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This study evaluates runoff predictions generated by GeoWEPP (Geo-spatial interface to the Water Erosion Prediction Project) and a modified version of WEPP v98.4 for forest soils. Three small (2 to 9 ha) watersheds in the mountains of the interior Northwest were monitored for several years following timber harvest and prescribed fires. Observed climate variables, percent ground cover, soil erodibility values, and GIS-derived slope data were used to drive the models. Predictions of total yearly runoff generated by GeoWEPP (WEPP v2002.7) and the modified WEPP model were compared to total yearly runoff measured at each watershed. In addition, measured seasonal values were compared to the predictions generated by the modified WEPP model. GeoWEPP significantly underpredicted the total yearly runoff for all three sites. The modified WEPP model, with algorithm changes to account for deep percolation and subsurface lateral flow, predicted total yearly runoff for two of the three sites with an index of agreement (d) of 0.8 and 0.9 for each. The third site performed less accurately, with d = 0.3. In the seasonal runoff predictions, the modified WEPP model was most accurate for the spring months (higher runoff) but was a poor predictor for other seasons when the measured runoff rates were low. The GeoWEPP model successfully incorporates digital elevation data, but the WEPP version used to process the data does not adequately represent the hydrological processes of forests. The lateral flow modifications that were added to the WEPP model improved predictions of runoff in forests, thus suggesting that further refinement of these calculations may improve the accuracy of WEPP-based models when applied to forest environments. [ABSTRACT FROM AUTHOR]

CLIGEN is the only weather generator that generates internal storm patterns, which are required by many agricultural system models such as the Water Erosion Prediction Project (WEPP) model. The lack of correlation between CLIGEN-generated storm variables may limit those models' abilities to predict surface runoff and soil erosion. The objectives of this study were to: (1) test a distribution-free method for inducing desired rank correlation between generated storm variables, and (2) compare WEPP-predicted runoff and soil loss using measured vs. variously generated storm patterns on eight U.S. sites. Four climate files containing four storm patterns (measured, original uncorrelated CLIGEN output, correlated CLIGEN output, and correlated output with exponentially generated storm durations), along with measured soil, slope, and crop management on each site, were used as input to WEPP. The distribution-free approach was simple to use and capable of inducing desired rank correlation between storm depth and duration and consequently between storm depth and relative peak intensity. Original CLIGEN output after inducing desired correlation considerably improved WEPP runoff and soil loss predictions on most sites where strong correlation between storm depth and duration existed. On average, the relative errors averaged over all sites were reduced from 15.0% to 4.6% for runoff prediction and from 11.1% to 1.5% for soil loss prediction. The use of exponentially distributed storm duration, compared with the original CLIGEN output, doubled the overall relative error for soil loss prediction due to the undesirable alteration of relative peak intensity estimates. Overall results indicate that for better runoff and soil loss prediction, correlated CLIGEN output should be used on sites where strong correlation between storm depth and duration exists. [ABSTRACT FROM AUTHOR]

Agricultural system models are useful tools for tailoring agricultural production systems to possible climate variations. The objectives of this work were: (1) to evaluate and calibrate the water balance and crop components of the Water Erosion Prediction Project (WEPP) model and to make improvements if necessary, and (2) to simulate hydrologic and crop responses to generated climate scenarios for winter wheat (Triticum aestivum L.). Precipitation, surface runoff, soil moisture, and wheat biomass collected between 1980 and 1996 on a 1.6 ha watershed near El Reno, Oklahoma, were used. Two contrasting (wet and dry) climate scenarios were generated using a climate generator (CLIGEN) for assessing the overall sensitivity of WEPP to climate variations. Optimized saturated hydraulic conductivity (Ks) agreed well with field--measured Ks, indicating that the infiltration routine of the model functioned properly. WEPP's original water use function substantially overpredicted plant water uptake and therefore was modified. The revised water use function resulted in better predictions of soil water balance, plant water stress, and biomass production. Predicted aboveground biomass agreed relatively well with measured data (model efficiency = 0.5). However, wheat grain yields were less well predicted because of inadequate adjustments to harvest index in the model. The general relationship between total aboveground biomass and growing--season evapotranspiration for winter wheat was reasonably simulated by the model. Model simulations under the generated wet and dry scenarios showed that each percent increase in growing--season precipitation would result in, on average, 3.38%, 0.34%, 0.73%, 1.09%, and 0.81% increases in surface runoff, plant transpiration, soil evaporation, deep percolation, and wheat grain yield, respectively, under the study conditions. This work has shown that WEPP is capable of simulating hydrologic and crop responses to climate variations. [ABSTRACT FROM AUTHOR]

This study investigates the modeling question: "What level of geometric detail is necessary for modeling surface erosion from a forest road network?" We used the Water Erosion Prediction Project (WEPP) model to predict road sediment yield from a road network in a timber sale on the Boise National Forest, Idaho. Using three levels of detail (high, intermediate, and low), the outsloped 4.4 km road was divided into road segments (145, 91, and 42) of different lengths. The model predicted that 44, 44, and 42 metric tons per year of sediment would be produced from the 4.4 km road network based on the high, intermediate, and low detail methods, respectively. The model also predicted that a corresponding 0.036, 0.058, and 0.061 metric tons of sediment per year would be delivered into the stream system. Most sediment was predicted to be deposited in the buffer. For the predicted sediment produced from the roads, the total difference among the three methods was less than 5%, and regression analysis showed excellent correlation, with no practical differences among the three methods. For the quantity of sediment predicted to be delivered into the stream system from the road, the total difference among the methods was 61%, 67%, and 4% for high vs. intermediate, high vs. low, and intermediate vs. low, respectively. There were no strong correlations between the three methods, and the regression analysis indicated significant differences. More delivered sediment was predicted when using lower detail methods than when using higher detail methods. We conclude from the study results that the low detail method (i.e., considering only road grade reversal) can be used for input requirements of road traveled-way geometry, but that a high level of detail (i.e., dividing a road system into more road sections and considering buffer slope breaks and meanderings) is required for modeling the buffer slope geometry. Considering that the results were not sensitive to road travele... [ABSTRACT FROM AUTHOR]