Spatially Targeted Social Interventions to Improve BMP Adoption in Maryland WatershedsAUTHORS: RENKENBERGER, JAISON; XIANG, ZHONGRUN; MAEDA, KANOKO; WANG, YAN; MONTAS, HUBERT; LEISNHAM, PAUL; CHANSE, VICTORIA; SHIRMOHAMMADI, ADEL; SADEGHI, ALI; BRUBAKER, KAYE; ROCKLER, AMANDA; HUTSON, THOMAS; LANSING, DAVID

Acknowledgements: This project was supposed by EPA Grant no. R835284 - “Sustainable Community Oriented Stormwater Management (S-COSM): A Sensible Strategy for the Chesapeake Bay”.

Study Areas

Study Areas

WB WLArea (Square Mile) 3.72 1.95Mean Elevation (Meter) 41.43 125.06Impervious Area (%) 32.1 14.5Mean Land Slope 8.63 7.53Dominant Landuse Types Residential area; Industrial &

commercialLow density residential; Forest

Mean Soil Erodibility 0.22 0.3

Methods: SWAT and Urban BMP Implementation

Best Management Practices (BMPs) are applied by a DDSS to reduce export of pollutants 8 different Urban BMPs were designed for our study areaThey model: Pervious Pavements,

Vegetated Filter Strips, Rain Barrels, Green Roof, Native Landscaping, Rain Gardens, Fertilizer Reduction, and Infiltration Trench

The Soil and Water Assessment Tool (SWAT) was used to model our study area. Built-in SWAT BMPs were not used since they are generally for

Agriculture Urban BMPs were designed by targeting specific parameters at the

HRU Level

Methods: Diagnostic Decision Support System (DDSS)

Diagnostic Decision Support System (DDSS)

Components Hotspot identifier Diagnostic expert system Prescriptive expert system

The areas at high And the

BMP Allocation by Soil, Topography, and LU Pollutant Export: SurQ,

Sediment, TN, and TP

0

0.2

0.4

0.6

0.8

1

1.2

5/1/87 5/6/87 5/11/87 5/16/87 5/21/87 5/26/87 5/31/87

Date

Prec

ipita

tion

(inch

es/d

ay)

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

Biody-namic Model

Results: BMP Allocation by DDSSDDSS targets 41% of watershed area for BMPs DDSS targets 37% of watershed area for BMPs

Results for Watts Branch: SurQ and TSS

Total Runoff Reduction Rate is 21% Total Sediment Reduction Rate is 53%

Results for Watts Branch: TN and TP

Total Nitrogen Reduction Rate is 38% Total Phosphorus Reduction Rate is 53%

Results for Wilde Lake: SurQ and TSS

Total Runoff Reduction Rate is 8% Total Sediment Reduction Rate is 35%

Results for Wilde Lake: TN and TPTotal Nitrogen Reduction Rate is 35% Total Phosphorus Reduction Rate is 41%

Methods: Social Model Integration by BMP Adoption Likelihood (LH)

Model Development Randomized door-to-door survey was

carried out in both watersheds. Questions targeted knowledge and

attitudes about BMPs and implementation.

A total of 311 responses were recorded at a 73.2% response rate.

Relationships between D, K, A, P were determined using generalized linear models in PROC GENMOD (SAS 9.3)

Resulting model is a BMP Adoption Likelihood (LH)

The Social model Found that factors such as

landowner tenure, race, education and association membership to be very important to LH

Observed data for each study area was gathered from the 2010 Census and the American Community Survey (ACS 2014) from U.S. Census Bureau

Methods: Factors to Likelihood (LH)

Education level is significantly different, but coefficient is very low. We need to increase the coefficient by introducing more watershed knowledge in school.

