Research Proposal: Multi-site Seasonal Streamflow Forecast on the Upper Colorado River Basin Cameron Bracken Advisor: Balaji Rajagopalan Abstract A process for predicting streamflows at multiple locations upstream of the Lees Ferry gage on the Colorado River is proposed. Lees Ferry is a crucial gage because it is considered the boundary between the upper and lower Colorado River basins. The method seeks to increase lead-time on spring streamflow forecasts to allow basin managers to make more informed operating decisions. Large-scale climate predictors such as sea surface temperature, and geopotential height will be identified by their correlation with historical natural streamflow data. An optimal subset of predictors will be determined and an ensemble of forecasts will be made for the Lees Ferry gage. Upon evaluating the skill of the predictions at Lees Ferry, a disaggregation model will be used to extend predictions to upstream gages. The forecasted streamflows can be used with an existing decision support model. Introduction and Motivation Due to rapidly growing populations, water resource managers in the western United States face increased demand on the region’s renewable water supply. In the western United States, 44% o the renewable water supply is consumed annually while only 4% is consumed in the rest of the country (Grantz et. al. 2005). The Colorado River provides a crucial water source to many states including Colorado, Utah, Wyoming, Arizona, New Mexico and California. Management of the Colorado River is a complex issue that relies in part on the on the skilful prediction of seasonal streamflows. Skilful predictions allow basin managers to understand the impact of operational policy on aspects of the Colorado River system (Prairie 2006). Recent droughts have increased the importance of not only skilful flow predictions but also long lead times. Currently, the primary predictor for streamflow in the upper basin of the Colorado River is winter snowpack or Snow Water Equivalent (SWE) (Grantz et. al. 2005). Predictions based on SWE are available in January at the earliest though skilful forecasts of spring flows are also important for planning and decision-making. A growing body of literature supports the idea that large-scale climate features (e.g. ENSO, PDO) influence the regional hydrology of the western United States in a significant way (Regonda et. al. 2006). The identification of large-scale climate predictors (e.g. SST, geopotential height) offers a forecast of spring streamflows during December through February. Lees Ferry is the boundary between the upper and lower Colorado River basin located near the border between Utah and Arizona. The gauging station located there is of interest in this study. A recently developed disaggregation model provides the means to expand flow predictions at the Lees Ferry gage to make predictions at upstream gages (Prairie et. al. 2007). A dissaggregation method captures the spatial interdependency of the upstream gages rather than simulating them all separately. The method is parsemoneous because predictors need only be developed for one gage. Objectives The objectives of this study are to: 1. Perform Climate diagnostics in order to identify large-scale climate predictors, which have an impact on the flow at the Lees Ferry gage (Grantz et. al. 2005). 2. Using an optimal subset of predictors, provide a seasonal forecast of the streamflow at various lead times and evaluate the skill. 3. Employ a disaggregation model to predict flows at gages upstream of Lees Ferry and evaluate the skill. 4. The final objective of this study is to provide input to the decision support model for the upper Colorado River Basin though this is out of the scope of this study. Figure 1 gives a graphical representation of the objectives of this study. Identify Predictors Seasonal Forecast Disaggregation Model Decision Support Model Figure 1-Flow Chart of Objectives Methods This section will parallel the objectives section with descriptions of the methodology at each step. 1. A forecast season will first be developed based on historical data. The task of identifying predictors involves identifying regions of high correlation to seasonally averaged flows for various lead times. This will be done using the Climate Diagnostics Center’s (CDC) website (www.cdc.noaa.gov). The average predictor value over the region of highest correlation is then used in a local polynomial fit model, which captures local nonlinearity. In addition to identifying predictors, extreme flow years will be singled out for composite analysis in hopes of identifying large-scale mechanisms that drive basin-scale hydrology. 2. A statistical method called Generalized Cross Validation (GCV) will be used to determine an optimal subset of predictors (Grantz et. al. 2005). The subset of predictors will then be used to generate an ensemble of forecasts in the form of probability density functions. The skill will be evaluated using ranked probability skill scores and likelihood functions (Grantz et. al. 2005). 3. The ensemble of forecasts will be run through the disaggregation model which may be modified slightly in order to fit the desired number of upstream gages. Expected Schedule The data analysis, generation of correlation and composite maps, identification of predictors, and determining an optimal set of predictors is expected to take 1-2 weeks. The end of June will be spent using the LOCFIT package to fit local polynomials and generate an ensemble of forecasts. July will be spent evaluating the skill of the predictions, using the disaggregation model to predict upstream flows, and interpreting data. The end of July and the beginning of August will be spent compiling and analyzing data and preparing the final report. References 1. Prairie, J., B. Rajagopalan, U. Lall and T. Fulp, A Stochastic Nonparametric Technique for Space-Time Disaggregation of Streamflows, Water Resources Research, 2007 2. Regonda, S., B. Rajagopalan, M. Clark and E. Zagona, Multi-model Ensemble Forecast of Spring Seasonal Flows in the Gunnison River Basin, Water Resources Research, 42, W09494, 2006 3. Grantz, K., B. Rajagopalan, M. Clark and E. Zagona, A Technique for incorporating large-scale climate information in basin-scale ensemble streamflow forecasts. Water Resources Research, 41, W10410, 1-13, 2005 4. Prairie, James. Stochastic Nonparametric Framework for Basin Wide Streamflow and Salinity Modeling: Application for the Colorado River Basin. PhD thesis, University of Colorado, 2006.
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