A Robust Decision Making Technique for Water Management Under Uncertainty Incorporating Decadal Scale Projections

Funding Agency


Research Team

  • Logan Callihan, M.S. Student
  • Edith Zagona, Principal Investigator
  • Balaji Rajagopalan

Project Overview

Robust decision making, fundamental for managing water resources in light of the deep uncertainties inherently associated with climate change, is an analytical framework that detects when a system is in or approaching a vulnerable state and seeks strategies to address the vulnerabilities that perform well over a wide range of plausible future scenarios. Various specific techniques have been developed to identify vulnerable conditions and to select the options that are most favorable in given situations. These may be based, for example, on probabilities of success (Groves et al., 2008) or on maximizing benefits and minimizing costs (Brown et al., 2012). Characterizing the possible options and strategies in terms of their potential success given various system states requires extensive modeling of the system under a wide range of conditions. Recent research that increases our understanding of decadal scale variability (Nowak et al., 2012; Nowak, 2011) has the potential to increase the success of decisions made in this framework. This research develops a comprehensive robust decision making framework that utilizes the power of extensive modeling to develop relationships between climate indicators and future system performance, incorporating decadal scale projections, hence to identify the type and severity of vulnerable conditions, and to evaluate a set of options and strategies in terms of benefits, costs, likelihood of mitigating vulnerable conditions, and resiliency of the system performance under a range of eventual conditions.

The research utilizes the RiverSMART suite of software modeling and analysis tools developed under Reclamation’s WaterSMART initiative and built around the RiverWare modeling environment. In order to provide a wide range of possible hydrologic futures, the framework generates stochastic streamflow scenarios using a K-nearest neighbor nonparametric bootstrap method, resampling observed flow and paleo reconstructed hydrology; other data sets can be produced that combine characteristics of the paleo, historic and climate change projections derived from downscaled global circulation model (GCM) data using various Markov Chain techniques (Prairie et al., 2008).

A case study is developed using the Gunnison Basin in Colorado, part of the Upper Basin of the Colorado River. Various demand scenarios are projected and system performance indicators measure the ability of the system to meet water demands for agriculture, municipalities and power plants; meet environmental flows, hydropower generation commitments and recreational needs. Options and strategies for addressing vulnerabilities include such measures as conservation, reallocation, adjustments to operational policy, and transbasin transfers. Projections that utilize teleconnections with decadal scale signals — specifically, the Pacific Decadal Oscillation (PDO) and the Atlantic Multi-decadal Oscillation (AMO) are evaluated for improving the efficacy of the decisions. Results of extensive simulations provide both guidance for decision-making and evaluation of the effects of the decisions.


Brown, C., Y. Ghile, M. Laverty and K. Li (2012) Decision scaling: Linking bottom-up vulnerability analysis with climate projections in the water sector, Water Resources Research, 48.

Groves, D., D. Yates and c. Tebaldi, (2008) Developing and applying uncertain global climate change prejections for regional water management planning, Water Resources Research, 44.

Nowak, K., M. Hoerling, B. Rajagopalan, and E. Zagona (2012), Colorado River Basin Hydroclimatic Variability, Journal of Climate, 25 (2), 4389-4403.

Nowak, Kenneth C. (2011), Stochastic Streamflow Simulation at Inter-decadal Times Scales and Implications for Water Resources Management in the Colorado River Basin,  Civil, Environmental, and Architectural Engineering Ph.D. Dissertation, University of Colorado, Boulder, CO.

Prairie, J., K. Nowak, B. Rajagopalan, U. Lall, and T. Fulp, (2008), A Stochastic Nonparametric Approach for Streamflow Generation Combining Observational and Paleo Reconstructed Data, Water Resources Research, 44.


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