Coupling Hydrologic, Economic, and Social Network Models to Improve Understanding of Surface Water-Groundwater Interactions for Protection of Instream Flows

In many regions of the world, agricultural, urban, and environmental water users share the same sources of water. In recent years, a major source of water conflict has been the increased extraction of groundwater from areas that are physically connected to rivers and streams with a resulting loss of instream flows. The impacts of decreased instream flows include reduction of habitat for fish and migratory birds, changes in stream and riparian zone form and habitat, and decreases in water availability for dam and reservoir operation, recreation, and downstream uses.

Although hydrologists have long conducted field studies of the physical aspects of groundwater-surface water exchange, little is known about the feedbacks operating between natural and human components of complex surface water-groundwater systems, which are uncertain, spatially variable, and may include nonlinear and threshold behavior. Two sites for studying surface water-groundwater systems where an understanding of the complex interactions between human and natural components is critical to effective policy design are the Kankakee River Basin in Illinois and the Republican River Basin in Nebraska and Kansas. As a result of groundwater pumping, both areas have experienced reduced stream flows that are of concern to policymakers. The two basins differ dramatically in terms of economics, institutions, history of management, stakeholder conflict, and current policies, however.

The objectives of this interdisciplinary research project are (1) to quantify the economic and social impacts of parameter, model, and behavioral uncertainty in coupled surface water-groundwater systems; (2) to evaluate how spatial and temporal variability in hydrologic processes, individual and social group behavior can affect policy design and decision consequences in surface water-groundwater systems; (3) to analyze the impacts of decision making processes on the development of socially acceptable surface water-groundwater management policies; and (4) to develop efficient and socially acceptable policies to manage surface water-groundwater systems in order to maintain instream water flows.

Methods to be used by the investigators in this study include numerical Bayesian modeling, spatial optimization, a stochastic multi-agent system, econometric analysis, social network analysis, geographical information systems, and advanced visualization techniques. The research will address fundamental hydrologic and socioeconomic questions while also integrating training and learning activities for K-12 and graduate students and outreach activities for local and international stakeholders.

This research will provide significant advances in the development and calibration of natural system models that integrate feedbacks between and uncertainty in natural processes and human behavior. The improved scientific models derived from this research will advance policy design that maintains or improves instream environmental conditions while improving economic prospects and minimizing the potential for stakeholder conflict. By evaluating alternative policy frameworks in the simulation model, the investigators will provide practical information regarding how to improve water resource management and reduce stakeholder conflict in the two study areas.

Research findings also will be of general interest in many parts of the world where coupled surface water-groundwater systems are the source of conflict. This project is supported by an award resulting from the NSF competition focusing on the Dynamics of Coupled Natural and Human Systems.

Investigator(s)
Lead Investigator: 
Characteristics
Topics: 
Threshold
Topics: 
Non-linearities
Topics: 
Feedbacks
Attributes
Model: 
multi-agent, bayesian, econometric, social network
Location: 
US Midwest
Temporal Scope: 
contemporary
Spatial Scope: 
river basin
Natural System: 
temperate surface, groundwater
Human System: 
agriculture
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