1. Application of DHSVM to Hydrologically Complex Regions as Part of Phase 2 of the Distributed Model Intercomparison Project Erin Rogers Dennis Lettenmaier Jessica Lundquist

2. Outline  Project Overview and Context NOAA’s NWS Distributed Model Intercomparison Project (DMIP) NOAA’s NWS Distributed Model Intercomparison Project (DMIP) NOAA’s ESRL Hydrometeorological Testbed program (HMT) NOAA’s ESRL Hydrometeorological Testbed program (HMT)  The American River Basin and DHSVM  Current Research Status  Future Work

3. Distributed Model Intercomparison Project  Designed to help NWS make decisions about operational forecasting models- specifically moving from lumped to distributed models  Goal is to determine if distributed models perform as well as lumped models at basin outlets and if they have the ability to model basin interior points accurately

4. DMIP Format  NWS picks basins and sets forth simulation requirements  NWS makes input and forcing data available  Participants are given a due date for submitting required simulations  NWS compiles and analyzes simulation results

5. DMIP 2

6. American and Carson River Basins

7. Hydrometeorological Testbed Program  Regional demonstration program focused on improving precipitation forecasting  Evaluating current observational tools wrt spatial and temporal distribution of precipitation

8. HMT  Dense network fixed and mobile advanced sensors  AR Basin 1st full scale deployment  Expected to run from 2006-2011

9. PRE-HMT Coverage P Precip Station Snow Station

10. HMT Instrumentation – Radar Locations (Polarimetric and Doppler)

11. HMT Instrumentation – Rain Disdrometers

12. HMT Instrumentation - 2875 MHz Precip Profilers

13. HMT Instrumentation – Soil Moisture and Temperature Probes

14. HMT Instrumentation – Water Vapor Sensors

15. HMT Instrumentation – Surface Met Stations

16. HMT Instrumentation – Stream Level Loggers

17. HMT Instrumentation - All

18. Pre/Post HMT Instrumentation

19. American River Basin  Critical resource for CA’s economy Water resources management Water resources management Hydroelectric power generation Hydroelectric power generation Fisheries Fisheries  Prone to flooding due to heavy winter precipitation from ‘atmospheric rivers’ originating in the tropical pacific  Heavily populated downstream area

20. Basin Characteristics - Climate Hydrology Rain and Snow Driven Mean Annual Temp Range Low: -1 to 2 C High: 26 to 34 C Mean Annual PE Range ~1030 mm to 1210 mm ~1030 mm to 1210 mm

21. Basin Characteristics – DEM Area: 866 km2 Elevation Range: 200-2600 m Median Elevation: 1270 m

22. Basin Characteristics - Precipitation Precipitation dominated by orographic effects Mean Annual Precip: 813 mm (393 m elev) 1651 mm (1676 m elev)

23. Basin Characteristics - Vegetation Heavily forested basin 75-85% coverage Vegetation Types Douglas Fir Ponderosa Pine Lodgepole Pine Fir-Spruce Western Hardwoods Shrub rangeland Forest Type Forest Percent

24. Basin Characteristics - Geology  Metasedimentary rock and granite  Shallow soils with areas of exposed rock  Soils are clay loams and coarse sandy loams  Depth ranges from 0-2.5 m

25. Impoundments and Diversions

26. Basin Characteristics - Road Density  Pink > 2 km/km 2 > 2 km/km 2  Yellow 0.9 – 2.0 km/km 2 0.9 – 2.0 km/km 2  Blue 0 – 0.9 km/km 2 0 – 0.9 km/km 2

27. Basin Characteristics - Road System

28. DHSVM  Has successfully been applied to similar watersheds  Has limited ability to model standing water  Baseflow is expected to be a small component

29. Current Research Status  No NOAA data yet  Initially forcing with Alan’s data set (5 stations)  Run DHSVM at 90m resolution  Soils data from SSURGO soil survey  Veg data from EPA  No roads

30. Gridded Data Locations

31. Future Work  Relating point to gridded data: compare Alan’s data set with local station data, adjust if necessary  Calibrate DHSVM with gridded forcing data  Model other basins in DMIP2 Carson Carson Blue Blue Elk Elk Illinois Illinois

32. Questions?

33. DMIP Phase 1 Simulation Requirements  Hydrographs generated using NEXRAD as ppt forcing  Calibrated and uncalibrated sims required  ‘Blind’ simulation at prescribed interior sub-basin points  Simulations in continuous retrospective mode  HL conducted an analysis of all simulations vs observed data as well as SAC-SMA simulations Interior PointUSGS Gage

34. DMIP Phase 1  DMIP1 ran from 2000-2002  Conducted in several basins in the southern great plains  Hydrologically simple, but prone to flash flooding Blue River Illinois River Elk River

35. DMIP2 Science Questions 1. Can Distributed Models provide increased simulation accuracy compared to lumped models? Are improvements constrained by forcing data quality? 2. What simulation improvements can be realized through the use of re-analysis forcing data? Can using the Multi-sensor precipitation estimation algorithm to process the raw NEXRAD data lead to improved simulations? 3. What is the performance of distributed models if they are calibrated with observed precipitation data but use forecasts of precipitation? How far out into the future can distributed models provide better forecasts than currently used lumped models? 4. Can distributed models reasonably predict processes such as runoff generation and soil moisture re-distribution at interior locations? At what scale can we validate soil moisture models given current models and sensor networks?

36. DMIP2 Science Questions 5. In what ways do routing schemes contribute to the simulation success of distributed models? 6. At what river forecast points can we expect distributed models to effectively capture spatial variability so as to provide better simulations and forecasts? 7. What is the potential for distributed models set up for basin outlet simulations to generate meaningful hydrographs at interior locations for flash flood forecasting? 8. What are the advantages and disadvantages associated with distributed modeling (versus lumped) in hydrologically complex areas using existing model forcings?

37. DMIP2 Science Questions 9. Is there a dominant constraint that limits the performance of hydrologic simulation and forecasting in mountainous areas? If so, is it the quality and/or amount of forcing data or is the constraint related to a knowledge gap in our understanding of the hydrologic processes in these areas? 10. Can improvements to rain-snow partitioning be made? Can advanced sensors planned for implementation in the American River lead to improved simulations and forecasts? 11. What are the dominant scales (if any) in mountainous area hydrology?

38. OK Mesonet Stations