A Technique for Incorporating Large-Scale ClimateInformation in Basin-Scale Ensemble Streamflow Forecasts Katrina Grantz1, 2, Rajagopalan Balaji1, 3, Martyn Clark3, and Edith Zagona2 1Dept of Civil, Environmental & Architectural Engineering (CEAE), University of Colorado, Boulder, CO2Center for Advanced Decision Support for Water and Environmental Systems (CADSWES)/CEAE, University of Colorado, Boulder, CO3CIRES, University of Colorado, Boulder, COAbstractWater managers throughout the Western U.S. depend on seasonal forecasts toassist with operations and planning. In this study, we develop a seasonal forecastingmodel to aid water resources decision-making in the Truckee-Carson River System.We analyze large-scale climate information that has a direct impact on our basin ofinterest to develop predictors to spring runoff. The predictors are snow waterequivalent (SWE) and 500mb geopotential height and sea surface temperature (SST)“indices” developed in this study. We use nonparametric stochastic forecastingtechniques to provide ensemble (probabilistic) forecasts. Results show that theincorporation of climate information, particularly the 500mb geopotential heightindex, improves the skills of forecasts at longer lead times when compared withforecasts based on snowpack information alone. The technique is general and could beapplied to other river basins. 11. IntroductionWater resource managers in the Western U.S. are facing the growing challengeof meeting water demands for a wide variety of purposes under the stress of increasedclimate variability (e.g., Hamlet et al., 2002; Piechota et al., 2001). Careful planningis necessary to meet demands on water quality, volume, timing and flowrates. This isparticularly true in the Western U.S., where it is estimated that 44% of renewablewater supplies are consumed annually, as compared with 4% in the rest of the country(el-Ashry and Gibbons, 1988). The forecast for the upcoming water year isinstrumental to the water management planning process. In the managed riversystems of the West, the skill of a streamflow forecast dramatically affectsmanagement efficiency and, thus, system outputs such as crop production and themonetary value of hydropower production (e.g., Hamlet et al., 2002), as well as thesustainment of aquatic species. Forecasting techniques for the Western U.S. have long used winter snowpackas a predictor of spring runoff. Because the majority of river basins in the West aresnowmelt dominated (Serreze et al., 1999), winter snowpack measurements provideuseful information, up to several months in advance, about the ensuing springstreamflow. More recently, information about large-scale climate phenomena such asEl Niño Southern Oscillation (ENSO) and the Pacific Decadal Oscillation (PDO)pattern has been added to the forecaster’s toolbox. The link between these large-scalephenomena and the hydroclimatology of the western U.S. has been well documentedin the literature (Ropelweski and Halpert, 1986; Cayan and Webb, 1992; Redmondand Koch, 1991; Gershunov, 1998; Dettinger et al., 1998). Clark et al. (2001) showed2that including large-scale climate information together with SWE improves theoverall skill of the streamflow predictions in the western United States. Souza andLall (2003) show significant skills at long lead times in forecasting streamflows inCearra, Brazil using climate information from the Atlantic and Pacific oceans. These teleconnection patterns, though dominant on a large scale, often fail toprovide forecast skill on the individual basin scale. This is because the surface climateis sensitive to minor shifts in large-scale atmospheric patterns (e.g., Yarnal and Diaz,1986). Because the standard indices of these phenomena are not adequate predictorsof hydroclimate in many individual basins, we investigate the existence of predictorsthat can improve forecasts for individual basins. In this paper we present a generalized framework for utilizing large-scaleclimate information to forecast streamflows at the basin scale. The framework firstidentifies the large-scale climate patterns and predictors that modulate seasonalstreamflows in the given basin. It next uses the predictors to develop a forecast modelof the seasonal flows and subsequently tests and validates the model. This frameworkis applied to forecasting spring streamflows in the Truckee and Carson river basinslocated in the Sierra Nevada Mountains. The paper is organized as follows. Section two presents a background onlarge-scale climate and its impacts on Western U.S. hydroclimatology. The studyregion and data used are described in sections three and four, respectively. This isfollowed by the proposed method of climate diagnostics and identification ofpredictors for forecasting spring streamflows in section five. Section six presents thedevelopment of the statistical ensemble forecating model using the identified3predictors. This section also discusses model testing and verification. Section sevenpresents the results and section eight summarzies and concludes the paper.2. Large Scale Climate and Western US HydroclimatologyThe tropical ocean-atmospheric phenomenon in the Pacific identified as ElNiño Southern Oscillation (ENSO) (e.g., Allan, et al., 1996) is known to impact theclimate all over the world and, in particular, the Western U.S. (e.g., Ropelweski andHalpert, 1986). The warmer sea surface temperatures and stronger convection in thetropical Pacific Ocean during El Niño events deepen the Aleutian Low in the NorthPacific Ocean, amplify the northward branch of the tropospheric wave train overNorth America and strengthen the subtropical jet over the Southwestern U.S.(Bjerknes, 1969; Horel and Wallace, 1981; Rasmussen, 1985). These circulationchanges are associated with below-normal precipitation in the Pacific Northwest andabove-normal precipitation in the desert Southwest (e.g., Redmond and Koch, 1991;Cayan and Webb, 1992). Generally opposing signals are evident in La Niña events,but some non-linearities are present (Hoerling et al., 1997; Clark et al., 2001).Decadal-scale fluctuations in SSTs and sea levels in the