Optimization procedure for cost effective BMP placement at a watershed scale

Tamie L. Veith, Mary Leigh Wolfe, Conrad D. Heatwole

Research output: Contribution to journalArticlepeer-review

104 Scopus citations


A combinatorial optimization procedure for best management practice (BMP) placement at the watershed level facilitates selection of cost effective BMP scenarios to control nonpoint source (NPS) pollution. A genetic algorithm (GA) was selected from among several optimization heuristics. The GA combines an optimization component written in the C++ language with spatially variable NPS pollution prediction and economic analysis components written within the ArcView geographic information system. The procedure is modular in design, allowing for component modifications while maintaining the basic conceptual framework. An objective function was developed to lexicographically optimize pollution reduction followed by cost increase. Scenario cost effectiveness is then calculated for scenario comparisons. The NPS pollutant fitness score allows for evaluation of multiple pollutants, based on prioritization of each pollutant. The economic component considers farm level public and private costs, cost distribution, and land area requirements. Development of a sediment transport function, used with the Universal Soil Loss Equation, allows the optimization procedure to run within a reasonable timeframe. The procedure identifies multiple near optimal solutions, providing an indication of which fields have a more critical impact on overall cost effectiveness and flexibility in the final solution selected for implementation. The procedure was demonstrated for a 1,014-ha watershed in the Ridge and Valley physiographic region of Virginia.

Original languageEnglish (US)
Pages (from-to)1331-1343
Number of pages13
JournalJournal of the American Water Resources Association
Issue number6
StatePublished - Dec 2003

All Science Journal Classification (ASJC) codes

  • Ecology
  • Water Science and Technology
  • Earth-Surface Processes


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