Ef coop meetingpres_11102011

Office of Research and Development National Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch November 10, 2011

East Fork Watershed Water Quality Monitoring and Modeling Cooperative (EFWCoop): November 10th Meeting.

1.

Overarching R&D Objectives for establishing the East Fork Watershed Cooperative 1. Integration of natural and built systems 2. Coupled modeling and monitoring programs for

decision support 3. BMP/GI performance to effectiveness linkages 4. Informational (data) architectures and required

cooperation for sustainable total water management

5. Consider scaling and extrapolation within and across systems.

R&D Projects Currently Supported by the EFWCoop Program

• Linked models to support decisions across the natural/built system interface

• Small stream ecology – monitoring/modeling • Small-scale modeling protocols for assessing BMP

effectiveness • Conservation Innovation Grant – Innovative AgBMPs • Treatability translations • Evaluation of water quality trading market models • Development and testing of data management and

exchange architectures

DWTP Sampling Update

Mike provide a review of his DWTP intake sampling effort. The next few slides summarize

Agriculture

Large Midwestern watershed draining to a National Scenic River and then the Ohio River

East Fork Lake

2000 acre water surface 890 km2 of upland drainage

• 64% agriculture • 26% forest

• 1.5 % imperviousness • 1.4% lawn

• 1.3% impounded

20 MGD DWTP

20 MGD DWTP MnO4 pre-oxidation

Coagulation Settling

Filtration Cl2

• THM levels exceed 80 ug/L MCL during summer

• increasing number of taste & odor episodes during summer

• increasing period of (reduced) Mn(II) (necessitating more pre-oxidant usage)

(spring-to- fall )

• Increasing number of sulfide episodes (more pre-oxidant usage) during summer

Adding deep-bed GAC to meet 2012 Stage-2 DBP Rule

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4/7/2009 7/16/2009 10/24/2009 2/1/2010 5/12/2010 8/20/2010 11/28/2010

CH

La (U

g/L)

DBP

(ug/

L)

date

THMs

HAA9

CHLa-surface

Clear-well DBP Concentration vs. Source Water Chlorophyll-a

Algal blooms

HABs

Reactive

DOM

Cl2

DBPs

algal toxin

s

Stage-2 violations

Consumer confidence! Future regulations?

Taste & odor cmpds

Consumer complaints! Consumer confidence!

$$$

CO2

btu

Algal and Harmful-algal Derived Water Treatment Challenges

Advanced Treatment

ozonation AOP

PAC/GAC

Algal Concerns • DBP precursors • Taste & Odors • Oxygen deficiencies • Toxins • Filter clogging • pH changes • Light limiting • Decreased recreational use • Decreased property values • Economy

Content courtesy of Richard Lorenz City of Westerville

Modeling Treatment Processes

Modeling fate and

transport

Reservoir Data -

various depths

Source Water Data -

1_depth

In Plant Data

River and/or Reservoir Ecology Processes

Treatment Plant Processes coagulation, settling, filtration,

chlorination, activated carbon, membrane filtration biogeochemistry, hydrology, ecology

Finished Water Data

chlorophyll a phycocyanin (cyanobact. pigment)

DO pH

ORP turbidity

Conductivity UV absorbance (DOM)

Real-time in-situ monitoring

chlorophyll a phycocyanin (cyano bact. pigment)

algal taxonomy (species level counting) nutrients

pH turbidity/sechi

DOC/TOC, UV absorbance (DOM) fluorescence EEMs (DOM)

DBP (THMs) formation potential

Grab sampling

DBPs -THMs, HAAs UV absorbance (DOM)

fluorescence EEMs (DOM) Chlorine demand, etc.

