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-SMA Parameter 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. 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 ~ 10 2 – 10 3 km 2 –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 Surface Soil Textures in a 600 km 2 Basin S LS SL L SIL CL SICL Other(water, rock, etc.) STATSGO vs. SSURGO

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

6. Complex Soil Survey Databases Must Be Simplified From Zhang et al. (2007) 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.

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-STATSGO_GLCC STATSGO: UZTWM STATSGO_GLCC: UZTWM Mean: 54 mm Mean: 51 mm STATSGO – STATSGO_GLCC STATSGO – STATSGO/GLCCForested Areas

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

11. Gridded A-priori Estimates for Two Snow-17 Parameters Derived from: 1.Aspect (500-m DEM) 2.Slope 3.Forest Type 3.Forest Cover, % 4.Anderson (2002) recommendations for MFMIN, MFMAX (Chapter 7-4) MFMIN MFMAX

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

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

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

15. Customized Algorithms for Analyzing DEMs with Low Accuracy in Flat Areas Identify flat areas and digitized streams Modify elevation grid 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 HRAP grid Cells flow to the wrong basin 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) Using networks derived from high-resolution DEMs improves the results

18. ABRFC ~33,000 cells MARFC ~14,000 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. HRAP Cell-to-cell Connectivity Examples

19. 1 2 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 Distributed Model Resolution Impacts the Accuracy of Basin Representation 1: 2258 km 2 2: 619 km 2 3: 365 km 2 HRAP ½ HRAP

20. Drainage Area Delineation Accuracies 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 Delineated from an HRAP Network Derived from 400-m Flow Directions Delineated directly from DEM resolution

21. Representative Slopes Are Extracted from Higher Resolution DEMS (North Fork of the American River (850 km 2 )) 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. Main Channel Slope (1/2 HRAP Resolution) Average = 0.06 Channel slopes are assigned based on a representative channel with the closest drainage area. Local Channel Slope (1/2 HRAP Resolution) Average = 0.11 Slope (m/m)

22. Main Tributary Main Channel Slope vs. Local Channel Slope (1)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. Segment Slopes (m/m) Cell slope -> pixel-wise local slopec Cell slope -> pixel-wise main slopec

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. CAP Example Graphics Snow Cover Analysis Forest Cover Analysis

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) 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B Image Plane A B A'A' B'B' AA Distances between points are elongated relative to true distances at latitudes below  o but shortened at latitudes above  o, e.g.: A'B' > AB South Pole Elevation View

29. HRAP grid is specified in the image plane of the polar stereographic map projection: 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. Polar Stereographic to HRAP

30. ESRI Polar Stereographic Projection Example See also: http://www.nws.noaa.gov/oh/hrl/distmodel/hrap.htm /*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 end **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. 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 Earth radius divided by 4762.5 (size of 1 HRAP cell)

31. HL-RDHM XMRG Grids to GIS and Back 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 xmrgtoasc Header output with ‘ster’ option: Header output with ‘HRAP’ option: Arc/Info: asciigrid/gridascii GRASS: r.in.gdal/r.out.gdal asctoxmrg Go to http://www.weather.gov/ohd_files/project-hydrology/index.phphttp://www.weather.gov/ohd_files/project-hydrology/index.php And click on ‘dhmworkshop’ link. 1 2 3

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 Calibration Assistance Program (CAP) – Arcview 3.x Prototype Tools Available to RFCs Parameter Grids HRAP/XMRG ESRI Grids and Shapefiles Hydrology Laboratory Distributed Hydrologic Model (HL-RDHM) ThreshR – ArcView 3.x In-house Procedures 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++ Derived Data Layers GRASS/ArcVie w/ArcInfo Asctoxmrg, xmrgtoasc Parameter Grids HRAP/ASCII Edit/dis play Grids ABRFC’s XDMS