This paper presents a short history of water resources systems analysis from its beginnings in the Harvard Water Program, through its continuing evolution toward a general field of water resources systems science. Current systems analysis practice is widespread and addresses the most challenging water issues of our times, including water scarcity and drought, climate change, providing water for food and energy production, decision making amid competing objectives, and bringing economic incentives to bear on water use. The emergence of public recognition and concern for the state of water resources provides an opportune moment for the field to reorient to meet the complex, interdependent, interdisciplinary, and global nature of today's water challenges. At present, water resources systems analysis is limited by low scientific and academic visibility relative to its influence in practice and bridled by localized findings that are difficult to generalize. The evident success of water resource systems analysis in practice (which is set out in this paper) needs in future to be strengthened by substantiating the field as the science of water resources that seeks to predict the water resources variables and outcomes that are important to governments, industries, and the public the world over. Doing so promotes the scientific credibility of the field, provides understanding of the state of water resources and furnishes the basis for predicting the impacts of our water choices.

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.

Water management agencies seek the next generation of modeling tools for planning and operating river basins. Previous site‐specific models such as U.S. Bureau of Reclamation's (USBR) Colorado River Simulation System and Tennessee Valley Authority's (TVA) Daily Scheduling Model have become obsolete; however, new models are difficult and expensive to develop and maintain. Previous generalized river basin modeling tools are limited in their ability to represent diverse physical system and operating policy details for a wide range of applications. RiverWare(tm), a new generalized river basin modeling tool, provides a construction kit for developing and running detailed, site‐specific models without the need to develop or maintain the supporting software within the water management agency. It includes an extensible library of modeling algorithms, several solvers, and a rich “language” for the expression of operating policy. Its point‐and‐click graphical interface facilitates model construction and execution, and communication of policies, assumptions and results to others. Applications developed and used by the TVA and the USBR demonstrate that a wide range of operational and planning problems on widely varying basins can be solved using this tool.

[1] We propose a multimodel ensemble forecast framework for streamflow forecasts at multiple locations that incorporates large-scale climate information. It has four broad steps: (1) Principal component analysis is performed on the spatial streamflows to identify the dominant modes of variability. (2) Potential predictors of the dominant streamflow modes are identified from several large-scale climate features and snow water equivalent information. (3) Objective criterion is used to select a suite of candidate nonlinear regression models each with different predictors. (4) Ensemble forecasts of the dominant streamflow modes are generated from the candidate models and are combined objectively to produce a multimodel ensemble, which are then back transformed to produce spatially coherent streamflow forecasts at all the locations. The utility of the framework is demonstrated in the skillful forecast of spring seasonal streamflows at six locations in the Gunnison River Basin at several lead times. The generated ensemble streamflow forecast provides valuable and useful information for optimal management and planning of water resources in the basin.