Validation of EPIC for Two Watersheds in Southwest Iowa

The Erosion Productivity Impact Calculator (EPIC) model was validated using long-term data collected for two southwest Iowa watersheds in the Deep Loess Soil Region, which have been cropped in continuous corn (Zea mays L.) under two different tillage systems (conventional tillage vs. ridge-till). The annual hydrologic balance was calibrated for both watersheds during 1988 to 1994 by adjusting the runoff curve numbers and residue effects on soil evaporation. Model validation was performed for 1976 to 1987, using both summary statistics (means or medians) and parametric and nonparametric statistical tests. The errors between the 12-yr predicted and observed means or medians were <10% for nearly all of the hydrologic and environmental indicators, with the major exception of a nearly 44% overprediction of the N surface runoff loss for Watershed 2. The predicted N leaching rates, N losses in surface runoff, and sediment loss for the two watersheds clearly showed that EPIC was able to simulate the long-term impacts of tillage and residue cover on these processes. However, the results also revealed weaknesses in the model's ability to replicate year-to-year variability, with r 2 values generally <50% and relatively weak goodness-of-fit statistics for some processes. This was due in part to simulating the watersheds in a homogeneous manner, which ignored complexities such as slope variation. Overall, the results show that EPIC was able to replicate the long-term relative differences between the two tillage systems and that the model is a useful tool for simulating different tillage systems in the region.

[1]  P. Gassman,et al.  Validation of EPIC for Two Watersheds in , 1999 .

[2]  Philip W. Gassman,et al.  Metamodeling Approach to Evaluate Agricultural Policy Impact on Soil Degradation in Western Canada (A) , 1996 .

[3]  Mark A. Nearing,et al.  Using soil erosion models for global change studies , 1996 .

[4]  Randy Wayne Clouse.  Evaluation of GLEAMS considering parameter uncertainty , 1996 .

[5]  Paul D. Mitchell,et al.  RAPS 1996: Agricultural and Environmental Outlook , 1996 .

[6]  S. Zacharias,et al.  Robust Quantitative Techniques for Validating Pesticide Transport Models , 1996 .

[7]  R. C. Izaurralde,et al.  Epic Model Parameters for Cereal, Oilseed, and Forage Crops in the Northern Great Plains Region , 1995 .

[8]  V. W. Benson,et al.  EPIC user's guide, version 5300. The EPIC model: Environmental Policy Integrated Climate formerly Erosion Productivity Impact Calculator , 1995 .

[9]  V. Singh,et al.  The EPIC model. , 1995 .

[10]  Philip W. Gassman,et al.  Use of a Linked Biophysical and Economic Modeling System to Evaluate Risk-Benefit Tradeoffs of Corn Herbicide Use in the Midwest , 1995 .

[11]  C. W. Lindwall,et al.  Comparison of five wheat simulation models In southern Alberta , 1995 .

[12]  Piyush Singh,et al.  Modification of RZWQM for Simulating Subsurface Drainage by Adding a Tile Flow Component , 1995 .

[13]  J. R. Williams,et al.  Use of the EPIC Model to Predict Runoff Transport of Surface-applied Inorganic Fertilizer and Poultry Manure Constituents , 1994 .

[14]  R. L. Elliott,et al.  Economic and environmental impacts of water quality protection policies: 2. Application to the Central High Plains , 1993 .

[15]  J. V. Baglio,et al.  Nonpoint source pollution impacts of alternative agricultural management practices in Illinois: A simulation study , 1993 .

[16]  A. Moulin,et al.  Evaluation of the CERES and EPIC models for predicting spring wheat grain yield over time , 1993 .

[17]  J. Foltz,et al.  Farm‐Level Economic and Environmental Impacts of Eastern Corn Belt Cropping Systems , 1993 .

[18]  C. W. Richardson,et al.  The Weather Factor: Incorporating Weather Variance Into Computer Simulation , 1992, Weed Technology.

[19]  G.A.A. Wossink,et al.  Environmental-economic policy assessment: A farm economic approach , 1992 .

[20]  Claudio O. Stöckle,et al.  A method for estimating the direct and climatic effects of rising atmospheric carbon dioxide on growth and yield of crops: Part II—Sensitivity analysis at three sites in the Midwestern USA , 1992 .

[21]  Stanley F. Miller,et al.  Farm level response to agricultural effluent control strategies : the case of the Willamette Valley , 1992 .

[22]  Jay D. Atwood,et al.  Compromise solution for economic-environmental decisions in agriculture* , 1991 .

[23]  Trevor M. Harris,et al.  Climate change, winter wheat yield and soil erosion on the English south downs , 1991 .

[24]  K. Loague,et al.  Statistical and graphical methods for evaluating solute transport models: Overview and application , 1991 .

[25]  Kurt D. Pennell,et al.  Evaluation of Five Simulation Models for Predicting Aldicarb and Bromide Behavior Under Field Conditions , 1990 .

[26]  John R. Williams,et al.  The erosion-productivity impact calculator (EPIC) model: a case history , 1990 .

[27]  John B. Braden,et al.  A Dynamic Programming Approach to a Class of Nonpoint Source Pollution Control Problems , 1990 .

[28]  J. Steiner.  Tillage and Surface Residue Effects on Evaporation from Soils , 1989 .

[29]  R. G. Spomer,et al.  Landform Changes Determined Photogrammetrically on Deep Loess Soils , 1987 .

[30]  John R. Williams,et al.  A modeling approach to determining the relationship between erosion and soil productivity [EPIC, Erosion-Productivity Impact Calculator, mathematical models] , 1984 .

[31]  R. E. Phillips,et al.  No-Tillage Agriculture , 1980, Science.

[32]  W. Rawls,et al.  Residue and Tillage Effects on SCS Runoff Curve Numbers , 1980 .

[33]  W. H. Wischmeier,et al.  Predicting rainfall erosion losses : a guide to conservation planning , 1978 .

[34]  Joe T. Ritchie,et al.  Model for predicting evaporation from a row crop with incomplete cover , 1972 .