The Role of Decision Support Systems in Sustainable Management of Water Resources

Transcription

1 The Role of Decision Support Systems in Sustainable Management of Water Resources Bernie Engel Purdue University Agricultural & Biological Engineering

2 Introduction Overview of Talk Water resources decision support system applications NAPRA biofeedstock production analysis Long-Term Hydrologic Impact Assessment Pollutant load analysis Optimization of management practice selection Future opportunities

3 The water footprint of a product

4 This is a global average and aggregate number. Policy decisions should be taken on the basis of: 1. Actual water footprint of certain coffee at the precise production location. 2. Ratio green/blue/grey water footprint. 3. Local impacts of the water footprint based on local vulnerability and scarcity. [Hoekstra & Chapagain, 2008]

5 [Hoekstra & Chapagain, 2008]

6 [Hoekstra & Chapagain, 2008]

7 Water footprint per capita 3000 Domestic water consumption Industrial goods Agricultural goods China India Japan Pakistan Indonesia Brazil Mexico Russia Nigeria Thailand Italy Water footprint (m 3 /cap/yr) USA Global average water footprint [Hoekstra & Chapagain, 2008]

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9 Water Quality

10 Decision Support Systems Computer-based information system that supports decision-making activities Use a number of computing technologies such as: Models GIS Databases Other information technology

11 Biofeedstock Impact Assessment Approach NAPRA pre-process Post-process Climatic data CLIGEN Graphs Matlab Soil Data SSURGO GLEAMS Model Perl Extraction of water quality metrics Spatial Display ArcMap 9.3 Management Statistical Analysis SAS/STAT Software Residue Removal C-Factors RUSLE 2.0 Cover management factor NAPRAWeb Model: 11

12 Groundwater Loading Effects of Agricultural Management Systems (GLEAMS) About GLEAMS: Serves as the engine for the NAPRA model. Developed by USDA Nutrient and pesticide algorithms are used by Soil & Water Assessment Tool (SWAT) model. 12

13 Erosion Losses (t ha -1 ) Erosion: No-till 5 Blount Silt Loam Hoytville Clay Oshtemo Sand b 2.70 bc 3.24 c c 0.88 a 0.64 b 0.82bc 0.04 a 0.06 b 0.08 b 0.13 c 0.25a r0 r38 r52 r70 r0 r38 r52 r70 r0 r38 r52 r70 Rate of Corn Stover Removal r0: no residue removal r38: removal rate at 38% r52: removal rate at 52.5% r70: removal rate at 70% Presented at the 2010 ASABE Meeting, Paper number

14 GLEAMS-NAPRA simulation of continuous corn cropping system Figure 1. Statewide annual estimates in atrazine concentration to the edgeof-field based on long-term (60-years) using an Aatrex application rate of 2 lb a.i/ac based on manufacturer s label rate.

15 Watershed Identification and Characterization

16 EPA HUC NHD Water layer Pour Point Purdue University is an Equal Opportunity/Equal Access institution.

17 Purdue University is an Equal Opportunity/Equal Access institution.

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19 L-THIA Long-Term Hydrologic Impact Assessment Based on the rainfall land cover runoff analysis method already used in many communities Input: Land Use Pattern(s) + Soils Pattern Process: Daily Runoff and Pollutant Loading Calculations (30 years) Output: Average Annual Runoff and NPS loads for Specific Land Use Patterns

20 40% 10% Natural Cover 50% 35% 35-50% Impervious 30% Land Use Decisions Affect Runoff, Recharge, and Water Quality 30% % Impervious 55% 35% 15%

21 Landuse and Soil are automatically entered into spreadsheet Purdue University is an Equal Opportunity/Equal Access institution.

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23 Preliminary Evaluation of LID for Pendleton, IN

24 Using L-THIA LID Lot Level Reduce street width from 26ft. to 18ft. Rain barrels for Residential Green Roofs for Commercial Bioretention/Raingardens Reduces Post-developed runoff by 46% Purdue University is an Equal Opportunity/Equal Access institution.

25 Extends L-THIA online tool to entire Great Lakes area.

26 Tool will now allow use of a polygon as an area of analysis. This will improve ability to model zoning and LID BMP areas.

27 New Results Options

28 Pollutant Load Analysis

29 Purdue University is an Equal Opportunity/Equal Access institution.

30 Enhancement of Web-based LDC Tool Data Retrievals User Side Purdue WQP Map-based User Interface (HTML/CGI) Parsing to Water Quality Data Table Web Services Request (CGI) with Organization ID and Monitoring Location ID WQ Data Handling (CGI)

31 Objective 3. Enhancement of Web-based LDC Tool about LDC Flow and Load Duration Curves Simple, quick, and statistical approach Cumulative frequency of historic data ov er a specified period * USEPA, An approach for using load duration curves in the development of TMDLs. U. S. Environme ntal Protection Agency

32 Objective 3. Enhancement of Web-based LDC Tool Result LOADEST Run Flow Duration Curve Load Duration Curve Time Series Plot Concentration Plot

33 Required Reduction Percentage Pollutant Constituent for Load (N, P, BOD, or Sediment) Target Load in each flow regime Estimated Load by LOADEST in each flow regime

34 BMP List for each Landuse The user needs to select an option in each landuse table for interactive results which may be used in models. Phosphorus reduction example The BMP suggestions were tested in STEPL. Required Reduction percentage from LDC was found to be similar to STEPL reductions.

35 Optimization of Management Practice Selection

36 SWAT Predicted Pollutant Load (before Conservation Practices) Phosphorus Nitrogen Sediment 4 Miles TP Load (lb/yr) TN Load (lb/yr) Sed. Load (lb/yr)

37 The process - Overview A. Stakeholders provide List of conservation practices to include Water quality goals Cost limits (budget) B. Modelers provide: SWAT model, calibrated and validated for the watershed C. Optimization tool: Develops thousands of sets of practices Calculates cost Calculates water quality benefits Identifies greatest benefit for each cost D. Stakeholders Receive results Provide feedback on how results can help make watershed decisions Suggest changes to inputs

38 Final solution is a curve with minimum cost, maximum benefits $500,000 $200,000 $100,000 $50,000

39 Optimization Results (SJRW) -Pesticides in the raw water reduced by 30% $135,000 -Nutrient levels in the raw water reduced by 50% -Sediment loading reduced by 50% -Cedar Creek Watershed Management Plan BMPs No BMP Cons. Till No Till Cons. Till + WASCOB No. Till + WASCOB Cons. Till + 10m Buffer No Till + 10m Buffer Cons. Till + WASCOB + 10m Buffer No Till + WASCOB + 10m BUffer

40 Mobile Apps

41 In Situ Input Remote Sensing Based Inputs Weather, Vegetation Vigor Soil Moisture Multiscale Models Databases Weather Data Site Output Field Scale Model 1 Field Scale Model 2 Database Downloadable Watershed Regional Scale Models SSURGO Soil Data Regional Scale Models Crop Data Weather Simulation Data Weather Generator

42 Summary Opportunities to use decision support systems to reduce water and environmental impacts of biofeedstock production (and other land use decisions) Can reduce negative impacts of land use and management decisions Water resources protection and utilization can be optimized