Remote Sensing Applications to Improve Seasonal Forecasting of Streamflow and Reservoir Storage in the Upper Snake River Basin Marketa McGuire, Andy W. Wood, and Dennis P. Lettenmaier Department of Civil and Environmental Engineering, Box , University of Washington, Seattle, WA ABSTRACT Mountain snowmelt contributes over eighty percent of the water supply in the Western U.S. Accurate estimates and forecasts of snow cover and snowmelt are important to many local, state, and Federal agencies that have interests in agriculture, hydropower, and recreation. Water resource managers depend on accurate water supply predictions to allocate a finite supply of water to often competing demands. Although quantitative forecasts of seasonal snowmelt runoff have been made at many locations in the West for over 50 years, these forecasts are limited by accurate knowledge of winter season precipitation and snow accumulation in remote areas, which presently are estimated via in situ networks like the NRCS’ SNOTEL. We evaluate the potential to improve on methods based solely on in situ observations through a strategy that combines nowcasts of soil moisture, snow water content, and other hydrologic variables using the Variable Infiltration Capacity (VIC) macroscale hydrologic model, updated with a combination of SNOTEL-based estimates of snow water equivalent and remote sensing (MODIS) estimates of snow areal extent. The method is evaluated for the Upper Snake River basin, for which a long-term retrospective VIC run for the period provides a model climatology, and the basis for a retrospective evaluation of seasonal streamflow forecast skill absent updating. For winters and , we evaluate the impact of both replacing and augmenting our updating scheme based on SNOTEL data with corrections from two remote sensing products: course spatial resolution MODIS snow-cover data and, on a more experimental basis, an AMSR snow water equivalent product. In addition to seasonal streamflow forecasts based on an adaptation of the Extended Streamflow Prediction (ESP) method, we implement and evaluate experimental reservoir forecasts produced with the SNAKESIM Snake River reservoir management model for forecasts made in the winters of and for the following summers. Background 1 4 Streamflow Forecasts Using MODIS Update Feb 9, 2003 In regions like the Snake River basin, where spring and summer streamflow is dominated by snow-melt, snow cover extent is an important variable for seasonal streamflow forecasts. This study evaluates the impact of utilizing remotely sensed snow cover to update initial conditions for streamflow forecasts. Previous work by Maurer et al (2003) suggests that MODIS (Moderate Resolution Imaging Spectrometer) remotely sensed snow cover products have the potential to improve hydrological modeling and prediction in the Missouri and Columbia basins. MODIS collects images of snow cover at 500m resolution once per day, subject to cloud cover, at 500 m spatial resolution. MODIS snow products are available from February 2000 in near- real time (availability of the images is within 3-6 days of collection) Given the limitations of meteorological station data, estimates of forecast initial conditions based on surface data alone have large uncertainty, which MODIS snow cover data have the potential to reduce. NCDC met. station obs. up to 2-4 months from current local scale weather inputs Initial Conditions: soil moisture, snowpack Hydrologic model spin up MODIS Update Hydrologic simulation Ensemble Forecast: streamflow, soil moisture, snowpack, runoff 25 th Day of Month years back LDAS/other real-time met. forcings for remaining spin-up End of Month Snake River 1/8° Resolution Routing Flow Network Hydrologic Model: The Variable Infiltration Capacity (VIC) Model is used in this study to produce streamflow forecasts. VIC is a gridded large scale model solves water and energy balance at the land surface for each model 1/8° grid cell. Among other things, the model produces daily baseflow and runoff from each grid cell. These are input to a routing model, which uses a predefined flow network to determine where water will flow and calculates the streamflow at a desired location. 2 6 Streamflow Forecasting Methodology MODIS Update for Nov 25, 2003 Forecast Steps: 1.VIC is run for a spin up period, typically 1-2 years, prior to the forecast date, forced with gridded observed data (precipitation and temperature). From the spinup, we obtain the initial state of the system. 