Caribbean Coastal Scenarios Project (CCSP):

1. Caribbean Coastal Scenarios Project (CCSP): Hydrological and Water quality Modeling using SWAT Assefa Melesse Florida International University

2. Outline • Modeling protocol • SWAT overview and Data requirement • Pilot watershed selection • Data collection • Calibration, validation and verification

3. Model • Model = simplification of reality • Purpose: To reduce a complex system to its essential processes, so that system behavior may be simulated under different conditions. • Model = software + data • A modeling program + data specific to that watershed • Climate, topography, hydrography, soils, and land use

4. Model cont. • A program to determine the cause of water quality problems and aquatic ecosystem disturbances • Sediment and nutrient load modeling require: • Soil loss data • Field operations and fertilizer/pesticide application data • Human impact activities – LULC change. • Max. daily sediment/nutrient loads for evaluating watershed management strategies • Recommend BMPs

5. Model cont. • Problem • Information • What questions need to be answered • Simplest model with acceptable accuracy • Question whether increased accuracy is worth the increased effort

6. Define the problem Field data Conceptual Model Mathematical Model Computer Program Yes Code Verified? NO Model Design Field data Calibration Compare with Field data Verification Field data Postaudit Modeling Protocol

7. Research questions • What are the major sources of the coastal and aquatic ecosystem degradation? • How much runoff, nutrient and sediment loading? • How much nutrient loads aquatic ecosystem sustain? • What BMPs will help reduce the sediment, nutrient and solute laden?

8. Input Model Output

9. SWAT (Soil water Assessment Tool) Some figures are taken from SWAT Manual

10. About SWAT • SWAT (USDA/ARS) • Objective: “...to predict the impact of land management practices on water, sediment and agricultural chemical yields in large complex watersheds with varying soils, land use and management conditions over long periods of time.” • Strengths • Best at NPS-pollution loads from agricultural practices; • improving routines for urban loads • Good interface with GIS software (ArcView)

11. SWAT features cont. MODEL OPERATION: • simulates hydrology, pesticide and nutrient cycling, erosion and sediment transport • spatially distributed • Daily time step with long term simulations • Basins subdivided to account for differences in soils,   land use, crops, topography, weather, etc. • Basins of several thousand square miles can be studied • SWAT accepts measured data & point sources • Windows Interface

12. Land phase Water phase Phases of hydrologic cycle simulated by SWAT

13. SWAT pesticide fate & transport

14. Pilot Watershedselection • Data availability and continuity • Significance to water quality problems • Accessibility for possible visit and data collection

15. Watershedselection: Suggestions? • Jamaica: Kingston Basin (Hope River watershed) • Purteo Rico: Loiza Basin • Cuba: Guama`@Hoyo de Guama` • DR: Haina Basin

16. Watershedselection: Jamaica

17. Watershedselection: Jamaica • Kingston Basin (Hope River watershed) • Home of the 25% of the island’s population • Urban discharge to the Kingston Harbour has been a concern  • Better data • But no agricultural areas Rio Cobre Basin • Sugar cane agriculture and urban areas • Data? 

18. Watershedselection cont.

19. Data Collection Geospatial/Physical • Land cover: • Detailed land-cover map from existing sources • Level I or higher land-cover classes • Soil: FAO or other sources • 1:250K or better scale • Elevation • DEM 90-m or better

20. Data Collection cont. Weather • Rainfall • air temperature (monthly min and max) • solar radiation • wind speed and • relative humidity • Location of weather station

21. Data Collection cont. Hydrological • Stream flow • Sediment and • Nutrient delivery • Continuous data

22. Data Collection cont. • Non-point and point source pollution data • Fertilizer and pesticide application data • Type, rate and characteristics (absorption, half-life • Point source of pollution, if any • Location and amount

23. Calibration, validation and verification • Calibration • Model adjustment by changing parameters using known input and output data • Validation • Comparison of the model with a different input dataset • Verification • Examining the numerical technique represents the conceptual model and no numerical problems

24. Calibration, validation and verification Calibration/validation periods • Enough time to adjust • Similar condition Calibration validation

25. Calibration, validation and verification Calibration/validation steps • Hydrology • Sediment • Water quality/nutrients

26. Calibration, validation and verification Calibration/validation common problems • Little data • Small range of conditions • Only small storms • Data discontinuity • Calibration can change the physical representation of the processes by the model

27. Calibration, validation and verification Calibration/validation evaluation • Mean and SD, errors of prediction • Regression coefficient, intercept, slope • RMSE, MAD, MAPE • Nash and Sutcliffe efficiency

28. Calibration, validation and verification Calibration/validation key considerations • Water balance • Total amount • Partitioning to other hydrologic components • Storm sequence • Time shift or lag • Time of concentration, travel time • Shape of hydrograph • Peak, time to peak and recession

29. Calibration, validation and verification Courtesy: SWAT Manual

30. Calibration, validation and verification : Hydrology Possible scenarios for hydrology • Model failed to simulate some peaks • Rain gage location • Prblem with rain gage • Use rainfall from representative rain gage • Examine the rainfall and flow data

31. Calibration, validation and verification : Hydrology Possible scenarios • Consistent over prediction • High surface flow, base flow • Less evapotranspiration • Decrease CN • Increase soil available water • Increase deep percolation loss • Increase GW revap coeff.

32. Calibration, validation and verification : Hydrology Possible scenarios • Time lag • Long time of concentration • High Surface roughness • Less slope • Increase slope • Lower Manning’s coeff

33. Calibration, validation and verification: Hydrology Possible scenarios • Model consistently over predicts peaks and under predicts at other parts • Less base flow • High overland flow

34. Calibration, validation and verification: Sediment Sediment calibration possible scenarios • Model consistently under predicts sediment • Low sediment yield • Adjust • Crop management factor • soil erodabiliy factor • USLE slope length factor • Channel cover factor

35. Discussion and Questions