Grab sampling

Office of Research and Development National Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch

UEFW SWAT Modeling Update and WQT Case Study – Large Scale Modeling

Overcoming model parameterization issues (UEFW Scale) 1. SSURGO vs. STATSGO Soils 2. SWAT Project Subbasin delineation

Summary of Working with the SSURGO data (11/9/2011 by SCK) •There are two commonly used soils databases: SSURGO and STATSGO. The scale of the SSURGO database generally ranges from 1:12,000 to 1:63,360. SSURGO is the most detailed level of soil mapping done by the Natural Resources Conservation Service (NRCS) (http://soils.usda.gov/survey/geography/ssurgo/description.html). For the 48 conterminous states, the STATSGO database is at the scale of 1:250,000 (http://www.il.nrcs.usda.gov/technical/soils/statsgo_inf.html). With regard to the information needed for modeling, the formats of SSURGO and STATSGO are the same. More information on the format details can be found at http://soildatamart.nrcs.usda.gov/documents/SSURGO%20Metadata%20-%20Tables%20and%20Columns%20Report.pdf. To summarize, the databases include both tabular (text files) and spatial (shape files) data. Depending on the area to be covered, multiple files need to be combined into one dataset (comprised of one tabular and one spatial). The East Fork Watershed (EFW) is covered by one STATSGO dataset. Six counties of SSURGO data need to be aggregated into one dataset for use in SWAT.

•To be used in SWAT, each soil classification listed in the SSURGO/STATSGO for the area being modeled (the EFW) must have a match in the SWAT soils reference database. Using ArcGIS tools, the area of the SSURGO/STATSGO shape files were clipped close (a buffer was applied) to the boundary of the EFW. A link was established between the clipped spatial data and the tabular data. Tabular data not related to the area associated to the buffered EFW was removed from further consideration. The EFW tabular databases (SSURGO and STATSGO) were each joined to the SWAT soils classification database. There were no orphan records in the STATSGO database, but there were orphans in the SSURGO database. The following table shows the orphan SSURGO soil classifications (COMPNAME) and also shows how the soil classification was renamed to match with the SWAT classification table.

•The substitutions were made based on an extensive review of the STATSGO data and the neighboring soils of the orphan SSURGO classifications. Information from the following site was also used http://soils.usda.gov/technical/classification/ when assigning a SWAT classification to the SSURGO orphans.

http://soils.usda.gov/survey/geography/ssurgo/description.html�

http://www.il.nrcs.usda.gov/technical/soils/statsgo_inf.html�

http://soildatamart.nrcs.usda.gov/documents/SSURGO Metadata - Tables and Columns Report.pdf�

http://soildatamart.nrcs.usda.gov/documents/SSURGO Metadata - Tables and Columns Report.pdf�

http://soils.usda.gov/technical/classification/�

The National Elevation Dataset 10 meter dataset was downloaded for each of the six counties in the East Fork Watershed. The files were aggregated into one for use with SWAT. The layer was reprojected from UTM 1983 17N to 16N. To reduce SWAT processing time, the NED layer was clipped to just larger than the East Fork Watershed. The watershed delineation was performed in the SWAT model using an area of 500 to get the default outlet locations at this scale. Then the watershed delineation was performed again using an area of 10. Both delineations used the NHD flowlines to burn in the stream network. In the SWAT model that was delineated using an area of 10, all the default outlet locations were removed, and the default outlet locations from the area of 500 delineation were added. Then, the default outlets that were in the lake were removed, and several other outlets were added. This delineation still needs to be fine tuned.

UEFW Preliminary Discretization

Downstream Direction

WWTPs

DWTP

Assumption: DWTP operator colludes with

WWTPs to reduce Ag Loadings. Nutrient Trading program leads to: • Fewer algal blooms • Shorter periods of eutrophication and hence…. easier/cheaper water treatment.

Water Quality Trading Case Study: Determining feasibility and advancing the market model

The central question is: can watersheds and drinking water plants be understood as a single system, and how can human decisions regarding water quality be improved by modeling the coupled human‐engineered‐natural system?

