Identifying Forests and Wetlands of Highest Value for Water Quality and

Economic Benefits

Chesapeake Bay Resource Lands Assessment

Chesapeake Bay Program

Introduction

• Valuable forests, farms, and wetlands are under pressure from land use change and other environmental stresses.

• The Chesapeake 2000 Agreement charged the Chesapeake Bay Program with conducting an assessment of its resource lands in order to identify the most important lands to conserve.• Commitment # 4.1.3.3

Resource Lands Assessment(RLA)

• Purpose: To identify the resource lands (i.e., forests, farms and wetlands) that have the highest water quality, habitat, cultural and economic value and are the most vulnerable to loss.

RLA Objectives

I. Habitat Value Assessment

II. Water Quality/Watershed Integrity Value Assessment

III. Cultural Value Assessment

IV. Economic Value (i.e., forest and farm production) Assessment

V. Vulnerability Assessment

Water Quality and Watershed Integrity Value Assessment

• Watershed integrity relates to physical and biological watershed functions that perform many functions to protect water quality.– Store precipitation– Retain and assimilate nutrients– Moderate runoff – Protect soils and critical riparian areas– Sustain aquatic ecosystems

• In general, forests and wetlands are the best land cover for providing these watershed functions.

The Role of Forests in Water Quality

Goals of this Analysis

1. To identify the nexus between a forest or wetland and the parameters that affect its ability to provide these functions.

2. To place value on forests and wetlands that if lost would have significant potential to compromise or degrade watershed and water quality.

Availability of specific and consistent data sets is a limiting factor in this analysis.

Assumptions

1. The characteristics of soil and vegetation at a particular site, and for the watershed within which they occur, have significant bearing on this assessment of value.

2. The GIS data layers used to represent the parameters are accurate enough for this coarse scale assessment.

GIS data layers collected for parameters known to affect water quality and watershed integrity (total of 13 parameters)

Methods

Data within every parameter classified into 4 ranges based on their influence on water quality (0 = no influence, 4 = highest influence)

Every parameter given a weight from 0 to 5 to emphasize variables with a greater influence on water quality

(0 = no influence, 5 = highest influence)

Data Types and Sources

Local Parameters

1. Proximity to water USGS NED

2. Erodible Soils NRCS STATSGO

3. Net Primary Productivity USDA Forest Service

4. Slope USGS DEM

5. Wetland Function NWI

6. Forest Fragmentation CBP

7. 100 Year Floodplains FEMA8. Hydrogeomorphic Regions USGS

Regional Parameters (summarized by HUC 11 watershed)

9. Stream Density (m/sq km) USGS NHD

10. Percent Forested MRLC 97

11. Percent Impervious RESAC 2000

12. Water Quality Rank USGS Sparrow/DU

13. Municipal Water Supplies USGS

Data Types and Sources

Ranks and WeightsConservation Priority Index

Data Source Scale 4 3 2 1 Weight

USGS NHD 1:100K 0 - 90 m 90 - 180 m 180 - 270 m > 270 m 5

STATSGO (kfact) 1:250K > .30 (High) .2 - .3 (Moderate) < .2 (None to Slight) 2

Net Primary Productivity USFS 1 km 4 3 2 1 3

DEM 1:100K > 15% 15 - 10% 5-10% < 5% 3

Functional Parameters (local)

NWI - cumulative score of water-

related functions1:100K score > 3.5 score = 3.0 score < 3.0 5

CBP 1:100K > 1000 400 - 1000 100 - 400 <100 2

FEMA Yes 2

USGS HGMR 1:250K CPU, PCA, VRS PCR, BR, APC CPD, ML, VRC, APS CPL 2

Regional Watershed Parameters

NHD 1:100K > 1.042 0.77 - 1.042 0.524 - 0.769 0 - 0.523 4

MRLC 1997 1:100K 40 - 65% 30 - 40; 65 - 75% 15 - 30; 75 - 85% < 15; > 85% 3

MRLC 1997 1:100K 5 to 15% < 5% 15 to 25% > 25% 2

Sparrow/DU 1:100K Very Good Good Fair Poor 5

USGS - pop. Served/ # of intakes

1:100K High/High High/Med, Med/HighMed/Med, Med/Low and

Low/Med Low/Low 4

Water Quality

Municipal Surface Water Supplies

Stream Density- meters/sqkm

% Forested

% Impervious Surface

Hydrogeomorphic Region

Proximity to water

Erodible Soils

Slope

Wetland Function a (wetlands only)

