .............................................................................................................................................. 44

3.6. Metals Toxicity Exceedences in Stormwater ................................................................. 46 3.6.1. Seasonality of Metals Toxicity.................................................................................................... 47

4. Summary .............................................................................................................................. 53

Part 1: Spatial and Seasonal Patterns in Water, Nutrients, Metals ................................ 53 4.1.1. Baseflow vs. Stormflow ................................................................................................................ 53 4.1.2. Spatial Variation .............................................................................................................................. 53 4.1.3. Seasonality ......................................................................................................................................... 54

CRWD Stormwater Monitoring Data Analysis Report 3

Part 2: Impact of Storm Event Characteristics on Water and Nutrient Loading ....... 55 4.1.4. Cumulative Rainfall Frequency ................................................................................................. 55 4.1.5. Antecedent Conditions .................................................................................................................. 56

Part 3: Impact of Land Cover and Drainage Characteristics on Water and Nutrients

in Stormflow ...................................................................................................................................... 56 Part 4: Exceedence Probabilities of Water Yields and Nutrient Loads ........................ 57 Part 5: Metals Toxicity Exceedences in Stormwater ........................................................... 58

References ................................................................................................................................ 59

Appendix A: Seasonal and Monthly Concentrations of Nutrients, TSS, Chloride, and

Metals in Stormflow and Baseflow .....................................................................................................

Appendix B: Summary of Regression Parameters (slope, R2, and p-value) for Linear

Regression of Stormwater Yield, Nutrients, Total Suspended Solids, Chloride, and

Metals against Antecedent Rainfall Parameters ...........................................................................

Appendix C: Summary of Regression Parameters (slope, R2, and p-value) for Linear

Regression of Stormwater Yield, Nutrients, Total Suspended Solids, Chloride, and

Metals against Land Cover and Drainage Characteristics .........................................................

Appendix D: Summary of p-values for Mann-Whitney U test of Seasonal Differences

in Metals Toxicity Exceedence Values in Baseflow at CRWD Monitoring Sites. ...............

Appendix E: Cumulative Rainfall Frequency Plots for Cumulative Stormwater

Volume and Nutrient Loads in CRWD Sub-watersheds .............................................................

Appendix F: Flow-Duration and Load-Duration Curves for Loading of Water,

Nutrients, Sediment, and Chloride in CRWD Sub-watersheds ................................................

Appendix G: Observed Concentrations and Toxicity Standards of Metals (Cd, Cr, Cu,

Pb, Ni, Zn) as a Function of Total Hardness in Stormflow of CRWD Sub-Watersheds ...

Appendix H: Toxicity Exceedence Probability Curves and Observations of Metals

Concentrations (Cd, Cr, Cu, Pb, Ni, Zn) in Stormflow of CRWD Sub-Watersheds ............

Appendix I: Cumulative Loading of Water, Nutrients, TSS, and Chloride in

Stormflow and Baseflow in CRWD Sub-watersheds ...................................................................


1. Introduction

This report describes the summary and analysis of portions of the extensive data set

collected in the Capitol Region watershed (CRWD) from 2005 – 2012 by the Capitol

Region Watershed District as part of its stormwater monitoring program. Monitoring data

included continuous flow measurements and water chemistry of samples collected during

stormflow and baseflow periods throughout the year at 13 sites (primarily storm drains

and outlets of stormwater best management practices). Water samples were analyzed for

a suite of nutrients, ions, solids, and metals.

The analyses and summaries described in this report are intended to expand on those

provided by CRWD in its annual monitoring reports, and can be categorized by objective

as follows: (1) investigating spatial and seasonal patterns in yields and concentrations of

water, nutrients, and metals, (2) examining the impact of storm event characteristics (e.g.

rainfall depth, antecedent conditions) on loading of water and nutrients, (3) investigating

the impact on water and nutrient loading of differences in land cover and drainage

characteristics among monitored sub-watersheds, (4) determining exceedence

probabilities of water yields and nutrient loads, and (5) quantifying probabilities and

seasonality of metals toxicity exceedences. A complete list of the analyses is included on

the following page.

The primary goals of these analyses were to provide a reference that could lead to a better

understanding of water and nutrient loading patterns in the watershed, inform the design

of future stormwater best management practices (BMPs), and aid in the development of

total maximum daily loads (TMDLs) or water quality goals for the CRWD. Several

analyses are intended to provide information to assess the appropriateness of the current

design storm and to potentially modify it if needed (for example, if the frequency of the

design storm is much different than expected or if it results in a different loading than

used to size certain BMPs). The analyses also provide several methods that may

eventually be used to compare data from future monitoring seasons to data from the

summary period (2005-2012). Such a comparison should provide a means of assessment

of BMP performance and quantification of progress towards water quality goals in the

watershed. A summary has also been included of major patterns in spatial and seasonal

variability among monitored sub-watersheds, which may help identify seasons and/or

specific sub-watersheds or BMPs that may be especially crucial for managing overall

water quality in the CRWD.


List of Analyses

A complete list of data summaries and analyses is included here, organized by objective.

Methods employed in the analyses are described in Section 2. Constituents for most

analyses included water, nutrients (total phosphorus, total nitrogen, nitrite-nitrate), total

suspended solids, chloride, and metals (cadmium, chromium, copper, lead, nickel, and

zinc).

(1) Seasonal and spatial patterns in water, nutrients, and metals:

(a) Mean and five-number summary (minimum, 1st quartile, median, 3

rd quartile, and

maximum) of water yield and concentrations of nutrients and metals, by season

(spring, summer, and fall for stormflow, all seasons for baseflow).

(b) Statistical tests for significant differences among seasonal concentrations of

nutrients and metals (stormflow and baseflow)

(c) Cumulative water and nutrient loading plots for each site (stormflow)

(d) Cumulative discharge and nutrient loading rates for each site (baseflow)

(2) Impact of storm event characteristics on water and nutrient loading:

(a) Cumulative rainfall frequency plots for rain event count and cumulative

stormwater runoff volume (similar to Bannerman et al. 1983, as published in Pitt

et al. 1999)

(b) Cumulative rainfall frequency plots for nutrient and sediment loads in stormwater

(similar to Bannerman et al. 1983, as published in Pitt et al. 1999)

(c) Simple linear regression analysis investigating impact of antecedent rainfall

characteristics (dry days, days since 0.5-in rainfall, and rainfall in last 7 days) on

stormwater yield and nutrient and metals concentrations

(3) Impact of land cover and drainage characteristics on water and nutrients in stormflow:

(a) Summary of land cover and drainage characteristics for CRWD sub-watersheds

for use in the regression analysis and to aid in interpretation of all results

(b) Simple linear regression analysis investigating the influence of these land cover

and drainage metrics on observed loads and concentrations of nutrients, metals

(Cu, Pb, Zn), and water for CRWD sub-watersheds

(4) Exceedence probabilities of water yields and nutrient loads:

(a) Flow-duration curves for all sites (volume and discharge for stormflow, discharge

for baseflow)

(b) Load-duration curves for nutrients, sediment, and chloride at all sites (loads for

stormflow, loading rates for baseflow)


(5) Metals toxicity exceedences in stormwater:

(a) Toxicity exceedence probability curves for metals (Cd, Cr, Cu, Pb, Ni, Zn) at all

sites (stormflow only)

(b) Statistical tests for significant differences among seasonal metals toxicity

exceedences (stormflow)

The data summaries and analyses are presented in the order listed above in the Results.

An introduction to the Results is also included in which CRWD sub-watersheds are

described in terms of land cover distributions, drainage characteristics, and stormflow

and baseflow hydrology, which are referenced in the interpretation of the plots and

analyses. A summary of the Results is included at the end of the report.


2. Data Collection and Methods

2.1. Data Collection

Data used in this work were collected by the Capitol Region Watershed District (CRWD)

as part of its stormwater monitoring program over the years 2005 – 2012. Monitoring

sites included major storm drains and outlets of best management practices (BMPs),

which were usually monitored from April through November of each year. For sites with

baseflow, sampling was also conducted during the interim period (winter and early

spring). Collected data included continuous flow rate and chemistry of water samples.

Samples were collected during both baseflow and stormflow during the monitoring

season using ISCO automatic water samplers, with baseflow sampling during winter and

early spring periods conducted using manual grab samples. Precipitation data were also

collected across the watershed using both manual and automatic gauges.

CRWD water samples were analyzed for a suite of nutrients, solids, ions, and metals by

Metropolitan Council Environmental Services (2005-2011) and by Pace Analytical

Services Inc. (2012). Of particular interest in this study were total phosphorus (TP), total

nitrogen (TN), nitrite and nitrate nitrogen (NO3), total suspended solids (TSS), chloride

(Cl-), and cadmium (Cd), chromium (Cr), copper (Cu), lead (Pb), nickel (Ni), and zinc

(Zn). Data were reported as concentrations: mg/L for nutrients, TSS, and Cl,- and g/L

for metals. For some metals (Cd and Cr) and nitrite, measurements were often below the

detection limits of the analyses. For the analyses these values were left at the detection

limits given the frequent low concentrations of metals (in baseflow especially), and the

very small fraction of either stormflow or baseflow TN generally comprised by nitrite.

CRWD analyzed the flow data from each monitoring site, correcting errors in the depth

and velocity measurements and determining water volumes (in ft3) for baseflow intervals

and storm events for the entire monitoring period. Gaps in flow data are present at some

sites due to equipment malfunction; most instances in which these gaps could influence

the analyses are identified in the Results.

A total of 13 CRWD-maintained monitoring sites were included in this work. These sites

included storm drains at the outlets of several large sub-watersheds within CRWD: East

Kittsondale (EK), Phalen Creek (PC), St. Anthony Park (SAP), Trout Brook East Branch

(TBEB), Trout Brook West Branch (TBWB), and Trout Brook Outlet (TBO). Several

smaller sites, generally located upstream or downstream of BMPs within CRWD were

also included: Arlington-Hamline Underground Stormwater Vault Inlet (AHUG), Villa

Park Inlet (VP Inlet), Villa Park Outlet (VP Outlet), Sarita Wetland Outlet (Sarita), and

the outlet of a pond at the Como Golf Course (GCP Outlet). Como 7 and Como 3, two

sub-watersheds of Como Lake, were also included.


Description of CRWD sub-watersheds, and data collection and processing methods are

detailed in the 2012 CRWD monitoring report (CRWD, 2012). Flow and chemistry data

used by site is listed in Table 2.1. For the Trout Brook sites, data prior to 2007 was not

used due to re-location of the TBEB and TBO sites in spring of 2007.

Table 2.1. Flow and water chemistry data intervals used in the analyses, by site.

Site Years of Monitoring Data Used

East Kittsondale 2005 - 2012

Phalen Creek 2005 - 2012

St. Anthony Park 2005 - 2012

Trout Brook - East Branch 2007 - 2012

Trout Brook - West Branch 2007 - 2012

Trout Brook - Outlet 2007 - 2012

Como 7 2007 - 2012

GCP Out 2008 - 2012

Villa Park Outlet 2006 - 2012

Sarita Outlet 2006 - 2012

AHUG 2007 - 2012

Como 3 2009 - 2012

Villa Park Inlet 2006 - 2012*

*no flow data available in 2009 due to equipment malfunction

2.2. Methods

2.2.1. Land Cover and Drainage Characteristics

Spatial data was used to determine a wide range of land cover and drainage

characteristics for the CRWD sub-watersheds (Table 2.2). Primary data sources included:

(1) a high-resolution (roughly 0.6-m) land cover map for assessing canopy coverage in

St. Paul, MN, developed by the Forestry Department at the University of Minnesota from

2009 satellite imagery, aerial photography, and LiDAR (Kilberg et al. 2011); and (2) a

GIS layer provided by CRWD for impervious cover, including designations for street,

alley, several roof types, and miscellaneous impervious cover. ArcMap GIS was used for

all spatial data analyses.

The two land cover layers were used to determine land cover fractions of the CRWD sub-

watersheds. Land cover classes included trees, lawn/shrubs, bare ground, open water,

rooftop, street, and “other” impervious (alleys, driveways, and lots). Composite land

cover classes included total impervious area and vegetated area (trees + lawn/shrubs).

The CRWD impervious layer was used to partition roof area into low-density residential,

high-density residential, industrial, commercial, and institutional. Overlay and buffer


analyses were used to determine the fraction of street directly covered by vegetation, as

well as the fraction of canopy coverage within 5ft, 10ft, and 20ft of the streets.

Drainage characteristics included a runoff coefficient, street density (total length of street

divided by watershed area), and curb density (total length of curb divided by watershed

area). Curb length was determined from calculating the perimeter of the street layer.

Street density and curb density were used as surrogates for drainage density, which could

not be determined due to the lack of storm drain spatial data at the time of writing. The

runoff coefficient is defined as the depth of total runoff normalized by the depth of total

rainfall. Pond density, in ponds per km2, was determined from visual inspection of

satellite imagery and should be considered a rough approximation of the actual pond

density (Ann Krogman, personal comm., March 7, 2012).

Note that the watersheds upstream of wetlands (Sarita in SAP) and lakes (i.e. Como Lake

and Lake McCarrons in TBWB/TBO) were not included in the calculation of the land

cover and drainage characteristics.


Table 2.2. Land cover and drainage characteristics of CRWD sub-watersheds.

Watershed Area (ac) Area (km2)

Drainage Characteristics

Runoff Coeff

Street Density

(km/km2)

Curb Density

(km/km2)

Pond Density

(ponds/km2)

EK 1116 4.52 0.373 15.26 26.41 0.22

PC 1433 5.80 0.279 14.78 27.18 1.03

SAP 2491 10.08 0.249 11.59 20.31 0.60

TBEB 808 3.27 0.248 13.15 22.70 4.28

TBWB 2379 9.63 0.381 8.73 18.32 3.43

TBO 5036 20.38 0.332 11.13 19.96 3.24

Como 7 298 1.21 0.052 12.84 24.53 n/a

GCP Out 298 1.21 0.408 12.84 24.53 n/a

VP Outlet 708 2.87 0.162 8.45 17.17 n/a

Sarita 930 3.76 0.067 7.95 n/a n/a

AHUG 41.5 0.17 0.157 13.03 26.71 0

Como 3 517 2.09 0.108 10.25 19.56 n/a

VP Inlet 622 2.52 0.168 8.61 17.09 n/a

Watershed

Land Cover Percentages

Trees Lawn / Shrubs

Bare Water Rooftop Street Alley Other

Impervious Total

Impervious Vegetated

EK 26.8 16.7 0.3 0.0 22.1 16.5 3.3 17.6 56.2 43.5

PC 23.7 17.0 0.5 0.0 22.3 16.9 3.4 19.4 58.7 40.8

SAP 23.0 14.4 0.5 0.7 19.0 14.0 1.5 28.3 61.3 37.4

TBEB 29.9 24.9 0.1 0.4 13.8 16.2 1.3 14.6 44.7 54.8

TBWB 31.7 25.2 0.8 3.2 13.1 10.5 1.5 15.5 39.2 56.9

TBO 26.7 23.5 0.7 1.8 14.2 13.8 1.3 19.3 47.3 50.3

Como 7 30.5 25.3 0.2 0.4 18.4 13.4 0.5 11.7 43.5 55.9

GCP Out 30.5 25.3 0.2 0.4 18.4 13.4 0.5 11.7 43.5 55.9

VP Outlet 45.4 24.8 0.3 1.2 9.2 11.3 0.0 7.8 28.4 70.2

Sarita n/a n/a n/a n/a n/a n/a 0.1 n/a n/a n/a

AHUG 26.8 21.9 0.3 0.0 24.7 13.3 3.6 12.9 51.0 48.7

Como 3 31.2 26.7 0.9 0.3 12.0 12.6 1.6 16.3 40.8 57.9

VP Inlet 44.0 25.4 0.3 0.9 9.3 11.7 0.0 8.4 29.4 69.4


Table 2.2. (Con’t). Land cover and drainage characteristics of CRWD sub-watersheds.

Watershed

Rooftop Percentages by Type Percentage of Street Covered by Canopy

Instit-utional

Residential, Low Dens.

Residential, High Dens.

Comm. Industrial Street Street +

5 ft Buffer

Street + 10 ft

Buffer

Street + 20 ft

Buffer

EK 0.9 11.9 1.4 2.2 1.2 30 34 37 41

PC 1.3 10.3 0.9 2.2 2.9 27 31 34 37

SAP 0.8 4.6 1.8 1.7 8.0 21 24 27 31

TBEB 0.3 7.2 1.8 0.3 0.6 22 26 29 34

TBWB 0.6 6.1 1.5 1.0 1.0 32 36 39 43

TBO 0.7 5.6 1.3 1.1 1.8 22 25 28 32

Como 7 0.9 13.5 0.2 0.3 0.0 43 46 48 51

GCP Out 0.9 13.5 0.2 0.3 0.0 43 46 48 51

VP Outlet 0.5 5.6 1.4 0.3 0.0 15 19 22 29

Sarita 3.5 2.0 3.6 0.6 0.0 n/a n/a n/a n/a

AHUG 3.0 16.9 0.1 0.0 0.0 36 40 43 48

Como 3 0.2 5.1 0.9 1.8 0.7 31 34 36 40

VP Inlet 0.6 5.4 1.6 0.3 0.0 14 17 21 27


2.2.1. Stormflow and Baseflow Water Yields

Seasonal water yields (in/season) of baseflow and stormflow were calculated for all

monitored sub-watersheds. The seasonal period was defined as Apr 1 – Oct 31, which

corresponded approximately to the monitoring period of each year. Gaps in flow data

were accounted for by linear extrapolation from the interval of existing flow data. Similar

to the drainage and land cover characteristics, watersheds of major lakes and wetlands

(i.e. Como Lake, Lake McCarrons, and the Sarita wetland) were not included in the

contributing areas used for the yield calculations, though these water bodies contribute

some flow to their downstream watersheds during both stormflow and baseflow periods.

2.2.2. Statistical Methods for Yield and Concentration Data

Concentration data for each site were grouped by month as well as by season: spring

(Mar – May), summer (June – Aug), and fall (Sep – Nov) for stormflow data, with winter

(Dec – Feb) included for baseflow data. It is acknowledged that these are somewhat

arbitrary breakpoints for seasons that do not always correspond to changes in flow

regimes, snow cover, or nutrient inputs, but are useful for the purposes of analyzing

seasonal changes in runoff concentrations. Mean, median, maximum, minimum, 1st

quartile, and 3rd

quartile of concentration data were determined by season for each

constituent. Volume-weighted mean concentrations by month were also calculated.

As is common for water quality data, the concentration data were not normally

distributed, instead tending to be positively-skewed due to lack of negative values and

occasional high concentrations and outliers (Helsel and Hirsch, 2002). This positive

skewness is illustrated in the probability distribution of TP measurements in stormflow at

EK (Figure 2.1a). Many methods are available for normality testing. One such method,

the Lilliefors test, has been used in previous analyses of stormwater data (e.g. Brezonik

and Stadlemann, 2002). This test shows that the TP data at EK are not normally

distributed (i.e. the null hypothesis that the data come from a normal distribution is

rejected at p = 3.74E-9), and thus transformation of the data was necessary for some

analyses. Log transformation, which is commonly used for concentration data (Brezonik

and Stadlemann, 2002; Helsel and Hirsch, 2002), improves the fit of the TP data to a

normal distribution (Figure 2.1b; p = 0.133 for the Lilliefors test).


Figure 2.1. (a) Probability density of stormflow TP concentrations at EK, and (b)

Probability density of log-transformed stormflow TP concentrations at EK.

Significant differences in nutrient concentrations and metals toxicity exceedences

between pairs of seasons (within sites) were assessed using the Mann-Whitney U test, a

non-parametric pairwise test appropriate for use on non-normally distributed data (Helsel

and Hirsch, 2002). The null hypothesis of this rank-sum test is that an observation from

one group has a 50% probability of being larger than that from the second group. The

null hypothesis is rejected if this probability is not 50% (i.e. observations from one group

tend to be higher or lower than those from the second group), meaning that the groups

differ in their medians. Differences were considered significant at p < 0.05.

Simple linear regression was used to investigate the effect of antecedent rainfall

conditions on stormflow water yield (in) and stormwater nutrient (TP, TN, NO3), TSS,

Cl-, and metals (Cd, Cr, Cu, Pb, Ni, Zn) concentrations. For all events in the data set of

each site, these water yield and chemistry variables were regressed against three

antecedent rainfall characteristics: days since last measureable rainfall (“dry days”), days


since last storm of 0.5 inch depth or greater (“days since 0.5-inch”), and total rainfall

depth in the previous 7 days (“weekly rain”). All data were log-transformed for this

analysis as recommended by Helsel and Hirsch (2002), and correlations were considered

significant at p < 0.05.

Simple linear regression was also used to investigate the influence of land cover and

drainage variables on mean values of stormwater volume and nutrients, TSS, Cl-, and

metals. For this analysis, the non-BMP sites were used (AHUG, EK, PC, SAP, TBEB,

and TBWB). The BMP outlet sites were not included in the analysis due to a less definite

link between the land surface and monitored stormwater (due to internal processing of

nutrients or increased hydrologic residence times), and TBO was excluded because it is

not independent of TBEB and TBWB. Como 3 was excluded due to large gaps in its

relatively short data record. Land cover and drainage metrics used as explanatory

variables are shown in Table 2.2. Dependent variables included event mean concentration

and mean seasonal yield of nutrients (TP, TN, NO3), TSS, Cl-, and selected metals (Cu,

Pb, Zn). The other metals (Cd, Cr, and Ni) were not included due to few observed

toxicity exceedences and generally lower concentrations. Data were not log-transformed

due to the use of mean quantities and because of the small number of sites (6), which also

limited the ability to interpret results.

R was used for all statistical analyses, as well as to generate most of the plots for

presentation of the data and analyses. Microsoft Excel was used for some plots and basic

statistical summaries.

2.2.3. Cumulative Loading and Cumulative Rainfall Frequency

Nutrient loads (lb) were calculated by multiplying observed concentrations by observed

water volumes for each event (stormflow) or flow interval (baseflow). For un-sampled

intervals, loads were calculated by using a volume-weighted mean concentration for the

month in which the volume interval occurred (determined from the whole set of samples

at a site for that month; see Tables A.3 and A.4). This monthly mean approach was used

in order to capture seasonality of nutrient concentrations and because flow-concentration

relationships were generally poor. Loads were normalized by watershed area to produce

yields (lb/ac) that allowed for comparisons among watersheds of varying size.

Cumulative loading curves for water and nutrients were developed for each site, with

separate curves for baseflow and stormflow. Snowmelt was included in baseflow

intervals from 2005-2010 (and rarely sampled directly), but CRWD identified snowmelt

events in 2011 and 2012 at most sites. Snowmelt intervals are therefore not included in

cumulative loading curves developed for 2011 and 2012, but are included in curves

developed from earlier data. Loading was normalized by the total load for the monitoring

season, and each year of available data is shown along with a mean seasonal (Apr – Oct)


loading line for all years, which was determined by taking the average across years of the

fraction of the cumulative loading added each day.

Cumulative rain count and cumulative water and nutrient loads were plotted as a function

of increasing rainfall depth for all sites, referred to for the purposes of this report as

cumulative rainfall frequency plots. These plots are similar to those constructed by

Bannerman et al. (1983), as shown in Pitt et al. (1999). This analysis first required

matching rainfall depths with associated runoff volumes for each sub-watershed.

Precipitation data utilized in this analysis was collected at a station maintained on the

University of Minnesota’s St. Paul campus by the Department of Soil, Water, and

Climate, as well as at six sites maintained by CRWD, including manual gauges at the

CRWD office, Villa Park, and Westminster-Mississippi stormwater pond, and automatic

gauges at Highland Park, the St. Paul Fire Station, and Trout Brook East Branch. Gauge

locations are shown in CRWD (2012). Mean precipitation depth for each storm was

determined using an inverse squared distance relationship, as in Brezonik and

Stadlemann (2002):

where Pi is the precipitation depth measured at gauge i, and dij is the inverse of the square

of the distance from gauge i to monitoring site j at the outlet of the watershed, i.e. dij =

(distance from gauge i to site j)-2

. Once runoff volumes (and nutrient loads) were matched

to rainfall depths, the plots were constructed by sorting the data records by increasing

rainfall depth.