WSC Proposal 2011 Submitted Oct 19th


CIG Effort Update- Cover Crop Sign-up Soil Sampling Small-scale Modeling

GRT Headwatershed (2.5 km2) where Innovative AgBMPs are being tested in cooperation with the CC SWCD, Farm

Services, and NRCS

HUC 12 Subwatershed (111 km2) with weekly monitoring points shown

Preliminary Modeling with AnnAGNPs and SWAT Models were

used to project high areas of sediment yield (rust colored area)

Those areas were used by SWCD to target fields for cover crop

placement. Land Owners were approached. Gray areas are slated

for cover crops

Model Application Development: Rules/Criteria for

1) Delineating Subbasins at GRT Scale 2) Accounting for differences in spatial

distributions of crop rotations 3) Modeling fertilizer and pesticide application

rates. 4) Estimating channel widths and depth at

subbasin outlets.

Rules to Delineate subbasins at GRT scale 1. A threshold area (critical source area) of 2 hectares was selected to define the stream network. 2.The stream network generated by ArcSWAT did not match the actual stream in the field (based on the aerial

photos). So this generated stream network was edited in ArcGIS to conform to the actual stream. This edited stream network was then burned in to the DEM. The flow direction and accumulation was recalculated after the burn in. The same threshold area of 2 hectares was selected and the stream network was generated again.

3.The GRT watershed outlet point was selected as the whole watershed outlet and the watershed delineation was done keeping all the default subbasin outlets generated by ArcSWAT.

4.The areas of the subbasins generated were analyzed to make sure that the individual subbasin areas were greater than 10% and less than 190% of the mean subbasin area. Any subbasin smaller than the threshold was removed by deleting the corresponding subbasin outlet and those subbasins which are higher than the threshold were divided by adding outlets.

5.Adding or deleting an outlet had to be done by redefining the stream network. After the changes are made, the whole watershed delineation is done again.

6.This process may take a few tries till you obtain a final watershed delineation.

Rules to account for differences in spatial distributions of crop rotations at GRT Scale 1.National Agricultural Statistics Service (NASS) provides the Cropland Data Layer (CDL), which contains crop

specific digital data layers. CDL is available for the Ohio from 2005 to 2010. 2.The CDL for the GRT watershed (Clermont County) was downloaded and for all the years available (2005-

2010). The land use layer for the GRT watershed was super-imposed on the CDL to determine the actual crop rotations for the watershed. The crop rotations for GRT are shown in Figure 1. The green cells represent soybean and the yellow cells represent corn.

3.The agricultural land within this watershed was further subdivided into separate land use classes based on the unique crop rotation pattern they follow. Figure 2 shows the polygon numbers of the agricultural parcels created within GRT. The crops currently (2011) planted in the GRT watershed was obtained from NRCS and Clermont County. Having the crop rotation data from 2006 to 2011, the same crop rotation was assumed to be present in those areas for the years starting at 1989. Table 1 was created based on this assumption and it shows what crop is grown in which parcel during the years 1989 to 2011. 1989 is the starting year for available climate data and the starting year of SWAT simulation. The different colors in the table represent unique crop rotation patterns and the parcels having the unique crop rotation were grouped together to form a unique land use. These land uses were further subdivided into parcels which would get a cover crop (in winter) and those that will not. The “NC” at the end of the land use title represents parcels with no cover crops. The land use/land cover data obtained from EPA for the Little Miami watershed for the year 2002 is shown in Figure 3 and it shows that only soybean was planted in the GRT watershed during that year. Based on the Table 1, for year 2002 all the parcels would have had only soybean planted and this further validates our assumption.

Figure 1 – Crop rotations for GRT from CDL data.

2006

2007

2008

2009

2010

Green represents soybean and yellow is corn

Crop rotations in recent years do not follow the Corn, Bean, Bean rotation rule of thumb

Figure 2 - GRT Parcel Numbers

Had to re-characterize LandUse for SWAT Model parameterization based on parcel and specific crop rotation schedule. Otherwise all field would be corn or bean in a given year. This matters when it comes to differences in fertilizer and pesticide application pending the crop type.