FEMA 100 year flood plain

Forest Fragmentation - Patch Size (HA)

Ranking of value ranges

Parameter

Bio-Physical Parameters (local)

Preliminary Results – Unweighted

Preliminary Results - Weighted

Comparison

Unweighted Weighted

New Data Layers to be Included

• Forest Productivity – cu.ft/ac/yr -based on species, geography, elevation, climate, soils, and atmospheric deposition. (USFS-PNet Model)

• Municipal Water Supplies – small-medium size systems, proximity to major river withdrawals (compile from USGS/State data)

Economic Value Assessment of Forest Land

Economic Value of Forests

• Timber management activities contribute significantly to the economy of the region.

• This assessment considers:– Economic returns of forest harvests;– Long-term economic sustainability of forest land; and– Local importance of the timber management and wood

products industry.

• It does not account for tourism, hunting, and other economic benefits.

• A similar economic analysis for agricultural lands is being considered.

Goals of this Analysis

1. To identify the connection between forests and the parameters that effect their ability to produce economic benefits.

2. To place value on forests that if lost would have significant potential to negatively impact the region’s economy.

Availability of specific and consistent data sets is a limiting factor in this analysis.

Assumptions

1. The physical, management, landscape, socioeconomic, and programmatic characteristics of forests have significant bearing on the economic value forests contribute.

2. The GIS data layers used to represent the parameters are accurate enough for this coarse scale assessment.

GIS data layers collected for parameters known to affect the economic benefits produced by forests (total of 17 parameters)

Methods

Data within every parameter classified and given a score based on its economic influence (0 = no influence, 10 = highest influence)

Every parameter given a weight from 0 to 10 to emphasize variables with a greater influence on the economy

(0 = no influence, 10 = highest influence)

Data Types and Sources

Local Parameters1. Species Composition GAP Vegetation (CBP)

2. Soil Productivity NRCS STATSGO

3. Precipitation Spatial Climate Analysis Service

4. Forest Density Landsat subpixel analysis

5. Riparian and Wetland Features MDP Streams (NHD)

NWI Wetlands

6. Steep Slopes USGS DEM

7. Rare, Threatened, and Endangered SSPRA (BCD? & PNDI) Species

Data Types and Sources

Regional Parameters8. Forest Fragmentation/ Patch Size CBP

9. Probability of Sustainable Commercial Census 2000 Timber Management (Compatibility)

10. Contiguity of Ownership/ Parcel Size Parcelization MD (Census 2000)

11. Local Importance of Timber and IMPLAN (CBP) Primary Manufacturing Industry

12. Local Importance of Secondary IMPLAN (CBP) Manufacturing Industry

Data Types and Sources

Regional Parameters (Continued)13. Historic Timber Harvests USDA Forest Service

14. Sourcing Areas/ “Timbersheds” USDA Forest Service (Mill Locations)

15. Impacts of Growth PFA’s (Census 2000)

16. Private Land Protection Designations Forest Legacy and Rural Legacy (N/A)

17. Public Land Management Activities CBP

Ranks and Weights: Local Parameters

SFLA RLA

Species Composition*

High Value Species

Associations(8-10)

Moderate Value Species Associations

(4-7)

Low Value Species

Associations(1-3)

See GAP alliance rankings 8

Soil Productivity

80 - 90(10)

75 - 79(7)

64 - 74(4)

55 - 63(1)

Scores based on

Average Site Index 5

Precipitation 2

Forest Density

% Forest Cover (subpixel

LANDSAT analysis)

Baywide map to be developed

(date unknown)75-100%

(10)50-75%

(7)25-50%

(4)< 25%

(1)

Data not currently available 5

Riparian and Wetland Features

MDP StreamsNWI

Wetlands

NHD StreamsNWI

Wetlands

Not in Stream/ Wetland or Buffer (10)

