Agriculture and water quality: Modeling NPS pollution under geographic state dynamics and biophysical simulation

Timothy Onukuri Randhir, Purdue University

Abstract

Spatial dynamics in decision making and multiple attributes are important components of water quality modeling, that are less studied. This study incorporates spatial dynamics, multiple attributes, GIS, and process simulation in the development of an integrated model to address water quality issues. The objective is to study the spatial effects in a watershed, develop a spatial multi-attribute dynamic programming model, and apply the model to develop spatially optimal agricultural landuse and crop management practices in a watershed.^ Using an eight cell experiment, Spatial Multi-attribute Dynamic Programming (SMDP) algorithm was developed to accurately estimate full enumeration frontier. A staging procedure was developed that enables scalable spatial modeling. The SMDP algorithm applies Bellman's dynamic programming over geographic space, rather than time, and is used in the development of an integrated decision model, WISDOM (Watershed Integrated Spatial Dynamic Optimization Model). This model consists of several modules that represent different sub-systems that include GIS, user interface simulation models, staging, multi-attribute, and spline fitting modules.^ The model was applied to the Animal Sciences watershed in Central Indiana to develop optimal crop plans. Four sets of preference regimes, that correspond to different weights in objectives, were used in the optimization. When environmental parameters are included, the linear model and the use of contribution ratios were found to be inferior to the dynamic solution. Efficient watershed plans were developed using spatial dynamics and multiple attributes. There existed tradeoffs among economic and environmental parameters that can be utilized in spatial optimization to achieve better water quality standards (compared to the pre-optimization levels) without substantial financial losses. The model is information-intensive and opens vistas to a revolutionary way of approaching water quality problems. Future applicability of the model included several areas of advanced economic and engineering research. ^

Degree

Ph.D.

Advisors

Major Professor: John G. Lee, Purdue University.

Subject Area

Economics, Agricultural|Engineering, Agricultural|Environmental Sciences|Operations Research|Urban and Regional Planning

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