Parameter sensitivity and uncertainty analysis of the WetSpa model using PEST ABDOLREZA BAHREMAND and FLORIMOND DE SMEDT Department of Hydrology and Hydraulic Engineering Vrije Universiteit Brussel Pleinlaan 2, 1050 Brussels Belgium Abstract: The spatially distributed hydrologic model WetSpa is applied to the Torysa river basin (1297 km 2 ) located in Slovakia. Daily hydrometeorological data from 1991 to 2000, including precipitation data from 14 stations, temperature data from 2 stations and evaporation data measured at one station are used as input to the model. The spatial characteristic of the basin are described by three base maps, i.e. DEM, landuse and soil type, in GIS form using 100 m cell size. Results of the simulations show a good agreement between calculated and measured hydrographs at the outlet of the basin. The model predicts the daily discharge values with a good accuracy, i.e. about 73% according to the Nash-Sutcliff criterion. Sensitivity and uncertainty analysis of the model parameters is performed using a model-independent parameter estimator, PEST. It is found that the correction factor for calculating the actual evapotranspiration from potential evaporation has the highest relative sensitivity. Parameter uncertainty analysis gives an insight of a proper parameter set and parameter interval. Key-Words: WetSpa model, PEST, Sensitivity analysis, Uncertainty, Flood prediction, Flow simulation, GIS- based hydrological modeling 1 Introduction Distributed hydrological models are usually parameterized by deriving estimates of parameters from the topography and physical properties of the soils, aquifers and land use of the basin. The reliability of model predictions depends on how well the model structure is defined and how well the model is parameterized. However, estimation of model parameters is difficult due to the large uncertainties involved in determining the parameter values, which can not be directly measured in the field. Therefore model calibration is necessary to improve the model performance (Liu et al., 2005). Manual calibration and automatic calibration are two types of parameter estimation approaches. Automatic calibration involves the use of a search algorithm to determine best-fit parameters, and it offers a number of advantages over the manual approach. Automatic calibration is fast, it is less subjective, and since it makes an extensive search of the existing parameter possibilities, it is highly likely that results would be better than that which could be manually obtained. Unfortunately, model calibration does not guarantee reliability of model predictions. The parameter values obtained during calibration and the subsequent predictions made using the calibrated model are only as realistic as the validity of the model assumptions for the study watershed and the quality and quantity of actual watershed data used for calibration and simulation. Therefore, even after calibration, there is potentially a great deal of uncertainty in results that arises simply because it is unlikely to have error-free observational data (e.g. precipitation, streamflow, topography) and because no simulation model is an entirely true reflection of the physical process being modeled (Muleta and Nicklow, 2004). Sensitivity analyses are valuable tools for identifying important model parameters, testing the model conceptualization, and improving the model structure. They help to apply the model efficiently and to enable a focused planning of future research and field measurement (Siebera and Uhlenbrook, 2005). Due to spatial variability, budget constraints or access difficulties model input parameters always contain uncertainty to some extent. However, a model user has to assign values to each parameter. The model is then calibrated against measured data to adjust the parameter values according to certain criteria. This implies that the modeler has a clear understanding of all the parameters used as input to the model and of the processes represented in the model. Parameters that are not well understood may be left unchanged even though they are sensitive or are adjusted to implausible values. Not knowing the Proceedings of the 2006 IASME/WSEAS Int. Conf. on Water Resources, Hydraulics & Hydrology, Chalkida, Greece, May 11-13, 2006 (pp26-35)