MULTIPLE CRITERIA DYNAMIC SPATIAL OPTIMIZATION TO MANAGE WATER QUALITY ON A WATERSHED SCALE
Transactions of the ASAE
E fforts to increase agricultural productivity can place a severe strain on land and water resources, often resulting in deteriorating water resources and ecosystems. During recent decades, unmanaged and rapid exploitation of natural resources has increased nonpoint source pollution (NPS) that has both onsite and offsite affects on human communities and ecosystems. The magnitude of this problem is reflected in the annual cost estimates on damage to water quality through agricultural sources
... ultural sources that range from $2.2 billion (Clark et al., 1985) to $7 billion (Ribaudo et al., 1989) . Given the multifarious nature of impacts of NPS pollution, many individuals, communities, and agencies are struggling with how to manage resources at watershed scales to achieve an acceptable mix of products and services (Lovejoy et al., 1997 ). An integrated approach to decision-making at a watershed level is necessary to combine information on spatial dynamics, multiple attributes, and processes. One such approach is to spatially optimize watershed land uses and to lessen NPS pollutants with minimal economic loss (Randhir, 1995) . This approach can be used to design spatial resource policies and structural practices, to identify efficient and sitespecific production decisions, to identify efficient productservice mix in planning production of food and fiber, to plan housing and urban development, to protect ABSTRACT. This article develops a dynamic spatial optimization algorithm for watershed modeling that reduces dimensionality and incorporates multiple objectives. Spatial optimization methods, which include spatially linear and nonlinear formulations, are applied to an experimental watershed and tested against a full enumeration frontier. The integrated algorithm includes biophysical simulation and economic decision-making within a geographic information system. It was observed that it is possible to achieve economic and water quality objectives in a watershed by spatially optimizing site-specific practices. It was observed that a spatially diversified watershed plan could achieve multiple goals in a watershed. The algorithm can be used to develop efficient policies towards environmental management of watersheds to address water quality issues by identifying optimal tradeoffs across objectives.