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PNWRC Snake River Basin-wide streamflow forecasting Dennis P. Lettenmaier Marketa McGuire Department of Civil and Environmental Engineering University.

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Presentation on theme: "PNWRC Snake River Basin-wide streamflow forecasting Dennis P. Lettenmaier Marketa McGuire Department of Civil and Environmental Engineering University."— Presentation transcript:

1 PNWRC Snake River Basin-wide streamflow forecasting Dennis P. Lettenmaier Marketa McGuire Department of Civil and Environmental Engineering University of Washington Climate Impacts Group Annual Fall Forecast Meeting Boise, ID September 15, 2003

2 What is PNWRC (Pacific Northwest Regional Collaboratory)?

3 “… to facilitate the integration of geospatial technologies and data to serve the needs of resource managers and decision-makers across the PNW region Strategic Objectives  Provide solutions to problems of critical regional importance  Create enduring collaborations that position the PNWRC as a key asset to government and industry in the region  Build geospatial capacity within the end user community and participant institutions  Ensure the financial sustainability of the enterprise PNWRC Mission & Objectives

4 Regional Partners of the PNWRC  Two National Laboratories and four regional universities  Partner base will be expanded in future years

5 Organizing the Effort Government policymakersPublic interest groupsCommercial interests PNW Sustainability Atlas Trend analysis Information integration Adaptive management Water management Snowpack Stream flow Water use efficiency Land management Rangeland & forest health Invasive species Wildland habitat Government agencies Natural Resources Conservation Service Bureau of Reclamation Washington Dept of Ecology Non-Governmental organizations & Tribes Watershed councils NW Environment Watch Duck Valley Reservation Private/Commercial entities Irrigators Forest land managers Hydropower producers Example End Users Sustaining Framework Technical Projects Example End Users Baseline characterization Sustainability indicators Forecasts and scenarios

6 Improved Streamflow Prediction and Water Resource Management in Snowmelt-Dominated Basins A Raytheon/SYNERGY Pacific Northwest Regional Collaboratory Project

7 BENEFITS: more optimal water use and informed decision-making Recreation Industry More optimal reservoir operations Water Resource Managers Farmers Conservation Planning

8 Important NASA programmatic goal: Utilize Earth Observing System data for “real world” decision-making

9 Improved Streamflow Prediction and Water Resource Management in Snowmelt-Dominated Basins COMPONENTS: Hydrologic Modeling Large scale: Upper Snake (Year 1); Klamath (Year 2 - ) (UW) Finer scale for critical sub-basins (ex: Big Lost,) (INEEL, UI, PNNL) Drivers for Hydrologic Models Snowcover and SWE (UI, INEEL) Forecast Met. Variables (UI, PNNL) Reservoir Operations (UW) Crop Water Demand (UI) Comprehensive Stream System Resource Management Big Lost Sub-Basin (INEEL) Regional scale (UW) Example: Upper Snake 36,000 mi 2

10 University of Washington Element (Snake River basin hydrology and water resources forecasting using remote sensing products)

11 Draws from UW Experimental Westwide Hydrologic Forecast System (see Hamlet presentation for details) www.hydro.washington.edu/ Lettenmaier/Projects/fcst/ index.htm

12 S/I Hydrologic Forecasting System Streamflow Reservoir System: storages releases derived variabl es Spatial variables: snow water equiv. soil moisture runoff Verifications Products

13 local scale weather inputs Initial Conditions: soil moisture, snowpack Hydrologic model spin up Forecasting Approach using MODIS Updating MODIS Update Hydrologic simulation Ensemble Forecast: streamflow, soil moisture, snowpack, runoff 25 th Day of Month 01-2 years back NCDC met. station obs. up to 2-4 months from current LDAS/other real-time met. forcings for remaining spin-up End of Month 6 - 12

14 MODIS Snowcover March 3, 2000 Snow Clouds Land No Data/No Decision/Saturated VIC Snowcover March 3, 2000 Snow (SWE >= 5mm) Land (within Columbia River Basin)

15 MODIS Snowcover April 4, 2000 Snow Clouds Land No Data/No Decision/Saturated VIC Snowcover April 4, 2000 Snow (SWE >= 5mm) Land (within Columbia River Basin)

16 Evaluation of the Snow-Covered Area Data Product from MODIS in the context of (Continental-Scale) Hydrological Modeling Edwin P. Maurer and others University of Washington Department of Civil and Environmental Engineering Originally presented at AGU Fall Meeting, December 2001

17 Study Area Selection Study areas: Columbia River Basin Missouri River Basin

18 MODIS Images (nominal 500 m res.) NOHRSC (AVHRR) images (1 km) Snow observations Missouri - 1325 observation points Columbia - 773 observation points Select days of interest: • Relatively cloud-free days • Both MODIS and AVHRR available Image Comparison Data (all in spring 2001)

19 Observation Point Locations

20 MODIS, AVHRR and Gridded Observations MODIS Gridded Observations AVHRR April 4, 2000

21 MODIS, AVHRR and Gridded Observations MODIS Gridded Observations AVHRR April 7, 2000

22 Pixel-by-Pixel Comparisons Classification grid: Error of Omission Error of Commission

23 Missouri River Basin 1) Using image resolutions of 30-arc seconds for AVHRR and 15 arc-seconds for MODIS Columbia River Basin Less Cloud for MODIS Snow Less Cloud for MODIS

24 2) Using aggregated MODIS image -- AVHRR and MODIS at 30 arc-seconds Missouri River Basin Columbia River Basin MODIS has more snow pixels correct Less Cloud for MODIS

25 3) Examining Differences in Cloud Cover and Vegetation Missouri River Basin Columbia River Basin Red shading indicates difference is significant at 95% confidence level

26 SUMMARY  MODIS generally classifies less as “cloud” than NOHRSC/AVHRR MODIS SCA product is a measurable improvement over AVHRR images where:  Terrain is complex – (greater relief)  Forested area is dominant MODIS exhibits greater errors than AVHRR in:  Cloudy days over grassland/low relief basin

27 Other plans for large area forecasting and water management Extend westwide forecast products over the Snake to include reservoir storage, releases, etc (via routing of forecast ensembles through SNAKESIM) Evaluation of AMSR SWE products Evaluation of MODIS-based crop water use (available in near-real time) – current SNAKESIM approach prescribes water use as fixed

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