Watershed Diagnostics for Improved Adoption of

Management Practices: Integrating Biophysical and

Social Factors

PAUL T. LEISNHAMDEPT. ENVIRONMENTAL SCIENCE & TECHNOLOGY,

COLLEGE OF AGRICULTURE & NATURAL RESOURCES, UNIVERSITY OF MARYLAND

NIWQP: 2012-51130-20209; $631,500

Talk Outline1. Background

2. Impacts of climate change on pollution Critical Source Areas (CSAs)

3. Impacts of climate change on Best Management Practice (BMP) effectiveness

4. Competing views on water pollution and BMPs among stakeholders

Paul Leisnham, Socio-Ecology

Dan Boward, MD-DNREcology

Tom Hutson & Nicole Barth,Extension

David Lansing, UMBC

Sociology

Adel Shirmohammadi, Hydrology

Victoria Chanse, Amanda Rockler & numerous

studentsExtension & Sociology

Hubert Montas, Diagnostic Tools

Jaison Rekenberger, Diagnostic Tools

Daniel Schall,UMBC

Sociology

Julianna Brightman, Sociology

Develop, implement, and evaluate a cross-disciplinary approach for watershed management that considers

both technical (biophysical) and social factors

Objectives1. Evaluate community attitudes towards water resources and

identify barriers to BMP adoption2. Develop a Diagnostic Decision Support System (DDSS) to

identify CSAs and prescribe targeted BMPs3. Develop and evaluate strategies for increasing awareness and

behaviors towards water quality to increase BMP adoption

Tom Fisher, Horn Point Laboratory, UMCES

Algae and turbidity are increasing over time, and summer bottom DO is decreasing

Little improvement in Chesapeake Bay in past 40 years

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Date

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Study Area Spatial Database

Pollutant Transport

ModelDiagnosis

Expert SystemPollutant Export Hot Spots BMP Allocation Plan

Prescription Expert SystemExcess Export Causes

Pollutant Hot Spots

Excess ExportCauses

Bio-Dynamic Models

DDSS Development

Components: GIS, Models, Expert Systems

Models: SWAT (SUSTAIN, AQUATOX, FIBI, EPA BASINS)

Expert Systems: MATLAB (predicate calculus to decision trees)

• DDSS will rank environmental causes to pollution and prescribe a BMP allocation plan

• Geo-referenced biophysical and land management data

• Simulate watershed

responses to selected stressors

• Identify CSAs & prescribe appropriate BMPs

• More “bang for the buck”

Climate Change Forecasts for the Northeastern United States↑ Elevated concentrations of atmospheric CO2

↑ Elevated mean temperature↑ Increased mean rainfall ↑ Shift in seasonal rainfall patterns

o Wetter springo Drier late summer

↑ Increases in extreme weather events

Climate Change: Precipitation ChangesPrecipitation Volume: Very Heavy Events i.e., top 1%

(1958-2012)

Model Construction and AnalysisSoftware Purpose and Progression

ArcGIS Spatial Data Analysis(Graphics and Database)

ArcSWAT Model developmentSWAT input file Generation

SWAT-CUP Model calibration(SUFI-2 Method)

SWAT Experimental engine(SWAT.exe)

Model Calibration Warm-up (3 yrs): 1/1/1990 to 12/31/1992 Calibration Period (15 yrs): 1/1/1990 to 12/31/2004

Data Type CharacteristicsTopography/DEM 10 meter

Landuse/Land Cover NLCD 2006 HRUSoils NRCS SSURGO

Weather (Calibration) 3 stations NCEP CFSR

Flow, Nutrients and Sediment

1 USGS Gauging Station Greensboro

Climate ChangeGFDL-CM2.1

CMIP3 (B1, A1B, A2)(Mid and End Century)

Model Validation Warm-up (2 yrs): 1/1/2005 to 12/31/2006 Validation Period (6 yrs): 1/1/2005 to

12/31/2010

Downscaled climate predictions from global model based around 3 IPCC scenarios that lead to low, medium, and high future levels of CO2

Definition of a Critical Source Area (CSA)

Rank SurQ (mm H20) TSS (tonnes/ha/yr) TN (kg/ha/yr) TP (kg/ha/yr)

