GIS in Environmental and Water Resources Engineering

1. GIS in Environmental and Water Resources Engineering Research Progress Report Jan 15, 1999

2. Texas data and water modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi Environmental Risk Assessment: Hay-Wilson, Romanek, Kim Global runoff: Asante, Lear Nonpoint source pollution: Melancon, Osborne Flood hydrology and hydraulics: Ahrens, Perales, Tate Internet: Favazza,Wei Research Areas

3. Texas data and water modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi Environmental Risk Assessment: Hay-Wilson, Romanek, Kim Global runoff: Asante, Lear Nonpoint source pollution: Melancon, Osborne Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate Internet: Favazza,Wei Research Areas

4. Brad Hudgens Geospatial Data Development for Water Availability Modeling

5. Determining Watershed Properties • Need to know at many points on a stream network: the upstream drainage area, average precipitation and SCS CN value, and the downstream flow length • Grids of these variables are computed using the flow accumulation function • An attribute table is obtained using the Combine function

6. Weighted Flow Accumulation AvgCN=flowaccumulation(fdr, CN)+CN flowaccumulation(fdr)+1

7. Combine Grids GRID : “combine”

8. David Mason Geospatial Data Development for Water Availability Modeling

9. Control Point Status • FINALLY, Acquired all control points for Nueces and Guadalupe River basins • STILL, Waiting for control points on the San Antonio River basin

10. Meanwhile….. • Finished development of a single-line stream network for all basins • Attached control points with ID numbers to line network • Obtained more clearly defined project goals • Which watershed parameters are needed? • Worked on streamlining database development • Develop tools to automate the process

11. Trinity River TMDL Subtask on Network AnalystKim Davis

12. Jona Finndis Jonsdottir Geospatial Data for Total Maximum Daily Loads

13. New Tool Development for Water Modeling Richard Gu

14. Rainfall Runoff in the Guadalupe River Basin Esteban Azagra

15. Objectives • Run HEC-PrePro and HMS programs for a sample area. • Comparison of the runoff with field data. • Calibration of the modeling system.

16. What have I done? • Run HEC-PrePro and HMS. • Analysis of parameters. • Comparison of the model with field data

17. Analyzing Parameters • For Vx constant: D X = 20 % D flow @ 3.7 % • For X constant: D VX@ 20 % D flow @ 28 % • Use of Manning to change the values of VX

18. Comparison and Future work • Precipitation data used for HMS showed big differences between the model and the field data. • The use of NEXRAD Precipitation could help for a more detailed comparison.

19. Surface/Subsurface Modeling By: Shiva Niazi 1/15/99

20. GMS Model

21. Argus ONE Model

22. Argus ONE Can create interface within software- inc. built-in functions Must manually create boundary, river arcs? GMS Supports more MODFLOW packages Time consuming Argus ONE vs. GMS

23. Texas data and water modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi Environmental Risk Assessment: Hay-Wilson, Romanek, Kim Global runoff: Asante, Lear Nonpoint source pollution: Melancon, Osborne Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate Internet: Favazza,Wei Research Areas

24. Lesley Hay Wilson Spatial Environmental Risk Assessment

25. Current Research Status • Completed dissertation proposal defense on Dec 11th • Objective is to develop the spatial risk assessment methodology with emphasis on application to large, complex sites • Working on the site conceptual model and linkages between Access and ArcView

26. Risk Assessment Data Model Forward Risk Estimation Cross-media pathways Receptor Source Human, Ecological Geographic pathways Target Level Calculation

27. Research ApproachSpatial Site Conceptual Model • Spatial representations of the site conceptual model elements (e.g., sources, receptors) • Individual data layers for each element • Supported by • database of exposure pathway components • spreadsheet of transport and transfer algorithms • grid-based models • Implemented in a tiered approach

28. Connection of SCM Database and RBSL Spreadsheets Identify COC Pathway Segments Source Concentrations Excel Spreadsheet Perform simple fate and transport calculations ODBC Access Site Conceptual Model Database Link Pathway Endpoint Concentrations

29. Other Activities • Marcus Hook Project team meetings completed Jan 11-13th (team) • EWRE seminar presentation of dissertation proposal scheduled for Jan 20th

30. Andrew Romanek Surface Representation of the Marcus Hook Refinery

31. Activities • 3 day meeting with BP, Langan, UT, and others (Mon. - Wed.) • Update of progress • Delineation of future tasks • COC Transport Extension • Thesis

32. Surface water model extension to predict concentrations Steady state, conservative, mixing model (only decreases in concentration from additional flow) Initial attempt yielded a maximum benzene concentration of 0.26 mg/L COC Tranport Extension

33. Thesis • Intro to risk assessment and project • Digital Facility Description • Spatial and Tabular Databases • Data development (Photogrammetry) • Connection between Spatial and Tabular • Map-Based Modeling • Surface and Groundwater models

34. Spatial Analysis of Sources and Source Areas on Marcus Hook Progress report by Julie Kim Friday, November 20, 1998

35. Texas data and water modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi Environmental Risk Assessment: Hay-Wilson, Romanek, Kim Global runoff: Asante, Lear Nonpoint source pollution: Melancon, Osborne Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate Internet: Favazza,Wei Research Areas

36. Global Runoff RoutingEstimating Flow Velocity Kwabena Asante

37. Methods • Lag Between Runoff Stations • Lag Between Rainfall and Runoff • Empirical Methods

38. Rainfall Distribution in November

40. Empirical Equations: Generally of the form: P = a * Q b Leopold and Maddock (1953): a = 1.3, b = 0.1 Matalas (1969): a = 1, b = 0.155

41. Grid Cell Translation from High to Low Resolution Mary Lear November 20, 1998

42. Texas data and water modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi Environmental Risk Assessment: Hay-Wilson, Romanek, Kim Global runoff: Asante, Lear Nonpoint source pollution: Melancon, Osborne Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate Internet: Favazza,Wei Research Areas

43. Patrice Melancon Pollutant Loading Model for Tillamook Bay

44. Flow Contribution Distribution matches values reported for the watershed

45. Flow vs Load Contribution by Landuse

46. Concentration Profiles

47. Katherine Osborne Water Quality Master Planning for Austin

48. Texas data and water modeling: Hudgens, Mason, Davis Jonsdottir, Gu, Azagra, Niazi Environmental Risk Assessment: Hay-Wilson, Romanek, Kim Global runoff: Asante, Lear Nonpoint source pollution: Melancon, Osborne Flood hydrology and hydraulics: Ahrens, Bigelow, Perales, Tate Internet: Favazza,Wei Research Areas

49. Seth Ahrens Flood Forecasting in Houston

50. Rainfall Data: Benefits of MATLAB over Visual Basic Lat. Lon. Rf. Time (min) Rf. (mm) Program A Program B Time interval is inconsistent. Final output is an ArcView ASCII grid in the proper projection. All data in one grid in ten-minute intervals. Each time interval in own file. Benefits: Can now more efficiently prepare rainfall data. Original technique incorporated Visual Basic in Excel. Though it worked, the method proved to be cumbersome, error-prone (relied too much on user), and time-consuming.