Click on “Download PDF” for the PDF version or on the title for the HTML version.

If you are not an ASABE member or if your employer has not arranged for access to the full-text, Click here for options.

Data-poor environments represent places where there are not enough data to accurately characterize and evaluate watershed systems. Available data are often scarce, incomplete or nonexistent, consequently are poor-quality and often uncertain. One-phase Monte Carlo Simulation methodology was used to characterize the uncertainty in SWATs outputs due to input parameters likely to be lacking in data-poor conditions. The analysis was performed for two experimental watersheds, Little River, GA and Reynolds Creek, ID. Uncertainty due to selected soil parameters was quantified as a function of DEM resolution (10, 30 and 90m) and soils database (SSURGO and STATSGO). Uncertainty was calculated for a 12-year period for streamflow, sediment yield, total nitrogen and total phosphorous. Changing the soils database resulted in a significant (p < 0.001) change in model predictions. A change in DEM resolution did not always impact the predictions; the results depended on the type of watershed, constituent of interest, and soil parameter. The combined effect (Soil_DB x DEM) on model predictions was less than the effect of each factor. In general, predictions had lower values and increased uncertainty as the input data got coarser, but there were some exceptions. The SSURGO soils database combined with the 30-m DEM appears to be an adequate level of resolution. Therefore, additional research should be focused on improving existing information in data-poor environments to represent similar resolution to SSURGO and 30-m DEM.