For all sites, flow-duration curves were developed for runoff and load-duration curves

were developed for nutrients (TP, TN, NO3), TSS, and Cl- in both stormflow and

baseflow. Event water volume (ft3), flow rate (cfs), and nutrient loads (lb) were used for

the stormflow plots, while loading rates (cfs for runoff, and lb/h for nutrients) were used

for the baseflow plots due to the dependence of baseflow loads on the length of the

sampling intervals. Following the recommendations of Helsel and Hirsch (2002), the

Weibull plotting parameter (plotting position, i = n / N+1) was used for these plots, where

n = rank and N = number of events or intervals.

2.2.4. Metals Toxicity

The toxicity of a metal is a function of water hardness. For CRWD watersheds, the

chronic toxicity standard was used, as defined in Minnesota Rules 7050.0222 for each of

the 6 metals (Cr, Cd, Cu, Pb, Ni, and Zn). Toxicity is assessed only for stormflow, given

the generally low concentrations of metals and high water hardness in baseflow, which

leads to very few toxicity exceedences. Equations for the chronic standard (CS) for each

P =Pidij( )ådijå


metal in g/L, as a function of total water hardness (TH) in mg/L, are listed below as well

as in CRWD (2012):

Cadmium: CSCd = exp(0.7852[ln(TH)] – 3.490)

Chromium: CSCr = exp(0.819[ln(TH)] + 1.561)

Copper: CSCu = exp(0.620[ln(TH)] – 0.570)

Lead: CSPb = exp(1.273[ln(TH)] – 4.705)

Nickel: CSNi = 297 g/L, for TH > 212 mg/L

CSNi = exp(0.846[ln(TH)] + 1.1645), for TH < 212 mg/L

Zinc: CSZn = exp(0.8473[ln(TH)] + 0.7615)

Toxicity exceedence curves were developed by sorting hardness concentration data by

decreasing concentration, then applying the toxicity standard to the hardness data.

Corresponding observed metal concentrations were also plotted on these curves.

Toxicity exceedence was defined as the difference between the observed metal

concentration and the toxicity standard, which is a function of the observed hardness.

Positive values of exceedences, which were not normally distributed, were assessed for

seasonality using the Mann-Whitney U test (similar to the nutrient concentration data).

All negative exceedence values (i.e. non-exceedences) were discarded, resulting in

sample sizes that varied considerably among sites and among seasons.


3. Results

3.1. Characterization of CRWD Sub-watersheds

To aid in explanation and interpretation of the results, differences in land cover, drainage

characteristics, and hydrology of the monitored sub-watersheds are described here.

Methods used to determine the land use and drainage metrics and to calculate stormflow

and baseflow water yields are described in Section 2.

3.1.1. Land Cover and Drainage Characteristics

A summary of land cover metrics and drainage characteristics of all monitored sub-

watersheds is shown in Table 2.2. Some variation in land cover is present among sub-

watersheds. For example, total impervious area varies from 39% (TBWB) to 59% (PC),

with much smaller percentages (28%, 29%) at the VP Inlet and Outlet sites, while total

vegetated area (tree canopy + lawn and shrubs) varies from 37% (SAP) to 70% (VP

Outlet). Street area varies only from 10% (TBWB) to 17% (PC), with roughly 14% (VP

Inlet) to 43% (Como 7) of this street area directly shaded by overhanging canopy. Among

roof types, low-density residential was the most common, as expected given the

dominance of this land use in the watersheds, ranging from 5.4% (VP Inlet) to 17%

(AHUG).

Drainage characteristics also showed some variability among sites. Runoff coefficients

(RC) ranged from 0.052 to 0.408, with the lowest values at the Como and BMP sites

(excepting GCP Outlet, which had the highest runoff coefficient due to water pumped in

from outside the watershed via Gottfried’s Pit). The highest values of RC for non-BMP

sites were observed at EK, TBWB, and TBO. Street and curb density were both lowest at

VP Outlet, and highest at EK and PC.

The amount of surface water in CRW also varied considerably among sub-watersheds,

and was anticipated to have an impact on water yields. Open water area, though relatively

insignificant in terms of total area, was greatest in TBWB, TBO, Villa Park, and SAP

(even neglecting the area of the Sarita wetland and Como Lake and Lake McCarrons). In

addition, a rough count of stormwater ponds in five sub-watersheds using aerial

photography showed the greatest density in TBEB (approximately 4.28 ponds per km2

watershed area) and TBWB (3.43 ponds/m2), with lower densities (0.22 – 1.02

ponds/km2) in EK, PC, and SAP (Ann Krogman, personal comm., March 7, 2012).

Potential implications of differences in drainage characteristics for stormflow and

baseflow are addressed below and throughout the Results.

3.1.2. Sub-watershed Stormflow and Baseflow Water Yields

Mean seasonal water yields (in/season) of baseflow and stormflow for all monitored sites

are shown in Figure 3.1a. The percentage of the combined seasonal water yield due to


baseflow is also shown. The seasonal period was defined as Apr 1 – Oct 31, which

corresponds approximately to the monitoring period of each year. Note that watersheds of

upstream lakes (e.g. Como Lake and Lake McCarrons for TBWB) or wetlands (e.g.

Sarita for SAP) are not included in the contributing area for the yield calculations.

Figure 3.1. (a) Mean seasonal (Apr – Oct) stormflow and baseflow water yields (inches)

for CRWD sub-watersheds during the monitoring period (2005-2012), and (b) Mean and

standard error of seasonal (Apr – Oct) stormflow yields (inches). In (a), percentages

indicate the baseflow portion of total seasonal water yield.

While variation in stormwater yields from year to year was relatively small at most sites

(i.e. small standard errors; Figure 3.1b), considerable variation was present among sites in

0

2

4

6

8

10

12

EK

PC

SAP

TBEB

TBW

B

TBO

VPIn

VPOut

Sar

Com

o7

GCP

Com

o3

AHUG

Seaso

nal S

torm

wa

ter

Yie

ld,

in

(b)

0

5

10

15

20

25

EK

PC

SAP

TBEB

TBW

B

TBO

VPIn

VPOut

Sar

Com

o7

GCP

Com

o3

AHUG

Sea

so

na

l W

ate

r Y

ield

, in

Stormflow

Baseflow

25%

67%

56%

44%

62%

61%

31% 28%

(a)


seasonal stormwater yields. The lowest yields were generally observed for the BMP sites

(VP Inlet, VP Outlet, and Sarita) as well as the Como sites (Como 3, Como 7, and

AHUG), which have a relatively large number of BMPs present that should reduce water

yields. The high water yields at GCP Outlet are probably due to pumping from

Gottfried’s Pit, which is outside the GCP/Como 7 watershed. With this exception, the

highest stormwater yields were observed at the non-BMP sites, including at EK, which

has very little surface water (i.e. few ponds and no open water; Table 2.2), and thus likely

has little storage capacity relative to the other sites. High stormwater yields at TBWB and

TBO (and to a lesser extent SAP) might be supplemented by flow from upstream lakes

and wetlands present in these sub-watersheds (the area of which is not included in the

yield calculations). Frequency of large events should vary from year to year, which may

also explain the higher standard errors at these sites.

For the non-BMP sites with baseflow (EK, PC, SAP, and the Trout Brook sites),

appreciable variation was present in combined seasonal water yields. This variation was

driven primarily by differences in baseflow yield, as stormflow yields were similar

among these sites (see Figure 3.1b). Baseflow was especially important at the largest

sites, including PC, SAP, TBWB, and TBO, where 56% to 67% of combined seasonal

water yield was delivered by baseflow. For several sites (EK, PC, SAP, TBEB, TBWB,

and TBO), year-round flow data was collected from 2010 - 2012. These results are

summarized in Table 3.1. While some gaps exist in these data, they illustrate the even

greater importance of baseflow on an annual scale, as baseflow comprises 32% (EK) to

71% (PC) of combined annual volume. The substantial contribution of baseflow to

combined water yield at several sites is noteworthy, as it represents a considerable flux of

water that is often not monitored, and rarely treated or considered in BMP development.

Table 3.1. Annual water yields for six CRWD sub-watersheds, averaged over 2010 to

2012 and separated by flow type (baseflow, stormflow, and snowmelt). Note that

snowmelt volumes were only available for 2011 and 2012; in 2010 these volumes are

included in the baseflow volumes and thus may slightly inflate baseflow estimates.

Site Baseflow Stormflow Snowmelt Combined Baseflow as % of

Combined

EK 5.93 11.11 1.41 18.46 32%

PC 23.15 8.37 1.20 32.72 71%

SAP 6.59 3.71 0.29 10.59 62%

TBEB 7.80 6.42 0.85 15.07 52%

TBWB 9.75 6.93 0.50 17.19 57%

TBO 11.50 5.62 1.35 18.46 62%


In CRWD, two primary baseflow sources are assumed to be present: (1) groundwater

seepage into storm drains that were constructed below the water table, and (2) outflow

from surface water (lakes, ponds, and wetlands) that is connected to storm drains.

Groundwater is presumed to be the dominant baseflow source in all sub-watersheds due

to generally high water tables and sandy or loamy soils (Kanivetsky and Cleland 1992,

Meyer 2007), in particular for the larger watersheds with extensive or deeper storm drain

networks (e.g. SAP, TBO). For several watersheds with a large number of lakes, ponds,

and wetlands, and in particular those with known outflows from major lakes or wetlands

to the storm drain network (e.g. Sarita Wetland in SAP, Como Lake and Lake McCarrons

in TBWB), surface water may contribute a small but important fraction of baseflow.

The developmental history of the watershed may be important in explaining baseflow

variation among sites. For example, the main trunks of the storm drains in PC and the

Trout Brook watersheds were constructed in existing stream channels beginning in the

late 1800’s (Brick 2008). Water tables are especially high in the vicinity of these drains

(Barr Engineering 2010), and this concentration of shallow groundwater may cause high

seepage rates into the aging storm drains, which would explain the high baseflow yields

for TBWB/TBO and PC in particular. Extent of the storm drain is also likely important,

as EK and TBEB, which had the smallest baseflow yields of the non-BMP sites, are the

smallest of these watersheds and have relatively small, shallow storm drain networks that

may be located mostly above the water table.

The importance of baseflow for water and nutrient loading, and the potential influence of

groundwater vs. surface water for baseflow nutrient chemistry has been investigated for

the 2005-2011 CRWD monitoring data set by Janke et al. (2013).

3.1.3. Stormflow Response

Inspection of hydrographs further illustrates the differences in stormflow response among

the monitored sub-watersheds, and in particular the influence of major BMPs. A

relatively large-scale, fast-moving frontal storm occurring on July 31, 2009 producing

0.60 in of rain was used for this illustration. Normalized hydrographs for this event are

shown for most sites in Figure 3.2. Note that no flow data was collected at VP Inlet

during 2009, and water level was used in place of flow rate for Sarita due to errors in

velocity data for that storm.

As expected, the outflow hydrographs for the BMP sites (VP Outlet, GCP Outlet, and

Sarita) were considerably different than for the other sites. The BMPs stored much of the

rainfall-runoff from this event and released it slowly over the next day, with peak outflow

rates occurring several hours after rainfall had ended. By contrast, hydrographs for EK,

PC, AHUG, and Como 7 were typical of very impervious watersheds with little surface

water for detention capacity: very early runoff peaks and short hydrographs (i.e. small

times of concentration). SAP, Como 3, and the Trout Brook watersheds had much longer


hydrographs and later peak flows. In the case of SAP and the Trout Brook sites this is

likely evidence of considerable surface water (especially detention ponds) present in

these watersheds that delays the movement of stormwater, as observed at the BMP sites.

For Como 3, the large amount of park and golf course area in the watershed may also

serve to slow stormwater movement.

Figure 3.2. Observed hydrographs at CRWD sub-watersheds for a spatially extensive,

0.60-in storm on July 31, 2009. Main sites are shown in the top plot, with smaller sites

and BMPs shown in the bottom plot. Note that VP Inlet is not included due to lack of flow

data in 2009, and level data is plotted at Sarita due to errors in velocity data.

0

0.5

1

1.5

2

2.5

3 0.0

0.2

0.4

0.6

0.8

1.0

1.2

22:00 23:00 0:00 1:00 2:00 3:00 4:00 5:00 6:00

Pre

cip

(c

m,

pe

r 1

5m

in i

nte

rva

l)

Dim

en

sio

nle

ss F

low

(Q

/Qp

ea

k)

EK

PC

SAP

TBEB

TBWB

TBO

Precip

EK

TBWB

TBEB

PC

SAP TBO

0

0.5

1

1.5

2

2.5

3 0.0

0.2

0.4

0.6

0.8

1.0

1.2

22:00 23:00 0:00 1:00 2:00 3:00 4:00 5:00 6:00

Pre

cip

(cm

, p

er

15m

in in

terv

al)

Dim

en

sio

nle

ss F

low

(Q

/Qp

ea

k)

AHUG VP Outlet

Como 3 Sarita (Lvl)

Como 7 GCP Outlet

Precip

AHUG

GCP Outlet

VP Outlet

Como 7

Como 3

Sarita


3.2. Seasonal and Spatial Patterns in Water, Nutrients, and Metals

Concentration data are summarized in terms of mean, median, minimum, maximum, 1st

quartile, and 3rd

quartile by season for all monitoring sites in Appendix A for stormflow

(Table A.1) and for baseflow (Table A.2). Concentrations of most constituents appear to

have right-skewed distributions common in these type of data (Helsel and Hirsch 2002),

with median concentrations considerably smaller than mean concentrations, the latter of

which can be influenced by a few samples with very high concentrations. Monthly mean

volume-weighted concentrations of nutrients, TSS, and Cl- are shown in Table A.3 for

stormflow and in Table A.4 for baseflow.

3.2.1. Stormflow Concentrations of Nutrients, TSS, Chloride, and Metals

The highest median TP and TN concentrations were observed at EK, PC, Como 7, and

AHUG. At EK and PC, high TN and TP may be due to high stormwater yields at these

sites (Figure 3.1b), which may result in the transport of more particulate N and P relative

to sites with smaller stormwater yields. The highest median NO3 concentrations were

observed at PC, SAP, TBWB, and TBO, which might be explained by mixing of

stormwater with NO3-rich baseflow at these sites (Table A.2). Median TSS

concentrations were highest at EK, PC, TBWB, and Como 7. For EK, PC, and TBWB

this again may be due to high stormwater yields (and runoff coefficients) for these

watersheds, but an explanation for high TSS at Como 7, which has very low stormwater

yields, is unclear.

By contrast, the lowest median TP, TN, and TSS concentrations were logically observed

at the BMP sites (Sarita, VP Inlet, VP Outlet, and GCP Outlet), suggesting that these

BMPs were effective in removing some particulate nutrients from stormwater relative to

the larger, non-BMP sites. The smallest median NO3 concentrations were also generally

observed at these sites, suggesting that NO3 uptake or denitrification may be occurring in

the ponds and wetlands present at these sites.

Median stormflow Cl- concentrations did not vary much among sites, and were well

below the Minnesota Pollution Control Agency (MPCA) water quality standard of 230

mg/L at all sites. The highest concentrations were observed at VP Inlet, VP Outlet, GCP

Outlet, and TBEB, and were driven by high values during spring. It is unclear why these

sites have higher spring Cl- concentrations than the others, but a high density of

stormwater ponds are present in TBEB and the other sites are located at outlets of

wetland and pond BMPs, suggesting that winter accumulation of road salt in the ponds

from snowmelt may be flushing out during early spring rains.

Cr, Cd, and Ni concentrations tended to be very low in stormwater, with Cr and Cd in

particular often below the detection limit of the analyses. Median Cu, Pb, and Zn

concentrations were highest in EK, PC, and SAP, which have the greatest total


impervious area of all monitored sub-watersheds, suggesting that these may be primary

source areas of metals. Cu, Pb, and Zn concentrations were generally several times lower

at the BMP sites (Sarita, VP Outlet, GCP Outlet, and VP Inlet), suggesting that the BMPs

were capturing metals through particle settling, similar to particulate nutrients.

3.2.2. Baseflow Concentrations of Nutrients, TSS, Chloride, and Metals

For those sites with baseflow (EK, PC, SAP, TBEB, TBWB, TBO, VP Inlet, and VP

Outlet), nutrient chemistry (Table A.2) was generally much different than in stormflow.

For metals, concentrations in baseflow were frequently below the detection limit for most

sites and constituents, and rarely exceeded toxicity standards (see Appendix D), and

therefore are not presented here.

For the non-BMP sites (EK, PC, SAP, TBEB, TBWB, and TBO), median TP

concentrations in baseflow were roughly 15% - 30% of values in stormflow, consistent

with the expectation that most P is delivered in stormflow due to mobility of particulates

in stormwater. Accordingly, median TSS concentrations were roughly 10-50 times higher

in stormflow than in baseflow at the non-BMP sites.

While median TN concentrations tended to be similar among baseflow and stormflow,

the form of N varied considerably: median NO3 concentrations were much higher in

baseflow than in stormflow across all sites, with the greatest difference at PC, where NO3

was 4-5 times higher in baseflow than in stormflow. Higher NO3 in baseflow relative to

stormflow is sensible if much of the baseflow in these larger drains is contributed by

groundwater, which should be much higher in dissolved than particulate N forms.

Median Cl- concentrations were generally several times higher in baseflow than in

stormflow across the non-BMP sites, including an order of magnitude higher in baseflow

at EK. Median Cl- concentrations at TBEB and EK exceed the MPCA water quality

standard of 230 mg/L during all seasons, and maximum concentrations at most of the

other sites exceed the standard for all seasons except summer. Given that groundwater is

the likely source of baseflow for these sites, the results suggest that road salt applications

during winter months are polluting shallow groundwater in these watersheds.

For the VP sites, TP, TN, NO3, and TSS concentrations were remarkably similar between

stormflow and baseflow, suggesting that the BMP is effectively capturing suspended

solids as well as reducing NO3 export. For Cl-, the slightly lower median concentrations

in stormflow are probably the result of dilution by a larger water volume during storms. It

is also worth noting that median Cl- concentrations at both VP sites exceed the MPCA

standard during winter months (Dec – Feb).

3.2.3. Seasonal Differences in Nutrient and Metal Concentrations

Nutrient and metal concentrations were tested for statistically significant differences

among seasons, both in stormflow and in baseflow. Pairwise testing was conducted using


the Mann-Whitney test, with differences considered significant for p < 0.05. Results are

shown for stormwater in Table 3.2a (nutrients and metals), and for baseflow in Table

3.2b (nutrients only). Note that no stormwater samples were collected during winter

months (Dec – Feb), and therefore this period was not included in the stormflow tests.

Stormflow

For TP, relatively few statistically significant differences (p < 0.05) existed among

seasons for any of the sites. At PC, SAP, and TBEB, differences between summer and

fall TP were significant, and in all cases fall concentrations were lower than summer,

suggesting a depletion of TP sources in the watershed. However, there are generally

fewer samples collected during fall (Sep – Nov) than during summer (June – Aug), and

the monitoring period may end before leaf fall has finished, and therefore a potentially

large input of phosphorus is not reflected in these results. In addition, TP was

significantly lower during spring at VP Outlet and significantly higher during summer at

VP Inlet. Seasonality of TP in this BMP, which is present to a greater extent in the

baseflow TP concentration data (Table 3.2b), does not have an obvious explanation but

may be related to processing or inputs, the latter of which may be greater in summer

storms.

Seasonality was much more prevalent in the nitrogen concentration data (TN and NO3)

than in the TP data. For most non-BMP sites (EK, PC, SAP, TBEB, and TBWB), TN

concentrations were significantly lower during fall than in the spring and summer,

suggesting a depletion of TN sources in the watersheds. As in the case of TP, most

samples were likely taken before leaf fall, and thus a large N input is probably not

included. NO3 concentrations showed a strong seasonality for nearly all sites, with

concentrations generally decreasing from spring to fall, and significant differences

present among all seasons at SAP, VP Outlet, and GCP Outlet. NO3 depletion by algal

uptake over the summer in relatively abundant surface water in these watersheds may

partially explain seasonal variation of NO3 concentrations. NO3 was not a large

component of stormwater TN at the non-BMP sites in general, and therefore seasonality

of TN concentrations were likely being controlled by seasonal dynamics of the organic

and particulate components, which may be largely contributed by lawns and trees.

Few patterns were readily apparent in the seasonal differences of TSS concentrations. No

significant seasonal differences were present in TSS in TBWB, TBO, or Sarita; for the

latter site this is unsurprising as it is located at a wetland outlet and settling of solids is

likely occurring upstream. For the other non-BMP sites (EK, PC, SAP, TBEB, and

AHUG), fall concentrations of TSS were significantly lower than summer and sometimes

spring as well. Higher TSS in summer may be due to high erosion rates during summer

storms, which tend to be more intense than during other seasons, while higher TSS in

spring (at EK and TBEB) may be due to flushing of winter-applied sand or erosion of

lawns by spring rains before grass is fully established.


Cl- concentrations showed strong seasonality for all sites except Sarita, with spring

concentrations significantly different from those observed in summer and/or fall.

Additionally, median spring concentrations at all sites were higher than in either summer

or fall. This result is consistent with the expectation that spring rains flush road salt

applied during winter months. In addition, due to solubility of Cl-, the BMP sites do very

little to remove it from runoff.

For all monitored metals, significant seasonal differences were present mostly at the non-

BMP sites, again highlighting the ability of BMPs to allow metals to settle out and

maintain relatively consistent concentrations in outflow throughout the year. Seasonal

differences tended to be similar among sites for the six metals, which may be evidence of

more uniform sources and delivery mechanisms among watersheds. Accordingly, the

greatest seasonality was present for the sites with the most impervious area (EK, PC,

SAP, and TBEB). Mean fall concentrations of most metals tended to be lower than in

spring at these sites, suggesting a winter build up on impervious surfaces followed by a

flushing that occurs over the warm season.

Baseflow

Grab samples were collected during the winter months at all sites with baseflow, and

while the number of samples is few relative to the other periods, the winter season was

considered in the tests for significant seasonal differences in nutrient concentrations.

Metals were not considered due to the generally low concentrations year-round.

For the non-BMP sites (EK, PC, SAP, TBEB, TBWB, and TBO), the strongest

seasonality was present for the Trout Brook sites. This seasonality could be evidence of

the influence of surface water, which may be more susceptible to seasonal nutrient

dynamics than groundwater. While groundwater is likely the largest baseflow water

source for all of the non-BMP sites, surface water is present in storm drain baseflow in

the Trout Brook watersheds due to connections to upstream lakes (TBWB/TBO) and a

large number of ponds (TBEB). Of note, median TP concentration at TBEB was higher in

summer than during the rest of the year, and was significantly different from the other

seasons; seasonal differences in TP were not significant in any of the other non-BMP

sites. TN concentrations showed some seasonality at SAP, TBEB, TBWB, and TBO,

which was similar but not identical to the seasonality in NO3 concentrations. Fall TN was

significantly lower than spring TN at all four of these sites, perhaps a result of N

depletion during summer in drain-connected surface waters, as a similar trend was not

present in the NO3 data. At all four sites, summer NO3 was significantly lower than

during spring, and lower than during winter (with the exception of TBEB). This pattern

suggests that NO3-rich groundwater, which may explain high winter NO3 concentrations,

might be diluted by NO3-poor outflow from ponds and wetlands in these watershed

during summer.


By contrast, EK and PC, watersheds with very few BMPs or surface water, showed few

significant differences among seasons for TP, TN, and NO3. This result is unsurprising as

groundwater is presumed to dominate baseflow at these sites given the lack of surface

water, and nutrient concentrations are not expected to vary as much throughout the year

in groundwater as in surface water. One exception is TN, which was significantly lower

during winter at EK; an explanation for this trend is not apparent, but due to the lower

proportion of NO3 relative to PC, the pattern may be evidence of the depletion of sources

of organic (non-nitrate) N during cold months.

