A method for identifying sources of model uncertainty in rainfall-runoff simulations Jonathan J. Gourley a, * , Baxter E. Vieux b a Cooperative Institute for Mesoscale Meteorological Studies, University of Oklahoma, Norman, OK, USA b Department of Civil Engineering and Environmental Science, University of Oklahoma, Norman, OK, USA Received 20 September 2004; received in revised form 7 November 2005; accepted 14 November 2005 Summary 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 rain- fall-runoff simulation system. This ensemble includes several quantitative precipitation esti- mates that serve as inputs to the Vflo TM 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 dis- charge 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 observa- tions 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 methodol- ogy is used in the model to compute soil infiltration rates. Evidence suggests additional abstrac- tions 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. ª 2005 Elsevier B.V. All rights reserved. KEYWORDS Modeling; Radar; Ensemble forecasting; Uncertainty estimation; Distributed hydrology 0022-1694/$ - see front matter ª 2005 Elsevier B.V. All rights reserved. doi:10.1016/j.jhydrol.2005.11.036 * Corresponding author. Present address: National Severe Storms Laboratory, 1313 Halley Circle, Norman, OK 73069, USA. Fax: +1 405 366 0472. E-mail address: jj.gourley@noaa.gov (J.J. Gourley). Journal of Hydrology (2006) 327, 68– 80 available at www.sciencedirect.com journal homepage: www.elsevier.com/locate/jhydrol