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Summary
Precipitation patterns in the tropics are characterized by extremely high spatial and temporal variability that are difficult to adequately represent with rain gauge networks. Since precipitation is commonly the most important input data in hydrological models, model performance and uncertainty will be negatively impacted in areas with sparse rain gauge networks. To investigate the influence of precipitation uncertainty on both model parameters and predictive uncertainty in a data sparse region, the integrated river basin model SWAT was calibrated against measured streamflow of the Pipiripau River in Central Brazil. Calibration was conducted using an ensemble of different precipitation data sources, including: (1) point data from the only available rain gauge within the watershed, (2) a smoothed version of the gauge data derived using a moving average, (3) spatially distributed data using Thiessen polygons (which includes rain gauges from outside the watershed), and (4) Tropical Rainfall Measuring Mission radar data. For each precipitation input model, the best performing parameter set and their associated uncertainty ranges were determined using the Sequential Uncertainty Fitting Procedure. Although satisfactory streamflow simulations were generated with each precipitation input model, the results of our study indicate that parameter uncertainty varied significantly depending upon the method used for precipitation data-set generation. Additionally, improved deterministic streamflow predictions and more reliable probabilistic forecasts were generated using different ensemble-based methods, such as the arithmetic ensemble mean, and more advanced Bayesian Model Averaging schemes. This study shows that ensemble modeling with multiple precipitation inputs can considerably increase the level of confidence in simulation results, particularly in data-poor regions.
