Watershed Models for Development and Implementation of Total Maximum Daily Loads
Publication: Journal of Hydrologic Engineering
Volume 24, Issue 1
Abstract
This paper reviews 14 prevalent watershed models for their capabilities, credibility, and suitability in total maximum daily load (TMDL) development and implementation. Brief descriptions of the models, including sources, capabilities, and applicability are presented. General information such as intended watershed and simulation types, simulated outputs, uncertainty analysis capabilities, graphical user interface, and availability are also presented. Mathematical bases of the hydrologic and water quality simulations, which indicate credibility, expected performance, and accuracy, and dictate model features (e.g., structure, input data, and parameters) are presented. Routing procedures, the backbones of the models, are compared and ranked. The Gridded Surface and Subsurface Hydrologic Analysis (GSSHA) and MIKE SHE (Système Hydrologique Européen) models rank high on overland and Storm Water Management Model (SWMM) on channel/pipe flow routing as the most accurate for representation of the physical processes and also the most numerically complex. The Generalized Watershed Loading Function (GWLF) and Spreadsheet Tool for the Estimation of Pollutant Load (STEPL) rank at the bottom on both the aspects. The rest of the models are in between, although the Dynamic Watershed Simulation Model (DWSM) is computationally efficient among kinematic wave models. Notable strengths and limitations of the models for TMDL development and implementation are presented. All these provide valuable information on the models, not readily available in a concise form elsewhere, to compare and help determine relative credibility and make informed selections for TMDLs and similar studies. Future research should focus on further comparisons of the models based on other key aspects such as simulation capabilities of processes, uncertainty analysis, required resources, and performances on watersheds followed by developing better models or improving existing ones by strengthening the weaknesses found. Robust physically based algorithms, uncertainty analysis capabilities, and use of remotely sensed and high-resolution data are recommended to be part of the model improvements.
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Acknowledgments
The paper is based on the work of the ASCE-EWRI TMDL Analysis and Modeling Task Committee (2017) as presented in its report. All the authors of this paper are current members of this committee. However, only Dr. Borah, Dr. Padmanabhan, and Dr. Mohamoud had participated in the preparation of the report along with other committee members, as listed in the report for their contributions. We like to acknowledge Dr. Saurav Kumar, a member of the committee, for sharing his recent search results from the TMDL Report Selection tool that he developed as part of the committee work and presented in its report.
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Journal of Hydrologic Engineering
Volume 24 • Issue 1 • January 2019
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©2018 American Society of Civil Engineers.
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Published online: Oct 31, 2018
Published in print: Jan 1, 2019
Discussion open until: Mar 31, 2019
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