For the two VP sites, more seasonal variability was present for nutrients and TSS than in

the other sub-watersheds, most likely because these sites are located in a BMP heavily

influenced by surface inputs. Significant seasonal differences in nutrients and TSS were

similar but not identical to those observed in stormflow. TP and TSS were significantly

lower in spring than in summer and fall at both VP sites, suggesting that TP and TSS are

predominantly from surface runoff inputs, which would be larger during summer and fall.

While TN was not strongly seasonal, NO3 was significantly different between winter and

both summer and fall, and between spring and summer. Median NO3 in winter at both

sites was several times higher than in any other season; the cause is uncertain, but may

result from NO3-rich shallow groundwater inputs that are not diluted by surface runoff in

winter, or from the decay of vegetation within the wetland.

Strong seasonality of Cl- in baseflow was present at all sites, especially between spring

and summer or fall. Significant differences among nearly all seasons were observed for

the two VP sites, with the lowest p values observed between winter and summer/fall and

between spring and summer, a trend similar to what was observed in stormflow data for

these two sites. In addition, significant differences were observed at all non-BMP sites

between winter and summer. Taken together, these results strongly suggest that road salt

applications during winter and spring (Nov – Apr) may be polluting both surface water

and shallow groundwater, the latter of which is likely present in all of the large non-BMP

watersheds.


Table 3.2a. Summary of p-values for Mann-Whitney U test of seasonal differences in nutrient, TSS, Cl-, and metals concentrations

(mg/L) in stormflow at CRWD monitoring sites. Seasonal differences significant at p < 0.05 are highlighted in blue.

Seasonal Comparison

Kittson-dale

Phalen Creek

St. Anthony

Park

Trout Brook East

Trout Brook West

Trout Brook Outlet

Sarita Outlet

Villa Park

Outlet

Como 7

GCP Outlet

Como 3

AHUG Villa Park Inlet

Total Phosphorus

spring-summer 3.0E-01 5.3E-02 4.5E-01 5.6E-01 2.0E-01 9.3E-01 8.3E-01 3.0E-05 2.4E-01 2.2E-01 7.8E-03 7.0E-01 7.9E-06

spring-fall 1.4E-01 4.4E-01 9.5E-02 3.9E-01 7.0E-01 7.1E-01 6.2E-01 1.0E-02 5.8E-02 1.6E-01 5.8E-02 2.0E-01 8.1E-01

summer-fall 5.4E-01 5.0E-03 1.6E-03 2.0E-02 7.6E-02 5.1E-01 4.2E-01 1.0E-01 1.9E-01 6.6E-01 6.3E-01 1.1E-01 2.0E-06

Total Nitrogen

spring - summer 1.4E-02 7.6E-01 6.7E-01 1.0E-01 6.7E-01 5.6E-02 4.3E-01 8.1E-02 5.8E-01 1.6E-01 8.1E-03 9.8E-01 1.3E-01

spring - fall 2.4E-04 3.3E-03 1.0E-04 7.2E-04 2.7E-02 1.6E-02 3.2E-02 1.4E-01 1.1E-01 1.8E-02 8.3E-03 2.0E-02 1.2E-04

summer - fall 2.7E-02 2.3E-04 1.5E-05 2.7E-03 1.8E-02 9.1E-02 4.8E-02 9.0E-01 3.3E-01 2.8E-01 7.9E-02 5.3E-03 1.4E-04

Nitrate-Nitrite




Total Suspended Solids




Chloride





Table 3.2a (con’t).

Seasonal Comparison

Kittson-dale

Phalen Creek

St. Anthony

Park

Trout Brook East

Trout Brook West

Trout Brook Outlet

Sarita Outlet

Villa Park

Outlet

Como 7

GCP Outlet

Como 3


Cadmium




Chromium




Copper




Lead




Nickel




Zinc


spring - fall 1.0E-04 1.4E-01 4.1E-01 2.1E-05 2.7E-02 8.2E-02 1.2E-03 1.0E+00 9.8E-01 1.8E-03 2.1E-02 4.5E-02 1.9E-02



Table 3.2b. Summary of p-values for Mann-Whitney U test of seasonal differences in

nutrient, TSS, and Cl- concentrations in baseflow at CRWD monitoring sites. Seasonal

differences significant at p < 0.05 are highlighted in blue.

Seasonal Comparison

Kittson-dale

Phalen Creek

St. Anthony

Park

Trout Brook East

Trout Brook West

Trout Brook Outlet

Villa Park

Outlet

Villa Park Inlet

Total Phosphorus

spring-summer 7.5E-01 9.8E-01 4.0E-01 3.7E-02 4.8E-01 4.1E-01 2.8E-09 1.1E-06

spring-fall 4.4E-01 6.4E-01 9.3E-02 9.2E-01 2.3E-01 2.5E-01 1.7E-07 5.1E-04

summer-fall 1.8E-01 5.0E-01 3.8E-01 2.3E-03 5.9E-01 2.7E-02 3.5E-02 1.9E-01

winter-spring 1.5E-01 3.5E-01 1.9E-01 7.2E-01 9.9E-01 9.6E-01 2.7E-01 2.7E-01

winter-summer 1.7E-01 2.8E-01 4.4E-01 7.3E-03 7.2E-01 3.2E-01 1.3E-05 1.8E-06

winter-fall 6.2E-02 8.7E-02 9.6E-01 6.8E-01 6.6E-01 5.1E-01 1.1E-04 1.6E-04

Total Nitrogen







Nitrate-Nitrite







Total Suspended Solids







Chloride








3.2.4. Cumulative Water Volume and Nutrient Loading -- Stormflow

Plots of cumulative seasonal loading of stormwater, nutrients, TSS, and Cl- are shown for

all years at each site in Appendix I-1. Note that significant gaps in flow or chemistry data

can exaggerate the contribution of all sampled events to cumulative loading (e.g. PC in

2007, SAP in 2008). Figure 3.4 shows the mean loading curves for the sites, grouped by

main sites and secondary sites, with separate plots for each constituent (volume and TP in

Figure 3.4a, TN and NO3 in Figure 3.4b, and TSS and Cl- in Figure 3.4c).

Cumulative stormwater loading for most sites (Figure 3.4a) followed a slight S-shaped

curve, with the largest increases from mid-summer through early fall, when some of the

larger, more intense storms tend to occur. This seasonality appears especially true of

some of the BMP sites (e.g. Sarita, GCP Outlet, VP Outlet), where these larger events

cannot be completely detained and more outflow may occur than in other times of the

year. For the non-BMP sites, in particular EK, PC, TBEB, and TBWB, the largest

increases in stormwater loads tended to occur in fall, perhaps due to the effect of a few

large fall storm events, or because of the loss of rainfall abstraction by vegetation as leaf

fall occurs. The largest watersheds, TBO and SAP, had more uniform seasonal

stormwater volume loading than the other sites, perhaps due to the substantial presence of

BMPs and surface water in both watersheds that at large scale might serve to smooth out

stormwater loading.

The nutrient loading plots are intended to illustrate the combined effect of seasonality in

both runoff yields and nutrient concentrations. Loading of nutrients (TP, TN, NO3) at

most sites was similar to stormwater, with perhaps slightly larger increases in nutrient

loads (relative to increases in stormwater volume) during summer and early autumn,

which may be related to event size. Some non-BMP sites (e.g. EK, PC, TBW, TBO)

showed substantial increases in TN and TP loads in early summer that could perhaps be

related to early-season inputs of leaves, seeds, and flowers as trees leaf out. However, the

similarity of nutrient and stormwater loading suggests that while some nutrient

concentrations do vary significantly among seasons during the monitoring period

(Section 3.2.3), these differences are not enough to substantially impact seasonal loading

(though extreme loading events may still be a concern during seasons with generally

higher nutrient concentrations). Instead, nutrient loading appears to be controlled

primarily by the seasonality of stormwater loads.

Seasonal patterns of cumulative TSS loading also tended to follow patterns in water

loading, though most sites showed much larger increases in TSS than in stormwater in

late summer and early fall, especially at the BMP sites. This late season TSS flux is

presumably due to larger or more intense storms occurring during this part of the year,

which may tend to cause greater erosion rates and carry more sediment into storm drains.

In the case of the BMP sites, the large or intense late summer storms may be pushing


sediment-laden water from the BMPs, which are better able to detain the smaller or less-

intense events that tend to occur during the rest of the year.

The cumulative loading curves for Cl- were generally dissimilar to the stormwater

loading curves at the non-BMP sites because of the significantly higher spring Cl-

concentrations at these sites. In particular, EK, SAP, TBEB, TBWB, and TBO showed

large increases in Cl- loading in late spring and early summer, with nearly uniform

(linear) loading the rest of the monitoring season. This is unsurprising for stormwater

since early season rains are expected to flush winter road salt applications from

impervious surfaces, and once this source has been depleted, stormwater concentrations

become more uniform as background sources (e.g. groundwater mixing, outflow from

ponds and wetlands) become the dominant contributors of Cl-.


Figure 3.4. (a) Mean cumulative seasonal stormwater volume loading at main sites (top left) and secondary sites (top right), and

mean cumulative seasonal stormwater TP loading at main sites (bottom left) and secondary sites (bottom right).


Figure 3.4. (b) Mean cumulative seasonal stormwater TN loading at main sites (top left) and secondary sites (top right), and mean

cumulative seasonal stormwater NO3 loading at main sites (bottom left) and secondary sites (bottom right).


Figure 3.4. (c) Mean cumulative seasonal stormwater TSS loading at main sites (top left) and secondary sites (top right), and mean

cumulative seasonal stormwater Cl- loading at main sites (bottom left) and secondary sites (bottom right).


3.2.1. Cumulative Water Volume and Nutrient Loading -- Baseflow

In general, loading of water and nutrients by baseflow (Appendix I-2) was much more

uniform than by stormwater due to the less dynamic nature of baseflow water sources,

which is likely groundwater at most sites. The exception to this was the two VP sites,

which are dominated by surface water in the upstream ponds and wetlands and therefore

exhibited some seasonal variation in nutrient and water loading.

Cumulative water loading was very uniform throughout the monitoring season for all

sites (the non-BMP sites in particular), as exhibited by the linear loading curves

(Appendix I-2). The Trout Brook sites in particular were remarkably constant over the

season, even showing small variation year-to-year. This suggests a consistent source of

baseflow, which in these watersheds is mostly groundwater that may be potentially

enhanced by the presence of buried streams.

As in the case of stormwater, cumulative nutrient loading (TP, TN, and NO3) was tied

strongly to hydrology, and was therefore relatively constant throughout the year on

average, especially for the non-BMP sites (EK, PC, SAP, TBEB, TBWB, and TBO). For

the VP sites, NO3 loading appears to be relatively more intense during spring than the rest

of the monitoring period (with a similar pattern for TN at VP Outlet), which is explained

by the significantly higher NO3 concentrations observed at both sites during winter and

spring relative to summer (Table 3.2b). This could be the result of build-up of NO3

during winter (perhaps due to decay of wetland vegetation) that is flushed out by storms

and baseflow in spring, or perhaps due to the dominance of potentially NO3-rich

groundwater during winter and early spring, when the upstream ponds and wetlands are

mostly frozen.

TSS, which is found in much lower concentrations in baseflow than in stormflow,

showed some seasonality in cumulative loading. In particular, a regular late spring – early

summer increase in loading was present at PC, and both VP sites showed relatively sharp

increases in TSS loading during fall. At the VP sites, this fall increase in TSS loading

may be caused by flushing of summer-deposited sediment from the wetland during

autumn rains, especially as macrophytes senesce and potentially reduce the ability of the

wetland to filter out and retain sediment. This explanation is also supported by the much

larger increases in fall TSS loading at VP Outlet vs. VP Inlet.

Cumulative baseflow loading of Cl- was more variable among sites than for nutrients and

TSS. Spring peaks in Cl- loading were apparent at the VP sites and to a lesser extent EK;

this is a logical observation for the BMP sites (VP) as road salt accumulated in the ponds

and wetlands of this BMP during winter are flushed out in spring outflow. An

explanation for EK is more difficult, but results suggest that shallow groundwater is the

primary baseflow component at EK. Some flushing of this reservoir may thus occur

during late spring, with more consistent loading of Cl- (which is above the MPCA water


quality standard year-round) during the rest of the season. At the other sites, the Trout

Brook sites in particular, Cl- loading is relatively constant throughout the monitoring

season as well as among years.

Cumulative Baseflow Loading – Annual

Annual flow data was collected for the non-BMP sites (EK, PC, SAP, TBEB, TBWB,

and TBO) during 2010, 2011 and 2012, allowing cumulative baseflow loading curves to

be developed for the entire year rather than just the Apr – Oct monitoring period. These

plots are shown in Appendix I-3. Note that in all years, the monitoring interval at SAP is

shorter than a year (Mar – Dec in 2010, Apr – Dec in 2011, parts of June and Nov in

2012) due to equipment issues. Note that while major snowmelt intervals were identified

by CRWD for 2011 and 2012 and are not included in these plots, some snowmelt input is

probably reflected in the loading curves.

As expected, baseflow water loading was relatively constant throughout the year at these

sites, especially at the Trout Brook sites. Annual water loading was more variable at EK,

with an increase in loading rates in early spring 2011 and 2012, and during summer of

2010. The cause of these patterns is uncertain, but may be related to seasonality of flow

rates of shallow groundwater, which is assumed to be the primary baseflow source in this

watershed.

Over the annual time scale, nutrient loading was very similar to water loading for these

sites. Much of TN is in dissolved form as NO3 in these large drains, and therefore the

annual loading curves for TN and NO3 were very similar for all sites and generally

followed patterns in water yields. Some site-to-site variability was observed for TP; while

loading was uniform over much of the year at PC, TBWB, and TBO, small increases in

loading rates were present in spring and again in fall for TBWB and TBO. As baseflow in

the Trout Brook watersheds is influenced by outflow from upstream lakes (Como and

McCarrons), increases in TP, especially in fall, may be related to seasonality of both

terrestrial and aquatic vegetation inputs. The spring TP increases occur simultaneously

with increases in TN, TSS, and Cl- (especially in 2012), suggesting inputs from snowmelt

or early season rainfall, which would influence outflow from upstream lakes in the

TBWB and TBO watersheds. Note that the sharp increases in TP during late fall at TBEB

in 2010 and at EK in 2011 are likely caused by single, high TP concentrations being

applied to long-duration loading intervals due to less frequent sampling during this time

of year, and thus may not reflect actual changes in loading rates.

TSS loading varied among sites and between years when considered on an annual time

scale, and did not appear to follow the relatively uniform patterns of water yield. For

example, increased loading rates were observed in July, Aug or Sep at EK, with similar

peaks observed for PC in Feb-Mar and May-June. All three Trout Brook sites showed

large increases in loading rates in Feb-Mar of 2011 and 2012 but not in 2010. The early


spring peaks are likely related to small snowmelt events, which would be expected to

flush sediment (likely from winter road deicing applications) into the drains and would be

higher in TSS than groundwater during baseflow periods. Spring of 2011 in particular

would have involved more snowmelt as snowfall was far above average during the winter

of 2010-2011.

For Cl- loading, patterns similar to those of TSS were observed for all sites, suggesting

that early season TSS (and Cl-) inputs may be due in part to flushing of sand and salt

applied to roads during winter. In 2011, all sites showed large increases in Cl- loading

rates in early spring (beginning in Feb or Mar), in particular at PC and EK, which have

less surface water than the Trout Brook sites. Smaller spring increases in Cl- loading rates

were observed in 2012, and especially in 2010; springs in both of these years followed

much warmer and drier winters than 2011. Interestingly, while peak loading rates of Cl-

were also observed in spring in the cumulative loading curves for the Apr-Oct monitoring

period, the annual data shows that the onset of Cl- loading peaks may be much earlier

than the start of the monitoring period, and also that, while data are limited, loading rates

may be highly dependent on antecedent winter conditions (e.g. snowfall, snowpack

depth, temperature).

3.3. Impact of Storm Event Characteristics on Water and Nutrient Loading

Cumulative rainfall frequency plots for rain count, cumulative runoff volume, and

cumulative loads of nutrients and sediment were determined for all monitored sub-

watersheds. In addition, a simple linear regression analysis was used to determine the

importance of antecedent conditions (e.g. days since last measurable rainfall, total rainfall

in previous 7 days) on runoff volume and nutrient, TSS, Cl-, and metal concentrations.

3.3.1. Cumulative Rainfall Frequency and Runoff Volume

Cumulative rainfall frequency plots (exceedence probability distributions for runoff

volume and rainfall events versus rainfall depth) are given for all sites in Appendix E-1.

Results for EK are also shown in Figure 3.4a as an example. As is generally the case for

all monitored sites, the cumulative runoff volume frequency distribution has a similar

shape to the rain event frequency distribution, but they are not coincident. As a result, the

median rainfall depth for EK is 0.46 inches, but rainfall events of this depth and smaller

only account for 21% of the total runoff volume; half of the runoff volume occurs for

events 0.81 inches and smaller. A 1-inch rainfall event, which is commonly used to size

BMPs, is in the 87th

percentile for EK, but events at or below this depth contribute only

63% of total runoff volume. These results show that the largest storms comprise the

majority of the total runoff volume for this site, an unsurprising result given that very

little area of this watershed is devoted to surface water storage or BMPs. By contrast, the


smallest events, which are the most frequent, are mostly captured by the watershed and

may not be of great concern in BMP design.

Figure 3.4. Cumulative rainfall frequency plots of rain event count and cumulative

stormwater runoff volume at (a) East Kittsondale (EK) and at (b) Villa Park Outlet.

A second example of rainfall and runoff volume exceedence probabilities is shown in

Figure 3.4b for VP Outlet. Both the rain event count and runoff volume curves are more

vertical and shifted slightly towards greater rainfall depth relative to EK because larger

rainfall events are completely captured by the wetland system compared to the EK

watershed. As a result, the median rainfall depth is much greater for VP Outlet (0.69 in)

than for EK (0.46 in). Events at and below this depth constitute 24% of the total runoff

volume from VP Outlet, while half the total runoff volume occurs for rainfall depths of


1.11 inches and smaller -- considerably larger than at EK (0.81 in). A 1-inch storm is in

the 74th

percentile by rainfall depth and 45st percentile for cumulative runoff volume.

These results illustrate the effect of BMPs to restrict runoff to greater rainfall depths,

especially relative to watersheds (such as EK) with very little surface storage or few

BMPs.

Rainfall and runoff exceedence probability characteristics are summarized for all sites in

Table 3.3. The BMP sites (GCP Outlet, VP Outlet, Sarita) have the largest median

rainfall depths and largest rainfall depths corresponding to median cumulative runoff

volumes, likely due to the ability of BMPs to store runoff from smaller events. Rainfall

depths of 1 inch or less contributed 32% - 42% of the total runoff volume at these sites,

suggesting that the BMPs were designed for storms smaller than 1-inch, or that other

factors (e.g. rainfall intensity, antecedent conditions, variable watershed area) are

increasing water loads to these BMPs. Similarly, among the non-BMP sites, those with

upstream connections to lakes (e.g. TBWB) or with relatively large numbers of ponds

and wetlands (e.g. TBEB, SAP) had higher median rainfall depths and/or greater rainfall

depths corresponding to median cumulative runoff volume when compared to sites such

as EK and PC, which have less surface water and fewer BMPs.

Table 3.3. Summary of rainfall and runoff frequency characteristics of CRWD sub-

watersheds.

Site

Median Rainfall by Count Rainfall Depth at

Median Cmltv Vol

1-inch Rainfall Median Event Vol by Count

Depth Cmltv Vol Cmltv Vol Rainfall

(in) (fraction) (in) (fraction) Percentile (ft3)

EK 0.43 0.20 0.82 0.62 0.87 534,105

PC 0.50 0.21 0.86 0.55 0.84 596,768

SAP 0.55 0.23 0.85 0.59 0.84 1,195,920

TBEB 0.59 0.24 1.01 0.50 0.77 358,771

TBWB 0.53 0.19 1.13 0.45 0.80 1,147,130

TBO 0.51 0.25 0.80 0.59 0.82 2,955,970

Como7 0.31 0.12 1.13 0.48 0.89 10,768

GCP 0.72 0.19 1.24 0.42 0.73 257,416

VP Out 0.74 0.25 1.26 0.41 0.70 268,419

Sarita 0.65 0.14 1.50 0.32 0.74 69,328

Como 3 0.28 0.11 1.05 0.47 0.84 53,914

AHUG 0.32 0.12 0.87 0.59 0.90 6,331

VP Inlet 0.56 0.23 0.94 0.54 0.79 224,831

Note that some of the smaller sites (e.g. AHUG, Como 3) have smaller median rainfall

event depths than the larger watersheds. This is likely the result of including in the


analysis only those events that produce runoff. In the smaller watersheds, less rainfall is

required to produce runoff that is above the sampling threshold (“trigger”) for the auto-

samplers; at the larger sites the trigger is set higher so that changes in baseflow rates are

not sampled as storm events. It is also possible that at the larger sites, especially those

with significant amount of surface storage, smaller events are mostly contained on the

watershed, resulting in little detectable effect on flow at the watershed outlet.

3.3.2. Cumulative Rainfall Frequency and Nutrient, TSS, and Cl- Loading

Cumulative rainfall frequency curves for loads of nutrients, TSS, and Cl- are shown for

all sites in Appendix E-2. In general, cumulative loading curves for TP, TN, NO3, and

TSS are very similar to each other, and to the cumulative stormwater volume curves

(shown also in the nutrient loading plots for reference). This suggests, much as in the

case of the seasonal cumulative loading curves (Appendix I-1), that hydrology is

controlling stormwater loading of nutrients and TSS.

For Cl-, the loading curves fall slightly above the other curves at most sites due to a larger

percentage of total Cl- loading being associated with smaller rainfall events. This is

perhaps caused by the high solubility of Cl-, which makes it mobile in even the smallest

rainfall-runoff events. The differences in Cl- loading are especially apparent at EK and

PC, which have less BMPs and surface water than most of the other sites, and therefore

less ability to retain water, even for small events. It should be noted that March rainfall

events were left out of these analyses due to their scarcity at most sites. This prevents

confounding of results among sites due to release of Cl- in snowmelt; the few early spring

rainfall-snowmelt events that exist in the record at all sites generally have very high Cl-

concentrations and loads, even for small rainfall events, which may tend to exaggerate

the Cl- curves even further. With a greater sample size, these events could potentially be

examined on their own to determine the impact of early spring rains on flushing of

nutrients, and Cl- in particular.

3.3.3. Effect of Antecedent Rainfall on Stormwater and Nutrient Loading

Stormflow water yield (in) and stormwater nutrient (TP, TN, NO3), TSS, Cl-, and metal

(Cd, Cr, Cu, Pb, Ni, Zn) concentrations were regressed against three antecedent rainfall

characteristics, including days since last measureable rainfall (“dry days”), days since last

storm of 0.5 inch depth or greater (“days since 0.5-inch rain”), and total rainfall depth in

the previous 7 days (“antecedent weekly rain”). Results are shown in Appendix B.

For stormwater yield, antecedent weekly rain was a statistically significant predictor (i.e.,

p < 0.05) for all sites. This is perhaps a logical result, as slope was positive for antecedent

weekly rainfall; positive slope suggests that as more rainfall occurs in the week before an

event, the watershed has less ability to capture water via infiltration or surface storage,

resulting in greater runoff. The other antecedent parameters were generally not

significant, except at SAP and VP Outlet, where both dry days and days since 0.5-inch


rain were significant. These sites are dissimilar in terms of size and land cover

composition and thus the correlations may be spurious, but stormwater yields from both

may be influenced to a significant degree by antecedent conditions.

