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Use of GIS for Hydrologic Model Parameter Estimation

Use of GIS for Hydrologic Model Parameter Estimation. OHD/HSMB/Hydrologic Modeling Group Seann Reed (presenter), Ziya Zhang, Yu Zhang, Victor Koren, Fekadu Moreda, Michael Smith, Zhengtao Cui Presented at the RFC GIS Workshop, OHRFC July 17, 2007. Outline.

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Use of GIS for Hydrologic Model Parameter Estimation

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  1. Use of GIS for Hydrologic Model Parameter Estimation OHD/HSMB/Hydrologic Modeling Group Seann Reed (presenter), Ziya Zhang, Yu Zhang, Victor Koren, Fekadu Moreda, Michael Smith, Zhengtao Cui Presented at the RFC GIS Workshop, OHRFC July 17, 2007

  2. Outline • Gridded a-priori parameter estimation procedures • SAC-SMA • PE, PE Adjustment Factors • Snow-17 • Distributed model routing • Calibration Assistance Program (CAP) • Polar stereographic/HRAP • Xmrgtoasc, asctoxmrg Pre-processing Delivery

  3. A priori SAC-SMAParameter Grids Victor Koren methodology inputs: • SCS curve number; assumed dry antecedent conditions • total soil column depth • texture by layer Three versions now being tested: • STATSGO only (original) • Miller and White (1998) 1-km gridded STATSGO • Curve numbers vary spatially as a function of hydrologic soil group but not land use; assumed “pasture or range land use” • CONUS coverage • STATSGO-GLCC • GLCC: Global Land Cover Characterization (1-km resolution) • Explicitly account for Land Use/Land Cover variations • CONUS coverage • SSURGO-NLCD • SSURGO: State Soil Geographic Database • NLCD: National Land Cover Database • Higher resolution inputs • Parameters derived for 25 states in southern US so far

  4. S S I L C L L S S L S I C L L Other(water, rock, etc.) Surface Soil Textures in a 600 km2 Basin STATSGO vs. SSURGO • State Soil Geographic Database (STATSGO) • A Mapunit groups similar soils and may contain several non-contiguous polygons; each polygon may contain multiple soil types • Mapunit sizes ~ 102 – 103 km2 • Attribute tables contain soil property information by layer • Soil Survey Geographic Database (SSURGO) • ~ 4 to 20 times more detail • Polygon data for all counties expected to be available in standard digital format by 2008

  5. SSURGO data schematic from Zhang et al. (2007), in review STATSGO and SSURGO contain both spatial and tabular information.

  6. Complex Soil Survey Databases Must Be Simplified • This slide describes our assumptions for SSURGO simplifications • Miller and White (1998) used similar assumptions to convert STATSGO polygon data to a 1 km grid and 11 standard layers for the conterminous U.S. From Zhang et al. (2007)

  7. Efficient processing of large data sets using GRASS, R, K Shell and Perl scripts • Phases 1 and 2 run for each soil survey area and then merged to state and regional domains in Phase 3 • Parameters aggregated to ¼, ½, and 1 HRAP resolutions for hydrologic modeling Phase 3 Zhang et al. (2007), in review

  8. Example SSURGO-NLCD Results: UZTWM Basic Result Basic with Gap Filling

  9. STATSGO_GLCC: UZTWM STATSGO: UZTWM Mean: 54 mm Mean: 51 mm STATSGO – STATSGO/GLCC Forested Areas STATSGO – STATSGO_GLCC STATSGO-STATSGO_GLCC

  10. PE and PE Adjustment Factor Grids PE PE Adjustment July January Koren, Schaake, Duan, Smith, and Cong (1998)

  11. Gridded A-priori Estimates for Two Snow-17 Parameters MFMIN • Derived from: • Aspect (500-m DEM) • Slope • Forest Type • Forest Cover, % • Anderson (2002) recommendations for MFMIN, MFMAX (Chapter 7-4) MFMAX

  12. Digital Elevation Model and Derivatives (DEMs)“Out-of-the-Box” DEM Analysis Flow Direction Grid

  13. “Out-of-the-Box” DEM Analysis Flow Accumulation

  14. 1 “Out-of-the-Box” DEM Analysis Stream links Streams 2 3 4 5 6 Sub-basins

  15. Customized Algorithms for Analyzing DEMs with Low Accuracy in Flat Areas Modify elevation grid Identify flat areas and digitized streams Compute new flow directions from Modified grid

