September 23, 2005 Watershed Hydrology and Management University of Arizona

1. TITLE A Spatial Decision Support System for Economic Analysis of Sediment Control on Rangeland Watersheds September 23, 2005 Watershed Hydrology and Management University of Arizona YANXIN DUAN

2. Contents I 1. Introduction M 2. Model Development S 3. SDSS Design & Integration C 4. Case Study E 5. Summary

3. 1. Introduction I 1.1. Problem Statement M 1.2. Study Objective S 1.3. Approach 1.4. Benefits C E

4. Problem Statement I • CWA→TMDL • LC = WLA + LA + MOS • BMP • Watershed analysis • Data • Geospatial analysis • Simulation models • Difficult for many users M S C E

5. Problem Statement (Cont.) I • Economics • “Allocations for a particular watershed or TMDL are likely to be based on compelling measures of desirability such as cost effectiveness and equity. Final allocation determinations are policy decisions and should reflect public perceptions about acceptable tradeoffs between these measures…” • From the demonstrations and tools of USPEA M S C E

6. Problem Statement (Cont.) I • Many non-professional users desire a DSS that provides easy access to perform rangeland watershed analysis, particularly from economic perspective with few requirements of hardware, software and expertise. M S C E

7. Problem Statement (Cont.) I • Simulation models: • Simple vegetation component • Rarely includes livestock and economic components • Parameterization and result interpretation • Few rangeland models Optimization models: • Search the optimum solution • Less complex in process simulation • More desirable in decision making • No such models for rangeland M S C E

8. Study Objective I • The overall objective is to develop a prototype spatial decision support system (SDSS) that can be used to assess the economics of sediment control on rangeland watershed in web-based environment. M S C E

9. Study Objective (Cont.) I • Develop integrated constrained optimization models that can simulate the bio-physical and production processes of range systems. • Develop a database to manage all the spatial and non-spatial data. • Develop a series of web page interfaces to help users create inputs, run models and view results. • Implement the SDSS that integrates database, models and interface in one system. • Apply the SDSS to a watershed to illustrate the functionality. M S C E

10. Approach I • Nonlinear optimization models of a representative ranch • GIS • Database • Web technology M S C E

11. Benefits I • Provide a platform to perform watershed analysis • Develop a prototype model for similar analysis • Environmental education • Potential User Community • Agency: DEQ, EPA, BLM, etc • Public: ranchers, residents • Other groups M S C E

12. 2. Model Development 2.1. Economic Theory I M 2.2. Model Structure S 2.3. Model Specification C E

13. Economic Theory I • Ranch Production Function • In physical units: • Y = F(X, E) • In monetary units: • PRO = G (PY, PX, PE, X, E) M S C E

14. Economic Theory I Grazing Forage Resources Ground Cover M Stocking rate BMPs Erosion S Profit Sediment Yield C E

15. Economic Theory I • Ranch Management: MCDM • Object: OBJi i = 1, …, I • St: Y = F(X, E) M S C E

16. Economic Theory (Cont.) I • MCDM -> Single objective • Max. PRO • St. Y = F(X, E) • U <= U* • SY <= SYO M S C E

17. Economic Theory (Cont.) I Add spatial index Max PRO (XVS, XVNS, XFS, XFNS) St. Y = F(XVS, XVNS, XFS, XFNS, ES, ENS) US < US* SY < SYO M S C E

18. Economic Theory (Cont.) I • Given Infrastructure • Max PRO • (XVS, XVNS ,XFNS) • St. Y = F(XVS, XVNS, XFS, XFNS, ES, ENS) • US < US* • SY < SYO • XFS are given M S C E

19. Economic Theory (Cont.) Abatement Cost Curve C(∆SY) = H(SY0) - H(SY0 - ∆SY) I • Production Frontier • PRO* = H(SYO) M S PRO0 COST PROFIT C E SY0 SEDIMENT YIELD REDUCTION SEDIMENT YIELD

20. Economic Theory (Cont.) I • Relationship of Two Production Frontiers M S PROFIT PROFIT C E SEDIMENT YIELD SEDIMENT YIELD

21. Economic Theory (Cont.) I • Cost Sharing Shifts Production Frontier M P0 I PROFIT P1 III S II C E SY1 SY0 SYO

22. Model Structure I • Spatial Configuration ------ Fence border ------ Ecological site M P2 32 E3 S 31 E2 22 C 12 P1 13 11 23 E E1 P3 21