Table 1 Land use Classification

Parcel No. 65 71 73 246 101 103 74 240 43 243 239 102 98 75 77 80 81 241 57 44 248 244 31 32 30 85 86 247 216 105 59

2011 C C C C C C B B B B B B B B B B B B B B B B B B B B B B B B B

2010 B B B B B B C C C C C C C C C C C C C C C C C C C C C B B B B

2009 C C C C C C B B B B B B B B B B B B B B B B B B B B B B B B C

2008 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B

2007 C C C C C C C C C C C C C C C C C C C C C B B B B B B B B B B

2006 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B C

2005 C C C C C C B B B B B B B B B B B B B B B C C C C C C B B B B

2004 B B B B B B C C C C C C C C C C C C C C C B B B B B B B B B B




2000 B B B B B B B B B B B B B B B B B B B B B C C C C C C B B B C





1995 C C C C C C C C C C C C C C C C C C C C C C C C C C C B B B B

1994 B B B B B B B B B B B B B B B B B B B B B B B B B B B B B B C




1990 B B B B B B B B B B B B B B B B B B B B B C C C C C C B B B B


Landuse title CBCB CBCBNC CBB CBBNC BCBBB BCBBBNC BBBB BBBBNC BBC

Landuse code 21 22 23 24 25 26 27 28 29

SWAT Landuse COR1 COR2 COR3 COR4 SOY1 SOY2 SOY3 SOY4 SOY5

“C” represents Corn and “B” represents Soybean

Reclassification of land use.

Figure 3 – EPA Land use/ Land cover data for 2002.

Dark Green represents soybean and yellow is corn. Light green is forest.

Rules to model fertilizer and pesticide application rates Fertilizer •Steve had provided the following application rates: Corn: N – 200 lbs per acre per planting season P2O5 – 37 lbs per acre per year K – 37 lbs per acre per year Soybean: P2O5 – 24 lbs per acre per year K – 52 lbs per acre per year He had mentioned that 20-30 lbs./acre of N and P would be applied before planting and the remaining amount would be applied 30-40 days after planting. Also due to the non-availability of phosphate, MAP (Mono-ammonium phosphate 11-52-0) and DAP (Di-ammonium phosphate 18-46-0) are being used. UAN (28%) is used as the source for nitrogen. •Lori had provided the following fertilizer recommendation: Corn: N – 1 lbs per acre per bushel yield P2O5 – 20-40 lbs per acre per year (for soil test level “H”) and 40-80 lbs per acre per year (for soil test level “M”) K – 20-40 lbs per acre per year (for soil test level “H”) and 40-80 lbs per acre per year (for soil test level “M”) Soybean: P2O5 – 40-80 lbs per acre per year (for soil test level “H”) and 80-160 lbs per acre per year (for soil test level “M”) K – 40-80 lbs per acre per year (for soil test level “H”) and 80-160 lbs per acre per year (for soil test level “M”) •NASS survey for fertilizer application in Ohio for the year 2010: Corn: N – 141 lbs per acre per year (Average) P2O5 – 64 lbs per acre per year (Average) K – 91 lbs per acre per year (Average) Soybean: - No data was available •Based on these recommendations, it was decided on the following application rates: (Since SWAT does not track Potash, only Nitrogen and Phosphorus were considered) P2O5 – 40 lbs per acre per year (for both corn and soybean). Out of which 20 lbs/acre will be applied before planting and 20 lbs/acre will be applied 35 days after planting. N – 200 lbs per acre per year (for corn). Out of which 30 lbs/acre will be applied before planting and 170 lbs/acre will be applied 35 days after planting. MAP was assumed to be source for P2O5. Since MAP contains 0.11kg N/kg, this would be deducted from the required N so that the total applied N would sum up to a total of 200 lbs/acre.

