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EVALUATION OF THREE DAILY RAINFALL GENERATION MODELS FOR SWAT

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

Citation:  Transactions of the ASAE. Vol. 48(5): 1697-1711. (doi: 10.13031/2013.20004) @2005
Authors:   B. M. Watson, R. Srikanthan, S. Selvalingam, M. Ghafouri
Keywords:   Hydrologic model, Rainfall generation, Stochastic model, SWAT, Water balance

The Soil and Water Assessment Tool (SWAT) is a hydrologic model that was developed to predict the long-term impacts of land use change on the water balance of large catchments. Stochastic models are used to generate the daily rainfall sequences needed to conduct long-term, continuous simulations with SWAT. The objective of this study was to evaluate the performances of three daily rainfall generation models. The models evaluated were the modified Daily and Monthly Mixed (DMMm) model, skewed normal distribution (SKWD) model and modified exponential distribution (EXPD) model. The study area was the Woady Yaloak River catchment (306 km2) located in southwest Victoria, Australia. The models were assessed on their ability to preserve annual, monthly, and daily statistical characteristics of the historical rainfall and runoff. The mean annual, monthly, and daily rainfall was preserved satisfactorily by the models. The DMMm model reproduced the standard deviation of annual and monthly rainfall better than the SKWD and EXPD models. Overall, the DMMm model performed marginally better than the SKWD model at reproducing the statistical characteristics of the historical rainfall record at the various time scales. The performance of the EXPD model was found to be inferior to the performances of the DMMm and SKWD models. The models reproduced the mean annual, monthly, and daily runoff relatively well, although the DMMm and SKWD models were found to preserve these statistics marginally better than the EXPD model. None of the models managed to reproduce the standard deviation of annual, monthly, and daily runoff adequately.

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