2.We adjust the initial snow extent by updating the VIC simulated snow water equivalent, using the MODIS snow cover product over 8 consecutive days. 3.A 6 to 12 month ensemble streamflow forecast is produced using methods described by Twedt et all (1977). The MODIS snow cover product can only be used to update the model state in absence of clouds. We have developed an updating technique that uses an update window of 8 days. During each day within the update window, the VIC model is run and the ending model state is saved. The decision to update VIC snow water equivalent (SWE), for each elevation band within each grid cell, is shown in the diagram, based on the fractions of no decision, fractions of cloud- and snowcover, and VIC SWE. Concluding Remarks 7 References: Maurer, E.P., J.D. Rhoads, R.O. Dubayah, and D.P. Lettenmaier, 2003, Evaluation of the snow-covered area data product from MODIS, Hydrol. Processes 17, 59-71, doi: /hyp Twedt, T.M., Schaake, J.C., Jr. and E.L. Peck, 1977: National Weather Service Extended Streamflow Prediction, Proc. Western Snow Conf., Albuquerque, New Mexico, 9 Pages, April. Updating Technique Using MODIS Snowcover Product 3 No Dec < 0.50 and Cloud < 0.50 VIC SWE < 0 and MODIS SC >= 0.50 VIC SWE > 0 and MODIS SC < 0.50 F VIC SC = 1 Add 5mm SWE VIC SC = 0 Remove VIC SWE TT Write New Model State T FF Read Model State Monthly Storage Forecast for the Boise River System Feb 2003 – Sep including Anderson Ranch, Arrowrock, and Lucky Peak reservoirs Without MODIS updateWith MODIS update Full Pool Storage (KAF) Full Pool Without MODIS updateWith MODIS update Monthly Storage Forecast for the Upper Snake System Feb 2003 – Sep including reservoirs above Milner Dam Forecasted streamflows were used to drive the SnakeSim reservoir management model to produce storage forecasts for Feb-Sep SnakeSim, developed at the University of Washington, is run in the Stella modeling environment. It has 21 inflow nodes, where either naturalized or simulated streamflows are connected. The model represents 18 reservoirs, which equates to approximately 13 million AF of storage. Storage Forecasts Using MODIS Update Feb 9, Storage (KAF) The plot in the upper left corner shows VIC simulated SWE on Feb 9, 2003 with no SWE adjustment. The plot in the upper right corner shows VIC simulated SWE after 8 days of consecutive updating. The lower plot shows the computed difference and summarizes the cumulative effect of the updating. This spatial plot shows VIC simulated SWE before and after updating for Nov 25, This date was chosen because it corresponds to the most current initial condition date used for Columbia River basin forecasts as part of the West-wide Seasonal Forecasting System, which has been developed by the UW Hydrology Group. Projects/fcst/index.htm Ensemble Streamflow Prediction (ESP) simulates streamflow utilizing initial conditions and historical forcings where each year in the historic record produces an ensemble member. This technique assumes that the future conditions are represented in a probabilistic sense by the historic past. Discussion: In general, the regions where snow was either removed (pink) or added (green) are low in elevation. I hypothesize that these regions experience greater fluctuations in temperature around the freezing point than those in higher mountainous areas. It is possible that model precipitation is accurate but deviations from observed temperature could cause the form of precipitation to be misrepresented in the model. Discussion: As seen in the spatial SWE difference plot in Section 4, snow is mainly removed in areas of comparatively low elevation. The reservoirs of the Boise River system are located in such a region. The plots of the Boise system total storage show that removal of snow cover reduces the ensemble mean of storage. However, the Upper Snake River basin is characterized by higher average elevation. The plots of Upper Snake basin storage suggest that, even with changes in low elevation snow cover, future storage is not significantly effected because of the influence of high elevation snowpack. Discussion: Unlike the similar plot in Section 4, this spatial SWE difference plot shows updating of VIC simulated SWE throughout the basin and not in concentrated regions. During the fall season, temperatures throughout the basin fluctuate around the freezing point and snowpack in mountainous areas is still low. Because MODIS only indicates areal extent of snow, updating does not reflect the effect of snowfall on already snow-covered areas. Updating over a greater extent of the basin suggests that the mountains have only a thin layer of snow that can be easily melted. Discussion: The two plots to the left show ESP forecasts beginning Feb 9, 2003, with and without a MODIS adjusted initial condition. As the above spatial SWE difference plot shows, snow coverage was reduced more than it was extended. A fixed amount of 5mm was added to any elevation band where coverage was extended, but any amount could be removed. The plots supports conclusions made from the above spatial plot by showing lower forecasted streamflows in low lying areas like the lower Boise River system and no significant change in the upper Snake basin above Milner dam due to SWE adjustment. The graphs the the right show how adjustment of the SWE from Feb 2–Feb 9 impacts SWE and snow cover area (SCA) from Feb 10 through Feb 24. SCA is defined as the fraction of model cells with SWE > 5mm. -Results suggest that the VIC model may be misrepresenting temperature or precipitation. Future work will entail studying the relationship between model forcings and results from updating VIC simulated SWE using the MODIS snow cover product. -A more sophisticated method for adding SWE will be developed in hopes of better capturing snow cover information from MODIS images. -All MODIS images are provided courtesy of Ryan Hruska, INEEL, Idaho