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Hydrologic Components of Watershed-Scale Models

Published by the American Society of Agricultural and Biological Engineers, St. Joseph, Michigan

Citation:  Transactions of the ASABE. 50(5): 1695-1703. (doi: 10.13031/2013.23955) @2007
Authors:   K. W. Migliaccio, P. Srivastava
Keywords:   AnnAGNPS, ANSWERS-2000, HSPF, Hydrologic modeling, Hydrology, SWAT, WAM, Watershed, WEPP

This article briefly reviews the hydrologic components of prominent models used in agricultural and mixed land use watersheds and presents the current state-of-the-art in agricultural watershed modeling. The models included are Annualized Agricultural Nonpoint Source (AnnAGNPS), Areal Nonpoint Source Watershed Environment Response Simulation (ANSWERS-2000), Hydrologic Simulation Program - Fortran (HSPF), Soil and Water Assessment Tool (SWAT), Watershed Assessment Model (WAM), and Water Erosion Prediction Project (WEPP). Hydrologic components (e.g., precipitation, potential evapotranspiration (PET), infiltration-surface runoff, groundwater, and stream flow) are discussed for each of these models. Simulation of PET differs among selected watershed models, with some offering multiple PET options and others providing one method. The primary difference in the infiltration and surface runoff algorithms among watershed models is their empirical (e.g., curve number (CN) and Green-Ampt) or physical (e.g., Philip's) basis and their simulation time step. Groundwater components (such as interflow, tile drainage, shallow aquifer, and deep aquifer) may be one of the most variable hydrologic components among watershed models. Stream flow was routed predominantly by the selected models using the continuity equation and Manning's equation; other algorithms used were the Muskingum routing method, finite difference integration, and kinematic wave. The use of watershed models by agricultural and biological engineers continues to expand as new technologies, such as the integration of remote sensing and Geographic Information Systems (GIS), and computer capabilities improve and the expectations for high-quality results (including uncertainty analyses and multi-objective functions) increase.

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