AN AGNPS-BASED RUNOFF AND SEDIMENT YIELD MODEL FOR TWO SMALLWATERSHEDS IN GERMANY

Abstract

The event-based Agricultural Non-Point Source (AGNPS) pollution model is used extensively to simulatesurface runoff, sediment yield and nutrient transport in unmonitored watersheds. Investigation, that compare AGNPSpredictions to measured data are rare. The objective of the present study was to compare surface runoff and sedimentyield predictions from AGNPS water quality simulation model and modified versions to measured data. Shortcomings ofthe AGNPS model were examined. The study was carried out using 52 rainfall-runoff events, 22 for calibration and 30 forvalidation, from two small watersheds (G1 and G2) in Bavaria, Germany. Evaluation of model outputs was based onstatistical comparisons between measured and predicted values for each rainfall-runoff event. We compared threedifferent surface runoff prediction methods: uncalibrated curve number (Q1), calibrated curve number (Q2), and Lutz(Q3). The modifications made to sediment yield calculations encompassed: (i) replacement of the Universal Soil LossEquation LS factor algorithm (S1) by one based on stream power theory (S2), and (ii) linkage of channel erosion byindividual categories of particle size to runoff velocity (S3). Measured median for surface runoff was under-predicted by55.5% using Q1, overpredicted by 36.8% using Q2 and over-predicted by 13.1% using Q3 in G1. The largest coefficientof 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 medianfor 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 of E was calculated by Q3 (0.83) followed by 0.76 for Q2 and 0.24 for Q1 inG2. Median sediment yield measurement was underpredicted by 57.2% using S1, underpredicted by 47.6% using S2 andunderpredicted by 4.8% using S3 in G1. The largest E was calculated with 0.90 for S3 followed by 0.57 for S2 and 0.26for S1 in G1. Measured median for sediment yield was underpredicted by 53.9% using S1, underpredicted by 38.5% usingS2 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 ofvariant S3 for sediment yield calculations with AGNPS model showed the highest merit to match measurements withpredictions at the drainage outlet.