  16. Digital Elevation Models (DEMs) and Derivatives • NOHRSC Data (CONUS by RFC) • 15 arc-second DEM (resampled from 3 arc-second) • RF1 (1:500,000 stream vectors) • Customized algorithms used to blend DEM and streamline data • Used in IHABBS, ThreshR, CAP, and to derive first-cut HL-RDHM connectivity files • National Elevation Dataset (NED) • 1 arc-second (30-m resolution) • Used NSSL derivative products for selected study areas (e.g. DMIP) • No correction with digitized streams or basin boundaries • NHDPlus Project DEM Derivatives • Multi-agency effort to develop attributes for National Hydrography Data set (NHD) • Uses several algorithms to forces consistency between DEM derivatives, NHD, and that National Basin Boundary Dataset • Not necessarily best algorithms to correct DEMs, but looks to be the most practical and best available product for basin and stream delineation

  17. Deriving Coarse Resolution (e.g. HRAP) Flow Directions from Higher Resolution DEMs Out-of-the-Box Steepest Descent Algorithm Works Well for High Resolution DEMs but not for HRAP resolution Cell outlet tracing with an area threshold (COTAT), Reed (2003) HRAP grid Using networks derived from high-resolution DEMs improves the results Cells flow to the wrong basin

  18. HRAP Cell-to-cell Connectivity Examples ABRFC ~33,000 cells • OHD delivers baseline HRAP resolution connectivity, channel slope, and hillslope slope grids for each CONUS RFC on the basis of higher resolution DEM data. MARFC ~14,000 cells

  19. Distributed Model Resolution Impacts the Accuracy of Basin Representation 1: 2258 km2 2: 619 km2 3: 365 km2 ½ HRAP HRAP 2 1 3 Must choose this cell to get only subbasin 3, losing cells in the red box. 2 km resolution allows more accurate delineation of subbasin 3

  20. Drainage Area Delineation Accuracies Delineated directly from DEM resolution Delineated from an HRAP Network Derived from 400-m Flow Directions • Open squares represent errors due to resolution only. • Black diamonds represent errors due to resolution and connectivity. • We correct for these errors by adjusting cell areas in HL-RDHM implementations. • Both higher resolution input DEMs and use of finer resolution distributed models (e.g. ½ HRAP) can be used to increase accuracy

  21. Representative Slopes Are Extracted from Higher Resolution DEMS (North Fork of the American River (850 km2)) Slope (m/m) Main Channel Slope (1/2 HRAP Resolution) Average = 0.06 Channel slopes are assigned based on a representative channel with the closest drainage area. Slopes from 30-m DEM Hillslope Slope (1/2 HRAP Resolution) Average = 0.15 Slopes of all DEM cells within the HRAP pixel are averaged. Local Channel Slope (1/2 HRAP Resolution) Average = 0.11

  22. Tributary Main Main Channel Slope vs. Local Channel Slope Segment Slopes (m/m) Cell slope -> pixel-wise local slopec Cell slope -> pixel-wise main slopec • Slopes of each stream segment are calculated on the DEM grid (2) Model pixel slopes are assigned from representative segments (DEM cell) that most closely match either the cell’s cumulative or local drainage area.

  23. Calibration Assistance Program (CAP) • Avenue-based , requires ArcView 3.x with the Spatial Analyst 1.1 • V. 1.0, 2000 (Seann Reed, Ziya Zhang, David Wang) • Initially intended to: • simplify initial parameter estimation for lumped modeling (assumed non-expert GIS user) • facilitate extensibility and creative exploration for GIS experts • V. 1.1, 2002: Added tools to automatically define MAPX areas for OFS based on zone or basin polygons (Lee Cajina) • 2003 – 2007 no updates • AWIPS migrates to Linux so future of ArcView 3.x applications is unclear • V. 1.2, 2007: Minor enhancements • Updated cover data from NOHRSC (1996-2003) • Two new grids to support the frozen ground model are now provided • Scripts updated to support new grids • Scripts modified to allow most functions to run properly on Windows XP operating system (not functions that interact with OFS, e.g. MAPX) • All data in Albers Equal Area Projection (equal area projection makes it easier to compute zone and basin areas)

  24. CAP v. 1.2 Functionality • Derive area-elevation curves • Export area-elevation to MCP input deck format • Sub-divide basins into elevation zones • Derive elevation-precipitation plots • Compute basin or zonal mean, max, and min values of: • precipitation (monthly, annual, and seasonal) • potential evaporation (monthly, annual, and seasonal) • potential evaporation adjustment factors • percent forest • percent of each forest type • soil-based estimates for 11 SAC-SMA parameters • Mean annual temperature (C) used in the frozen ground model (TBOT) • Compute the dominant soil texture in a basin’s upper layer (STXT) used in the frozen ground model • Display NOHRSC historical snow images from (1990-2003) • Display basin boundaries and defined zones on top of other data layers (e.g. snow cover, SAC parameters, etc.) • Derive/export geographic information required to run NWSRFS-MAPX routines (must run on HP)

  25. Snow Cover Analysis Forest Cover Analysis CAP Example Graphics

  26. Future of CAP? Needs • Re-engineer CAP to move out of ArcView 3.x. • Maintain original goals: (1) friendliness for non-GIS experts, (2) extensible for intermediate GIS users. • Deliver refined a-priori parameter grids as they are developed (no problem) • Deliver parameter estimation procedures via the new CAP (as opposed to delivering only pre-processed data) • Many others . . .

