AN AGNPS-BASED RUNOFF AND SEDIMENT YIELD MODEL FOR Two SMALL WATERSHEDS IN GERMANY

The event-based Agricultural Non-Point Source (AGNPS) pollution model is used extensively to simulate sud~ice runoff, sediment yield and nutrient transport in unmonitored watersheds. Investigation, that compare AGNPS predictions to measured data are rare. The objective of the present study was to compare surface runoff and sediment yield predictions from AGNPS water quality simulation model and modified versions to measured data. Shortcomings of the AGNPS model were examined. The study was carried out using 52 rainfall-runoff events, 22 for calibration and 30for validation, from two small watersheds (GI and G2) in Bavaria, Germany. Evaluation of model outputs was based on statistical comparisons between measured and predicted values fior each rainfall-runoff event. We compared three different surface runoff prediction methods: uncalibrated curve number (Q]), calibrated curve number (Q2), and Lutz (Q3). The modifications made to sediment yield calculations encompassed: (i) replacement of the Universal Soil Loss Equation LS fiactor algorithm (SI) by one based on stream power theory (S2), and (ii) linkage of channel erosion by individual categories of particle size to runoff velocity (S3). Measured median for surface runoff was under-predicted by 55.5% using Q1, overpredicted by 36.8% using Q2 and over-predicted by 13. 1 % using Q3 in G1. The largest coefficient of efficiency (E) was calculatedfor Q3 with 0.96 followed by 0.93 for Q2 and 0.25 for QJ in Gl. In G2, measured median for surface runoff was underpredicted by 80.0% using Q1, overpredicted by 45.0% using Q2, and overpredicted by 35.0% using Q3 in G2. Best performance in terms E was calculated by Q3 (0.83) followed by 0. 76 for Q2 and 0.24 for Q1 in G2. Median sediment yield measurement was underpredicted by 57.2% using S1, underpredicted by 47.6% using S2 and underpredicted by 4.8% using S3 in G1. The largest E was calculated with 0. 90 for S3 followed by 0.57for S2 and 0. 26 for S1 in G1. Measured median for sediment yield was underpredicted by 53.9% using S1, underpredicted by 38.5% using S2 and overpredicted by 3.3% using S3 in G2. E was largest with 0. 72 (S3) followed by 0.60 (S2) and 0.57 (SI) in G2. Results of this study illustrated that a calibration Qf CN and Lutz method for surface runoff calculations and the use of variant S3 for sediment yield calculations with AGNPS model showed the highest merit to match measurements with predictions at the drainage outlet.