Top 10% >406 >1.03 >24 >1.9

Top 20% >359 >0.73 >16 >1.6

Top 10%: Value for which the top ~770 HRUs is separated from the other 7705 HRUs

Top 20%: Value for which the top ~1540 HRUs is separated from the other 7705 HRUs

An area that exports a target pollutant at concentrations significantly above average

Always defined a watershed area (or HRU) a CSA if it exported a given pollutant at these fixed thresholds

Impacts of Climate Change on Pollution

CSAs

Watershed Response: Baseline and Climate Change Scenarios

B a se l i n e M i d C e n t u r y ( 2 0 4 6 - 2 0 6 4 )

E n d C e n t u r y ( 2 0 8 1 - 2 1 0 0 )

400450500550600650700750800850

B1 A1B A2

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B a s e l i n e M i d C e n t u r y ( 2 0 4 6 -2 0 6 4 )

E n d C e n t u r y ( 2 0 8 1 -2 1 0 0 )

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B1 A1B A2

tota

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B a se l i n e M i d C e n t u r y ( 2 0 4 6 - 2 0 6 4 )

E n d C e n t u r y ( 2 0 8 1 - 2 1 0 0 )

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B1 A1B A2

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B a se l i n e M i d C e n t u r y ( 2 0 4 6 - 2 0 6 4 )

E n d C e n t u r y ( 2 0 8 1 - 2 1 0 0 )

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Surface Run-off & Total Suspended Solids

Present Day CSAs SRES A2 End Century CSAs

25-30% ↑ rainfall

3.9X ↑ 3.0X ↑

Present Day CSAs SRES A2/A1B End Century CSAs

Nitrogen & Phosphorous

25-30% ↑ rainfall

3.7X ↑ 2.5X ↑

Impacts of Climate Change on BMP Effectiveness

Targeting Method: Dense CSAs𝐶𝑟𝑖𝑡𝑖𝑐𝑎𝑙𝑙𝑦 𝐷𝑒𝑛𝑠𝑒𝐴𝑟𝑒𝑎𝑠𝑎𝑡 h𝑡 𝑒𝑡𝑜𝑝 20% 𝐵𝑟𝑒𝑎𝑘𝑉𝑎𝑙𝑢𝑒

Rank SurQ (mm H20) TSS (tonnes/ha/yr) TN (kg/ha/yr) TP (kg/ha/yr)Top 10% >406 >1.03 >24 >1.9Top 20% >359 >0.73 >16 >1.6

Targeting Dense CSAs of TodayResidual CSA Density with Baseline BMP Design Subjected to Current, A1B and A2 Climate Conditions

TMDL targets met 64%-143% above TMDL Targets

Targeting Dense CSAs of the Future

Residual CSA Density with A2 BMP Design Subjected to Current, A1B and A2 Climate Conditions.

TMDL targets met TMDL targets met

Competing Views on Water Pollution &

Management Among Stakeholders

Two dominant discourses from Interviews and Q-sort

Scientists and environmental professionals: Stressed the role of agriculture in water degradation, & importance of regulating all adverse land-use practices

Farmers and agricultural extension agents: Stressed the importance of industry-driven regulation, local knowledge & were skeptical of “outside” knowledge, including science.

Axes of Regulation and Cultural Knowledge

Legend

Increasing Regulation

Deregulation

Outside knowledge

Local knowledge

Highlights Summary1. Climate change will likely increase areas that export

excessive levels of pollutants

2. Targeting CSAs works, but a BMP allocation plan based on today’s CSAs fail to meet TMDL goals

3. We need a BMP allocation plan based on future climate scenarios demanding greater community-based participation

4. We need to overcome existing divergent viewpoints among stakeholders on pollution and its management

Final Work: Integrating Social Dimensions into DDSS

KAP surveys with farmers and urban residents.

Example Research-Education-Extension Outputs

3 journal papers in press or advanced peer-reviewo 2 in Special Climate Change edition of Transactions of the ASABE

3 journal articles in prep.

