An AGNPS-based runoff and sediment yield model for two small watersheds inGermany

The event-based Agricultural Non-Point Source (AGNPS) pollution model is us ed extensively to simulate surface runoff sediment yield and nutrient trans port in unmonitored watersheds. Investigation, that compare AGNPS predictio ns to measured data are rare. The objective of the present study was to com pare surface runoff and sediment yield predictions from AGNPS water quality simulation model and modified versions to measured data. Shortcomings of t he AGNPS model were examined. The study was carried out using 52 rainfall-r unoff events, 22 for calibration and 30 for validation from two small water sheds (G1 and G2) in Bavaria, Germany. Evaluation of model outputs was base d on statistical comparisons between measured and predicted values for each rainfall-runoff event. We compared three different surface runoff predicti on methods: uncalibrated curve number (Q1), calibrated curve number (Q2), a nd Lutz (Q3). The modifications made to sediment yield calculations encompa ssed: (i) replacement of the Universal Soil Loss Equation LS factor algorit hm (S1) by one based on stream power theory (S2), and (a) linkage of channe l 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 calculated for Q3 with 0.96 followed by 0.93 for Q2 and 0.25 for Q1 in G1. In G2, measured median for surface runoff was underpredicted by 80.0% using Q1, overpredicted by 45.0% using Q2, and over predicted by 35.0% using Q3 in G2. Best performance in terms of 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% us ing S1, underpredicted by 47.6% using S2 and underpredicted by 4.8% using S 3 in G1. The largest E was calculated with 0.90 for S3 followed by 0.57 for S2 and 0.26 for S1 in G1. Measured median for sediment yield was underpred icted 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 (S1) in G2. Results of this study illustrated that a calibration of CN and Lutz method for surface runoff calculations and the use of varian t S3 for sediment yield calculations with AGNPS model showed the highest me rit to match measurements with predictions at the drainage outlet.