In Wetland (including 100' wetland buffer

(5)In 100' Stream

Buffer (1) 5

Steep Slopes*

0-10%(10)

11-20%(7)

21-25%(4)

> 25% (1) 7

Rare, Threatened

and Endangered

Species

Sensitive Species Project Review Areas (SSPRA)

Biological Conservation

DatabaseNot in SSPRA

(10) IN SSPRA (1) 3

Biophysical Influences

(influences what is grown)

Management Constraints

(influences what is harvested)

Categories Factors Interpretation (Possible Scores) Weight

DEM/Slope

30 year average total precipitation (1961-1990)

STATSGO

GAP Vegetation

Data

Ranks and Weights: Regional ParametersSFLA RLA

Landscape (Influences of forest

land distribution)

Fragmentation/ patch size analysis*

Mean Forest Patch Size at MD 8-digit watershed scale

FRAGSTAT metric (consider using CBP data)

Consider using forest patch data from CBP - need to

reclass data into patch size classes

> 100 Acres (10)

50 - 99 Acres (7)

25 - 49 Acres (5) 10 - 24 Acres (3)

1 - 9 acres (1) 5

Probability of Sustainable Commercial

Timber Management

> 75% (10)

50-75% (7)

25-50% (5)

< 25% (3)

Near 0% (1) 7

Contiguity of Ownership* Parcelization MD PropertyView Road Density

> 100 Acres (10)

50 - 99 Acres (7)

25 - 49 Acres (5) 10 - 24 Acres (3)

1 - 9 acres (1) 5

Local Importance of Timber and

Primary Manuf. industry

IMPLAN - % Total Industry Output for 1) and 2) relative to Total County Industry Output

(1) Timber Mngmt/ Harvesting (2) Prim. Manufac.

IMPLAN (Adjust MD ranking relative

to Bay region)3.02-15.81%

(10)0.74-3.02%

(7)0.35-0.74%

(5)0.14-0.35%

(3) 0-0.09%

(1) 6

Local Importance of

Secondary Manuf. industry

IMPLAN - % Industry Output relative to Total County Industry

Output IMPLAN2.09-11.25%

(10) 1.26-2.09%

(7)0.61-1.26%

(5)0.17-0.61%

(3)0-0.17%

(1) 4

Historic Timber Harvests

Harvested Acres over 5 Year period (1995 - 2000) ?

6424 - 10301 acres(8-10)

4321 - 6423 acres(5-7)

374 - 4320 acres(1-4)

See Historic Timber Harvest

Ratings 6

Sourcing Areas/ Timbersheds*

MD and adjacent (within 50 miles) Sawmill Locations NE Sawmill Locations

< 10 Miles to Sawmill

(10)

10-20 Miles to Sawmill

(5)

> 20 Miles to Sawmill

(1)

Most land area within 10 miles

of sawmill location 1

Impacts of Growth PFA’s, Water or Sewer Municipal Boundaries (?)

Outside a PFA (10) Inside a PFA (0) 7

Private Land Protection

Designations Forest Legacy, Rural Legacy Forest Legacy (?)

In Rural or Forest Legacy

Area (10)

Not in Rural or Forest Legacy

Area (0) 3

Public Land Management

Activities*Public Lands (including mngmt

zones, wildlands) Public Lands (?)10 (where

applic.)See Rules Table

Categories Factors Interpretation (Possible Scores)

Socioeconomic (existing external

influences)

Programmatic (intended external

influences)

Weight

Timber Management Probability ModelCensus 2000

Data

Status of Economic Assessment

• Maryland assessment completed• Model will be adapted for PA and VA

– Have data for most parameters– Need to obtain data for rare, threatened, and endangered

species information– Need to develop weighting system for public lands data– Population density will be substituted for Probability of

Sustainable Timber Management, Parcel Size, and Impacts of Growth

– No “Forest Legacy” land equivalent

• Data sets for three states will be combined

When do we have enough data?

How will this information be used?

• Combined with other analysis – especially vulnerability

• Some ground proofing with local governments and land conservancies?

• Identify priority landscapes and guide future protection efforts

• Provide estimates of future program needs ($)• Provide information for “State of the Forests”

Report

Questions?