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TECHNICAL REPORTS

Ground Water Quality

Accuracy and Performance of Three Water Quality Models for Simulating Nitrate Nitrogen Losses under Corn

^a USDA-ARS, Northern Plains Agricultural Research Laboratory, 1500 North Central Avenue, Sidney, MT 59270
^b Robert Morris University, Moon Township, PA 15108
^c Pennsylvania State University, University Park, PA 16802

^* Corresponding author (jjabro{at}sidney.ars.usda.gov)

Received for publication November 1, 2005. Simulation models can be used to predict N dynamics in a soil-water–plant system. The simulation accuracy and performance of three models: LEACHM (Leaching Estimation And CHemistry Model), NCSWAP (Nitrogen and Carbon cycling in Soil, Water And Plant), and SOILN to predict NO₃–N leaching were evaluated and compared to field data from a 5-yr experiment conducted on a Hagerstown silt loam (fine, mixed, mesic Typic Hapludalf). Nitrate N losses past 1.2 m from N-fertilized and manured corn (Zea mays L.) were measured with zero-tension pan lysimeters for 5 yr. The models were calibrated using 1989–1990 data and validated using 1988–1989, 1990–1991, 1991–1992, and 1992–1993 NO₃–N leaching data. Statistical analyses indicated that LEACHM, NCSWAP, and SOILN models were able to provide accurate simulations of annual NO₃–N leaching losses below the 1.2-m depth for 8, 9, and 7 of 10 cases, respectively, in the validation years. The inaccuracy in the models' annual simulations for the control and manure treatments seems to be related to inadequate description of processes of N and C transformations in the models' code. The overall performance and accuracy of the SOILN model were worse than those of LEACHM and NCSWAP. The root mean square error (RMSE) and modeling efficiency (ME) were 10.7 and 0.9, 9.5 and 0.93, and 20.7 and 0.63 for LEACHM, NCSWAP, and SOILN, respectively. Overall, the three models have the potential to predict NO₃–N losses below 1.2-m depth from fertilizer and manure nitrogen applied to corn without recalibration of models from year to year.

Abbreviations: MD, mean difference • ME, modeling efficiency • RMSE, root mean square error