1. Drought Prediction (In progress) Besides real-time drought monitoring, it is essential to provide an utlook of what future might look like given the current status of moisture. We are currently evaluating an ensemble climate prediction approach for estimating drought indices that provide outlook information – e.g., recovery period length (return to normal). Shraddhanand Shukla, Andrew W. Wood, Department of Civil and Enviornmental Engineering, University of Washington APPLICATION OF LDAS-ERA LAND SURFACE MODELS FOR DROUGHT CHARACTERIZATION AND PREDICTION IN WASHINGTON STATE Introduction Accurate appraisal of the current and future status of drought is still a major challenge for scientists and water managers. Traditional drought indices are based on meteorological inputs whereas hydrologic variables such as soil moisture and runoff describe the hydro- meteorological processes of a watershed, and can be used to derive indicators of hydrological and agricultural -- as well as meteorological -- drought status We compare drought metrics based on modeled soil moisture and runoff with traditional drought indices for retrospective droughts in Washington State We also suggest that ensemble hydrologic predictions of these fields can be used to extend both traditional and model- based drought indices into the future and provide uncertainty estimates for the future evolution of a drought. 2007 Rationale Drought is mainly driven by the lack of precipitation, but its severity and duration depends on the antecedent moisture condition, hydrologic and socio- economic conditions of the region. Most drought indices being used, however, either depend solely on climate variables or incorporate a highly simplified water balance scheme. Retrospective Simulation A VIC run for the period 1915-2005 was made at 1/16th degree and a daily time-step over the Washington State domain to simulate the soil moisture and runoff data used in the present study. Methodology Soil-moisture Percentiles ) Daily soil-moisture was averaged to monthly values for the period 1950-2006. These historical monthly values of soil moisture for each grid were sorted for each month and percentiles of the values were estimated based on empirical climatological distributions (using the Weibull plotting position) Standardized Precipitation and Runoff Indices The Standardized Precipitation Index (SPI: McKee et al., 1993) is a probability index based on precipitation only. Computation of the SPI involves fitting a gamma probability distribution function of precipitation total for a station. In this study we also devise a Standardized Runoff Index (SRI: Wood and Shukla, 2007) based on the same analogy as SPI but using the simulated runoff data. SRI is an indicator of the hydrologic drought. Model-based products can improve on the spatial resolution of current products vs Historical Drought Events in Washington State 62 Water Resources Inventory Agencies NOAA PDSI smoothed SM %-ile Statewide 1976-77 Drought : A very dry year The above normal precipitation of 1975-76 was followed by major precip deficits in water year 1977. Drought began in Sep 1976. Persisted through the February 1977. Precipitation less than 75 nearly everywhere and for some areas even less than that. A series of storms brought rainfall well above normal in the month of September and October 1977 Statewide 2005 Drought : A warm winter with moderate precipitation deficits precipitation deficits Owing to extremely low snowpack, a statewide drought was declared on March10, 2005. As of April 1 st Mountain snow pack was 26% of average. Precipitation was between 51 and 76% of average Streamflows were between 22 and 90% of average (Source: WSDA, http://agr.wa.gov/Environment/Drought/) Land Surface Model The VIC model (Liang, 1994) balances both surface energy and water over each grid cell Accounts for the feedback of vegetation on land-atmosphere moisture and energy fluxes like other soil-vegetation-atmosphere transfer schemes. It represents the sub-grid variability of the soil, topography and vegetation 10 Climate Divisions Standardized Runoff Index (SRI) vs. Standardized Precipitation Index The percentile of soil moisture aggregated over the Upper Yakima is compared with the PDSI values (available at NOAA web) Water Balance plot of the Upper Yakima Water Balance plot of the state during 2005 drought Water Balance plot of the state during 1976-77 drought Plot of the monthly SM percentile during 1976-77 Estimated Instrumental PDSI for year 1976 and 1997 Source: http://www.ncdc.noaa.gov/paleo/pdsiyear.html Severity of the 2005 drought as estimated by the United State Drought Monitor and the monthly SM percentiles Results Conclusions Model-based soil moisture and runoff can characterize major drought events. The VIC model is capable of simulating cold season processes and thus its use in drought monitoring is of particular importance to Washington State where water resources are snow-melt runoff driven. Runoff-based index, e.g., SRI, incorporates seasonal hydrologic variations that are not captured by meteorological drought indices (e.g. SPI and PDSI) thus can augment such indices in describing drought. Hydrologic models show promise as central decision tools for monitoring drought and may warrant more attention in assessments such as the Drought Monitor Hydrologic models can be implemented in real-time and forecasting mode, and improve upon the spatial and temporal resolution of current drought products Bibliography Liang, X., D. P. Lettenmaier, E. F. Wood, and S. J. Burges, 1994: A simple hydrologically based model of land surface water and energy fluxes for GSMs, J. Geophys. Res., 99(D7), 14,415–14,428. McKee, T. B., N. J. Doesken, and J. Kleist, 1993: The relationship of drought frequency and duration to timescales, paper presented at 8 th Conference on Applied Climatology, Anaheim, Calif., 17– 22 Jan. Wood, A. W. and S. Shukla 2007: Use of a standardized runoff index for characterizing hydrologic aspects of drought, Geophysical Research Letters (accepted) Further Information http://www.hydro.washington.edu/forecast/sarp/ Expected Soil Moisture (or Runoff) Deficit Recovery Period Soil Moisture Deficit Current Soil Moisture Normal Soil Moisture Ensembles of Expected Recovery Period time surplus deficit