A modified version of the U.S. Bureau of Reclamation (Reclamation) long-term planning model, Colorado River Simulation System (CRSS), is used to evaluate whether hydrologic model choice has an impact on critical decision variables within the San Juan River Basin when evaluating potential effects of climate change through 2099. The distributed variable infiltration capacity (VIC) model and the lumped National Weather Service (NWS) River Forecast System (RFS) were each used to project future streamflow; these projections of streamflow were then used to force Reclamation’s CRSS model over the San Juan River Basin. Both hydrologic models were compared to evaluate whether or not uncertainty in climatic input generated from general circulation models outweighed differences between the hydrologic models. Differences in methodologies employed by each hydrologic model had a significant effect on projected streamflow within the basin. Both models project decreased water availability under changing climate conditions within the San Juan River Basin, but disagree on the magnitude of the decrease. On average, total naturalized inflow within the San Juan River Basin into the Navajo Reservoir is approximately 15% higher using inflows derived using the VIC model than those inflows developed using the RFS model; average projected tributary inflow from the San Juan River Basin to the Colorado River is approximately 25% higher using inflows derived by using the VIC model than those inflows developed by using the RFS. Overall, there is a higher risk and magnitude of shortage within the San Juan River Basin using streamflow developed with the RFS model as compared with inflow scenarios developed by using the VIC model. Model choice was found to have a significant effect on the evaluation of climate change impacts over the San Juan River Basin.

Water managers in the western United States and throughout the world are facing the increasing challenge of supplying a variety of water demands under the growing stresses of climate variability and of population and economic growth. Accurate streamflow forecasts are key to the water resources planning and decision-making process. There is growing evidence that variability in western streamflow is modulated by large-scale ocean-atmospheric features. Traditional forecasting techniques, however, do not systematically utilize large-scale climate information. In this study we present a framework for incorporating climate information into water resources decision-making and demonstrate the method on the semiarid Truckee-Carson Basin in Nevada where environmental, agricultural, and municipal demands compete for a limited supply of water. In this basin, water managers must plan carefully to meet the demands of timing and required flowrates. The framework presented in this paper consists of a streamflow forecast system that is based on large-scale climate information. The forecasts are then incorporated in a decision-support tool to aid in water resources planning and decision making. Previous work on the Truckee-Carson Basin has demonstrated that incorporating climate information into the forecast can increase the accuracy (or skill) and lead time of forecasts. This paper focuses on the decision-support system that is used to evaluate decision strategies and demonstrates possible water management improvements. The performance of the framework and the improved forecasts are evaluated on the Truckee-Carson system by using a suite of years from the historical record.

Analysis is performed on the spatiotemporal attributes of North American monsoon system (NAMS) rainfall in the southwestern United States. Trends in the timing and amount of monsoon rainfall for the period 1948–2004 are examined. The timing of the monsoon cycle is tracked by identifying the Julian day when the 10th, 25th, 50th, 75th, and 90th percentiles of the seasonal rainfall total have accumulated. Trends are assessed using the robust Spearman rank correlation analysis and the Kendall–Theil slope estimator. Principal component analysis is used to extract the dominant spatial patterns and these are correlated with antecedent land–ocean–atmosphere variables. Results show a significant delay in the beginning, peak, and closing stages of the monsoon in recent decades. The results also show a decrease in rainfall during July and a corresponding increase in rainfall during August and September. Relating these attributes of the summer rainfall to antecedent winter–spring land and ocean conditions leads to the proposal of the following hypothesis: warmer tropical Pacific sea surface temperatures (SSTs) and cooler northern Pacific SSTs in the antecedent winter–spring leads to wetter than normal conditions over the desert Southwest (and drier than normal conditions over the Pacific Northwest). This enhanced antecedent wetness delays the seasonal heating of the North American continent that is necessary to establish the monsoonal land–ocean temperature gradient. The delay in seasonal warming in turn delays the monsoon initiation, thus reducing rainfall during the typical early monsoon period (July) and increasing rainfall during the later months of the monsoon season (August and September). While the rainfall during the early monsoon appears to be most modulated by antecedent winter–spring Pacific SST patterns, the rainfall in the later part of the monsoon seems to be driven largely by the near-term SST conditions surrounding the monsoon region along the coast of California and the Gulf of California. The role of antecedent land and ocean conditions in modulating the following summer monsoon appears to be quite significant. This enhances the prospects for long-lead forecasts of monsoon rainfall over the southwestern United States, which could have significant implications for water resources planning and management in this water-scarce region.

Multiple reservoir management requires the consideration of competing demands for the water for irrigation, domestic consumption, hydroelectric generation, recreation, transportation, and ecosystem management. Such management requires sophisticated tools along with well educated personnel. Many point to the need for universities and professional societies to partner to assure that newly educated professionals will be available. This paper described water resources planning and management curriculum that incorporates the river basin modeling package RiverWare. RiverWare is a flexible general river basin modeling tool that allows water resources engineers to both simulate and optimize the management of multipurpose reservoir systems for daily operations as well as for planning studies. The curriculum was developed by faculty and staff at Humboldt State University and University Colorado Boulder. In the fall of 2005, ten Environmental Resources Engineering seniors at Humboldt State University experienced the RiverWare curriculum in ENGR 445, Water Resources Planning and Management. RiverWare assignments were developed for the following topics: storage yield analysis, firm yield/firm water, storage allocation zones, rule curves, operation rules, operation of multi-purpose reservoirs, and hydropower concepts. These assignments are further described as well as students' responses to the RiverWare tutorials and assignments. By the end of the semester, students had enough expertise to develop their own models. One student developed a rule curves model of the Shasta and Keswick reservoirs in Northern California. Finally, the paper describes further planned curricular developments. This curriculum is available at no cost for faculty that want to teach Water Resources Planning and Management using RiverWare. The authors anticipate receiving valuable feedback on the content of this curriculum from conference attendees.

[ 1] Water managers throughout the western United States depend on seasonal forecasts to assist with operations and planning. In this study, we develop a seasonal forecasting model 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 of interest to develop predictors to spring runoff. The predictors are snow water equivalent (SWE) and 500 mbar geopotential height and sea surface temperature (SST) "indices'' developed in this study. We use local regression methods to provide ensemble (probabilistic) forecasts. Results show that the incorporation of climate information, particularly the 500 mbar geopotential height index, improves the skills of forecasts at longer lead times when compared with forecasts based on snowpack information alone. The technique is general and could be used to incorporate large-scale climate information into ensemble streamflow forecasts for other river basins.

Ensemble Streamflow Forecasting: Methods & Applications Balaji Rajagopalan, Katrina Grantz, Satish Regonda, Martyn Clark and Edith Zagona 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 (CADSWES)/CEAE, University of Colorado, Boulder, CO