Pesticide •Steve had provided the following application rates : Spring application: 2,4-D – 0.5 to 1.0 lb per acre Roundup – 0.56 to 1.12 lbs per acre for Corn and 0.56 to 1.5 lbs per acre for Soybean Atrazine – 1.4 to 2 lbs per acre Spring application: 2,4-D – 1.0 qt. per acre (that would be 1 lb/Acre) Roundup – 0.75 to 1.5 lbs per acre Canopy – 2.25 oz per acre (Since Canopy is not in SWAT database and since we do not monitor for Canopy, it is not applied). •For the Spring application, it was decided to apply all three pesticides at the rate of 1/3rd the recommended rates for all agricultural land. For the Fall application, it was decided to apply both 2,4-D and Roundup at half the recommended rate for all agricultural land.

Criteria for estimation of channel widths and depths at subbasins (GRT) •The actual stream bank full width and depth was measured at the GRT watershed outlet and near the EPA monitoring point near Cornwell farm. •SWAT assumes the channel sides have a 2:1 run to rise ratio. Based on this assumption, the channel cross-section area at the watershed outlet is 60 sq.ft. and the cross-section area near Cornwell farm is 9.63 sq. ft. •The total area of the GRT watershed is 623 acres and the area of the watershed draining at the monitoring point near Cornwell farm is 277 acres. •The width to depth ratios at the two cross-sections were almost the same(0.2). So it was decided to keep this ratio a constant throughout the entire stream reach of the watershed and linearly interpolate the widths and depths between the Cornwell site and the watershed outlet. For the channel reaches upstream of the Cornwell site, the same width-depth ratios will be maintained. The cross-sections at the different reaches will also be cross checked from the lidar DEM. •The slopes of the different reaches will be calculated based on the DEM elevations at the start and the end of the stream reaches.


Lake Sampling in Oct 2011


October Sediment Sampling Funded by USACE

Site ID LOI (%)

Bethel-Surface 8.37

EFLMR-Surface 14.49

EFL-Surface 2EFRWT 19.90

DAM-Surface 2EFR20001 21.17

Site ID LOI (%)

Field 6 3.42

Field 7 3.17

Field 8 3.21

Field 9 3.63

Lake Sediment

GRT Fields Accumulated Stream Beds 10.23

OM contents came up in the discussion for comparison I’ve provided %OM contents from lake, crop fields, and stream beds. All from east fork areas. *Note, these need to be corrected for bulk density to be directly comparable, but BD would probably be highest n lake sediment, so….

DATE TIME SITE ID DEPTH UNIT TN TDN TNH4 DNH4 TNO23 DNO23 TUREA DUREA TP TDP TRP DRP

20111025 01:30:00 PM EUS 0 ug N(P)/L 914 814 81.3 97.7 344 324 32.8 19.5 76 52.7 60 51.520111025 01:30:00 PM EUS 2M ug N(P)/L 959 840 94.7 101 355 320 42.5 23.9 85.4 54.8 65.4 51.520111025 02:30:00 PM EEN 0 ug N(P)/L 894 826 123 130 312 303 51.7 26.4 73.3 50.7 56.5 48.420111025 02:30:00 PM EEN 7.5M ug N(P)/L 873 814 109 122 342 312 24.2 26 72 48.4 55.6 46.920111025 03:30:00 PM EWN 0 ug N(P)/L 840 810 129 138 307 295 23.8 16.1 70.8 51.6 53.9 5120111025 03:30:00 PM EWN 8M ug N(P)/L 853 793 146 144 302 290 30.5 31.9 69.5 55.4 57.8 5320111025 04:30:00 PM EDW 0 ug N(P)/L 831 824 126 132 306 301 31.4 22.9 67.7 55.4 55.9 53.220111025 04:30:00 PM EDW 14M ug N(P)/L 839 827 123 132 308 301 30.8 17.5 67.8 54.8 55.2 57.720111025 05:30:00 PM EOF 0 ug N(P)/L 873 849 201 206 263 256 27 27.3 73.9 58.1 57.7 56.720111025 05:30:00 PM EOF 27M ug N(P)/L 1280 1160 211 205 392 383 59 53 259 211 237 21620111024 10:59:00 AM ELI (inflow) 0 ug N(P)/L 1720 56.3 617 65.2 453 39120111024 12:38:00 PM DAM (outflow) 0 ug N(P)/L 915 230 245 23.5 83.4 51.4