For TP and TN, all three antecedent parameters were statistically significant predictors of

nutrient concentration at most sites; of these, antecedent weekly rainfall appeared to be a

slightly better predictor than the other two parameters for TN and TP, as it generally

explained more variance (higher R2). A notable exception to this is at several of the

smaller sites: none of the antecedent parameters were useful predictors of TP

concentration for VP Inlet or of TN concentration for Sarita or Como 3. These

differences, at least for VP Inlet and Sarita, may result from the large BMPs at these sites

that capture particulate N and P, potentially reducing the impact of antecedent rainfall. In

addition, for all sites, regressions of TP and TN concentration had negative slopes with

antecedent weekly rainfall, which was opposite the trend for stormwater yield. This

suggests that N and P source dilution may be occurring as antecedent rainfall and/or

stormwater volume increases.

For TSS, fewer significant correlations existed with antecedent parameters at most of the

sites. For the significant relationships, positive slopes associated with dry days and days

since 0.5-inch rainfall and negative slopes associated with 7-day antecedent rainfall

suggest that TSS may be subject to build-up and wash-off. This effect could explain the

strong correlations of TP and TN with antecedent rainfall parameters, as N and especially

P tend to be transported as particulates in stormwater (e.g. Waschbusch et al. 1999,

Easton and Petrovic 2008). The significant effect of all three antecedent conditions on

TSS concentrations at TBWB, TBO, and GCP Outlet suggests that in addition to build-up

and wash-off, flushing of sediment from abundant surface water in these watersheds may

be occurring, as concentrations of TSS could increase with drier antecedent conditions

(e.g. due to evaporation).

NO3 was correlated with almost none of the antecedent rainfall parameters at any sites.

While atmospheric deposition may be a potentially important source of NO3 in urban

watersheds, if it were the dominant source it would be expected to have greater

correlation with antecedent rainfall (evidence of build-up and wash-off). NO3 is a

relatively small component of stormwater TN at most sites, and the dominant source is

likely fertilizer, pet waste, and/or vegetation rather than dry deposition.

Cl- concentration in runoff was significantly correlated with antecedent rainfall

parameters at most sites, particularly for antecedent weekly rainfall and days since last

0.5-inch rainfall. An explanation for these trends is not readily apparent given the

seasonality of Cl- in storm runoff, but dry deposition on impervious surfaces between

events may play a role in enhancing Cl- concentrations. For example, as in the case of TN

and TP, all slopes for Cl- vs. antecedent weekly rainfall are negative, suggesting that as


more rainfall occurs prior to an event, less Cl- is available and/or it is being diluted by

larger stormwater volumes.

All metals were significantly correlated with antecedent rainfall parameters at most sites

(with perhaps fewer total significant correlations present for Cu and Cd), though the

amount of variance explained (R2) was generally low overall. Similar to TSS, among

significant relationships the slopes associated with dry days and days since 0.5-inch

rainfall were positive and those associated with 7-day antecedent rainfall were negative,

suggesting build-up and wash-off as the primary transport mechanism for metals. Finally,

none of the antecedent parameters appeared to be more frequently significantly correlated

with metals concentration than the others, although R2 was usually higher for weekly

antecedent rainfall than for the other parameters.

3.4. Impact of Land Cover and Drainage Characteristics on Water and

Nutrients in Stormflow

Simple linear regression was used to investigate correlations of stormflow nutrients and

metals with 21 land cover and drainage characteristics of several of the non-BMP sub-

watersheds (AHUG, EK, PC, SAP, TBEB, and TBWB). A complete list of land cover

factors is shown in Table 2.2. Dependent variables included stormwater yield, and event

mean concentration and event yield of nutrients (TP, TN, NO3), TSS, Cl-, and selected

metals (Cu, Pb, Zn). Linear regression parameters from the analysis (slope, R2, and p-

value) are summarized in Appendix C.

In general, very few useful relationships emerged from this analysis for parameters

expected to be good predictors of nutrient and metal concentrations (e.g. total impervious

area, street density, lawn). Of the explanatory variables, canopy over street, ‘other’

impervious, alley, and several roof types (institutional, high-density residential,

commercial, and industrial) were the only factors that were statistically significant (p <

0.05) predictors of nutrient concentration. Of these, the most sensible factors are probably

canopy over street and ‘other’ impervious area (parking lots, alleys, and driveways).

Alley area and the specific roof types are generally scarce in the monitored watersheds

(with the exception of low-density residential), and thus most of those correlations are

likely spurious.

Relationships of nutrient concentrations with canopy over and near the street were

positive, and significant for TP and TSS, suggesting that this near-street tree cover may

enhance TP concentrations, perhaps by leaching nutrients from leaves or through wash-

off of atmospheric deposition onto the street surface. However, if litterfall was a major

source of nutrients, TN would also be expected to be significantly correlated with near-


street canopy cover, and TSS is the only other nutrient for which a near-street canopy is a

statistically significant predictor.

NO3 concentration was significantly and positively correlated with ‘other’ impervious

area. This is a sensible result if atmospheric deposition is a primary source of NO3, as

these areas may serve as collectors of deposition. However, if impervious areas were also

primary pathways of transport, factors such as street density or total impervious area

should also be correlated with NO3, but none of these parameters are significant

predictors of either NO3 concentration or yield.

Very few factors were significant predictors of water, nutrient, sediment, or metal yields.

This is a surprising result, especially for water yield, given that impervious surfaces in

particular are expected to be primary conveyances of water and nutrients. The near lack

of correlations for yields suggests that water and nutrient sources in CRWD may be

relatively diverse, and that a single source or transport factor is not primarily responsible

for nutrient and metal loading. The watershed areas used to calculate yields may also be

inaccurate, particularly for the watersheds with upstream lakes and wetlands (SAP,

TBWB/TBO), as these upstream areas were not included in the yields despite possibly

contributing some water and nutrients during storm events.

3.5. Exceedence Probabilities of Water Yields and Nutrient Loads

Flow-duration curves were constructed for runoff, and load-duration curves for nutrients

(TP, TN, NO3), TSS, and Cl- in both stormflow and baseflow. All flow-duration curves

are shown for stormflow and baseflow in Appendices F-1 and F-3, respectively, and load-

duration curves are shown in Appendix F-2 for stormflow and in Appendix F-4 for

baseflow.

3.5.1. Stormflow

Flow-duration curves for stormflow showed the expected S-shaped patterns for volume

and flow rate at most sites (e.g. EK in Figure 3.5a). Stormwater volumes and flow rates

varied over 3 or 4 orders of magnitude at several of the smaller non-BMP sites (e.g.

AHUG, Como 3, and EK), as well as at two of the BMP outlet sites (GCP Outlet and

Sarita). For the non-BMP sites, these ranges may be related to lower sampling thresholds

for the auto-samplers (some small events go undetected at the larger sites) or less

capacity for surface water storage relative to the larger sites, thus causing more small

events to be included in the analysis for these sites. By contrast, flatter loading curves

were observed for the larger watersheds, in particular SAP and the Trout Brook sites,

which may be related to upstream lakes, ponds, and wetlands in these watersheds that

tend to moderate flow rates of larger or more intense storms.


As in the case of the cumulative loading plots, the nutrient, TSS, and Cl- loading

exceedence curves for a given site were similar in shape to each other. Likewise, for most

sites, load-duration curves followed similar patterns as their respective flow-duration

curves, with some exceptions, particularly for small events, that might be related to the

smaller data sets used for the load-duration curves (i.e., samples were not collected for all

events for which flow was measured.) However, the results generally support the

conclusion that hydrology has a stronger influence than nutrient or sediment sources on

stormwater loading in CRWD watersheds.

Some discrepancies existed between the load-duration curves for TSS and the other

constituents. For example, at several sites, including PC, TBEB, TBO, and VP Inlet, TSS

curves appeared to have slightly steeper slopes overall than the other loading curves. This

is due to greater amounts of sediment being mobilized for the larger (i.e. low exceedence

probability) storms, but these increases do not appear to also correlate to higher nutrient

and Cl- loads for these sites. In addition, at some sites (TBEB, PC and to a lesser extent

AHUG, SAP, and TBO) TSS loading decreased more than the other constituents for

smaller (high exceedence probability) events, which may be related to low mobility of

sediment for small storms at these sites.

3.5.2. Baseflow

Baseflow flow-duration curves were much flatter and much less variable than those for

stormflow at a given site (e.g. for EK in Figure 3.5b) due to relatively uniform flow rates

throughout the monitoring season at those sites with baseflow. At all sites except EK and

the VP sites, baseflow rate generally varied over less than an order of magnitude.

For TBWB and VP Inlet/Outlet, the loading curves were similar among nutrients, TSS,

and Cl-, with similar shapes to their respective flow-duration curves resulting from

relatively uniform nutrient concentrations and baseflow rates. At the remaining sites,

some variation was present among constituents. For example, at TBEB, TP, TSS, and Cl-

loading were less uniform, particularly at the extreme low- and high- exceedence

probabilities. The higher loading rates may perhaps be explained by the influence of

snowmelt or the receding limbs of storm events. This may also be the case at SAP and

EK, where some higher loading rates are present in Cl- and TSS in particular. At PC, a

large range in loading rates are present at the extreme low- and high- exceedence

probabilities, which is somewhat surprising given the nearly steady baseflow rates and

relatively linear cumulative loading curves (Appendix I). This suggests a large but

infrequent change in nutrient concentrations, perhaps due to snowmelt (for Cl-) or input

of water with low nutrient content, although the source of such an input is unknown.


Figure 3.5. Flow-duration curves for the East Kittsondale (EK) site, for (a) stormflow

and (b) baseflow.

0.1

1.0

10.0

100.0

1,000.0

1,000

10,000

100,000

1,000,000

10,000,000

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Sto

rm E

ven

t F

low

Rate

(cfs

)

Sto

rm E

ve

nt

Vo

lum

e (

cu

ft)

Probability of Exceedence

Volume

Flow Rate

0.001

0.010

0.100

1.000

10.000

100.000

0.00 0.10 0.20 0.30 0.40 0.50 0.60 0.70 0.80 0.90 1.00

Bas

efl

ow

Rate

(c

fs)

Probability of Exceedence


3.6. Metals Toxicity Exceedences in Stormwater

Plots of metals concentrations vs. hardness along with the chronic toxicity standard for 6

metals (Cr, Cd, Cu, Pb, Ni, and Zn) are shown for all sites in Appendix G. Toxicity

exceedence probability curves based on the toxicity standard and water hardness data are

shown in Appendix H. Only stormflow is considered, as metals concentrations in

baseflow are low and toxicity exceedences are rare.

Figure 3.6 shows an example of toxicity and metal concentration vs. hardness for Cu and

Zn measurements at TBEB. These plots show that the toxicity standards increase non-

linearly for increases in hardness. For Zn, it is apparent that a few storm events at TBEB

exceed the toxicity standard (mostly at lower hardness), while for Cu roughly half of the

events exceed the toxicity standard for a range of hardness values. The corresponding

toxicity exceedence probability plots for Cu and Zn at TBEB are shown in Figure 3.7.

For Zn, the few toxicity exceedences were mostly for lower hardness values, which

correspond to lower values of the toxicity standard (and a higher exceedence probability)

at this site. For Cu, toxicity exceedences were distributed across the whole range of

hardness values (and toxicity exceedence probabilities).

Figure 3.6. Observed stormflow metal concentrations in g/L and toxicity standards at

TBEB as a function of observed total hardness mg/L for zinc (left) and copper (right).


Figure 3.7. Toxicity exceedence probability curves at TBEB for zinc (left) and copper

(right). Observations of metals concentrations (in g/L) are also shown.

3.6.1. Seasonality of Metals Toxicity

For many sites, Cu, Pb, and Zn frequently exceeded chronic toxicity standards. The

seasonality of these exceedences was investigated to determine if certain times of year

were more likely to have exceedences than others, which might aid in the design of future

BMPs where metals toxicity is an issue. The percentage of samples in each season

(spring, summer, fall) that exceed the standard, as well as the mean value of the

exceedences are summarized in Table 3.4 by metal and by site. Only stormflow results

are included here; the corresponding table for baseflow is shown in Appendix D.

No toxicity exceedences were observed in stormflow at any of sites for Cr or Ni, while

exceedence percentages for Cd, Cu, Pb, and Zn varied considerably among sites, and

among seasons within some sites. For Cd, most non-BMP sites had fewer than 20%

exceedences across seasons; exceedences of greater than 28% were present during

summer and fall at the Como sites (Como 7, GCP Outlet, Como 3, and AHUG). For Cu

and Pb, all sites except the VP sites had toxicity exceedences of roughly 60% or higher

for all seasons. The most among-site variability in exceedence percentages was observed

for Zn: exceedences were highest (60% to 100%) at EK, Como 7, Como 3, and AHUG,

and lowest or non-existent at the VP Sites.

On average, the percentage of events exceeding standards did not vary much among

seasons, with a few exceptions where considerable variability was observed, such as at

AHUG, Sarita, Como 7, and GCP Outlet for Cd, and at most sites for Zn. At the main

sites, summer Zn exceedences were more common than in spring or fall, while spring

exceedences were more common at many of the smaller sites.

Toxicity exceedence values, defined as the difference between the observed

concentration and the toxicity standard for the observed hardness, also showed some


variability among sites and among seasons (Table 3.4). For Cu, Pb, and Zn, exceedence

values were generally largest at EK, PC, and SAP, with the smallest values observed at

the BMP sites (Sarita, GCP Outlet, VP Outlet). The largest Cd exceedences were found at

TBO, but were an order of magnitude higher than at any other site, suggesting an error in

the data or presence of a point source (the large exceedences were found only in the

spring). Exceedences generally decreased in value from spring to fall at most sites,

suggesting that spring or summer may be the most important time of year for metals

toxicity management.

Toxicity exceedence values in stormflow were tested for statistically significant

differences among seasons using the Mann-Whitney test. Table 3.5 summarizes p-values

for all sites and metals.

Very few statistically significant differences among seasons (at p < 0.05) were found for

Cd and Zn, in which the percentage of exceedences among seasons appeared to vary the

most. One exception is EK, in which Zn exceedences were significantly lower in fall and

decreased throughout the year from spring to fall, suggesting source depletion of Zn (or

an increase in water hardness) over the season. Similar trends, while not statistically

significant, were present at a few other sites (TBEB, TBWB, and AHUG).

Statistically significant seasonal differences in Cu and Pb toxicity exceedences were

present at all of the main sites except TBO. Due to lower Cu and Pb exceedence values in

the fall, differences between summer and fall toxicity exceedences were nearly all

significant at these sites, with several spring-fall differences significant as well. As in the

case of Zn at EK, this may be related to source depletion or an increase in water hardness

(which would increase the toxicity standard), though a uniform increase in water

hardness across sites is less likely than a depletion of Cu and Pb sources to stormwater.

As streets and impervious surfaces could be considered the primary collectors and vectors

for metals transport (by providing substrate for atmospheric deposition and connecting

directly to storm drains), it is possible that summer and fall rains deplete this source at a

faster rate than deposition recharges it, leading to low exceedences in the fall and higher

exceedences in spring and summer. This conclusion is supported in part by the metals

concentration data; concentrations of Cu and Pb in particular decline over the spring

and/or summer seasons in stormwater at many sites (Table A.1), and many of these

decreases were found to be statistically significant, especially at the main sites (Table

3.2a).


Table 3.4. Seasonality of metals toxicity exceedences in stormflow of CRWD sub-watersheds.

Season Parameter Kittson-

dale Phalen Creek

St. Anthony

Park

Trout Brook East

Trout Brook West

Trout Brook Outlet

Sarita Outlet

Villa Park

Outlet

Como 7

GCP Outlet

Como 3


Cadmium

sp

ring

No. of Samples 26 21 16 24 22 26 12 17 24 11 13 23 24

Exceedences (%) 11.5 0.0 0.0 0.0 4.5 73.7 8.3 0.0 37.5 9.1 0.0 30.4 0.0

Mean Exc. (ug/L) 0.103 N/A N/A N/A 0.148 0.686 0.083 N/A 0.186 0.036 N/A 0.141 N/A

su

mm

er No. of Samples 68 57 57 53 62 55 48 55 48 32 25 50 66

Exceedences (%) 13.2 10.5 7.0 7.5 6.5 7.3 29.2 0.0 33.3 28.1 32.0 50.0 3.0

Mean Exc. (ug/L) 0.219 0.571 0.595 0.649 0.245 0.594 0.094 N/A 0.129 0.115 0.123 0.128 1.204

fall

No. of Samples 31 26 27 25 21 20 24 24 17 14 10 21 24

Exceedences (%) 25.8 15.4 3.7 4.0 4.8 0.0 45.8 0.0 29.4 50.0 40.0 90.5 4.2

Mean Exc. (ug/L) 0.297 1.316 0.014 0.059 0.059 N/A 0.301 N/A 0.046 0.148 0.160 0.183 0.111

Chromium

sp

ring

No. of Samples 26 21 16 24 22 27 12 17 24 11 13 23 24

Exceedences (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0

Mean Exc. (ug/L) N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

su

mm

er No. of Samples 68 57 57 53 62 55 48 55 48 32 25 50 66

Exceedences (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


fall

No. of Samples 31 26 27 25 21 20 24 24 17 14 10 21 24

Exceedences (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0



Table 3.4 (con’t). Seasonality of metals toxicity exceedences in stormflow of CRWD sub-watersheds.


dale Phalen Creek

St. Anthony

Park

Trout Brook East

Trout Brook West

Trout Brook Outlet

Sarita Outlet

Villa Park

Outlet

Como 7

GCP Outlet

Como 3


Copper

sp

ring

No. of Samples 26 21 16 24 22 27 12 17 24 11 13 23 24

Exceedences (%) 100.0 95.2 87.5 79.2 81.8 66.7 91.7 0.0 100.0 63.6 100.0 100.0 4.2

Mean Exc. (ug/L) 36.9 14.9 15.4 8.3 15.8 16.8 4.3 N/A 15.5 2.1 15.6 12.1 0.4

su

mm

er No. of Samples 68 57 57 53 62 55 48 55 48 32 25 50 66

Exceedences (%) 98.5 94.7 75.4 77.4 90.3 83.6 81.3 0.0 97.9 65.6 96.0 98.0 4.5

Mean Exc. (ug/L) 26.3 23.9 19.8 8.1 15.5 11.8 4.8 N/A 12.7 9.4 8.9 12.6 10.0

fall

No. of Samples 31 26 27 25 21 20 24 24 17 14 10 21 24

Exceedences (%) 100.0 84.6 77.8 64.0 81.0 65.0 91.7 0.0 94.1 50.0 100.0 95.2 0.0

Mean Exc. (ug/L) 21.9 13.5 12.9 4.2 8.4 13.0 3.3 N/A 9.6 1.6 9.3 7.9 N/A

Lead

sp

ring

No. of Samples 26 21 16 24 22 27 12 17 24 11 13 23 24

Exceedences (%) 100.0 100.0 93.8 83.3 90.9 77.8 100.0 0.0 100.0 90.9 100.0 100.0 8.3

Mean Exc. (ug/L) 46.7 36.2 22.4 10.8 18.7 22.4 12.2 N/A 19.4 3.2 25.8 17.6 3.9

su

mm

er No. of Samples 68 57 57 53 62 55 48 55 48 32 25 50 66

Exceedences (%) 97.1 96.5 80.7 92.5 98.4 90.9 95.8 9.1 100.0 93.8 100.0 100.0 10.6

Mean Exc. (ug/L) 48.1 53.7 27.2 11.4 22.4 21.6 11.9 2.1 18.0 1.6 13.6 20.5 7.3

fall

No. of Samples 31 26 27 25 21 20 24 24 17 14 10 21 24

Exceedences (%) 100.0 92.3 77.8 72.0 95.2 90.0 100.0 16.7 100.0 92.9 100.0 100.0 8.3

Mean Exc. (ug/L) 28.4 31.0 18.9 4.9 13.4 17.7 9.3 1.5 14.0 1.4 10.1 15.5 0.8


Table 3.4 (con’t). Seasonality of metals toxicity exceedences in stormflow of CRWD sub-watersheds.


dale Phalen Creek

St. Anthony

Park

Trout Brook East

Trout Brook West

Trout Brook Outlet

Sarita Outlet

Villa Park

Outlet

Como 7

GCP Outlet

Como 3


Nickel

sp

ring

No. of Samples 26 21 16 24 22 27 12 17 24 11 13 23 24

Exceedences (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


su

mm

er No. of Samples 68 57 57 53 62 55 48 55 48 32 25 50 66

Exceedences (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


fall

No. of Samples 31 26 27 25 21 20 24 24 17 14 10 21 24

Exceedences (%) 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.0


Zinc

sp

ring

No. of Samples 26 21 16 24 22 27 12 17 24 11 13 23 24

Exceedences (%) 100.0 76.2 50.0 12.5 40.9 25.9 83.3 0.0 95.8 27.3 92.3 95.7 0.0

Mean Exc. (ug/L) 146.4 69.1 63.3 43.3 60.3 44.6 11.7 N/A 83.8 5.0 58.5 64.0 N/A

su

mm

er No. of Samples 68 57 57 53 62 55 48 55 48 32 25 50 66

Exceedences (%) 95.6 87.7 66.7 26.4 54.8 29.1 41.7 0.0 93.8 3.1 84.0 96.0 4.5

Mean Exc. (ug/L) 102.1 93.1 81.5 25.0 45.1 47.8 15.8 N/A 60.8 2.8 32.9 56.5 101.8

fall

No. of Samples 31 26 27 25 21 20 24 24 17 14 10 21 24

Exceedences (%) 93.5 50.0 48.1 4.0 33.3 25.0 16.7 0.0 76.5 0.0 60.0 90.5 0.0

Mean Exc. (ug/L) 68.5 63.0 61.7 7.8 23.7 56.5 14.1 N/A 54.8 N/A 40.4 36.3 N/A


Table 3.5. Summary of p-values for Mann-Whitney U test of seasonal differences in metals toxicity exceedence values in stormflow at

CRWD monitoring sites. Seasonal differences significant at p < 0.05 are highlighted in blue.

Seasonal Comparison

Kittson-dale

Phalen Creek

St. Anthony

Park

Trout Brook East

Trout Brook West

Trout Brook Outlet

Sarita Outlet

Villa Park

Outlet

Como 7

GCP Outlet

Como 3 AHUG Villa Park Inlet

Cadmium

spring-summer 1.0E+00 NA NA NA 1.0E+00 NA 8.1E-01 N/A 7.1E-01 2.9E-01 N/A 1.0E+00 N/A

spring-fall 9.2E-01 1.2E-03 6.5E-01 6.5E-01 1.0E+00 1.4E-01 7.7E-01 N/A 9.3E-02 5.0E-01 N/A 3.1E-01 1.6E-01

summer-fall 9.2E-01 3.9E-01 8.0E-01 1.0E+00 1.0E+00 2.2E-01 3.4E-01 N/A 1.8E-01 3.9E-01 7.3E-01 7.5E-02 6.7E-01

Chromium

spring-summer N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

spring-fall N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

summer-fall N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Copper

spring-summer 5.8E-02 8.4E-03 1.8E-01 4.6E-01 9.0E-01 8.5E-02 5.3E-01 N/A 2.7E-01 5.3E-01 9.5E-02 6.1E-01 N/A

spring-fall 6.3E-03 5.4E-01 9.9E-01 5.2E-02 4.9E-02 2.6E-01 6.1E-01 N/A 8.6E-01 3.2E-01 1.7E-01 5.1E-01 N/A

summer-fall 2.5E-01 9.1E-04 1.6E-02 1.3E-01 1.7E-02 8.2E-01 8.4E-02 N/A 4.9E-01 1.6E-01 1.0E+00 7.4E-02 N/A

Lead

spring-summer 5.0E-01 2.5E-02 1.3E-01 5.8E-01 3.5E-01 5.6E-01 7.1E-01 N/A 2.1E-01 4.4E-01 4.8E-02 6.7E-02 8.9E-01

spring-fall 1.7E-02 1.9E-01 7.8E-01 1.6E-02 2.1E-01 1.3E-01 1.4E-01 N/A 6.7E-01 3.1E-01 6.5E-03 7.4E-01 3.3E-01

summer-fall 9.7E-03 1.1E-04 3.7E-02 3.1E-02 1.2E-02 1.7E-01 1.8E-01 7.3E-01 1.3E-01 4.3E-01 3.6E-01 1.3E-01 5.0E-01

Nickel

spring-summer N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

spring-fall N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

summer-fall N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A

Zinc

spring-summer 8.9E-02 1.8E-01 3.5E-01 8.9E-02 3.4E-01 8.2E-01 6.2E-01 N/A 7.8E-01 N/A 4.8E-02 9.4E-01 N/A

spring-fall 1.2E-03 6.5E-01 6.5E-01 5.0E-01 1.4E-01 8.8E-01 5.4E-01 N/A 6.3E-01 N/A 4.4E-01 1.6E-01 N/A

summer-fall 2.2E-02 9.8E-02 2.7E-01 7.3E-01 2.2E-01 7.8E-01 1.0E+00 N/A 4.8E-01 N/A 4.4E-01 1.2E-01 N/A


4. Summary

Part 1: Spatial and Seasonal Patterns in Water, Nutrients, Metals

4.1.1. Baseflow vs. Stormflow

Appreciable differences existed between stormflow and baseflow in the loading of water,

nutrients, and metals. Baseflow water yields were quite variable among sites, and with

the exception of EK, were substantial, comprising 56% to 67% of combined seasonal

volume at PC, TBEB, TBWB, and TBO. These percentages are even greater when

considered on an annual scale. TP concentrations were much higher in stormflow as

expected, but TN concentrations were roughly similar between stormflow and baseflow,

suggesting that baseflow is potentially important for N loading. For metals (Cd, Cr, Cu,

Pb, Ni, Zn), baseflow concentrations were very low, often below the detection limit and

very rarely exceeded toxicity standards; stormflow concentrations were generally much

higher, and in particular for Cu, Pb, and Zn, frequently exceeded toxicity standards. For

Cl-, stormwater concentrations were generally much lower than in baseflow, and never

exceeded the MPCA standard of 230 mg/L; baseflow Cl- exceeded the standard at TBEB

and EK.

