A method for identifying sources of model uncertainty in rainfall-runoff simulations

A major goal in environmental modeling is identifying and quantifying sources of uncertainty in the modeling process. A forecast ensemble is developed in this study for a rainfall-runoff simulation system. This ensemble includes several quantitative precipitation estimates that serve as inputs to the Vflo™ hydrologic model. The rainfall estimates are derived from rain gauges, radar, satellite, and combinations, and their probability distribution is assumed to encompass the true, but unknown, rainfall. Sensitive model parameters in the model are also perturbed within their physical bounds to create a combined input-parameter ensemble. If all major sources of uncertainty are accounted for, then observations of river discharge should fall within simulation bounds. Otherwise, there may be additional errors that lie within the model structure. Probability distributions derived from the forecast ensemble encompass streamflow observations for three hydrologic events examined during October and December on the Blue River Basin in Oklahoma. It is discovered, however, that all simulations from an ensemble created for a warm season case overforecast discharge peaks and volumes. Climatological rain gauge, discharge, and soil moisture observations are introduced to illuminate the source of uncertainty that was not accounted for in the combined input-parameter ensemble. Observations show a strong correlation between dry, deep-layer soils and significantly reduced runoff production (provided the same rainfall inputs) during the summer months. The Green and Ampt methodology is used in the model to compute soil infiltration rates. Evidence suggests additional abstractions such as interception and evapotranspiration by vegetation and deep cracks in the soil structure contribute to enhanced infiltration rates during the warm season. These effects need to be considered for future infiltration models.