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Winter Runoff Prediction by WEPP with an Energy Budget Approach to Simulating Snow and Soil Frost

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

Citation:  Paper number  032068,  2003 ASAE Annual Meeting . (doi: 10.13031/2013.13769) @2003
Authors:   Chun-hsu Lin, Donald K. McCool, Dennis C. Flanagan
Keywords:   winter hydrology, energy budget, snow melt, soil frost, hydraulic conductivity, WEPP

In cold regions, the occurrence of snow and soil frost influences hydrology and, in turn, the mechanisms of soil erosion processes. For these regions, modeling the dynamics of snow and soil frost is necessary prior to predicting runoff and erosion accurately under different management practices. With better winter hydrology simulation, the schemes for predicting the rates and amounts of soil erosion by water can be established on a firm hydrological footing. This project examined the potential of an energy budget approach to simulating the magnitude and variations of snow and soil frost depths. It is assumed that the net sum of all energy components in the environment is consumed or compensated by water phase changes that occur near or under the ground surface, such as snow melting or soil freezing-thawing. After being tested, this energy budget approach shows promising potential to simulate winter hydrology and can be adapted to erosion prediction models. It was then incorporated into an operational erosion prediction model, the Water Erosion Prediction Project (WEPP) of USDA-ARS.

Earlier evaluation of WEPP indicated the winter hydrology subroutines in this model predict longer soil freezing periods and deeper frost depths than actually observed in many areas. After incorporated with the source code of the energy budget approach, not only can WEPP simulate winter hydrology better than did it before, with the new adjustment in hydraulic conductivity, the prediction performance of runoff events under the circumstance with snow and frost by the original WEPP was significantly improved for some locations. We also concluded several directions for future research and model revision.

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