4.1.2. Spatial Variation

Spatial (i.e. site-to-site) variation was present to some extent in the water and nutrient

data for stormflow. As expected, the BMP sites (Sarita, VP Inlet/Outlet, GCP Outlet)

tended to have steadier flow regimes (flatter hydrographs, lowest stormflow water yields

and runoff coefficients) and the lowest median TP, TN, and TSS concentrations, which

logically suggests that the BMPs are effective in reducing nutrient export by detaining

water and capturing particulates. By contrast, non-BMP watersheds with very little

surface water (ponds, lakes, or wetlands) or BMPs, including EK and PC, had the

flashiest hydrographs and the highest stormwater yields, runoff coefficients, and TP, TN,

and TSS concentrations, consistent with expectations for highly urbanized watersheds.

Sites with some surface water in their watersheds (SAP and the Trout Brook sites) tended

to have longer hydrographs and variable stormflow water yields and nutrient

concentrations, likely the result of a relative diversity of water and nutrient sources and

transport pathways in these watersheds. For metals, in particular Cu, Pb, and Zn, median

concentrations were highest in the watersheds with the greatest total impervious area

(EK, PC, and SAP), suggesting that these surfaces may be both sources and vectors of

metals transport.


4.1.3. Seasonality

Seasonal patterns were present in concentrations and loads of nutrients and metals in

CRWD watersheds, with considerable site-to-site variability present in these seasonal

trends. These patterns are summarized separately for stormflow and baseflow.

Stormflow

In stormflow, very few statistically significant (p < 0.05) differences among seasons

existed at any of the sites for TP concentration. Summer and fall differences in TP at PC,

SAP, and TBEB were significant, with lower concentrations observed in the fall,

suggesting source depletion over the summer (likely due to the establishment of lawns,

which may prevent erosion, and to the seasonal maturing of trees and plants, causing less

plant material to enter the sewers). However, it is possible that extending the monitoring

season into the early winter to capture the effect of leaf fall would lead to a sharp increase

in TP in the autumn, as it is a potentially large TP input that is likely not reflected in the

results.

Stronger seasonality was present in the TN data, with significantly lower concentrations

of TN observed in the fall at several of the non-BMP sites (EK, PC, SAP, TBEB, and

TBWB). A similar explanation to the TP data is likely; the monitoring season may not

always capture events after leaf fall. Leaf fall would be expected to produce a large flux

of TN to the landscape following a summer depletion of organic and particulate N, which

is generally derived from soil and vegetation, and comprises the majority of TN at most

sites.

Cumulative N, P, and TSS loading curves at most sites tended to follow patterns in

cumulative stormwater volume, with the largest increases in loading rates generally

occurring in late summer and early fall. This suggests that while significant seasonality is

present in stormwater nutrient concentrations at some sites, nutrient loading is primarily

driven by hydrology.

Cl- concentrations were higher in the spring at most sites, and statistically different from

the other seasons (fall and summer), consistent with the expectation that spring rains

flush winter applications of road salt. As a result, loading curves for Cl- tended to show

large increases in spring, with more uniform loading the rest of the season.

For all monitored metals, significant seasonal differences were present mostly at the non-

BMP sites, again suggesting that BMPs are capturing metals and particulates. Seasonal

differences tended to be similar among sites for the six metals, which may be evidence of

more uniform sources and delivery mechanisms among watersheds for metals.


Baseflow

In baseflow, very few significant differences existed among seasons in nutrient and TSS

concentrations at EK and PC. This is a sensible result given that the two sites are likely

dominated by groundwater, for which nutrient chemistry is not expected to change as

dynamically as surface water. At the remaining non-BMP sites (SAP, TBEB, TBWB, and

TBO), which all have storm drain connections to surface water, some seasonality was

present in TP, TN, and NO3. However, no consistent patterns emerged other than

significantly lower fall TN relative to spring TN at these four sites.

At the VP sites, seasonal variability was more prevalent in baseflow than at the other sites

due to the logical influence of surface water at these sites. The most noteworthy

differences were observed for TP and TSS, which were significantly higher in summer

and fall than in spring (suggesting a surface runoff or internal source), and for NO3,

which was several times higher in winter than any other season, suggesting an influx of

NO3-rich groundwater undiluted by stormwater or an internal source such as decay of

vegetation within the wetland.

Cl- concentrations were significantly different between winter/spring and summer at most

sites in baseflow (and significantly different among nearly all seasons at the VP sites),

with concentrations generally highest in winter and lowest in the summer. These results

strongly suggest that road salt applications during winter and spring (Nov – Apr) may be

polluting both surface water and shallow groundwater, the latter of which is likely present

in all of the large non-BMP watersheds.

Cumulative loading curves for nutrients and TSS in baseflow were generally much more

linear than in stormflow, due to much more uniform loading rates of water. The Trout

Brook sites were especially constant over the season and among years. For Cl-, loading

curves were less linear, though primarily for the VP sites, which had sharp increases in

spring loading rates similar to stormflow Cl- loading curves.

Part 2: Impact of Storm Event Characteristics on Water and Nutrient Loading

4.1.4. Cumulative Rainfall Frequency

Some variability existed in rainfall-runoff characteristics of the main sub-watersheds.

Among the non-BMP sites, the rainfall depth corresponding to median cumulative runoff

volume ranged from 0.80 in (TBO) to 1.13 in (TBWB), while the 1-inch rainfall

corresponded to a range in cumulative volume fractions of 0.45 (TBWB) to 0.62 (EK)

and ranged in depth from 77th

percentile at TBEB to 87th

at EK. Therefore slightly more

than half of the total stormwater volume from most watersheds is contributed by events

of 1-inch and smaller. In addition, the smallest 50 percent of rainfall events (by count)

contribute only 19% (TBWB) to 25% (TBO) of total runoff volume at the major sub-

watersheds. This logically suggests that the larger, less common rainfall events are


disproportionately important in terms of stormwater loading. A design storm larger than

1-inch may be needed to further reduce stormwater (and nutrient) loading by BMPS in

some watersheds.

BMPs shift importance to larger rainfall events by design, and rainfall-runoff results from

the BMP sites (GCP Outlet, VP Outlet, Sarita) show this to be the case. Median rainfall

depths are higher for these sites relative to the non-BMP sites, and the rainfall depth

corresponding to median cumulative runoff volume is also much higher for the BMP

sites. These results suggest that the BMPs monitored by CRWD are effective in

controlling runoff volume to some degree, and that placing such BMPs in other

watersheds might further reduce runoff volumes in those watersheds.

4.1.5. Antecedent Conditions

Antecedent conditions were important for stormwater yield and concentrations of TN,

TP, and Cl-. In particular, the amount of rainfall occurring in the week prior to an event

appeared to be important for explaining variance in these quantities, producing generally

higher R2 than the other two parameters. In addition, as antecedent weekly rainfall

increased, stormwater yield increased (positive slope) and TN, TP, and Cl- concentrations

decreased (negative slope), suggesting that source depletion or dilution may be occurring

for greater antecedent rainfall and/or increasing stormwater volume.

Fewer significant correlations with antecedent rainfall parameters were observed for TSS,

though significant relationships showed positive slopes for dry days and days since 0.5-

inch rainfall and negative slopes for 7-day antecedent rainfall, suggesting that build-up

and wash-off may be a dominant transport process in stormwater. Additionally, TN and

TP, which are predominantly found in particulate form in stormwater in CRWD (Janke et

al. 2013) are well-correlated with antecedent rainfall, and therefore may also be subject to

build-up and wash-off. By contrast, the near lack of significant correlations between NO3

and antecedent rainfall suggests that the dominant source of NO3 is perhaps fertilizer, pet

waste, or vegetation, rather than dry deposition.

No obvious patterns emerged for metals and antecedent rainfall. Cu and Cd were perhaps

less commonly significantly correlated with antecedent parameters than the other metals,

and none of the antecedent parameters appeared to be a better predictor of metal

concentration than the others.

Part 3: Impact of Land Cover and Drainage Characteristics on Water and

Nutrients in Stormflow

Very few land cover and drainage characteristics were found to be sensible or useful

predictors of concentration of nutrients and metals in CRWD sub-watersheds in the linear

regression analysis. Of the explanatory variables, almost none were significantly


correlated with nutrient, metal, or stormwater yields, while canopy over street, ‘other’

impervious, alley, and several roof types were the only statistically significant (p < 0.05)

predictors of nutrient concentration.

Of these explanatory variables, the most sensible factor is tree canopy over street. The

positive slope of the regression of TP with tree canopy suggests that near-street tree cover

may enhance TP concentrations, perhaps through litterfall inputs or wash-off of

atmospheric deposition. In addition, NO3 concentration was significantly and positively

correlated with ‘other’ impervious area, suggesting that atmospheric deposition, which

may collect on these surfaces, is a potentially significant source of NO3.

Overall, the general lack of statistically significant correlations between nutrients and

land cover or drainage metrics in the single linear regression analysis suggests the

presence of multiple sources and transport pathways in the CRWD watersheds. However,

very little should be concluded from this regression analysis for several reasons: (1) the

large size of many sub-watersheds might have obscured some source signals; (2) the

relatively uniform land use (i.e. residential) for many of the watersheds, particularly at

large scale, resulted in small ranges of the land cover metrics; and (3) the small number

of sub-watersheds (6) used reduced the statistical power of the analysis.

Part 4: Exceedence Probabilities of Water Yields and Nutrient Loads

Flow-duration curves showed the expected S-shaped patterns for volume and flow rate in

stormflow at most sites. Flatter curves were observed for the larger watersheds (e.g. SAP

and the Trout Brook sites), likely reflecting the presence of surface water and/or BMPs in

these watersheds that help to reduce flow rates for large storms and detain most of the

runoff from smaller storms. By contrast, flow rates and volumes varied over several

orders of magnitude at many of the smaller non-BMP sites.

The nutrient, TSS, and Cl- load-duration curves for a given site were similar in shape to

each other and to their respective flow-duration curve. Some exceptions exist for the high

exceedence probability events, which are small events that are often monitored for flow

but not sampled for nutrients. However, as in the case of the cumulative loading curves,

the results suggest that hydrology rather than nutrient or sediment sources dominate

stormwater loading in these watersheds.

Baseflow loading tended to be more uniform than stormflow, in particular due to

relatively constant baseflow rates; at all but EK and the VP sites, baseflow rates varied by

less than an order of magnitude. However, some unexpected variability was observed in

the load-duration curves at a few sites (PC and TBEB, and to a lesser extent EK and

SAP). In particular, Cl- and TSS loading rates varied at the extreme low- and high-


exceedence probabilities, perhaps due to the influence of snowmelt events or the receding

limbs of storm events that were treated as baseflow.

Part 5: Metals Toxicity Exceedences in Stormwater

Due to relatively lower concentrations of metals and much higher water hardness in

baseflow, chronic toxicity standards as defined by Minnesota Rules 7050.0222 were very

rarely exceeded in baseflow for the six metals sampled by CRWD (Cd, Cr, Cu, Pb, Ni,

Zn).

In stormflow, no toxicity exceedences were observed for Cr or Ni, while for Cd fewer

than 20% of sampled events exceeded standards at most sites, with the exception of the

Como sites, which had much higher exceedence percentages (28% or greater). More

frequent exceedences were observed for Cu and Pb (60% or higher at all sites except VP),

while the most variability among sites occurred for Zn: greater than 60% to 100% of

events exceeded standards at EK, Como 7, Como 3, and AHUG, with almost no

exceedences at the VP sites. Cu, Pb, and Zn are therefore likely the metals of greatest

interest in terms of management due to the frequency of toxicity exceedences.

Toxicity exceedence values of Cu, Pb, and Zn were generally largest at EK, PC, and

SAP, and smallest at the BMP outlet sites. Exceedence values tended to decrease in value

from spring to fall at most sites, suggesting the effect of source depletion or dilution over

the summer. Concentration data showed that Cu, Pb, and Zn concentrations decreased

from spring to fall, and these seasonal differences were statistically significant (p < 0.05)

at many sites. Accordingly, exceedence values of Zn were statistically lower during fall

than in other seasons at EK, while fall exceedence values of Cu and Pb were significantly

lower at most sites. These results suggest that spring or summer may be the most

important time of year for metals toxicity management, though the percentage of events

exceeding standards does not vary much (on average) among seasons except for Zn.


References

Bannerman RT, Baun K, Bohn M, Hughes PE, Graczyk DA (1983). Evaluation of Urban

Nonpoint Source Pollution Management in Milwaukee County, Wisconsin. Vol 1. PB

84-114164. EPA Water Planning Division.

Barr Engineering (2010). Evaluation of groundwater and surface-water interaction:

guidance for resource assessment, Twin Cities Metropolitan Area, Minnesota. June

2010. 27 pp. http://www.metrocouncil.org/Wastewater-Water/Publications-And-

Resources/Evaluation_of_Groundwater_and_Surface_Water_Intera.aspx. Accessed 12

Sep 2013

Brick G (2008). Historic waters of the capitol region watershed district, Ramsey County,

Minnesota. In: Capitol Region Watershed District 2010 watershed management plan.

Capitol Region Watershed District (CRWD) (2012) Capitol region watershed district

2012 monitoring report. 195 pp.

Easton ZM, Petrovic AM (2008). Determining phosphorus loading rates based on land

use in an urban watershed. In: Nett MT, Carroll MJ, Horgan BP, Petrovic MA (eds)

The fate of nutrients and pesticides in the urban environment. American Chemical

Society, Washington, D.C. pp 43-62.

Fissore C, Hobbie SE, King JY, McFadden JP, Nelson KC, Baker LA (2011). The

residential landscape: fluxes of elements and the role of household decisions. Ecol App

15:1-18.

Helsel DR and Hirsch RM (2002). Statistical Methods in Water Resources Techniques of

Water Resources Investigations, Book 4, Chapter A3. U.S. Geological Survey. 522

pages.

Janke BD, Finlay JC, Hobbie SE, Baker LA, Sterner RW, Nidzgorski D, Wilson BN

(2013). Contrasting influences of stormflow and baseflow pathways on nitrogen and

phosphorus export from an urban watershed. Submitted to: Biogeochemistry, Jan 2013.

Kanivetsky R and Cleland JM (1992). Geologic Atlas of Ramsey County: Surficial

Hydrogeology. County Atlas Series, Atlas C-7, Plate 6. Minnesota Geological Survey.

Kilberg D, Martin M, Bauer M (2011). Digital classification and mapping of urban tree

cover: City of St. Paul. University of Minnesota. Jan 2011. 17 pp.

Meyer GN (2007). Surficial Geology of the Twin Cities Metropolitan Area, Minnesota.

Miscellaneous Map Series, Map M-178. Minnesota Geological Survey.


Pitt R, Lilburn M, Durrans SR, Burian S, Nix S, Vorhees J, Martinson J (1999). Guidance

Manual for Integrated Wet Weather Flow (WWF) Collection and Treatment Systems

for Newly Urbanized Areas (New WWF Systems). U.S. Environmental Protection

Agency, Urban Watershed Management Branch, Edison, New Jersey.

Waschbusch RJ, Selbig WR, Bannerman RT (1999). Sources of Phosphorus in

Stormwater and Street Dirt From Two Urban Residential Basins in Madison, Wisconsin,

1994-95. USGS WRI 99-4021, 47 pp., U.S. Geological Survey, Washington, D.C.

Our Mission is to protect, manage and improve the water resources of Capitol Region Watershed District.

DATE: October 31, 2013 TO: CRWD Board of Managers FROM: Anna Eleria, Water Resource Project Manager RE: Statistical Analysis of Lake Data in CRWD

Background In early January 2013, CRWD hired Wenck Associates, Inc. to conduct a more in-depth analysis of the four CRWD lakes water quality data collected by Ramsey County to better understand temporal, seasonal and climatic trends and the factors driving these trends. Specifically, CRWD sought answers to several questions for each of the lakes including: 1) Is the lake water quality data generally getting better or worse; 2) What are the trends; and 3) What factors are driving these trends. Issues Wenck Associates, Inc. has completed a statistical and graphical analysis of CRWD lake water quality data, trend analysis of selected water quality parameters, and a qualitative assessment of potential trend drivers. Enclosed for the Board’s review and comment is a draft technical memorandum summarizing the results and recommendations for further analysis and monitoring. Action Requested None, for your information only enc: Draft Technical Memorandum of Statistical Analysis of CRWD Lakes Data W:\07 Programs\Monitoring & Data Acquisition\Lakes\2013 Lakes Analysis - Wenck\Board Memos\BM CRWD Lakes Analysis Presentation 11-06-13.docx


IV. Special Report - B) CRWD Lakes Statistical Analysis

W:\07 Programs\Monitoring & Data Acquisition\Lakes\2013 Lakes Analysis ‐ Wenck\Technical Memo\Technical Memo FINAL\Lake Trend Assessment Technical Memo FINAL CRWD comments.docx

TECHNICAL MEMORANDUM

TO: Anna Eleria, Capitol Region Watershed District FROM: Joe Bischoff, Wenck Associates, Inc. DATE: September 18, 2013 SUBJECT: Statistical Analysis of Lake Data in the Capitol Region Watershed District

Purpose The purpose of this technical memorandum is to present results from a statistical analysis of lake data in the Capitol Region Watershed District. The intent of the statistical analysis is to answer the following questions:

Can the water quality of CRWD lakes be described as generally getting better or worse than was

recorded in the past?

What trends in water quality exist? Can these trends be verified through statistical methods?

What factors are driving the trends in water quality among the different lakes?

What qualitative statements can be made regarding the causes and effects in the observed

water quality trends?

To answer these questions, Wenck employed a number of statistical analyses including trend analysis, hypothesis testing, and general descriptive statistics. Results of the analyses for each lake are presented below. Approach CRWD provided Wenck with water quality and biological data for the four lakes to be assessed (Table 1). After review, the project team decided to focus on the water quality parameters (TP, chlorphyll‐a, and Secchi) first, and use the biological data where possible to provide context for the water quality data. Fairly long records of phytoplankton and zooplankton data are available for Como Lake, Crosby Lake, and Lake McCarrons.

Wenck Associates, Inc. 1800 Pioneer Creek Center P.O. Box 249 Maple Plain, MN 55359‐0249 800‐472‐2232 (763) 479‐4200 Fax (763) 479‐4242 [email protected] www.wenck.com

Technical Memo Statistical Analysis of Lake Data in the Capitol Region Watershed District Capitol Region Watershed District September 18, 2013

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Table 1. Available data for the lake statistical analyses.

To evaluate trends in lake water quality, Wenck employed a series of statistical tests for the period of record. Wenck conducted the following steps in the statistical analysis of the lake data:

1. Evaluate the normality of the data including the residuals and log‐transformed data and

residuals. Levene’s test was used to assess equal variance among the years and the Shapiro‐

Wilks test was used to test for normality of the entire data set as well as each year. These tests

are critical to ensure statistical test assumptions are not violated. Analyses can be conducted on

the data, residuals, log‐transformed data, or log‐transformed residuals.

2. Run pairwise comparisons among the years using either ANOVA (parametric test if assumptions

are met) or Kruskall‐Wallace (non‐parametric test if assumptions are not met) tests to assess

difference among the years. Post‐hoc testing was completed using the Bonferonni test at a 0.1

significance level.

3. Visually plot data sets using time series and box plots to identify potential trends, seasonality, or

other exogenous factors that may be influencing the data set.

4. Evaluate potential exogenous variables for their influence on the data set. An exogenous

variable is an outside variable that may demonstrate trends that will influence the analysis

variable. For example, lake water quality data may be influenced by precipitation patterns

causing the analyst to interpret precipitation trends as water quality trends.

5. Monthly plots to evaluate the potential for seasonality in the data set.

6. Autocorrelograms to determine the level of autocorrelation in the data set. Autocorrelograms

lag the data sets to evaluate correlation between sequentially collected samples. So, at lag 1,

two sequentially collected samples are compared for correlation. At lag 2, the first and third

samples in a series are compared for correlation and so on.

Data Description Como Crosby Loeb McCarrons Little Crosby

Aquatic vegetation and species survey 2012 2012 2012 2012

Como Lake Turtle Study 2011

Crosby Lake Sediment Data 2010

Daphnia Size 1984‐2007 1999‐2007 2003‐2007 1988‐2007

DNR Fisheries‐Lake Management Plan 2005 2010

Lake Elevations 1978‐2012 2003‐2004, 2006‐2012 1924‐2012

Lake Sampling Data 1982, 1984‐2012 1999‐2012 2003‐2012 1988‐2012 2011‐2012

Macrophyte Surveys 2005, 2010 2009 2005 2005

Phytoplankton Data 1984‐2011 1999‐2011 2003‐2011 1988‐1998, 2000‐2011

Zooplankton Data 1984‐2011 1999‐2011 2003‐2011 1988‐1998, 2000‐2011


3


7. Trend analysis for the data set corrected for autocorrelation and seasonality of necessary. The

Mann Kendall‐Tau test was used in most cases which performs the trend assessment on the

ranks of the data sets.