Ex. from Wild Lake

High School College Graduat

eGeneral

Watershed Knowledge

Likelihood from General Watershed Knowledge

Min 0.13 0.39 0.12 2.25 63%Max 0.47 0.72 0.26 2.44 64%

Mean 0.31 0.52 0.17 2.35 64%× Coefficient

Ex. from Wild Lake

Rent OwnLikelihood

from Ownership

Min 0.04 0.41 56%Max 0.59 0.96 65%

Mean 0.31 0.69 61%

The LH from ownership has the largest difference. Owners are more likely to adopt BMPs. In addition, owners contribute more on the likelihood fraction of BMP knowledge. It is reasonable that owners care about their lands more than tenants. Should increase the ownership, and increase the awareness of tenants.

All studied areas have BMP adoption likelihood higher than 59%, which is good to see. However they didn’t vary much.

Other factors. Advertisements can be used to increase public’s awareness Fashion trends of BMPs such as building rain gardens can increase the LH in a

community

× Coefficient

Results: The Social Model

Hous

e As

socia

tion

Mem

bers

hip

Owne

rshi

pRa

ceSocial Model

% High School

% College

% Graduate

General Watershed Knowledge Point =

% High School * 1.92 +% College * 2.47 +% Graduate * 2.74

LH Fraction from General Watershed Knowledge =

0.556 + General Watershed

Knowledge Point * 0.0339

% African American

% Caucasian

% Other

BMP Knowledge Point Fraction One =

% AA. * 3.43 +% Cau. * 4.72 +% Other * 5.41

% Own

% Rent

BMP Knowledge Point Fraction Two =

% Own * 4.92 +% Rent * 4.12

BMP Knowledge

Point =Average of

Fraction One and Fraction

Two

LH Fraction from General Watershed Knowledge =

0.556 + General Watershed

Knowledge Point * 0.0339

LH Fraction from Ownership=

% Own * 0.662 +% Rent * 0.488

% Member

% Not a Member

LH Fraction from House Association Membership=

% Member * 0.497 +% Not a Member * 0.649

BMP Adoption Likelihood = Average of All Four Fractions

Educ

atio

n

Results: Census Tracts and LHWatts Branch is divided into 23 Census TractsBMP Adoption Likelihood varies from 59% to

63%

Wild Lake is divided into 6 Census TractsBMP Adoption Likelihood varies from 59% to

61%

Social Effects on Results

W/O Social Model

Best Situation with Social Model

Worst Situation with Social Model

Runoff 21% 16% 8%Sediment 53% 46% 14%Nitrogen 38% 29% 21%Phosphorus

53% 42% 19%

W/O Social Model

Best Situation with Social Model

Worst Situation with Social Model

Runoff 8% 5% 5%Sediment 35% 31% 12%Nitrogen 35% 30% 6%Phosphorus

41% 32% 17%

Question: Would Social Data affect the reduction rate much? Answer: Yes, it would!However, there are still some uncertainties because the unit area of census tracts are much larger than that of BMP model (HRU). So the SWAT results with the consideration of social model vary -- best situation and worst situation make a big difference.Reduction Rates at Watts Branch Watershed Reduction Rates at Wild Lake Watershed

Future Work Climate Change factors

It is crucial that people see further when making plans. Climate change is an important factor would affect the result.

Cost and benefit analysis It is also costly to increase public awareness and knowledge by education or

advertisements, so the efficiency needs to be analyzed. Analysis social data on different BMPs

For example, people prefer to reduce fertilizer than to build a rain garden. And select a second, or a third alternate BMP if the most efficient BMP has a very low social willingness.

Consider TMDL targets It is a better way to decide the hotspots and BMP Implementation based on

the TMDL requirement.

Questions ?

Paul LeisnhamEcology

Adel ShirmohammadiHydrology

Hubert MontasModeling

Jaison RenkenbergerModeling

Ali SadeghiHydrology

Victoria Chanse,Amanda Rockler

& numerous studentsExtension & Sociology

Yan WangDecision Support

Zhongrun XiangModeling

Kanoko MaedaSocial Science

Kaye BrubakerModeling

Thomas HutsonExtension

David LansingSocial Science