Next Article in Journal
Effect of Nitrogen and Irrigation Application on Water Movement and Nitrogen Transport for a Wheat Crop under Drip Irrigation in the North China Plain
Previous Article in Journal
Validation of a Locally Revised Topographic Index in Central New Jersey, USA
Article Menu

Export Article

Open AccessArticle
Water 2015, 7(11), 6634-6650; doi:10.3390/w7116634

An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty

1
Sino-France Institute of Earth Systems Science, Laboratory for Earth Surface Processes, College of Urban and Environmental Sciences, Peking University, Beijing 100871, China
2
Laboratory of the Pear River Estuarine Dynamics and Associated Process Regulation, Pearl River Hydraulic Research Institute, Guangzhou 510611, China
3
Center for Environmental Science, University of Maryland, Cambridge, MD 21613, USA
4
Department of Water Environmental Planning, Chinese Academy for Environmental Planning, Beijing 100012, China
*
Authors to whom correspondence should be addressed.
Academic Editor: Benoit Demars
Received: 20 July 2015 / Accepted: 6 November 2015 / Published: 20 November 2015
View Full-Text   |   Download PDF [1988 KB, uploaded 20 November 2015]   |  

Abstract

An indirect simulation-optimization model framework with enhanced computational efficiency and risk-based decision-making capability was developed to determine optimal total maximum daily load (TMDL) allocation under uncertainty. To convert the traditional direct simulation-optimization model into our indirect equivalent model framework, we proposed a two-step strategy: (1) application of interval regression equations derived by a Bayesian recursive regression tree (BRRT v2) algorithm, which approximates the original hydrodynamic and water-quality simulation models and accurately quantifies the inherent nonlinear relationship between nutrient load reductions and the credible interval of algal biomass with a given confidence interval; and (2) incorporation of the calibrated interval regression equations into an uncertain optimization framework, which is further converted to our indirect equivalent framework by the enhanced-interval linear programming (EILP) method and provides approximate-optimal solutions at various risk levels. The proposed strategy was applied to the Swift Creek Reservoir’s nutrient TMDL allocation (Chesterfield County, VA) to identify the minimum nutrient load allocations required from eight sub-watersheds to ensure compliance with user-specified chlorophyll criteria. Our results indicated that the BRRT-EILP model could identify critical sub-watersheds faster than the traditional one and requires lower reduction of nutrient loadings compared to traditional stochastic simulation and trial-and-error (TAE) approaches. This suggests that our proposed framework performs better in optimal TMDL development compared to the traditional simulation-optimization models and provides extreme and non-extreme tradeoff analysis under uncertainty for risk-based decision making. View Full-Text
Keywords: total maximum daily load (TMDL) allocation; eutrophication; tradeoff analysis; water-quality modeling; uncertainty total maximum daily load (TMDL) allocation; eutrophication; tradeoff analysis; water-quality modeling; uncertainty
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

Supplementary material

  • Supplementary File 1:

    default (PDF, 520 KB)

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

MDPI and ACS Style

Zhou, F.; Dong, Y.; Wu, J.; Zheng, J.; Zhao, Y. An Indirect Simulation-Optimization Model for Determining Optimal TMDL Allocation under Uncertainty. Water 2015, 7, 6634-6650.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Water EISSN 2073-4441 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top