Watershed model parameter estimation and uncertainty in data-limited environments

Parameter uncertainty and sensitivity for a watershed-scale simulation model in Portugal were explored to identify the most critical model parameters in terms of model calibration and prediction. The research is intended tohelpprovideguidance regardingallocationof limiteddata collection andmodel parameterization resources for modelers working in any data and resource limited environment. The watershed-scale hydrologyandwaterquality simulationmodel, Hydrologic Simulation Programe FORTRAN (HSPF),was used to predict the hydrology of Lis River basin in Portugal. Themodel was calibrated for a 5-year period 1985e1989 and validated for a 4-year period 2003e2006. Agreement between simulated and observed streamflow data was satisfactoryconsidering theperformancemeasures suchasNasheSutcliffe efficiency (E), deviation runoff (Dv) and coefficient of determination (R2). TheGeneralizedLikelihoodUncertaintyEstimation (GLUE)method was used to establish uncertainty bounds for the simulated flow using the NasheSutcliffe coefficient as a performance likelihood measure. Sensitivity analysis results indicate that runoff estimations are most sensitive to parameters related to climate conditions, soil and land use. These results state that even though climate conditions are generally most significant in water balance modeling, attention should also focus on land use characteristics as well. Specifically with respect to HSPF, the two most sensitive parameters, INFILT and LZSN, are both directly dependent on soil and land use characteristics. 2013 Elsevier Ltd. All rights reserved. Science Integrating point & Non-point Sources) with a non-proprietary, open source, free GIS apWindow.org) sultants and Idaho State University tact/index.php ilable for free download at USEPA (United States Environmental Protection Agency) website. open source programmable GIS (VB, Cþþ, .NET, and Active X controls) that supports ysis, and viewing of geospatial data and associated attribute data in several GIS data formats.