23. Model Structure I • Spatial Configuration ------ Fence border ------ Ecological site  (sub)watershed border M P2 S1 E2 E3 321 S 311 320 310 22 220 C 120 P1 110 130 230 E E1 210 P3

24. Model Structure (Cont.) I • Component Configuration M S C Geospatial Factors E

25. Model Specification Plant growth • Climax production • Brush and grass • Adjusted with climate, vegetation type, ecological condition • Use seasonal growth curve in dynamic model • Two types of grazing impacts: no grazing impact, inverted ‘U’ curve I M S C E

26. Model Specification (Cont.) Livestock grazing • Adjusted with distance to water point, slope and forage condition • Utilization under defined level • Grazing equilibrium: AUM demand = Forage grazed • Two types of distribution: rangemap, regression I M S C E

27. Model Specification (Cont.) Erosion • Upland erosion: RUSLE2 R uniform for whole watershed K from soil map LS from DEM C combine vegetation cover P 1 • Sediment yield Upland erosion X area X SDR - sediment detailed by pond • Constraints sediment yield < control objective I M S C E

28. Model Specification (Cont.) Ranch operation • Cow-calf system / cow-calf yearling system • Herd management Economics & policy • Revenue: sale of calf, yearling and cow • Cost: fixed cost, variable cost, environmental cost • Cost shared is deducted from ranch cost I M S C E

29. 3. SDSS Design & Integration 3.1. Architecture I 3.2. Analysis flow chart M 3.3. Interface design S 3.4. Database C 3.5. System Integration E 3.6. Application types

30. Architecture I M S C E

31. SDSS analysis Flow Chart Price & Cost I Pasture M Project Pond S Login Water Point Run C Environment Result E Policy Model

32. Interface Design Technology • Servlet • JSP • JavaScript • Map Server I M S C E

33. Database Application Logic I Requestto change data change DATA EDIT ANALYSISMODEL M Registration Response USER Response USER INFO MANAGEMENT Data request S Login Add/delete Data request Logout C DATA QUERY ORACLE DBMS E Add DBMSDATA REPOSITORY

34. System Integration I • Backbone: Servlet • Database: JDBC • Geospatial analysis: AML • Optimization model: GAMS M S C E

35. 3. Application Types I • A project - a constrained optimization model • Sensitivity analysis • Abatement cost curve / production frontier • Compare two alternatives M S C E

36. 4. Case Study I 4.1. Study area M 4.2. Parameterization S 4.3. Validation C 4.4. Sample applications E

37. Study Area • Location Southeastern Arizona • Vegetation desert brush/grass • Environmental Issue Turbidity by sediment I M S C E

38. Parameterization I • Ranch economics and operations Teegerstrom and Tronstad. 2000 • Ecologic site data NRCS ecological report for Area 41 M S C E

39. Parameterization (Cont.) Map inputs and geoprocessing I Original input layers Current Water Point Rock Cover M Soil DEM Eco-Site Current Fence Current Pond S New WP New Fence New Pond K LS C FlowDir Basic Unit I Sub-watershed E KLS Stream Pond Capa Basic Unit II Watershed Sed-DR

40. Current Infrastructure Parameterization (Cont.) I M • Parameterization (Cont.) S C E

41. Validation I M S C E

42. Sample Applications I • Current Condition Prediction • Reducing Sediment Yield through Grazing Management • Reducing Sediment Yield through Improving Ecological Condition • Comparison of Management Combinations • Adaptive Management of Climate Variation Using the Dynamic Model M S C E

43. 1: Default project • Current infrastructure • Normal climate • Fair ecologic condition • No sediment control requirement I M S Result: • Stock rate  276 cow/calf pair • Erosion  16,000 tons/yr • Sediment yield  5000 tons/yr C E

44. 1: (Cont.) Erosion Distribution I M S C E

45. 1: (Cont.) Vegetation I M S C E

46. 1: (Cont.) Model Types I M S C E

47. 2: Grazing Management I M S C E

48. 3: Comparing Managements I M S C E

49. 3: (Cont.) I M S C E

50. 4: Ecologic Condition Improvement I M S C E