American Society of Agricultural and Biological Engineers

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Evaluating a Drainage Model Using Soil Hydraulic Parameters Derived by Various Methods

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

Citation:  Paper number  062318,  2006 ASAE Annual Meeting . (doi: 10.13031/2013.20795) @2006
Authors:   Zhiming Qi, Matthew J. Helmers, Ranvir Singh
Keywords:   Soil hydraulic parameters, DRAINMOD, subsurface drainage, statistical measures

Soil hydraulic parameters are often indispensable input in hydrological modeling. The required input parameters can be obtained by measuring soil texture, bulk density, organic matter content, soil water retention and hydraulic conductivity. To minimize soil measurements, information is needed on how well hydrologic models perform with varying levels of soil hydraulic parameters. The objective of this study is to determine which level of soil information would be sufficient to use with DRAINMOD in predicting subsurface drainage volumes. Three groups of parameters were obtained by various methods: 1) determining the soil texture and bulk density (BD) data from the Soil Survey Database, then inputting them into a pedotransfer function model (ROSETTA) to determine soil hydraulic parameters (denoted as SP_1); 2) analyzing the soil texture and organic matter(OM) content in laboratory and deriving the BD, field capacity (è33kPa) and wilting point (è1500kPa) from literature, then inputting them into ROSETTA to determine soil hydraulic parameters (SP_2); and 3) calibrated soil hydraulic parameters based on initial inputs from the Soil Survey Database plus ROSETTA (SP_3). Parameters obtained from these three methods were used with DRAINMOD under the same weather, crop and soil conditions for 14 consecutive years at the subsurface drainage plots located in Pocahontas County, IA. Predicted subsurface drainage based on those three levels of soil hydraulic parameter inputs were compared to the observed ones through four statistical measures: Root Mean Square Error (RMSE), Co-efficient of Mass Residual (CRM), Index of Agreement (IoA) and Model Efficiency (EF). The statistical results indicated that output from SP_3 had the best fit with respect to observed values during the calibration period (1990-1993) and that from SP_2 has the best fit when considering all 14 years. However, all methods provided reliable estimates of subsurface drainage. ROSETTA in combination with Soil Survey offers a quick and easy way to derive the soil hydraulic parameters, which were found reliable for DRAINMOD simulations to predict long-term subsurface drainage volumes for the site studied.

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