1. NWS Calibration Workshop, LMRFC March, 2009 Slide 1 Sacramento Model Derivation of Initial Parameters

2. NWS Calibration Workshop, LMRFC March, 2009 Slide 2 Methods Derived through hydrograph analysis –Observed hydrographs contain a lot of information! –See handouts for manual methods Derived from GIS-soils information –From STATSGO (NRCS) state level data Developed by Victor Koren of HL Delivered to RFCs in CAP –From SSURGO (NRCS) county level data Shows improvement over STATSGO based parameters

3. NWS Calibration Workshop, LMRFC March, 2009 Slide 3 Observed hydrographs contain a lot of information

4. NWS Calibration Workshop, LMRFC March, 2009 Slide 4 Initial SAC-SMA Parameters from STATSGO Data Objective estimation procedure that can produce spatially consistent and physically realistic parameter values (Koren et al. 2000, 2003) –Improve starting point estimates of conceptual model parameters based on national database of soil property data –Constrained calibration so that parameter adjustment occurs within range of values to maintain conceptual consistency Provide spatial distributions of parameters to make simulation possible as NWS moves to smaller scale distributed and flash flood modeling

5. NWS Calibration Workshop, LMRFC March, 2009 Slide 5 Parameter Estimation Methodology using STATSGO Data Physically-based approach to quantify relationships for 11 major parameters of SAC-SMA (16 total) –STATSGO dominant soil texture grids for 11 soil layers for conterminous U.S. are used. –Hydraulic soil properties (e.g., θ s, θ fld, θ wlt, K s ) estimated for each USDA textural class using empirical relationships (Campbell 1974, Clapp and Hornberger 1978, Cosby et al. 1984) –Split between upper and lower layers based on NRCS curve number (function of soil type, land use, hydrologic condition, moisture status) –SAC-SMA storages are defined using these assumptions. Additional physical reasoning, empirical relationships establish remaining estimated parameters (Koren 2000 ). Current NWS Approach, contd.

6. NWS Calibration Workshop, LMRFC March, 2009 Slide 6 Current Limitations of using STATSGO Data Limitations of present methodology (some noted in Koren et al. 2003) –Data Resolution Uses STATSGO (1:250K) data intended for multi-state, regional scale analysis (~100 – 200 km 2 polygons) leading to limitations on reliability of parameters due to spatial sampling Measurements typically don’t extend below 152 cm, so unable to account for certain local conditions, e.g., areas of deep groundwater Assumed a single generic land use type and condition (“range” land use, “fair” condition), leading potentially to bias –Other uncertainties and scientific questions related to derived property-parameter and property-property relationships Current NWS Approach, contd.

7. NWS Calibration Workshop, LMRFC March, 2009 Slide 7 Strategies for Improvement –Using data of finer resolution Use county level (SSURGO) field surveys (~1:24K) with polygons typically consisting of a single soil component type Use USGS land cover grids at 30m scale –Incorporating data on impervious layers in soil column leading to local restrictions on parameter values –Applying base flow analysis to develop more accurate estimates of lower layer dynamic (free water) storage GOAL: Explore potential to improve parameter estimates by:

8. NWS Calibration Workshop, LMRFC March, 2009 Slide 8 Time, days Comparison of Hydrographs: STATSGO vs. SSURGO Data (coarse vs fine scale) Catchment 3: 2–7/1975 Linear scale Semi- Log scale Catchment 2: 2–7/1974 Time, days Results, contd.

9. NWS Calibration Workshop, LMRFC March, 2009 Slide 9 Demonstration of scale difference between STATSGO and SSURGO SSURGO STATSGO Background

10. NWS Calibration Workshop, LMRFC March, 2009 Slide 10 Objective estimation procedure that can produce spatially consistent and physically realistic values for 11 of the 16 SAC-SMA parameters STATSGO + Assumed spatially constant “pasture or range land use” under “fair” hydrologic conditions, (Koren et al. 2000, 2003) STATSGO + Spatially variable land use land cover data SSURGO + Spatially variable land use land cover data, (Anderson et al., 2005, Zhang et al., 2008) Background

11. NWS Calibration Workshop, LMRFC March, 2009 Slide 11 small basins large basins all basins Results R m : Modified correlation coefficient. It is calculated by reducing normal correlation coefficient by the ratio of the standard deviations of the observed and simulated hydrographs.

12. NWS Calibration Workshop, LMRFC March, 2009 Slide 12 Conclusions Use of land cover data and higher resolution soil data results in different a-priori SAC-SMA parameters. Overall simulation results for three sets of a-priori parameters are similar. The effect of using higher resolution soil data and land use land cover data is different between smaller basins and larger basins. Improvements are mainly for smaller basins when SSURGO data are used. Generally similar results for large basins for three sets of a priori parameters. Improvement from using detailed soil data is greater than using gridded land cover data. Results suggest that the SSURGO based parameters are preferable for smaller scale applications.

13. NWS Calibration Workshop, LMRFC March, 2009 Slide 13 Status of SSURGO – Based SAC-SMA Parameter Derivation