Uncertainty Quantification of Evapotranspiration and Infiltration from Modeling and Historic Time Series at the Savannah River F-Area

The objectives of this presentation are: (a) to illustrate the application of Monte Carlo and fuzzy-probabilistic approaches for uncertainty quantification (UQ) in predictions of potential evapotranspiration (PET), actual evapotranspiration (ET), and infiltration (I), using uncertain hydrological or meteorological time series data, and (b) to compare the results of these calculations with those from field measurements at the U.S. Department of Energy Savannah River Site (SRS), near Aiken, South Carolina, USA. The UQ calculations include the evaluation of aleatory (parameter uncertainty) and epistemic (model) uncertainties. The effect of aleatory uncertainty is expressed by assigning the probability distributions of input parameters, using historical monthly averaged data from the meteorological station at the SRS. The combined effect of aleatory and epistemic uncertainties on the UQ of PET, ET, and Iis then expressed by aggregating the results of calculations from multiple models using a p-box and fuzzy numbers. The uncertainty in PETis calculated using the Bair-Robertson, Blaney-Criddle, Caprio, Hargreaves-Samani, Hamon, Jensen-Haise, Linacre, Makkink, Priestly-Taylor, Penman, Penman-Monteith, Thornthwaite, and Turc models. Then, ET is calculated from the modified Budyko model, followed by calculations of I from the water balance equation. We show that probabilistic and fuzzy-probabilistic calculations using multiple models generate the PET, ET, and Idistributions, which are well within the range of field measurements. We also show that a selection of a subset of models can be used to constrain the uncertainty quantification of PET, ET, and I.