Resilience in natural and human systems is defined as the capacity to cope with disturbance. Adaptive capacity refers to a system's ability to change in basic structure or function when it is perturbed beyond its capacity for resilience. The concepts of sustainability, resilience, and adaptability are clearly related but few studies have systematically analyzed how interactions among them affect system-level dynamics, fragility, uncertainty, or risk. This interdisciplinary research project will investigate how regional coupled natural and human systems respond to changes in climate, economic, and policy conditions that operate over larger geographic and temporal scales. The investigators will explore these concepts in an agriculturally based Midwestern watershed (the Iowa/Cedar River Watershed in Iowa) within the context of sustainability, resilience, and adaptability. They will define sustainability using Elkington's Triple Bottom Line concept (people, planet, profit) in which social and environmental values are added to the traditional economic measures of success. In the context this particular study, they will consider surface and groundwater resources that will not be polluted or withdrawn so that intended agricultural uses can no longer be supported and agriculture practices that do not deplete the soil or groundwater, and, at a minimum, supports existing levels of economic activity. The investigators will evaluate the multidimensional tradeoffs that exist among traditional measures of optimality, such as maximization of environmental quality and economic return, and measures of resilience, adaptability, and sustainability. To conduct this analysis, they will develop models that simulate how landscapes change in response to changes in natural and socioeconomic factors. Land-use decision making will be modeled to simulate how humans respond to changing conditions, and these models will be linked to models of surface and ground water quality to quantify environmental impact. Evolutionary computation techniques will be developed to produce production possibility frontiers that quantify tradeoffs among competing objectives. Stakeholder preferences for various objectives will be solicited through focus groups, and surveys and cyberinfrastructure will be developed to support the significant computational needs of this study.

While landscapes are in a continual state of change, the national and global-scale factors that currently affect landscapes in the Midwest are without precedent. The generation of precise predictions regarding future conditions is an unrealistic goal under such conditions. This project therefore will evaluate a suite of plausible scenarios to identify those strategies that are most likely to result in sustainable agricultural landscapes. The investigators hypothesize that sustainable outcomes are produced by systems that have an ability to adapt to changing and uncertain conditions and that possess the resiliency needed to respond to unexpected and significant perturbations. Natural (e.g., climate) and human (e.g., policy and economic) processes can constrain or enhance the ability of systems to adapt and respond to change and uncertainty. It is important to promote processes that enhance sustainability, and avoid those that produce constraints. This project will advance the theory and practice of sustainability science by constructing models that link adaptability, resilience, and sustainability to more traditional measures of economic and environmental optimality. From a more practical perspective, the project will have an impact on policy and land management in the Midwest by providing better data and analytical tools to decision makers as they address complex problems and refine policy. The project also will provide information and examples that help the general public understand the imperative of sustainability in the context of changing climatic and economic conditions. Because of the uncertainty associated with the magnitude and spatiotemporal pattern of regional, national, and international changes, research designed to bound uncertainty, to document plausible natural and socioeconomic outcomes, and to analyze the impact of alternative adaptation strategies can be especially timely and important. This project is supported by the NSF Dynamics of Coupled Natural and Human Systems (CNH) Program.

Agency
National Science Foundation (NSF)
Institute
Division of Behavioral and Cognitive Sciences (BCS)
Type
Standard Grant (Standard)
Application #
1114978
Program Officer
Thomas J. Baerwald
Project Start
Project End
Budget Start
2011-09-01
Budget End
2016-02-29
Support Year
Fiscal Year
2011
Total Cost
$1,011,832
Indirect Cost
Name
University of Iowa
Department
Type
DUNS #
City
Iowa City
State
IA
Country
United States
Zip Code
52242