12 presentations at national conferenceso Best student poster (Renkenberger) at 2014 AWRA Annual Meeting

2 MS students graduated

6 undergraduates trained

Graduate class developed (“Watershed Ecosystem Sustainability”)

4-H EcoStewards program developed (850 participants)o 4-H youth and adult water quality education aids

Adult water quality in-service trainings for educators (190 participants)

Extra Slides

Extension Outreach

Interested Not interested020406080

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12

Farmer interest of additional in-formation on BMPs (n=71)

Interest

Num

ber o

f far

mer

s

Improving awareness Leveraging connections with trusted knowledge sources (Extension & State agencies)

4-H youth programs County fair exhibits Town-hall meetings

Extension Outreach

Pamphet Youtube Workshop Extension Govt Other0102030405060

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BMP information delivery of 59 re-spondent farmers

'Existing Delivery (n=90)'Desired Delivery (n=156)

Delivery Method

Num

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f res

pons

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Interested Not interested0

20406080

59

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Farmer interest of additional information on BMPs (n=71)

Interest

Num

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f far

mer

s

Polarizing statements

There is very strong evidence that agriculture is the single largest source of nitrogen, phosphorous and sediment going into the Chesapeake Bay.

Water problems like Pfiesteria should not be blamed on chicken manure because of all of the sewage being pumped into the bay right in the port of Baltimore. Pollutants like this in the bay aren’t from the farmers.

The most effective regulators are people who came up from the industry of whatever it is they’re regulating because they understand what actually happens.

Large companies like Perdue control all aspects of growing chickens, they should pay for Agricultural Best Management Practices for farmers.

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Water problems like Pfiesteria should not be blamed on chicken...

The most effective regulators are people that come up from industry...

There is very strong evidence that agriculture is the single largest source of nitrogen...

Large companies like Perdue control all aspects of growing chickens...

Strongly Disagree

Disagree Agree Strongly Agree

Farmer (n = 66)

responses as part of survey

These statements stimulated the strongest responses and were the most polarizing, from 26

stakeholder interviews and Q-sort analysis

Paired T-TEST: T = -11.60, p < 0.0001

Biophysical ModelingOnce we have a working model…

1. We can use best available science to find Critical Source Areas (CSAs)

2. We don’t have to physically install BMPs (now or in the future), saving money

3. We can use the model to test BMP configurations that will achieve TMDL targets

Effects of Climate Change

B1

A2A1B

SWAT(Soil Water and

Assessment Tool)

Land Use/CoverTopography

SoilsWeather

Other

Flow PredictionSediment LoadingsNutrient Loadings

Water Balance/Availability Nutrient Output x BMP Removal Eff (%) = TMDL Attainment

BMP Design and Targeting MethodsTypical Design

StrategyBaseline Model

(SWAT)

Apply targeting method and calculate BMP efficiencies

Repeat for A2 and A1B scenarios using baseline

conditions

Future Proof Design Strategy

End Century Model(SRES A2 SWAT)

Apply targeting method and calculate BMP efficiencies

Repeat for Baseline and A1B scenarios using A2 conditions

Examine effect of climate change on BMP implementation

Compare/Evaluate Two Targeting Methods:• CSA Based (Dense CSAs)• Non-CSA Based (Land Use)

Apply Generic SWAT BMP

Removal Required to Meet Bay TMDL Targets

Scenario TSS TN TP

Baseline 33% 45% 23%B1 63% 64% 49%

A1B 69% 70% 53%A2 71% 67% 57%

Results: In-Stream Validation, MonthlyPoints falling on SWAT generated

streams were correlated by month

1.Reach Export Correlation◦ Discharge is significant in the same month as

well as the month prior◦ TP is significant in the same month of

sampling◦ TSS and TN are significant in month prior to

sampling

2. Two month lag was considered but may be anomalous due to infrequent sampling

Constituent IBI (%) Habitat Index (%)Month Discharge (cm/s) -0.83 -0.86 Sediment (mg/L) 0.38 -0.16 Total N (mg/L) 0.80 0.35 Total P (mg/L) -0.83 -0.52

Month-1 Discharge (cm/s) -0.79 -0.98 Sediment (mg/L) -0.60 -0.67 Total N (mg/L) -0.32 -0.66 Total P (mg/L) 0.05 0.52

Month-2 Discharge (cm/s) -0.12 -0.19 Sediment (mg/L) 0.20 -0.36 Total N (mg/L) 0.82 0.39 Total P (mg/L) -0.91 -0.58

Point Sources + Non-Point Sources + Margin of Safety

Motivation: EO 13508 and Regulation

TSS, TN and TP