1. 1 OHD/HL Distributed Hydrologic Modeling Pedro Restrepo Hydrology Group HIC Conference Jan. 24-27, 2006

2. 2 Goal R&D for improved products and services: –RFC Operations –WFO Flash Flood Prediction –NOAA Water Resources Program

3. 3 R&D Topics Prototype Water Resources products (e.g. soil moisture) Parameterization/calibration (with U. Arizona and Penn State U.) Flash Flood Modeling: statistical distributed model Impacts of spatial variability of precipitation Data assimilation Snow (Snow-17 and energy budget models in HL- RDHM) Spatial and temporal scale issues Data issues Links to FLDWAV

4. 4 NOAA Water Resources Program: Prototype Products Initial efforts focus on soil moisture Soil moisture (m 3 /m 3 ) HL-RDHM soil moisture for April 5m 2002 12z

5. 5 UZTWC UZFWC LZTWC LZFSC LZFPC UZTWC UZFWC LZTWC LZFSC LZFPC SMC1 SMC3 SMC4 SMC5 SMC2 Sacramento Model Storages Sacramento Model Storages Physically-based Soil Layers and Soil Moisture Modified Sacramento Soil Moisture Accounting Model In each grid and in each time step, transform conceptual soil water content to physically-based water content Modified Sacramento Soil Moisture Accounting Model Gridded precipitation, temperature CONUS scale 4km gridded soil moisture products using SAC and Snow-17

6. 6 Distributed Model Parameterization-Calibration Explore SSURGO fine scale soils data for initial SAC model parameters (deliverable: parameter data sets in CAP) Investigate auto-calibration techniques –HL: Simplified Line Search with Koren’s initial SAC estimates. –U. Arizona: Multi-objective techniques with HL-RDHM and Koren’s initial SAC parameters. Continue expert-manual calibration Evaluate gridded values of Snow-17 parameters

7. 7 Hydrograph Comparison __ Observed flow __ SSURGO-based __ STATSGO-based Distributed Model Parameterization Use of SSURGO Data for SAC Parameter Derivation SSURGO data has show improvements in certain cases; more work is needed

8. 8 Forecasted frequencies A Statistical-Distributed Model for Flash Flood Forecasting at Ungauged Locations HistoricalReal-time simulated historical peaks (Q sp ) Simulated peaks distribution (Q sp ) (unique for each cell) Archived QPE Initial hydro model states Statistical Post-processor Distributed hydrologic model (HL- RDHM) Real- time QPE/QP F Max forecasted peaks Why a frequency- based approach?  Frequency grids provide a well-understood historical context for characterizing flood severity; values relate to engineering design criteria for culverts, detention ponds, etc.  Computation of frequencies using model- based statistical distributions can inherently correct for model biases

9. 9 14 UTC 15 UTC 16 UTC 17 UTC Statistical Distributed Flash Flood Modeling- Example Forecasted Frequency Grids Available at 4 Times on 1/4/1998 In these examples, frequencies are derived from routed flows, demonstrating the capability to forecast floods in locations downstream of where the rainfall occurred.

10. 10 Method to Calculate “Adjusted” Peaks Probability matching was used to compute adjusted flows at validation points. For implementation we can only assume a similar implicit correction if we are considering frequency-based flood thresholds at ungauged locations. Simulated Observed 157 cms (simulated) 247 cms (adjusted)

11. 11 Eldon (795 km2) Dutch (105 km2) Implicit statistical adjustment ~11 hr lead time ~1 hr lead time Statistical Distributed Flash Flood Modeling - Example Forecast Grid and Corresponding Forecast Hydrographs for 1/4/1998 15z

12. 12 Distributed Model Intercomparison Project (DMIP) Nevada California Texas Oklahoma Arkansas Missouri Kansas Elk River Illinois River Blue River American River Carson River Additional Tests in DMIP 1 Basins 1.Routing 2.Soil Moisture 3.Lumped and Distributed 4.Prediction Mode Tests with Complex Hydrology 1.Snow, Rain/snow events 2.Soil Moisture 3.Lumped and Distributed 4.Data Requirements in West Phase 2 Scope HMT

13. 13 DMIP 2 Science Questions Confirm basic DMIP 1 conclusions with a longer validation period and more test basins Improve our understanding of distributed model accuracy for small, interior point simulations: flash flood scenarios Evaluate new forcing data sets (e.g., HMT) Evaluate the performance of distributed models in prediction mode Use available soil moisture data to evaluate the physics of distributed models Improve our understanding of the way routing schemes contribute to the success of distributed models Continue to gain insights into the interplay among spatial variability in rainfall, physiographic features, and basin response, specifically in mountainous basins Improve our understanding of scale issues in mountainous area hydrology Improve our ability to characterize simulation and forecast uncertainty in different hydrologic regimes Investigate data density/quality needs in mountainous areas

14. 14 Basic DMIP 2 Schedule Feb. 1, 2006: all data for OK basins available July 1, 2006: all basic data for western basins available Feb 1, 2007: OK simulations due from participants July 1, 2007: basic simulations for western basins due from participants

15. 15 DMIP 2: Potential Participants Witold Krajewski Praveen Kumar Mario DiLuzio, Jeff Arnold Sandra Garcia (Spain) Eldho T. Iype (India) John McHenry Konstantine Georgakakos Ken Mitchell (NCEP) Hilaire F. De Smedt (Belgium) HL Thian Gan, (Can.) Newsha Ajami (Soroosh) Vazken Andreassian (Fra) George Leavesley (USGS) Kuniyoshi Takeuchi (Japan) Baxter Vieux John England (USBR) Dave Garen, Dennis Lettenmaier Martyn Clarke

16. 16 DMIP 2 Website http://www.nws.noaa.gov/oh/hrl/dmip/2/index.html

17. 17 Impact of Spatial Variability Question: how much spatial variability in precipitation and basin features is needed to warrant use of a distributed model? Goal: provide guidance/tools to RFCs to help guide implementation of distributed models, i.e., which basins will show most ‘bang for the buck’? HOSIP documents in preparation

18. 18 flow time output input precipitation at time t precipitation at time t +  t precipitation at time t + 2  t Impact of Precipitation Spatial Variability ‘filter’

19. 19 Data Assimilation Strategy based on Variational Assimilation developed and tested for lumped SAC model HOSIP documents in preparation

20. 20 Distributed Snow-17 Strategy: use distributed Snow-17 as a step in the migration to energy budget modeling: what can we learn? Snow-17now in HL-RDHM Tested in MARFC area and over CONUS Further testing in DMIP 2 Gridded Snow-17 parameters for CONUS under review. Related work: data needs for energy budget snow models

21. 21 Thank You! North Fork Dam, American River, California. Used with permission