8. A multivariate analysis of lake Trophic Status Index to evaluate drivers of lake water quality.

Data Description The first step is to describe the data set focusing on the assumptions for each potential statistical test. For example, most parametric statistical hypothesis tests such as ANOVA require the data have a normal distribution and equal variances among groups. In the case where this is not true, alternative nonparametric hypothesis tests such as the Kruskall‐Wallace test can to be used. Because parametric testing is more powerful in determining differences among groups or trends, it is worthwhile to check the data and residuals as well as the log transformed data and residuals for the assumptions of equal variance and normality. Data Visualization The second step is to visualize the data set to develop a general understanding of potential trends in the data set or other factors that may be causing trends in the data set. Many potential trends can be recognized using data visualization techniques such as notched box plots, histograms, scatter plots and other plots of the data or residuals of the data. Once any trends are identified at this level, they can be further evaluated using the appropriately selected statistical test. Wenck developed notched box plots by year and month to evaluate trends and statistical differences among the years. The notches in the box plots represent the 95% confidence internal around the mean, so when the notches overlap, there is no statistical difference in the means of the individual data sets. If they do not overlap, the means are likely statistically different. Trend Assessment To evaluate trends, Wenck first evaluated the necessary statistical assumptions for using trend analysis including normality of the data set and equal variance over time. In almost all cases, the data sets were determined to be non‐normal. The Mann‐Kendall Tau test for trends is nonparametric and is therefore appropriate where the data are non‐normal although it still requires equal variance over time. The Mann‐Kendall Tau test can also be adjusted for serial autocorrelation, the condition where a previous sample in time is correlated to the current sample, and seasonality. Differences Among Years The data set was also evaluated for differences among the years to identify groups of years that may be similar. The groupings can then be evaluated for similar conditions such as rainfall or fish abundance to


4


evaluate potential causes. For this analysis, the grouping was completed; however a detailed review of other factors was outside the scope of this assessment. Multivariate Assessment using the Lake Trophic Status Index The interrelationship between simultaneously collected variables can be used to identify conditions in a lake that affect those measured variables. One way this can be done is by evaluating the differences among TSI values for each of the three collected variables (Carlson 1992). Theoretically, the empirical relationships between TP, chlorophyll‐a, and Secchi should result in the same TSI value. Because these empirical relationships are derived from regressions that have error terms, some variability can be expected. However, in some situations the differences are not random and can be used to identify factors interfering with the relationship. The figure below represents a plot of the TSI differences with 4 primary zones for interpretation. Table 2 provides some interpretation of the data in each zone. If points fall below the x axis, chlorophyll‐a is under predicted suggesting that P is not limiting algal growth, rather algal growth is limited by light availability, nitrogen limitation or zooplankton grazing. Points lying to the right of the y axis indicate better clarity than expected which may be a result of larger algae such as aphanizomenon, a colony forming blue‐green algae. Points to the left of the y‐axis suggest smaller particles dominate suggesting water color or turbidity is a critical factor. Points lying along the diagonal and to the left of the axis suggest that P and clarity are correlated, but the expected chlorophyll response is not demonstrated. This suggest non‐algal turbidity such as clay is controlling water clarity and keeping the P unavailable for algal production.


5


Carlson and Simpson, 1996. Following is a discussion of the results of the statistical analysis for each lake. LAKE MCCARRON Descriptive Statistics Total phosphorus, chlorophyll‐a and Secchi depth were assessed for basic statistical assumptions such as normality, equal variance, and central tendencies (Table 2). None of the data sets as a whole are normal or lognormal, although Secchi depth demonstrates a lognormal distribution in all but one year.


6


Table 2. General statistical description of Lake McCarrons water quality data.

Statistic TP (mg/L) Chl‐a (µg/L) Secchi (m)

No. of observations 177 177 177Minimum 0.009 0.317 0.700Maximum 0.175 74.800 8.4001st Quartile 0.016 2.780 1.700Median 0.026 6.500 2.7503rd Quartile 0.039 13.370 4.000Mean 0.031 9.760 2.952Variance (n‐1) 0.001 107.056 2.199Standard deviation (n‐1) 0.025 10.347 1.483Skewness (Pearson) 3.258 2.856 0.697Kurtosis (Pearson) 14.539 12.298 0.206Standard error of the mean 0.002 0.787 0.114Geometric mean 0.026 6.197 2.580Geometric standard deviation 1.862 2.686 1.715

Summer average phosphorus, chlorophyll‐a and Secchi depth were plotted for Lake McCarrons (Figure 1). Both TP and chlorophyll‐a demonstrate a decreasing trend in concentration with Secchi depth demonstrating an increasing trend in water clarity. It is important to note that an alum treatment was performed on the lake in 2004 which essentially breaks the data set into two distinct periods: pre‐ and post‐alum application. Long term notched box plots demonstrate an improving trend in water quality (Figure 2). Pre‐alum variability was quite high with extreme values in TP and chlorophyll‐a. After the alum treatment the spread of the data decreased significantly for chlorophyll‐a and TP. It is also interesting to note that the lake appears to have taken a few years after the alum treatment to reach the maximum effectiveness (2007‐2010). The most recent two years demonstrate a broader spread in the data suggesting that the effectiveness of the alum treatment may be weakening. Annual Pairwise Comparisons Data and residuals, including log transformations, for Lake McCarrons was evaluated to test for normality and equal variance among the sample years (Table 3). To use parametric testing such as an ANOVA to test for differences among years, the test groups must be normally distributed and have equal variance. Although some of the Secchi depth data was normally distributed or had equal variances among years, none occurred in the same grouping. Therefore, the nonparametric Kruskall‐Wallace test was selected with a Bonferonni post‐hoc pairwise comparison.


7


Table 3. Evaluation results for tests of normality and equal variance among groups.

1Levene’s test 2Shapiro‐Wilks test The Kruskall‐Wallace and Bonferonni tests demonstrated significant differences among the years for all three parameters (Attachment 1). A total of 81 pairs were significantly different than one another for TP. Most of the years prior to the alum treatment were significantly higher in TP than those years after the treatment except for 1989, 1992, 1994, and 1998 with 2008 and 2009 significantly lower than all the other years. Only 27 pairs of years were significantly different than one another for chlorophyll‐a. The most recent two years are statistically similar than almost all the other years, although based on visual inspection (Figure 2) the spread of the data is tighter in more recent years. The fact that the most recent years of chlorophyll‐a data are similar to most years suggests that other factors may be affecting mean algal abundance or that the effectiveness of the alum treatment is diminishing. Overall, the post‐alum treatment chlorophyll‐a abundance is significantly lower. However, the reduced phosphorus concentrations appear to have reduced significant algal blooms (decreased spread in the data). Only 2008, when Secchi depth was at its highest, demonstrated a significant difference than most of the pre‐alum treatment period. This is slightly surprising given the significant reductions in chlorophyll‐a and total phosphorus. Other factors are likely controlling Secchi depth at these lower chlorophyll‐a concentrations and 2008 likely presents the best achievable Secchi depth when addressing only phosphorus and chlorophyll‐a.

Parameter Data Logs of Data Residuals1 Residuals of Logs1

Equal Variance?1

Normally Distributed?2

Equal Variance?

Normally Distributed?

Equal Variance?


Equal Variance?


Total Phosphorus

No No Yes No No No Yes No

Chlorophyll‐a

No No Yes No No No Yes No

Secchi Depth

Yes No No Yes Yes No No Yes


8


Trend Assessment Based on the visual assessment of water quality data in Lake McCarrons it is clear that there are two distinct periods to evaluate trends including the pre‐ and post‐alum treatment periods. An assessment of trends needs to focus on these two periods. Exogenous Variables Before evaluating trends in a lake data set, exogenous variables that may be causing a pattern in the data must be evaluated and removed if present. An exogenous variable is a variable other than time that may have considerable influence on the response variable. These variables are usually natural such as rainfall, temperature or stream flow. For lakes, especially those with long residence times such as Lake McCarron, the most common potential exogenous variable is rainfall. To test for rainfall as a factor, monthly total precipitation was regressed against monthly average TP concentrations for the period of record (Figure 3). No relationship between TP and monthly precipitation totals was found for the data or the logs of the data. Consequently, it was concluded that rainfall totals does not need to be accounted for in the trend analysis. Seasonality and Autocorrelation Two other factors that need to be accounted for in any trend analysis including serial autocorrelation is seasonality. Seasonality is important in lakes since they demonstrate a clear growing season along with a dormant season. However, most of the monitoring data were collected during the growing season meaning that year to year comparisons are not likely to include much seasonality in the data. Monthly notched box plots confirm this assumption (Figure 4). Only Secchi depth for one month (August) demonstrated a significant difference among the months. Based on this assessment, using the seasonally adjusted Kendall Tau trend test is not necessary. Lake data tend to be serially autocorrelated due to long residence times. To evaluate serial autocorrelation, correlograms were developed for each of the three parameters (Figure 5). Autocorrelograms evaluate autocorrelation using time lags in the data. For our analysis, we chose a lag period of 12 to account for annual data. Typical sampling in Lake McCarrons was 7 samples over the summer growing season. However, a lag period of 12 allows for evaluation of autocorrelation within a sampling year and between years. All three parameters demonstrated autocorrelation within any given year but not between years. Consequently, autocorrelation must be accounted for in the trend analysis.


9


Trend Assessment Because a major event (alum treatment) occurred in Lake McCarron, the trend assessment must account for both the pre‐ and post‐alum conditions. Total phosphorus conditions before and after the alum treatment were statistically different (Figure 6). No trends were detected in either the pre‐ or post‐alum treatment data sets using the Mann‐Kendall trend test with a significance value of 0.05. Trends tests on the overall data set do demonstrate an improving trend in water quality although this is solely a result of the alum treatment conducted in 2004. Multi‐Variate Assessment The multivariate TSI comparison for Lake McCarrons did not present a great deal of information about the lake (Figure 7). Essentially, Lake McCarrons appears to be a typically P limited lake. Much of the data do fall right of the y‐axis suggesting that larger particles such as Aphanizomenon dominate water clarity. This is further corroborated by the lack of significant improvements in water clarity after the alum treatment where algae were reduced but the water clarity was already relatively good. Potential Drivers of Water Quality Lake McCarrons Conclusions

1. Water quality in Lake McCarrons after the alum treatment was statistically better than the pre‐

alum water quality for all three parameters. Water quality appears to have peaked in 2008

through 2010 and may be trending poorer in the past two years. However, it is impossible to

tell if this is just annual variability and visual observations of the data suggest that the alum

treatment is still effective.

2. Prior to the alum treatment, peak total phosphorus concentrations were typically observed in

the spring (April‐May) samples suggesting high runoff loads during these periods.

3. No statistical trends were detected in water quality data for either pre‐ or post‐alum conditions

in Lake McCarrons. However, recent spread in total phosphorus and Secchi depth data suggest

that water quality may be changing and the alum treatment effectiveness may be weakening.

However, other data such as sediment cores are needed to evaluate current sediment release.

4. Other than 2008 and 2009, mean chlorophyll‐a data after the alum treatment was statistically

similar to many of the pre‐treatment years suggesting there was not a great overall reduction in

algal abundance in the lake. However, mean algal abundance has been reduced and it does

appear to have eliminated significant algae blooms in the lake.


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5. Water clarity overall did increase significantly after the alum treatment. However, year to year

comparisons suggest that the clarity is not significantly different than many of the previous

years. It is important to note that the year to year tests have less statistical power due to the

lower sample size in each given year. So, water clarity was improved for much of the year, but

some years still may demonstrate water clarity similar to past years even though overall algal

abundance is lower. 2008 is likely the best achievable Secchi depth by controlling phosphorus

alone.

6. Based on the multi‐variate assessment of the Trophic Status Index, Lake McCarrons appears to

be a typical P‐limited lake where larger particles dominate and zooplankton grazing likely plays a

factor is algal abundance.

Como Lake Descriptive Statistics Total phosphorus, chlorophyll‐a and Secchi depth were assessed for basic statistical assumptions such as normality, equal variance, and central tendencies (Table 4). Chlorophyll‐a had a wide range of values resulting a large standard deviation. Table 4. General statistical description of Lake McCarrons water quality data.

Statistic TP (mg/L) Chl‐a (ug/L) Secchi (m)

No. of observations 307 307 307Minimum 0.031 0.1 0.20Maximum 0.970 223.3 4.201st Quartile 0.089 6.8 0.70Median 0.129 20.1 1.203rd Quartile 0.228 49.4 2.20Mean 0.182 32.8 1.58Variance (n‐1) 0.023 1261.7 1.09Standard deviation (n‐1) 0.150 35.5 1.05Skewness (Pearson) 2.358 1.9 0.86Kurtosis (Pearson) 6.879 4.8 ‐0.31Standard error of the mean 0.009 2.1 0.06Geometric mean 0.142 16.5 1.26Geometric standard deviation 1.974 3.9 2.00

A visual review of the summer average total phosphorus concentrations for Como Lake suggest a somewhat cyclical pattern of several years of high phosphorus concentrations followed by a few years of


11


lower concentrations (Figure 8 and Figure 9). Both response variables follow similar patterns. The patterns may reflect cyclical life cycles of fish, particularly panfish, which can follow boom‐bust patterns. The fishery may ultimately affect zooplankton grazing and chlorophyll‐a abundance. Although there appears to be a cyclical pattern, there is no apparent trend in the data or major shift at any point in time. Annual Pairwise Comparisons Data and residuals, including log transformations, for Como Lake were evaluated to test for normality and equal variance among the sample years (Table 5). To use parametric testing such as an ANOVA to test for differences among years, the test groups must be normally distributed and have equal variance. None of the groups followed these assumptions. Therefore, the nonparametric Kruskall‐Wallace test was selected with a Bonferonni post‐hoc pairwise comparison. Table 5. Evaluation results for tests of normality and equal variance among groups.

1Levene’s test 2Shapiro‐Wilks test Pairwise comparisons for Como Lake TP suggest that there are not many significant differences from year to year since only 22 pairs demonstrated statistically significant differences and where most differences were between extreme years. These results suggest that although there appears to be differences in the spread of the data among years, average conditions are not significantly different. Both chlorophyll‐a and Secchi follow a similar cyclical pattern, however they demonstrate more difference among pairs, especially Secchi depth (Figure 9). The fact that more differences were not picked up in chlorophyll‐a is likely a result of the high variances in many of the years. For Secchi, there were 70 statistically different pairs. The greater number differences among years for water clarity suggest that water clarity is controlled by multiple factors and not just TP and chlorophyll‐a abundance.


Equal Variance?1


Equal Variance?


Equal Variance?


Equal Variance?


Total Phosphorus

No No No No No No No No

Chlorophyll‐a


Secchi Depth



12


Further exploration of the cyclical patterns in the lake data may reveal other factors affecting water clarity such as changes in the fish community, patterns of vegetation change, and potential climatic patterns. Trend Assessment Exogenous Variables Precipitation was evaluated as a potential factor affecting water quality trends in Como Lake (Figure 10). No relationship was found between monthly TP concentrations and monthly precipitation totals. Seasonality and Autocorrelation Box plots of monthly data suggest some seasonality in the data collected for Como Lake (Figure 11). It is important to note that the majority of the data were collected in the summer months. Because so few of the data are collected outside of the summer months, seasonality in the trend assessment can be ignored. The data do present autocorrelation, especially in those data collected in that same year (Figure 12). Data collected between years do not appear to be autocorrelated which is expected since the residence time of Como Lake is likely relatively short. Serial autocorrelation was accounted for in the trend assessment. Trend Assessment Total phosphorus in Como Lake did demonstrate a significant decreasing trend, although it was not significant if seasonality is included. Based on the relatively small data set outside of the summer season, the non‐seasonally adjusted test is acceptable. A trend test on the summer average TP did not result in a significant trend in the data. Neither chlorophyll‐a or Secchi depth resulted in a significant trend. Multi‐Variate Assessment The multivariate assessment resulted in a number insights about Como Lake including:

1. Phosphorus is not limiting algal growth, and that a phosphorus surplus may exist in the lake

2. Zooplankton grazing plays a large role in controlling water clarity in Como Lake. This is similar to

conclusions by Noonan (1998) who determined cyclical patters in lake water quality are a result

of complex interactions between submerged aquatic vegetation, zooplankton grazing, nutrient

cycling and fish abundance.


13


3. Vegetation in Como Lake is currently sparse (CRWD 2012) correlating to lower daphnia

abundance (Figure 14) and poorer water quality.

Potential Drivers of Water Quality Como Lake Conclusions

1. Cyclical patterns in water quality suggest that outside factors that follow cyclical patterns may

be affecting water quality in Como Lake. Noonan (1998) concluded that although “bottom‐up”

nutrient controls play a factor in Como Lake, other factors such as plant abundance, fisheries,

and zooplankton abundance are also critical in controlling water quality.

2. Secchi depth demonstrated many more statistically different years than either chlorophyll‐a or

TP, suggesting that other factors may be affecting water clarity. Some potential factors include

wind resuspension of sediment, changes in zooplankton abundance, TSS inflow, or rough fish

activity.

3. A statistically significant decreasing trend in TP was detected in Como Lake, although a trend

test on the summer average data was not significant. However, statistical differences among the

years in TP did not pick up significant patterns, confounding the results. It appears that TP is

possibly decreasing in Como Lake, but more data will improve the prediction.

4. Based on the data, management of water quality in Como Lake should focus on the submerged

aquatic vegetation community as well as nutrient reductions. Fish abundance is also an

important factor.

Crosby Lake Total phosphorus, chlorophyll‐a and Secchi depth were assessed for basic statistical assumptions such as normality, equal variance, and central tendencies (Table 6). None of the parameters were normally or log‐normally distributed.


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Statistic TP (mg/L) Chl‐a (µg/L) Secchi (m)

No. of observations 87 87 87Minimum 0.010 0.2 0.50Maximum 0.330 47.0 4.90Range 0.320 46.8 4.401st Quartile 0.032 2.8 1.63Median 0.051 4.7 2.003rd Quartile 0.091 8.8 2.88Mean 0.066 8.0 2.26Variance (n‐1) 0.002 75.6 0.85Standard deviation (n‐1) 0.050 8.7 0.92Skewness (Pearson) 2.415 2.2 0.55Kurtosis (Pearson) 8.414 5.0 ‐0.23Standard error of the mean 0.005 0.9 0.10Geometric mean 0.053 5.1 2.07Geometric standard deviation 1.895 2.6 1.55

Plots of the summer mean water quality for Crosby Lake show a decrease in water quality over the past five years with increasing TP and chlorophyll‐a concentrations and decreasing water clarity (Figure 15). It is important to note that although chlorophyll‐a demonstrates an increase over the past 8 years, the concentrations still remain below the state standard of 20 µg/L as a summer average. Secchi disk transparency has decreased over the years but still remains greater than the state standard of greater than 1 meter. Notched box plots for water quality suggest that water quality may be degrading with the most recent period showing greater extremes and spread in the data especially for TP and chlorophyll‐a (Figure 16). TP demonstrated statistically significant increases in the past three years. Annual Pairwise Comparisons Data and residuals, including log transformations, for Crosby Lake were evaluated to test for normality and equal variance among the sample years (Table 7). To use parametric testing such as an ANOVA to test for differences among years, the test groups must be normally distributed and have equal variance. None of the parameters had normal distributions or equal variance among the groups. Therefore, the nonparametric Kruskall‐Wallace test was selected with a Bonferonni post‐hoc pairwise comparison.


15



1Levene’s test 2Shapiro‐Wilks test Pairwise comparisons for TP show the last three years being statistically higher than most other years 2001, 2002, 2005 and 2006. So, although the last three years are higher, these TP levels in the lake are not unprecedented. Chlorophyll‐a doesn’t follow the same pattern as TP with 2012 statistically similar to all other years and only 2010 and 2011 being statistically higher than the lowest of the previous years. Secchi depth follows chlorophyll‐a patterns suggesting that algal abundance is likely the primary driver for water clarity in Crosby Lake. Trend Assessment Exogenous Variables Precipitation was evaluated as a potential factor affecting water quality trends in Crosby Lake (Figure 17). No relationship was found between monthly TP concentrations and monthly precipitation totals. Seasonality and Autocorrelation The majority of data collected for Crosby Lake were collected in the summer months which did not demonstrate statistical differences for TP or chlorophyll‐a but did have some differences for Secchi (Figure 18). Because the three parameters are related, a non‐seasonally adjusted Kendall Tau is appropriate, but both should be evaluated for Secchi. Crosby Lake demonstrated serial autocorrelation in any given year’s data set, but not between years (Figure 19). Consequently, serial autocorrelation needs to be accounted for in the trend assessment.


Equal Variance?1


Equal Variance?


Equal Variance?


Equal Variance?


Total Phosphorus


Chlorophyll‐a


Secchi Depth



16


Trend Assessment A Mann‐Kendall Tau test was positive for all three parameters with increasing trends in both TP and chlorophyll‐a and a seasonally adjusted decreasing trend in Secchi depth. The trend was also significant for the annual average data for all three parameters. Crosby Lake is trending toward poorer water quality. One factor that may be affecting water quality for Crosby Lake is interaction with the Mississippi River. Table 8 shows the number of days by year that the Mississippi River was at an elevation that would discharge to Crosby Lake (Elev. 697 feet). Although the Mississippi River interacts with Crosby Lake periodically over the past 15 years, the lake has received inputs from the River for the past 4 years with the Lake being flooded for 103 days in 2011. Similarly, in 2001 and the following year, water quality was poor following 63 days of inundation by the River. It appears likely that inundation from the Mississippi River is a significant factor affecting water quality in Crosby Lake. Table 8. Annual days the Mississippi River is at an elevation that interacts with Crosby Lake (Wenck 2012).

Year Number of Days Mississippi River Interacts with Crosby Lake

1999 14

2000 0

2001 63

2002 0

2003 0

2004 0

2005 0

2006 19

2007 0

2008 0

2009 15

2010 36

2011 103

2012 10

Multi‐Variate Assessment The multivariate TSI approach suggests that Crosby Lake is not typically limited by P, but may be limited by other factors such as light or P‐availability (Figure 20). The graph suggests that not all of the TP in the


17


water column is available for algal production and may be adhered to small particles such as clay or other TSS components. TP may be increasing in the lake, but it does not appear to all be readily available for algal production. Because there is a P surplus, other factors need to be considered in managing Crosby Lake including fisheries and submerged aquatic vegetation abundance. Potential Divers of Water Quality Crosby Lake Conclusions

1. Water quality in Crosby Lake’s three most recent years demonstrates an increase in total

phosphorus and a decrease in water clarity. Algal abundance is high in 2010 and 2011, although

water quality in 2012 was typical of previous years even though TP was higher. This suggests

that water quality in Crosby Lake is degrading but that algal abundance is not necessarily

controlled directly by TP (some fraction of phosphorus may be unavailable or zooplankton

grazing may play a role).

2. Water clarity appears to be primarily driven by algal abundance.

3. Statistical trend testing verifies that water quality in Crosby Lake is trending poorer with

increases in total phosphorus and chlorophyll‐a and decreases in water clarity. However, this

may be a function of inundation by the Mississippi River which occurred for 164 days over the

past 4 years.

Loeb Lake Total phosphorus, chlorophyll‐a and Secchi depth were assessed for basic statistical assumptions such as normality, equal variance, and central tendencies (Table 9). Secchi depth is normally distributed for Loeb Lake. TP and chlorophyll‐a were not normally or log‐normally distributed. Variance for all three parameters was low.


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Statistic TP (mg/L) Chl‐a (ug/L) Secchi (m)

No. of observations 70 70 70 Minimum 0.012 1.1 1.90 Maximum 0.093 21.8 4.60 Range 0.081 20.7 2.70 1st Quartile 0.017 2.4 2.80 Median 0.022 3.4 3.35 3rd Quartile 0.027 6.3 3.80 Mean 0.024 4.5 3.29 Variance (n‐1) 0.000 10.5 0.45 Standard deviation (n‐1) 0.012 3.2 0.67 Skewness (Pearson) 3.166 2.5 ‐0.25 Kurtosis (Pearson) 14.803 10.1 ‐0.75 Standard error of the mean 0.001 0.4 0.08 Geometric mean 0.022 3.7 3.22 Geometric standard deviation 1.459 1.9 1.24

Loeb Lake does not demonstrate much variability in water quality between years (Figure 21 and 22). 2003 appears to have the worst water in the data record although the lake still met state water quality standards. Annual Pairwise Comparisons Data and residuals, including log transformations, for Loeb Lake was evaluated to test for normality and equal variance among the sample years (Table 8). To use parametric testing such as an ANOVA to test for differences among years, the test groups must be normally distributed and have equal variance. Secchi depth was normally distributed in all years and demonstrated equal variance. Chlorophyll‐a was log‐normally distributed and logs had equal variance among the years. TP residuals were normally distributed and had equal variance among the groups. Therefore, the parametric GLM (ANOVA) test was selected with a Bonferonni post‐hoc pairwise comparison.


