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This study explores groundwater management policies and the effect of modeling assumptions on the projected performance of those policies. The study compares an optimal economic allocation for groundwater use subject to streamflow constraints, achieved by a central planner with perfect foresight, with a uniform tax on groundwater use and a uniform quota on groundwater use. The policies are compared with two modeling approaches, the Optimal Control Model (OCM) and the Multi-Agent System Simulation (MASS). The economic decision models are coupled with a physically based representation of the aquifer using a calibrated MODFLOW groundwater model. The results indicate that uniformly applied policies perform poorly when simulated with more realistic, heterogeneous, myopic, and self-interested agents. In particular, the effects of the physical heterogeneity of the basin and the agents undercut the perceived benefits of policy instruments assessed with simple, single-cell groundwater modeling. This study demonstrates the results of coupling realistic hydrogeology and human behavior models to assess groundwater management policies. The Republican River Basin, which overlies a portion of the Ogallala aquifer in the High Plains of the United States, is used as a case study for this analysis.

Abstract. This study tests the performance and uncertainty of calibration strategies for a spatially distributed hydrologic model in order to improve model simulation accuracy and understand prediction uncertainty at interior ungaged sites of a sparsely gaged watershed. The study is conducted using a distributed version of the HYMOD hydrologic model (HYMOD_DS) applied to the Kabul River basin. Several calibration experiments are conducted to understand the benefits and costs associated with different calibration choices, including (1) whether multisite gaged data should be used simultaneously or in a stepwise manner during model fitting, (2) the effects of increasing parameter complexity, and (3) the potential to estimate interior watershed flows using only gaged data at the basin outlet. The implications of the different calibration strategies are considered in the context of hydrologic projections under climate change. To address the research questions, high-performance computing is utilized to manage the computational burden that results from high-dimensional optimization problems. Several interesting results emerge from the study. The simultaneous use of multisite data is shown to improve the calibration over a stepwise approach, and both multisite approaches far exceed a calibration based on only the basin outlet. The basin outlet calibration can lead to projections of mid-21st century streamflow that deviate substantially from projections under multisite calibration strategies, supporting the use of caution when using distributed models in data-scarce regions for climate change impact assessments. Surprisingly, increased parameter complexity does not substantially increase the uncertainty in streamflow projections, even though parameter equifinality does emerge. The results suggest that increased (excessive) parameter complexity does not always lead to increased predictive uncertainty if structural uncertainties are present. The largest uncertainty in future streamflow results from variations in projected climate between climate models, which substantially outweighs the calibration uncertainty.

[1] A watershed can be simulated as a multiagent system (MAS) composed of spatially distributed land and water users (agents) within a common defined environment. The watershed system is characterized by distributed decision processes at the agent level with a coordination mechanism organizing the interactions among individual decision processes at the system level. This paper presents a decentralized (distributed) optimization method known as constraint-based reasoning, which allows individual agents in an MAS to optimize their behaviors over various alternatives. The method incorporates the optimization of all agents' objectives through an interaction scheme, in which the ith agent optimizes its objective with a selected priority for collaboration and forwards the solution and consequences to all agents that interact with it. Agents are allowed to determine how important their own objectives are in comparison with the constraints, using a local interest factor (b i ). A large b i value indicates a selfish agent who puts high priority on its own benefit and ignores collaboration requirements. This bottom-up problem-solving approach mimics real-world watershed management problems better than conventional ''top-down'' optimization methods in which it is assumed that individual agents will completely comply with any recommendations that the coordinator makes. The method is applied to a steady state hypothetical watershed with three off-stream human agents, one in-stream human agent (reservoir), and two ecological agents.