Cost Optimization for Robust Optimal TMDL Allocation

Abstract

During development of a Total Maximum Daily Load (TMDL) allocation, the costs associated with the requisite control measures, typically Best Management Practices (BMPs), should be considered. The simple premise that minimizing load reductions will also minimize cost cannot be substantiated, since the costs are governed by factors beyond performance level. This study develops a methodology to integrate the BMP or BMP treatment train costs into a robust optimal allocation model. Costs of BMPs and BMP treatment trains, as a function of reduction goal and land use, are determined. In previous studies, the watershed model (HSPF 11.0) and the Generalized Likelihood Uncertainty Estimation (GLUE) technique have been used to characterize mechanistic parameter uncertainty and loading rate uncertainty. A genetic algorithm was then used to perform robust optimization of TMDL allocations and predict the reliability of compliance associated with each allocation policy. In the previous studies, the objective was to minimize load reductions. This study follows a similar modeling approach, except the objective function of the robust optimization process is modified using the annualized BMP implementation costs. The modified robust optimization process determines reliable, low costs TMDL allocations given mechanistic parameter uncertainty or loading rate uncertainty. This methodology is demonstrated for fecal coliform contamination in the Moore's Creek watershed in Virginia. The results are then compared to the previous studies that determined optimized allocations requiring minimum load reduction. Results indicate that cost optimization significantly influences TMDL allocations with respect to land uses. High load reduction goals are set for low cost land uses with little or no impervious cover.