19



1Levene’s test 2Shapiro‐Wilks test Pairwise comparisons for Loeb Lake suggest that there is some variability from year to year especially in TP, but water quality generally has remained consistent over the past 9 years. 2003, 2006, and 2012 were higher in TP than most other years but no differences were identified in chlorophyll‐a concentrations. Trend Assessment Exogenous Variables Precipitation was evaluated as a potential factor affecting water quality trends in Loeb Lake (Figure 23). No relationship was found between monthly TP concentrations and monthly precipitation totals. Seasonality and Autocorrelation The majority of data collected for Loeb Lake were collected in the summer months but there is some variability (Figure 24). Consequently, a seasonally adjusted Mann‐Kendall Tau should be applied. Crosby Lake demonstrated serial autocorrelation in TP, but not in Secchi or chlorophyll‐a data (Figure 25). Autocorrelation is accounted for in TP, but not the other parameters. Trend Assessment No water quality trends were detected in Loeb Lake.


Equal Variance?1


Equal Variance?


Equal Variance?


Equal Variance?


Total Phosphorus

Yes No Yes No Yes Yes Yes No

Chlorophyll‐a

Yes No Yes Yes Yes No Yes Yes

Secchi Depth

Yes Yes Yes Yes Yes Yes Yes Yes


20


Multivariate Assessment The multivariate TSI assessment for Loeb Lake suggests that the lake is typically P‐limited although zooplankton grazing may play a role in expected algal abundance (Figure 26). Potential Drivers of Water Quality Loeb Lake Conclusions

1. Water quality is fairly consistent in Loeb Lake with no trends detected in TP, chlorophyll‐a or

Secchi depth.

2. Comparisons among years yield few differences with only TP showing some differences among

years.

3. The multivariate TSI assessment suggests that Loeb Lake is a fairly typical P‐limited lake.

SUMMARY Following is a summary of the results of the analysis. Can the water quality of CRWD lakes be described as generally getting better or worse than was recorded in the past? In general, water quality in CRWD lakes is fairly stable with a few demonstrating signs of eutrophication. Lake McCarrons demonstrates improved water quality as a direct result of the alum treatment conducted in 2004. Although the alum treatment visually demonstrates some signs of weakening, no statistical trends were identified suggesting that water quality is degrading. Como Lake demonstrates a cyclical pattern in water quality that is likely directly tied to changes in submerged aquatic vegetation and zooplankton abundance. Total phosphorus concentrations in Como Lake appear to be improving. Water quality in Crosby Lake appears to be degrading with higher TP and chlorophyll‐a concentrations resulting in decreased water clarity. Water quality in Loeb Lake appears to be stable with relatively good water quality. What trends in water quality exist? Can these trends be verified through statistical methods? Statistical methods applied to CRWD lake water quality demonstrated relatively stable water quality in the lakes except for Crosby Lake. Lake McCarrons had no significant trends prior to or after the alum treatment suggesting that water quality is stable in the lake. Detecting statistical trends in water quality in Como Lake is very difficult due to the complex interactions of vegetation and zooplankton on water quality. Although statistical results were weak, total phosphorus concentrations appear to be improving suggesting that other factors are controlling water clarity. Crosby Lake demonstrates a statistically


21


significant trend toward poorer water quality with increased phosphorus and chlorophyll‐a concentrations and decreased water clarity. Loeb Lake remains stable. What factors are driving the trends in water quality among the different lakes? The lakes demonstrated a variety of factors controlling water quality. Both Lake McCarrons and Loeb Lake appear to be phosphorus limited lakes where nutrient controls remain the best approach for controlling eutrophication. Both Crosby Lake and Como Lake are more typical shallow lakes that demonstrate other factors affecting water quality including vegetation, zooplankton and fish abundance. Crosby Lake is further complicated by its connection to the Minnesota River which has the potential to bring in large quantities of sediment and nutrients during flood periods. The degrading water quality trend in Crosby Lake is most likely attributed to flooding frequency since there are no apparent changes in the watershed that lead to additional nutrient loading. What qualitative statements can be made regarding the causes and effects in the observed water quality trends? Lakes in the CRWD watershed have relatively stable water quality; however both Como Lake and Crosby Lake are sensitive to factors other than nutrient loading including submerged vegetation, zooplankton and fish abundance. Crosby Lake has the additional pressures of nutrient and sediment loading from the Mississippi and Minnesota Rivers. Monitoring and managing biological conditions in these two lakes is critical to successfully improving and maintain water quality. For Crosby Lake, managing the input of sediment and nutrients from the Mississippi and Minnesota Rivers could stabilize water quality, although managing flood water inputs is very difficult. It may take direct management such as alum addition or other phosphorus inactivation to be effective. Long term nutrient load management is effective for both Loeb and Lake McCarrons. The long term effectiveness of Lake McCarrons alum treatment poses the greatest risk for water quality degradation in the lake. What monitoring recommendations can be made to improve assessing lake conditions in the CRWD? How should lake health be assessed moving forward? For the two deep lakes, continuing the current monitoring (TP, chlorophyll‐a, and Secchi plus field parameters) is sufficient for assessing the health of the lake. Monitoring of the phytoplankton and zooplankton communities provide some insight into the health of the lake too, but are not critical. For the shallow lakes, the standard water quality parameters are important, but so is the submerged aquatic vegetation community. The best measure of healthy shallow lake is the clarity of the water and the diversity and robustness of the submerged aquatic vegetation community. So, annual (or every few years) vegetation surveys are critical in assessing lake health. Zooplankton, phytoplankton, and fish surveys can be useful in assessing mechanisms controlling water quality. Following is a description of the analytical results for each of the lakes.


22


Lake McCarrons Lake McCarrons is a deep lake that received an alum treatment in 2004. Watershed BMPs have also been introduced over the periods of record, specifically the Villa Parks wetland complex. Water quality improved significantly after the alum treatment with reduced TP and chlorophyll‐a and increased Secchi depth. Prior to the alum treatment, no trends were identified in water quality suggesting that conditions were fairly stable. After the alum treatment, water quality improved greatly over a 3 to 4 year period; however recent data is demonstrating that the effectiveness of the alum treatment may be diminishing although no water quality trend was detected. Como Lake Como Lake, a shallow lake, demonstrates a cyclical pattern in water quality that may be related to other factors such as a boom‐bust fishery. The pattern should be explored further in relation to fish and zooplankton data. Como Lake did have a significant decreasing trend in TP, although statistical testing among years could not pick up the differences. There may be a long term, slow decrease in TP concentrations, albeit a very small one. Water clarity appears to be affected by factors beyond algal abundance, but the lake is phosphorus limited. Crosby Lake Although water quality in Crosby Lake is fairly good, water quality is decreasing in the lake over the past 13 years. Total phosphorus and chlorophyll‐a had significant increasing trends while Secchi depth had a significant decreasing trend. Water clarity followed a similar trend as algal abundance suggesting algal abundance is the primary factor controlling water clarity. Loeb Lake Overall, Loeb Lake demonstrated consistent water quality over the period of record with little to no variation in chlorophyll‐a or Secchi depth. Loeb Lake appears to be a P‐limited lake that has not experienced any major changes in water quality in the past 9 years. REFERENCES Wenck Associates Inc. 2012. Crosby Lake Management Plan. Report to the Capitol Region Watershed District.


Regular Meeting of the Capitol Region Watershed District (CRWD) Board of Managers, for Wednesday,

October 16, 2013 6:08 p.m. at the office of the CRWD, 1410 Energy Park Drive, Suite 4, St. Paul, Minnesota.

REGULAR MEETING

I. Call to Order of Regular Meeting (President Joe Collins)

A) Attendance

Joe Collins

Mike Thienes

Shirley Reider

Seitu Jones

Mary Texer – absent

w/notice

Others Present

Mark Doneux, CRWD

Michelle Sylvander, CRWD

Forrest Kelley, CRWD

Anna Eleria, CRWD

Lindsay VanPatten, CRWD

Elizabeth Beckman, CRWD

Public Attendees Todd Shoemaker, Wenck

B) Review, Amendments and Approval of the Agenda

President Collins asked for additions or changes to the agenda. Administrator Doneux stated that

representatives from Hamline University will deliver proto types of the education display designs. After a

discussion with the board, item VI. A) Education Display Designs Update could be elevated to an action item.

Motion 13-187: Approve the October 16, 2013 Agenda.

The consensus of the board approved the October 16, 2013 Agenda.

II. Public Comments – For Items not on the Agenda

There were no public comments.

III. Permit Applications and Program Updates

A) Permit # 13-026 Associated Bank (Kelley)

Mr. Kelley, reviewed Permit #13-026 Associated Bank Project. The applicant is Associated Bank. The permit

is for demolition and construction of a new bank at the corner of Snelling and Dayton. The applicable rules are

Stormwater Management (Rule C), Flood Control (Rule D), Erosion and Sediment Control (Rule F). The

disturbed area of this project is 1.5 Acres and .93 Acres impervious surface.

Motion 13-188: To approve Associated Bank Permit #13-026 with 4 conditions:

1. Receipt of $4,650 surety and maintenance agreement.

2. Provide a copy of the NPDES permit.


V. Action Item A) Approve Minutes

of October 16, 2013

DRAFT Regular Board Meeting

(Sylvander)


3. Increase filtration volume to provide at least 3,949 cf of storage between the outlet invert

elevation and the top of the sand. Currently, 1,993 cf is provided between elevation 926.3 and

925.56.

4. Clarify placement of the 4” draintile. Detail B on sheet C8-02 states the 4” draintile shall be on

the sides and outlet, but sheet C5-01 indicates it is a 6” draintile.

Reider/Thines

Unanimously approved

B) Permit # 13-029 Island Station Demolition (Kelley)

Mr. Kelley, reviewed Permit #13-029 Island Station Demolition. The applicant is Frattalone. The permit is for

the demolition of Island Station Power Plant. The applicable rule is Sediment Control (Rule F). This project

has 3.5 Acres of disturbed area and no impervious surface.

Motion 13-189: To approve Island Station Demolition Permit 13-029 with nine conditions:

1. Receipt of $7,000 surety.


3. Revise construction limits, perimeter controls, and revegetation areas to encompass the temporary

parking area..

4. Provide native seed mix appropriate for the river corridor and floodplain such as Mn/DOT 300 series.

5. Provide a note on the plans that stockpiles, equipment and other demolition materials shall not be

placed within the 100 yr floodplain, and that the floodplain shall be fenced or flagged to prevent

encroachment.

6. Identify and provide protection for catch basins on Randolph Avenue.

7. Provide a flood response plan to minimize floodwater contact with demolition materials and exposed

soils.

8. Quantify the net change in floodplain storage and provide compensatory storage for any fill within 100-

yr floodplain.

9. Provide final plans signed by a professional engineer per the Minnesota Board of AELSLAGID.

Reider/Thienes


President Collins asked for clarification on item number 8. Mr. Kelley replied that in a floodplain, to prevent a

change in elevation, projects can not add fill.

C) Permit #13-030 Western U Plaza (Kelley)

Mr. Kelley reviewed permit #13-030 Western U Plaza. The applicant is St. Paul Old Home Plaza. The permit

is for redevelopment and reuse of former Old Home property at Western and University. The applicable rules

are Stormwater Management (Rule C), Flood Control (Rule D), Erosion and Sediment Control (Rule F). This

project has 1.6 Acres of disturbed area and 1.03 Acres of impervious surface.

Motion 13-190: Table the permit application for Western U Plaza Permit 13-030 with 10 Conditions:

1. Receipt of $5,150 surety and maintenance agreement.


3. Provide plans signed by a professional engineer per the Minnesota Board of AELSLAGID.


4. Place inlet protection on curb catch basins on University Avenue.

5. Show location of existing storm sewer for catch basins at corner of University Avenue and Western

Avenue. Two catch basins appear to be detached from storm sewer system.

6. Remove geotextile fabric from bottom of the rock reservoir, provide on top and sides only

7. Revise grading plan so that, in the event the underground StormTech system outlet manhole overflows,

runoff flows to the west and into the street. The current grading promotes runoff flowing into the parking

garage ramp.

8. Revise plans, drainage area map, and HydroCAD to correspond:

a) Specify within the plan set or include a detail to show the elevation of underground StormTech

system. Confirm the values correspond with the HydroCAD model.

b) Area 4 (new building) is draining to the underground facility in HydroCAD, but there is a storm

sewer inlet on the east side of the building on sheet C5.

c) Porch area is draining to the underground facility in HydroCAD, but a separate storm sewer for

the porch is on plan sheet C5.

9. Define location and dimension for the pretreatment system for the StormTech underground infiltration

system. Isolator row is selected as pretreatment in plan set but location and orientation is not defined in

the plan set.

10. Identify whether the existing storm sewer in Lot 2 will be removed. Removal is not specified on sheet C5.

11. Revise plans to show pavement replacement where existing storm sewer is being disconnected and

removed.

Thienes/Reider


Manager Jones abstained from voting due to possible conflict of interests.

D) Permit Program/Rules Update (Kelley)

There will be three permit applications at the November 6th meeting.

IV. Special Reports

No Special Reports

V. Action Items

A) AR: Approve Minutes of the October 2, 2013 Regular Meeting (Sylvander)

Motion 13-191: Approve Minutes of the October 2, 2013 Regular Meeting.

Jones/Reider


B) AR: Approve Accounts Payable/Receivables for September 2013 (Sylvander)

Motion 13-192: Approve Accounts Payable/Receivables for September 2013

Thienes/Reider



C) AR: Approve letter of support for CCLRT GLGI (Eleria)

Ms. Eleria reviewed at the October 2, 2013 Board meeting, the City of Saint Paul presented its work and

findings over the past two years on shared, stacked-function green infrastructure (SSGI) as a tool for more

robustly achieving transit-oriented redevelopment in the Green Line corridor (formerly known as the Central

Corridor). The City has prepared a draft final project report titled “Strategic Stormwater Solutions for Transit

Oriented Development” and is seeking stakeholder comments until October 18, 2013.

CRWD staff have prepared a draft comment letter and detailed memorandum on the draft final report for the

Board’s review and approval. The letter and memorandum include the Board’s verbal comments to the City at

the Oct. 2nd

meeting as well as CRWD staff comments.

Motion 13-193: Approve the comment letter and detailed memorandum to the City of Saint Paul for the draft

final report titled, “Strategic Stormwater Solutions for Transit-Oriented Development”.

Thienes/Jones


Motion 13-194: Approve Resolution for Shared, Stacked-Function Green Infrastructure. Therefor be it

resolved that CRWD Board of Managers support the incorporation of shared, stacked-function green

infrastructure into (re) development projects when doing so would result in economic, environmental and social

benefits to the community. Be it further resolved, CRWD will support the implementation of shared, stacked-

function green infrastructure by:

1. Providing education materials of shared, stacked-function green infrastructure;

2. Encouraging consideration of shared, stacked-function green infrastructure in pre-development

discussions.

3. Considering regulatory measures to facilitate shared, stacked-function green infrastructure.

4. Considering conducting pilot studies to better understand and refine the shared, stacked-function

green infrastructure framework.

5. Considering integration of shared, stacked-function green infrastructure where prudent in CRWD-led

and CRWD-funded projects.

Thienes/Reider


VI. Unfinished Business

A. Education Display Designs Update (Beckman)

Ms. Beckman reviewed in July of 2011 the Board of Managers authorized staff to explore options for creating

education displays. In September 2012, a committee consisting of Managers Jones and Texer and CRWD staff

selected Hamline University’s Center for Global and Environmental Education (CGEE) to design and fabricate

the displays. The Board of Managers reviewed the proto types of the Education Displays. Overall the Board

was very pleased with the displays. A few modifications were requested by Administrator Doneux, the Board

of Managers and the committee.

Motion 13-195: Motion to begin fabrication of the Educational Displays with recommended changes.

Jones/Reider



Manager Thienes asked if the displays would be ready to share at the December 6th

, 2013 MAWD meeting. Ms.

Beckman felt that was enough time for the modifications to be made and the final fabrications to be made for

one display.

B. CAC Revitalization Update (Reider)

Ms. Reider attended the October 9th

, 2013 CAC meeting. The focus of the meeting was about strengthening the

roles of the CAC. Former State Senator Ellen Anderson was the facilitator. Ms. Reider felt the ideas that the

CAC had were very similar to ideas from the Board of Managers. The CAC showed an interest in more social

events that would involve more opportunities to interact and meet the staff of CRWD. The attendance of the

meetings continues to average around 50% of the total membership.

VII. General Information

A. CAC Update and identify a Board Member Attendee for November 13th

CAC

Meeting

Ms. Reider will attend the November 13th

CAC meeting.

B. Administrator’s Report

Administrator Approved or Executed Agreements

General updates including recent and upcoming meetings and events

Staff attended and Administrator Doneux presented at the Ramsey County State of the Waters meeting on

September 26, 2013.

CRWD Staff, Mark Doneux, Bob Fossum, Forrest Kelley and Nate Zwonitzer attended the WEF TEC

conference in Chicago that was held from 10/7/13 – 10/9/13.

Lake McCarron’s Shoreline Residents Meeting, 6:00 PM, Thursday, October 3rd

, Roseville City Hall

Council Chambers. – Twenty-nine lakeshore residents, five agency staff and Managers Thienes, Texer, and

Collins attend this meeting. The meeting generated many questions about managing aquatic plants in Lake

McCarrons especially along the shallow western shore. CRWD will be starting a process to develop a plan

to manage aquatic plants in the lake. The focus of the plan will be less on invasive species and more specific

to navigation and aesthetics. Administrator Doneux felt the meeting went very well with a good exchange

of information. The Neighbors thanked the Board Members and Administrator for their time and explaining

the problems of Lake McCarron’s.

CRWD Staff will be participating in the Minnesota Water Resources Conference in Saint Paul, October 15 –

16. Ms. Eleria and Mr. Fossum will both be presenters at the conference.

A Partner Grant committee will need to meet and review applications. Applications are due October 25,

2013. Managers Reider and Jones will meet on November 6th

at 4:30 to review the applications.

1) Upcoming events and meetings

a) Metro MAWD Meeting is Tuesday, October 15, 2013 at 7:00 PM.


b) Next Board Meeting is Wednesday, November 6, 2013 at 6:00 pm.

c) Next CAC Meeting is Wednesday November 13, 2013 from 7:00-9:00 pm.

d) MAWD Annual Meeting and Trade Show, December 5-7, 2013, Arrowwood Resort, Alexandria.

The Villa Park Wetland Restoration Project is one of the featured presentations at this conference.

2) Project Updates

a) Villa Park Wetland Restoration Project

Dredging at Villa Park is complete and all dried sediment has been removed. Frattalone is now

completing the site restoration phase and will be done by the end of October.

b) TBI – Cayuga Relocation Project

The TBI Realignment Project at 35E/Cayuga is substantially completed. The new TBI alignment has

been fully constructed and is on-line. Over the next couple of weeks, the old TBI alignment will be

abandoned.

VIII. Next Meeting

A) Wednesday, November 6, 2013 Meeting Agenda Review

IX. Adjournment

Motion 13-196: Adjournment of the October 16, 2013 regular Board Meeting at 7:03 p.m.

Reider/Jones

Unanimously Approved

Respectfully submitted,

Michelle Sylvander




FROM: Anna Eleria, Water Resource Project Manager

RE: Approve Contract Amendment #4 for Engineer for Highland Ravine Stabilization Project

Background

In early November 2012, CRWD’s Board of Managers approved Wenck Associates as the engineer for

the Highland Ravine Stabilization Project for an original contract amount of $45,476. To date, CRWD

has approved three contract amendments for additional engineering work at cost of $8,110 for a total

engineering budget of $53,586. The additional work included stabilization designs for ravines

discovered during field work, addressing another round of comments, and covering other design changes

that were outside the original scope of work.

Issues

Due to the expanding scope of the project and the complexities of working with private property owners,

Wenck has exceeded the engineering budget and seeks additional funds to cover portion of the unpaid

expenses to date and the remaining tasks including finalizing the stabilization plans for all ravines,

completing the contract documents, assisting CRWD with easements/agreements, and bidding. Wenck

has incurred over $17,000 to date since their last paid invoice and anticipates spending another $17,000

for the remaining tasks with a majority of that combined amount used to convert the plans to CAD.

Wenck is willing to assume a significant portion of the outstanding and future engineering costs and is

requesting $7,634 to complete the engineering work. See enclosed Wenck memo.

Currently, the Wenck contract deadline is December 31, 2013, which needs to be extended through

2014. CRWD staff anticipates all plans will be completed and easements/agreements secured in

February 2014. The project will go out for bid in March 2014 and construction will commence in

summer 2014. CRWD staff believes the budget request by Wenck is justified and recommends the

Board approve a Wenck contract amendment to increase the budget by $7,634 and extend the contract

deadline to December 31, 2014.

Action Requested

Approve Contract Amendment #4 for Wenck Associates, Inc. for the Highland Ravine Stabilization

Project in an amount not to exceed $7,634.00 for a total budget not to exceed $61,220 and a contract

deadline of December 31, 2014.

enc: Wenck Memo dated October 28, 2013

W:\06 Projects\Highland Ravine\Board-CAC Memos\BM Highland Ravine Engineer Contract Amendment #4 11-06-13.docx


V. Action Item – B) Contract

Amendment for Engineer for

Highland Ravine Project (Eleria)

W:\06 Projects\Highland Ravine\Design and Engineering\Wenck Scope of Work and Budget\Design Scope Changes\M ‐ Eleria Anna re Scope Change #5 FINAL.docxC:\Documents and Settings\anna\Local Settings\Temporary Internet Files\Content.Outlook\BELLGC1X\M ‐ Eleria Anna re Scope Change #5.docx

MEMORANDUM TO: Anna Eleria, Capitol Region Watershed District FROM: Todd Shoemaker, PE, CFM DATE: October 28, 2013 SUBJECT: Scope of work change #5 for Highland Ravine Stabilization Project

INTRODUCTION The purpose of this memorandum is to request additional compensation to finalize the project plans and specifications. BACKGROUND Capitol Region Watershed District (CRWD) contracted with Wenck Associates, Inc. (Wenck) to provide stabilization plans for multiple ravines adjacent to Highland Park in St. Paul. Wenck has completed preliminary plans which have been reviewed by CRWD staff, City of St. Paul staff, and affected property owners. Wenck and CRWD staff recently met to discuss the project status, pending tasks, and future schedule. CRWD staff advised Wenck to submit this memorandum to document anticipated future costs to finalize the plans and specifications. As this project has grown in size and scope, Wenck has notified CRWD staff and requested the scope and project budget be revised accordingly. Below are the four changes in scope for the project that have been approved by CRWD’s Board of Managers:

1. Add design plans, profiles and calculations for Ravine 2 (formerly known as the Stolpestad Ravine);

2. Revise the plans per comments submitted by CRWD, City of St. Paul and affected property owners;

3. Stabilize an eroding slope on 1590 Edgcumbe Rd; and 4. Include the eroding slope at 1626 Edgcumbe Rd in the stabilization plans.

SCOPE OF WORK CHANGE #5 Wenck is requesting a fifth change to accomplish the necessary tasks for finalizing the Highland Ravine plans and specifications, which include:

Finalize sanitary sewer design based on City of St. Paul comments.

Revise plans based on CRWD comments.

Finalize project manual and assist CRWD with bidding.

Wenck Associates, Inc. 1802 Wooddale Drive Suite 100 Woodbury, MN 55125‐2937 (651) 294‐4580 Fax (651) 228‐1969 [email protected] www.wenck.com

Technical Memo Scope of work change #5 Highland Ravine Stabilization Project October 28, 2013

2 W:\06 Projects\Highland Ravine\Design and Engineering\Wenck Scope of Work and Budget\Design Scope Changes\M ‐ Eleria Anna re Scope Change #5 FINAL.docxC:\Documents and Settings\anna\Local Settings\Temporary Internet Files\Content.Outlook\BELLGC1X\M ‐ Eleria Anna re Scope Change #5.docx

Finalize easements with homeowners and agreement with Deer Park.

The table below indicates the Wenck staff members that will accomplish the remaining tasks. (The task ID’s and task description headings have been maintained from our original proposal.) The total estimated cost for this change in budget is $7,634.00. This amount includes 8 hours for the Wenck Project Manager to manage the additional tasks.