GHG Sampling and closing the lake C and N budget

First event of new project looking at Green House Gas production in the Lake along with providing help to close the N and C budget for the lake. Leads are colleagues Jake Beaulieu from EPA and Amy Townsand-Small from UC. The work will be the master’s thesis of Becky Smolenski

Nutrient Data I collected below suggest that lake anoxia is limiting nitrification (Ammonia increasing over LG profile of lake).


Tipping Point Research - initiated

COLLABORATIVE RESEARCH: ROLE OF ORGANIC MATTER SOURCE ON THE PHOTOCHEMICAL FATE OF PHARMACEUTICAL COMPOUNDS – LEAD PI: ALLISON MACKAY, UCONN - FUNDED PROLOGUE An earlier version of this proposal was reviewed by a CBET panel and recommended for funding. The panel noted that we had “identified an important problem … that deserves attention … [because] there are knowledge gaps regarding their [pharmaceutical compounds] fate and the contribution of different degradation mechanisms in actual aquatic systems.” The panel was “impressed by the … collaboration with [the] Pomperaug River Watershed Coalition, which will serve as a way to disseminate results and offer basic community training.” “However, the panel felt that a more unified experimental plan would have strengthened the proposal.” In response to our panel comments, we have developed a new proposal that articulates in more detail how the experimental tasks are integrated to meet our project objectives. We have also changed our second field site from Boulder Creek, CO to the East Fork of the Little Miami River, OH to collaborate with the USEPA (Collaborator Nietch) in this networked experimental watershed. This proposal targets the CBET emphasis area of “emerging contaminants.”

Figure 2. Fate processes for pharmaceutical compounds in aquatic systems. Arrow width is proportional to the relative importance.

Merged Data Interpretation• NOM/EfOM physiochem contrasts• Photochem / physiochem relationships• Photochem / spectral relationships• OM contribution to PO influence in kfield

• Variability in NOM/EfOM contribution toPO influence in kfield vs season

• Variability in NOM/EfOM contribution toPO influence in kfield vs site

Engineer (PI MacKay), a geochemist (PI Chin), a photochemist (PI Sharpless) and a systems ecologist (Collaborator Nietch)


Monitoring Program Issues: Flow Gauge in the UEFW! We talked about where to best install and how to obtain the funding. Seemed most logical to partner with USACE who already has flow monitoring contracts with USGS. Need to get Erich’s input on this. In the meantime we (EPA) will install a sonteck depth integrated velocity and level gauge at a location in a stretch above the Williamsburg Treatment Plant.

A. Aerial and Streams Data Files

B. Subcatchment discretization

C. Land Cover and Properties Delineation

D. SWMM Project-Existing Conditions

Stormwater BMP Retrofit Project?

E. Alternative Scenarios

13.

John discussed the potential for a stormwater BMP retrofit demonstration project. We turned to some of the headwatershed locations that EPA has studied in the past and are currently part of the weekly monitoring program. The headwatershed at left could be an appropriate one, and it is already modeled

Office of Research and Development National Risk Management Laboratory, Water Supply and Water Resources Division, Water Quality Management Branch November 10, 2011

*The ideas and opinions expressed herein are those of the primary author and do not reflect official EPA position or policy.

Next meeting scheduled for December 10th; 9:00am

Technology

Ef coop meetingpres_11102011