Ensemble Streamflow Forecasting Methods & Applications
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Ensemble Streamflow Forecasting Methods & Applications
CHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationsh Zagona3‘Dept of Civil, Environmental & Architectural Engineering (CEAE), University of Colorado, Boulder, co, USA:CIRES, University of Colorado, Boulder, co, USA^Center for Advanced Decision Support for Water and Environmental Systems (CADSWESJ/CEAE, University of Colorado, Boulder, coKey words: S Ensemble Streamflow Forecasting Methods & Applicationstreamflow, Climate Variability, Climate Diagnostics, Ensemble Forecast, Local Polynomials, Bootstrap7.1. IntroductionThe chapter is organized as folloEnsemble Streamflow Forecasting Methods & Applications
ws. The theme of the chapter is introduced in Section 7.1 . Section 7.2 presents a background on large-scale climate and its impacts on the western USCHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationsand identification of predictors for forecasting spring streamflows in section 7.5. Section 7.6 presents the development of the statistical ensemble forecating model using the identified predictors. Model application and validation are*Corresponding author address: Balaji Rajagopalan, Dept, of Civil Ensemble Streamflow Forecasting Methods & Applications, Env. and Arch. Engineering, University of Colorado, ECOT-541, Campux Box 428, Boulder, co, 80309-0428E-mail: balajir@colorado.edu*Corresponding ữtHhEnsemble Streamflow Forecasting Methods & Applications
or-ữddress: Balaji Rftjflgopaldii, Dept, of Civil, Env. and Arch. Engineering, University of Colorado, ECOT-541, Campus Box 428, Boulder. co. 80309-04CHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationser resources worldwide are faced with increasing stresses due to climate variability, population growth and competing growth - more so in the Western US (e.g.. Hamlet et al., 2002; Piechota et al., 2001). Careful planning is necessary to meet demands on water quality, volume, timing, and flow rates. Ensemble Streamflow Forecasting Methods & Applications This is particularly true in the western US, where it is estimated that 44% of renewable water supplies are consumed annually, as compared with 4% inEnsemble Streamflow Forecasting Methods & Applications
the rest of the country (el-Ashry and Gibbons, 1988). Consequently, the forecast for the upcoming water year is crucial to the water management plannCHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationshe sustenance of aquatic species.Majority of river basins in the western USA are snowmelt driven in that, snow accumulates in the winter and melts in the spring thus producing a peak in the streamflow. Therefore, it is intuitive to use winter snowpack as a predictor of the runoff in the following sp Ensemble Streamflow Forecasting Methods & Applicationsring (Serreze el al., 1999). More recently, information about large-scale climate phenomena such as El Nino Southern Oscillation (ENSO) and the PacifiEnsemble Streamflow Forecasting Methods & Applications
c Decadal Oscillation (PDO) pattern has been added to the forecaster’s toolbox. The link between these large-scale phenomena and the hydroclimatology CHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationsormation together with Snow Water Equivalent (SWE) improves the overall skill of the streamflow predictions in the western United States. Souza and Lail (2003) showed significantskills al longer lead limes in forecasting streamflows in Cearra, Brazil using climate information from the Atlantic and P Ensemble Streamflow Forecasting Methods & Applicationsacific oceans.lypically. streamflow forecasts arc issued by fitting a linear regression with SWE and sometimes with standard indices that describe theEnsemble Streamflow Forecasting Methods & Applications
ENSO and PDO phenomena. The disadvantages with this approach are (i) the relationship is not always linear; (ii) the tclcconnection patterns from ENSCHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationse is sensitive to minor shifts in large-scale atmospheric patterns (e.g., Yarnal and Diaz, 1986), and (iii) inability to provide realistic ensemble forecasts and thus, the probability of exceedences of various thresholds useful for water resources management.Evidently there is a need for a generaliz Ensemble Streamflow Forecasting Methods & Applicationsed framework for ensemble streamflow forecast that utilizes large-scale climate information. We propose such a framework in Fig. 7.1. In this, large-sEnsemble Streamflow Forecasting Methods & Applications