Wenck Staff Matthiesen Shoemaker Jonett BoellTitle Sr. Engineer PM/WR Eng LA CAD

Hourly Rate $179 $144 $101 $144 $93TASK 1 Data Collection and Review $

TASK 01 TOTAL: $0.00

TASK 2 Field Work and Site Evaluation $TASK 02 TOTAL: $0.00

TASK 3 Project Design $A Sanitary sewer design and meeting with St. Paul $1,808.00 6 7B Revise plans based on City and CRWD comments $977.00 1 2 2 2 $20C Receive comments from City, DP, and CRWD $288.00 2D Finalize plans based on homeowner, City and CRWD comments $977.00 1 2 2 2 $20

TASK 03 TOTAL: $4,050.00

TASK 4 Construction Bidding $A Revise and resubmit project manual to CRWD for review $490.00 2 2B Finalize project manual $1,048.00 4 4 $100C Pre-bid meeting $462.00 3 $30D Respond to bidder questions $288.00 2E Attend bid opening $462.00 3 $30F Advise CRWD of lowest, qualified bidder and draft memo $432.00 3

TASK 04 TOTAL: $2,000.00

TASK 5 Technical Support for Easement Agreements $A Provide plans and easements to Deer Park (DP) homeowners $144.00 1B Finalize easements with homeowners and agreement with Deer Park $144.00 1C Board approval of homeowner easements and Deer Park agreement $0.00


TASK 6 Permitting $A Resubmit plans to City for site plan review $144.00 1


TASK 7 Project Coordination and Meetings $A Project coordination $1,152.00 8

TASK 07 TOTAL: $1,152.00

PROJECT TOTALS $7,634.00 2 38 6 13 4 $200

Admin ExpensesTASK ID TASK DESCRIPTION




FROM: Mark Doneux, Administrator

SUBJECT: Establish Monitoring, Research and Maintenance Division

Background

Minnesota Statue 103D.325, Subdivision 1 provides the District with employment authority and states that the

CRWD Board of Managers may employ a chief engineer, professional assistants, and other employees, and

provide for their qualifications, duties, and compensation. CRWD Board of Managers first hired professional

staff in 2003 and currently employs 14 Full Time Equivalents (FTE). The District recognizes the need to

regularly evaluate and assess staff size, skills and structure. Since the District first started hiring employees

with the Administrator position in 2003, the staff organizational structure has been flat. All employees report

directly to the Administrator.

Issues

Over time staff organized around the monitoring and BMP maintenance has grown in numbers with 5.5 FTE

working in this area currently. Because of this growth and need to provide more direct supervision for these

staff, I believe there is benefit in establishing functional Divisions as an organizational tool to provide enhanced

and more direct staff supervision, training and mentoring. In addition to the District benefit, staff desires the

opportunity to grow and gain management experience at CRWD. Establishing functional units (Division)

within the District’s staff structure recognizes the benefit for both staff and the District to provide professional

advancement for staff to strengthen the organization, reduce turnover and maintain high employee satisfaction.

To clarify how this would work I have drafted an organizational chart that illustrates both the proposed 2013

implementation of the Monitoring, Research and Maintenance Division as well as hypothetical future structure.

I would like to emphasize that staff structure beyond establishing the Monitoring, Research and Maintenance

Division is not known and the organizational chart provided is something that will need to be regularly

reviewed and evolve overtime with our programs, projects and staffing requirements.

This plan has been reviewed with the Personnel Committee and the Board of Managers. I recommend the

Board of Managers create and establishes the Division of Monitoring, Research and Maintenance. I would also

recommend that the Board of Managers regularly evaluate the District’s staff structure to ensure an efficient and

effective structure that harnesses staff skills, abilities and professional development goals.

Requested Action

Adopt Resolution Creating and Establishing the Monitoring, Research and Maintenance Division

enc: 2020 Organization Chart

Draft Resolution Creating and Establishing the Monitoring, Research and Maintenance Division W:\03 Human Resources\Staff Structure\Board Memo- Monitoring, Research and Maintenance Division 10-31-13.docx

April 3, 2013 Board Meeting

V. Action Item – C) Establish

Monitoring, Research and

Maintenance Division (Doneux)

Citizens

Board of Managers

Administrator

Program ManagerMonitoring, Research

& Maintenance

Water Resource Technician

Water Resource Technician

Water Resoure Technician (Seasonal)

Monitoring Coordinator

Water Resource Technician (.25 FTE)

Maintenance Coordinator

Program ManagerRegulatory Program

Water Resource Technician (.75 FTE)

Technician

Program ManagerCapitol Improvements

and TBI

Water Resource Specialist

Technician

Program ManagerEducation & Outreach

Education Assistant (.50 FTE)

Education Assistant

Program ManagerGrants & BMPs

Technician

Office Manager

Administrative Assistant (.50 FTE)

Engineer Attorney

Ramsey County Commissioners CAC

Technician

CAPITOL REGION WATERSHED DISTRICT

2020 ORGANIZATIONAL CHART

October 31, 2013

Currently same person performing two roles

Future Position

Implement in 2013

Implementation(TBD)

Future implementation of this Organizational strucutre will be regularly evalutated by the Board of Managers. Divisions, staff positions and titles to the right of the red dashed line are for illustration purposes only. These positions are not established until they are reviewed, updated adopted by the Board of Managers.

Resolution Capitol Region Watershed District

In the matter pertaining to: Establishing the Monitoring, Maintenance and Research Division Board Member __________ introduced the following resolution and moved its adoption, seconded by Board Member ________. WHEREAS, Minnesota Statue 103D.325, Subdivision 1 provides the District with employment authority; and WHEREAS, The CRWD Board of Managers may employ a chief engineer, professional assistants, and other employees, and provide for their qualifications, duties, and compensation; and WHEREAS, CRWD Board of Managers first hired professional staff in 2003 and currently employ 14 Full Time Equivalents (FTE); and WHEREAS, CRWD Board of Managers recognizes the need to regularly evaluate and assess staff size, skills and structure; and WHEREAS, CRWD Board of Managers recognizes the benefit of establishing functional Divisions as an organizational tool to provide enhanced and more direct staff supervision, training and mentoring; and WHEREAS, CRWD Board of Managers recognizes that staff desire the opportunity to grow and gain management experience at CRWD; and WHEREAS, CRWD Board of Managers recognizes the benefit for both staff and the District to provide professional advancement for staff to strengthen the organization, reduce turnover and maintain high employee satisfaction; and THEREFORE BE IT RESOLVED, that CRWD Board of Managers creates and establishes the Division of Monitoring, Research and Maintenance. BE IT FURTHER RESOLVED, CRWD Board of Managers will regularly evaluate the District’s staff structure to ensure an efficient and effective structure that harnesses staff skills, abilities and professional development goals.

*Approval must receive minimum of 3 Yeas

Vote: Approved/Denied W:\04 Board of Managers\Motions\Resolutions 2013\Resolution 13-xx-xx Establishing Monitoring, Research and Maintenance Division 10-29-13.docx

Requested By: Mark Doneux Recommended for Approval: Approved by Attorney: N/A Funding Approved: N/A

Manager Yeas* Nays Absent Abstain Collins Texer Jones Thienes Reider TOTAL

Resolution # 13-194 Date Adopted: November 6, 2013

Resolution Adoption Certified By the Board of Managers: By: ______________________________________ Date: November 6, 2013





SUBJECT: Approve Program Manager III Position

Background

Over time the number of staff organized around the monitoring and BMP maintenance has grown with 5.5 FTE

working in this area currently. Because of this growth and a need to provide more direct supervision for these

staff, I believe there is benefit in establishing a Program Manager to manage the Monitoring, Research and

Maintenance Division in order to provide enhanced and more direct staff supervision, training and mentoring.

In addition, staff desires the opportunity to grow and gain management experience at CRWD. Establishing a

Program Manager within the District’s staff structure recognizes the benefit for both staff and the District to

provide professional advancement for staff to strengthen the organization, reduce turnover and maintain high

employee satisfaction.

Issues Currently the District does not have a Program Manger III position. I have drafted a position description and

have reviewed it with the Personnel Committee. The Primary Objective of this position is to manage the

Monitoring, Research and Maintenance Division. In addition, this position will perform skilled to highly skilled

duties providing water resource management, protection and planning as it relates to the implementation of

District’s Watershed Management Plan and annual work plan. The Program Manager coordinates the

implementation of their Division’s area of responsibility within the District’s Watershed Management Plan.

Major areas of accountability and essential job functions include: program and project Management, Fiscal

management and employee supervision.

In addition to creating the Program Manager III position, I am requesting Board approval to promote Bob

Fossum into this position. Bob Fossum has been with the District since 2004 and has been instrumental in

implementing many major projects and programs with the District including, Rules, the 2010 Watershed

Management Plan and the Arlington Pascal project. Most recently Bob Fossum has brought his leadership and

management skills to help stabilize the Monitoring and BMP Maintenance programs of the District after

significant staff turnover.

In accordance with the District Salary Administration Policy, the Personnel Committee must approve any

change in Grade for an existing employee. The Personnel Committee has met and supports this promotion and

is seeking Board Approval of this action.

Requested Action

1) Approve Program Manager III Position and Position Description

2) Approve Promotion of Bob Fossum to Grade 11 and to fill Program Manager III Position.

enc: Draft Program Manager Position Description W:\03 Human Resources\POSITIONS\Program Manager\Board Memo- Program Manger III 10-31-13 #2.docx


V. Action Item – D) Approve

Program Manager III Position

(Doneux)

Board Adopted: November 6, 2013

GRADE: 11 JOB CLASSIFICATION: Program Manager III POSITION TITLE: Program Manager – Monitoring, Research and Maintenance Division REPORTS TO: Administrator PRIMARY OBJECTIVE: Perform skilled to highly skilled duties providing water resource management, protection and planning as it relates to the implementation of District’s Watershed Management Plan and annual work plan. POSITION OBJECTIVE: The Program Manager coordinates the implementation of their Division’s area of responsibility within the District’s Watershed Management Plan. The Program Manager is responsible for the development and implementation of District projects and programs, and the oversight of capital projects. The Program Manager is responsible for implementing projects that address water quality issues. This position will coordinate watershed management activities involving other local units of government, City Departments, agencies, and private and non-profit sectors in the Watershed. MAJOR AREAS OF ACCOUNTABILITY/ESSENTIAL JOB FUNCTIONS Program and Project Management: Engages the Division’ direct reports in the portions of the comprehensive Watershed Management Plan and the area’s Annual Program Work Plan. Develops corresponding budgets, secures Administrator’s approval for, and oversees the implementation of, the above plans. Identifies goals and corresponding strategies to address the watershed plan content areas and annual work plans. Ensure that the plans reflect best practices and fulfill all requirements as outlined in MN Statute 103B. Ensure their Division’s compliance with the District’s practices and policies. Fiscal Management: Involve direct reports in contributing data to be considered for inclusion in the budget. Formalize final budgets for their Division. Obtain Administrator and/or Board approval. Tracks program expenditures and monitors activities against budget. Secure Administrator and/or Board approval for expenditures outside of established budgets. Identifies, provides corresponding rationale, and advocates for appropriate staffing levels, material resources and professional development for direct reports to perform their jobs. Comply with all financial reporting requirements, as documented in the CRWD Policies and Procedures Manual.


Supervision: Supervise staff as assigned by Administrator in accordance with CRWD Organizational structure established by the Board of Managers. Manage the hiring process and decisions related to the selection, promotion, and transfer of assigned personnel. Has authority to terminate program area personnel, interns, and contractors as long as the Administrator has been apprised of the situation and the details are documented according to the organization’s progressive disciplinary process, outlined in the Employee Handbook. Provide clear, specific, and timely directions. Delegate without removing assistance or accountability. Works with direct reports to develop their annual Work Plans in a timely manner; approves Individual Work Plans and ensures they are in response to the Watershed Management Plan, support the area’s Annual Program Work Plan, and link to the Individual Performance Goals. Monitors deadlines and takes the appropriate actions to ensure that all goals/projects stay on track. Adjusts deadlines when the unexpected occurs, or per Administrator or Board directive. Ensures direct reports receive ongoing training/education and certification to perform their existing jobs, increase skills and knowledge, improve current performance, and/or develop new competencies for other assignments/positions. Provides regular formal performance reviews. Responsible for making salary adjustments based on Policies and subject to the approval of the Administrator. Contract Management: Manage the selection of contractors, creation of contract documents, and management of contracted services and personnel consistent with District policies, subject to the approval of the Administrator and/or Board of Managers. Orient contracted personnel to the organization’s policies and procedures. Communicate, both verbally and in writing, performance specifications and expectations. Monitors the work performance of contracted personnel on a continual basis, provides timely feedback, and if applicable, takes corrective action. Administer the organization’s policies and procedures as related to contractor selection, payment, contract deliverables and corresponding schedule, applicable amendments, and closeout. ADDITIONAL FUNCTIONS:

1. Provides technical support to District programs. 2. Represent the District on special committees. 3. Effectively represent water and watershed issues at meetings, conferences, before the media,

and to other local units of government, City Departments, the Board of Managers, partner organizations and the public.

4. Coordinate watershed-related activities in the District, and activities involving other governmental agencies and private and non-profit entities.


(The examples given above are intended only as illustrations of various types of work performed and are not necessarily all-inclusive. This position description is subject to change as the needs of the employer and requirements of the position change.) SALARY Grade 11, depending on qualifications and experience, plus benefits. MINIMUM QUALIFICATIONS Degree and/or experience appropriate for the position. Experience with stream hydrology and water quality monitoring and chemistry are essential. Minimum of eight years professional experience including project management is preferred. Appropriate advance degree and/or certificates are preferred. Good communication and computer skills are required. KNOWLEDGE, SKILLS and ABILITIES 1. Knowledge of watershed management, surface and groundwater hydrology, natural resource

management, soils and biology. Demonstrated knowledge and working experience related to local, state, and federal programs and requirements.

2. Effective communication skills, both oral and written. 3. Ability to develop effective cooperative relationships with technical and policy staff, state and

local government officials, and private entities and citizens. Ability to effectively lead teams of technical and policy staff, including those of partner and stakeholder organizations.

4. Demonstrated ability in team building and effective coaching. 5. Demonstrated knowledge of budget preparation and contract development. 6. Demonstrated knowledge of procurement, permitting and other processes, and design and

construction contracting. 7. Extensive knowledge of project management techniques. 8. Extensive negotiating skills. 9. Ability to analyze technical reports and construction diagrams. 10. Proven ability to achieve goals, ability to work successfully with considerable independence. 11. Excellent analytical, conflict management, interpersonal, and leadership skills. 12. Ability to write successful grant requests, including knowledge of grant writing requirements. 13. Proficiency with a personal computer (PC) and Microsoft software packages for word

processing, spreadsheet, database management and computer generated graphics. Specifically, but not limited to, Microsoft Office, Excel, Word, Access, PowerPoint. Ability to effectively use email and internet applications and other common software applications.

14. Ability to take direction, work independently with a minimum of supervision, use good time management practices, possess the ability to set priorities and balance large volumes of diverse work.

15. Ability to develop and maintain effective working relationships with, the Administrator, CRWD Board of Managers, Citizens Advisory Committee, Ramsey SWCD staff, Ramsey County staff, City and agency staff, members of the public, and other interested parties.

16. Must have valid Minnesota driver’s license and have vehicle available for periodic business use on a mileage reimbursement basis. The vehicle must have insurance approved by the District.


RESPONSIBILITY FOR PUBLIC CONTACT High level of public contact requiring tact, courtesy and good judgment. EMPLOYMENT CLASSIFICATION: Salaried, exempt from the provisions of the Fair Labor Standards Act. NON-DISCRIMINATION POLICY The Capitol Region Watershed District will not discriminate against or harass any employee or applicant for employment because of race, color, creed, religion, national origin, sex, disability, age, marital status, sexual orientation, or status with regard to public assistance.

PROGRAM MANAGER

PHYSICAL DEMANDS AND JOB DESCRIPTION SUPPLEMENT WORK ENVIRONMENT 1.) Normal shift = eight (8) hours for five (5) consecutive days. 2.) Work location normally in controlled environment. 3.) Stress level varies from low to very high. PHYSICAL DEMANDS

Type of Activity

Frequency

Walking/standing: M

Sitting: M

Standing in One Place:

M

Climbing:

O

Pulling/Pushing:

M

Crawling/Kneeling/Squatting:

M

Bending/Stooping:

M

Twisting/Turning:

M

Repetitive movement:

M

Lifting waist to shoulder:

M

Lifting knee to waist:

M

Lifting floor to knee: M

S = Significant M = Moderate O= Occasional



TO: Board of Managers

FROM: Mark Doneux

RE: 2014 Employee Benefit Program

Background

Since 2003, Bearence (formally TC Fields) has provided insurance to the District. During late 2011 the

District looked at other options for benefits for employees. Staff worked with Bearence to review and

determine a new benefits package with the goal of attempting to meet the following objectives:

1. Realize a cost savings for the District and District employees.

2. Obtain similar benefit coverage’s to those currently available to District employees through

Ramsey County.

The District has purchased health benefit package from Health Partners through Bearance for 2012 and

2013. The District purchased dental and other ancillary coverage’s (Life, Short Term and Long Term

Disability) from Ramsey County. The District is now ready to take the next step and obtain all benefit

coverage through Bearance.

Issues

Staff has obtained benefit quotes from Bearance for health, dental and ancillary coverage’s. Bearance

obtains quotes from at least three vendors when soliciting benefit quotes. The action requested by the

Board of Managers is to set the employee contribution rates for 2014. Table 1 below summarizes

Table 1- Current 2013 and Proposed 2014 Monthly Health Insurance Coverage Contributions

Current 2013 Health Coverage 2013 Employee 2013 District Total Cost

Single Health Insurance $11.66 $290.94 -$312.74 $302.60 - $324.40

Family Health Insurance $68.12 $537.08 - $1,329.48 $605.20 - $1,397.60

Proposed 2014 Health Coverage 2014 Employee 2014 District* Total Cost*

Single Health Insurance $40.00 $299.41 $339.41

Single + 1 Insurance $80.00 $606.11 $686.11

Family Health Insurance** $120.00 $815.71 - $1,314.91 $935.71 - $1,434.90

*2014 District and Total Costs are average based on all age groups and assume the same enrollment

for 2014

** 2014 District and Total Costs are based on using 20-29 age with spouse and 1 child for low end of

range, 40-49 age, spouse and 3 children for high end of range.

November 6, 2013

V. Action Items E) Approve 2014

Employee Benefit Program

(Doneux)

2

Table 2 - Current 2013 and Proposed 2014 Monthly Dental Insurance Coverage Contributions

Current 2013 Dental Coverage 2013 Employee 2013 District Total Cost

Single Dental Insurance $16.18 $28.27 $44.45

Family Dental Insurance $43.43 $55.57 $99.00

Proposed 2014 Dental Coverage 2014 Employee 2014 District Total Cost

Single Dental Insurance $10.00 $30.85 $40.85

Single + 1 Dental Insurance $20.00 $61.29 $81.29

Family Dental Insurance $40.00 $82.55 $122.55

Staff has reviewed potential changes to the benefits with the Employee Committee (Managers Texer and

Collins) and the committee is bringing forward the recommendation listed below.

Requested Action

Approve the 2014 Employee Benefit Program as follows:

1) Effective January 1, 2014, the District move all employee benefit programs from Ramsey County

to those offered through the District’s Insurance Company, the Bearence Management Group.

2) The District requires a monthly employee contribution of $40.00 for single, $80.00 for Single Plus

One and $120.00 for Family health insurance, effective December 1, 2013.

3) The District requires a monthly employee contribution of $10.00 for single, $20.00 for Single Plus

One and $40.00 for Family dental insurance, effective January 1, 2013.

4) The District will continue to provide ancillary employee benefits including life, short term

disability and long term disability insurance. These programs and the employee/District

contributions will be consistent to those offered by Ramsey County. The District will continue to

provide Life Insurance and Long Term Disability coverage consistent with the Ramsey County

Program and allow employees to purchase additional coverage at their cost.

5) The District provide payroll deductions and employee contributions to Health Care Flexible

Spending Accounts and Dependent Care Spending Accounts.

W:\03 Human Resources\Benefits\2014 Benefits\Board Memo- 2014 Employee Benefit Program 10-31-13.docx




FROM: Anna Eleria, Project Manager

Gustavo Castro, Water Resource Specialist

RE: Inspiring Communities Program Updates (Former Neighborhood Stabilization Program)

Background

Since August 2011, CRWD has partnered with the City of Saint Paul’s Planning and Economic

Development (PED) Department on creating water-friendly landscapes on single-family residential

properties that the City is acquiring and redeveloping through its Neighborhood Stabilization Program

(NSP). For each NSP property, CRWD has prepared a landscape plan that includes stormwater BMP(s)

(i.e., rain gardens, swales, etc.) to provide proper drainage away from the property, minimize stormwater

runoff from the property, enhance the property’s aesthetics and improve water quality of the Mississippi

River.

Issues

The Neighborhood Stabilization Program, now called Inspiring Communities Program has been

undergoing some changes. Up to now, CRWD has been working directly with the City of St Paul, and

they have been implementing many of the rehabs of vacant single family buildings over the last couple

of years. Moving forward, the program is shifting to a developer driven model that allocates property

and subsidy through an open bid process to developers. To that end, the City released an RFP in early

October with around 77 properties, which includes a mix of owner occupied and rental, as well as vacant

building rehabs and new construction.

In this new format, the developer is ultimately responsible for initiating work with CRWD. The

enhancement of the RFP properties to achieve water quality benefits is still a requirement of the

program, however, developers are not required to work with CRWD. Nevertheless, CRWD will

continue to offer a free landscape design and rebates, generally ranging from $500 - $1,000, for the

installation of rain gardens. In cases where CRWD identifies a unique opportunity for the installation of

other management practice, higher rebates could be considered.

Action Requested

No action requested. This is intended to be only an update to the board members on the undergoing

changes in the program’s format.

enc: Map of current and future project locations

\\CRwDC01\company\07 Programs\Stewardship Grant Program\Saint Paul NSP\Board Memos\BM Inspiring Communities Program Updates.docx

November 6, 2013

VI. Unfinished Business A)

Inspiring Communities

Program Updates (Eleria,

Castro)

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Copyright: ©2013 Esri, DeLorme, NAVTEQ

Capitol Region Watershed DistrictInspiring Communities Program | Project Locations

0 0.6 1.2 1.8 2.40.3Miles I

DISCLAIMER: This map is neither a legally recorded map nor a survey, and is not intended to be used as one. This map is a compilation of records, information anddata located in various city, county, state and federal offices

and other sources regarding the area shown, and is to be used for reference purposes only.

! 2013 Sites! 2011-2012 Sites

Major HighwaysMajor WaterbodiesParksCRWDSubwatersheds

Falcon Heights


TO: CRWD Board of Managers and Staff


RE: November 6, 2013 Administrator’s Report

Administrator Approved or Executed Agreements

Stewardship Grant Agreement with Great River Greening for a fall intern. - $1,500.

Board Approved or Executed Agreements

TBI Work Order No. 5 Amendment No. 2 with Barr Engineering for additional rail monitoring program.

Not to exceed $56,500 for a total work order amount of $992,865

General updates including recent and upcoming meetings and events

1) Upcoming events and meetings

a) Next CAC Meeting is Wednesday November 13, 2013 from 7:00-9:00 pm.

b) Next Board Meeting is Wednesday, November 20, 2013 at 6:00 pm.

c) MAWD Annual Meeting and Trade Show, December 5-7, 2013, Arrowwood Resort, Alexandria.

The Villa Park Wetland Restoration Project is one of the featured presentations at this conference.

The deadline to register for lodging is November 15, 2013. The deadline to register for the Annual

Conference is November 20, 2013.

2) Project Updates

a) Villa Park Wetland Restoration Project

Dredging at Villa Park is complete and all dried sediment has been removed. Frattalone is now

completing the final site restoration phase.

b) TBI – Cayuga Relocation Project

The TBI Realignment Project at 35E/Cayuga is completed. The new TBI alignment has been fully

constructed and is on-line. The old TBI alignment is now abandoned.

W:\04 Board of Managers\Correspondence\Administrator's Report 2013\Administrator's Report 11-6-13.docx