  27. Future of CAP? Possible Development Paths • Organize collaborative development project by hydrologists (‘local application’ in GRASS or ArcGIS?) • PROS: Less expensive, short wait, easily customizable to meet local needs • CONS: Requires field expertise and high level of coordination (from where?), risks lack of coordination and multiple versions, informal support • Push for official AWIPS development project by software engineers • PROS: Would yield a more polished user friendly application, formal AWIPS support • CONS: Higher cost, longer wait, greater risk of no future enhancements if funds dry up, may be difficult to get a high enough priority to receive funding

  28. Secant Polar Stereographic Map Projection (Basis for the HRAP coordinate system used in NEXRAD processing and distributed hydrologic modeling) Elevation View Image Plane B' A' A • Points are projected from the model earth to the image plane along a straight line drawn from the South Pole • The “secant” image plane intersects the earth at 60 N (the standard latitude, o) B • Distances between points are elongated relative to true distances at latitudes below o but shortened at latitudes above o, e.g.: • A'B' > AB o B A South Pole

  29. Polar Stereographic to HRAP True Side Lengths and Areas for HRAP Cells at Different Latitudes Although not ideal for hydrologic modeling, we can readily adjust HRAP cell areas to represent the true area when converting runoff depths to flow volumes. HRAP grid is specified in the image plane of the polar stereographic map projection:

  30. /*Example Arc/Info projection file /*to go from geographic to polar /*stereographic input projection geographic spheroid sphere units dd parameters output projection polar spheroid sphere units meters parameters -105 0 0 60 0 24.5304792 /* stand. latitude (dd mm ss) 0.0 0.0 end ESRI Polar Stereographic Projection Example GRASS Input and Output Location Projections name: Lat/Lon proj: ll ellps: sphere name: Stereographic proj: stere a: 1337.784777 es: 0.0 f: 0.0 lat_0: 90.0000000000 lat_ts: 60.0000000000 lon_0: -105.0000000000 k_0: 1.0000000000 x_0: 401.0 y_0: 1601.0 **TRICK: Standard latitude is adjusted so that the HRAP earth radius of 6371.2 km can be used instead of the ESRI default 6370.997 km. As of Arc/Info 7.2, ESRI did not support a user defined radius for this projection. Earth radius divided by 4762.5 (size of 1 HRAP cell) See also: http://www.nws.noaa.gov/oh/hrl/distmodel/hrap.htm

  31. HL-RDHM XMRG Grids to GIS and Back 1 xmrgtoasc <infilename> <outfilename> <ster|HRAP> Header output with ‘ster’ option: Header output with ‘HRAP’ option: ncols 1060 nrows 821 xllcorner -1905000.000000 yllcorner -7620000.000000 cellsize 4762.500000 NODATA_value -1.000000 ncols 1060 nrows 821 xllcorner 1.000000 yllcorner 1.000000 cellsize 1.000000 NODATA_value -1.000000 Arc/Info: asciigrid/gridascii GRASS: r.in.gdal/r.out.gdal 2 3 asctoxmrg <infilename> <outfilename> <ster|HRAP> Go to http://www.weather.gov/ohd_files/project-hydrology/index.php And click on ‘dhmworkshop’ link.

  32. Summary • GIS data and tools provided valuable assistance in estimating hydrologic model parameters • Because algorithms to derive apriori parameters are complex, work cannot be done with out-of-the-box GIS functions • Recently, products delivered to the field from OHD are derived data set rather than data and software • Reasons include • algorithm complexity (no need for everyone to learn) • lack of a common GIS platform • limited resources • Efforts to deliver data and programs should be considered in the future (potential added value by field developers and possibility of using better local data sources) • New CAP should be considered

  33. GIS-based Parameter Estimation for Lumped and Distributed Hydrologic Models Prototype Tools Available to RFCs Derived Data Layers Calibration Assistance Program (CAP) – Arcview 3.x ThreshR – ArcView 3.x In-house Procedures ESRI Grids and Shapefiles Tools to derive A-priori Parameter Grids • ArcView 3.1 w/ Spatial Analyst (HP-UX) • Arc/Info 7.x (HP-UX) • GRASS 6.2 • R Statistical Software • FORTRAN/C/C++ Hydrology Laboratory Distributed Hydrologic Model (HL-RDHM) Parameter Grids HRAP/XMRG ABRFC’s XDMS Asctoxmrg, xmrgtoasc Parameter Grids HRAP/ASCII Edit/display Grids GRASS/ArcView/ArcInfo

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