cale climate predictors are first identified via climate diagnostics. The identified predictors are then used in a nonparametric framework to generateCHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationswe focus primarily on the climate diagnostics and ensemble forecast methods, and then demonstrate their utility on the Truckcc/Carson River basin and GunnisonRiver basin, both located in the western USA.7. 2. Large Scale Climate and Western US HydroclimatologjThe tropical ocean-atmospheric phenomeno Ensemble Streamflow Forecasting Methods & Applicationsn in the Pacific identified as El Nifio Southern Oscillation (ENSO) (e.g., Allan, et al., 1996) is known to impact the climate all over the world and,Ensemble Streamflow Forecasting Methods & Applications
in particular, the Western US (e.g., Ropelewski and Halpen, 1986). The warmer sea surface temperatures and stronger convection in the tropical PacifiCHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationsrth America and strengthen the subtropical jet over the southwestern US (e.g. Rasmussen, 1985). These circulation changes are associated with below-normal precipitation in the Pacific Northwest and above-normal precipitation in the desert Southwestern US (e.g., Redmond and Koch, 1991; Cayan and Webb Ensemble Streamflow Forecasting Methods & Applications, 1992). Generally opposing signals are evident in La Nifia events, but some non-linearities are present (Hoerling et al., 1997; Clark et al., 2001; CEnsemble Streamflow Forecasting Methods & Applications
lark and Serreze, 2001).Decadal-scale fluctuations in SSTs and sea levels in the northern Pacific Ocean as manifested by the PDO (Mantua et al., 1997)CHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationsardless, the influence of PDƠ and ENSO on North American hydroclimate variability has been well documented (e.g., Regonda et al.. 2004a).Incorporation of this climate information has been shown to improve forecasts of winter snowpack (McCabe and Dettinger, 2002) and streamflows in the western US (Cl Ensemble Streamflow Forecasting Methods & Applicationsark et al., 2001, Hamlet et al., 2002) while increasing the lead-time of the forecasts. Use of climate information enables efficient management of watEnsemble Streamflow Forecasting Methods & Applications
erresources and provides socio-economic benefits (e.g.. Pulwany and Melis, 2001; Hamlet et al., 2002).Often, however, the standard indices of these phCHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applicationsimpact the western US hydroclimate (as described earlier). Furthermore, certain regions in the western US (e.g., basins in between the Pacific Northwest and the desert Southwest) can be impacted by both the northern and southern branches of the subtropical jet, potentially diminishing apparent conne Ensemble Streamflow Forecasting Methods & Applicationsctions to ENSO and PDO. The Truckee and Carson basins are two such examples, hence, predictors other than the standard indices have to be developed foEnsemble Streamflow Forecasting Methods & Applications
r each basin.7.3. Water Management Issues in the Basins StudiedOur motivation for the development of the ensemble streamflow approaehes stems from theCHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applications Gunnison River basin, a tributary of Colorado River, also in the western USA that can be seen in Fig. 7.3. On the Truckcc/Carson basin flows at two gaging stations arc to be forecast, while in the Gunnison streamflow forecasts are required at six sites simultaneously. In both the basins, for that m Ensemble Streamflow Forecasting Methods & Applicationsatter over much of the western USA. the bulk of the annual streamflow arrives during spring (April - July) from the melting of snowpack accumulated ovEnsemble Streamflow Forecasting Methods & Applications
er winter. This is evident in the climatology of precipitation and streamflows for the Truckee River (Fig. 7.4) - similar feature is observed on the GCHAPTER 7Ensemble Streamflow Forecasting: Methods & ApplicationsBalaji Rajagopalan’1,2, Katrina Grantz1'3,Satish Regonda'1* *, Martyn Clark2 and Edith Ensemble Streamflow Forecasting Methods & Applications through the seiniarid desert of western Nevada. The Truckee River originates as outflow from Lake Tahoe in California and terminates approximately 115 miles (185 km.) later in Pyramid Lake in Nevada. The Carson River has its headwaters approximately fifty miles (80 km) south of Lake Tahoe, runs alm Ensemble Streamflow Forecasting Methods & Applicationsost parallel to the length of the Truckee River and terminates in the Carson Sink area. The areas of the basins are comparable and are approximately 3Ensemble Streamflow Forecasting Methods & Applications
000 sq. miles (7770 km2). The Bureau of Reclamation (BOR) Lahontan Basin area office manages operations on the Truckee and Carson Rivers and relies heGọi ngay
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