Watershed model parameter estimation and uncertainty in data-limited environments

  • Authors:

  • André Fonseca;Daniel P. Ames;Ping Yang;Cidália Botelho;Rui Boaventura;Vítor Vilar

  • Affiliations:

  • LSRE - Laboratory of Separation and Reaction Engineering - Associate Laboratory - LSRE/LCM, Department of Chemical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal;Department of Civil and Environmental Engineering, Brigham Young University, Provo, UT, USA;Texas Institute for Applied Environmental Research, Tarleton State University, Stephenville, TX, USA;LSRE - Laboratory of Separation and Reaction Engineering - Associate Laboratory - LSRE/LCM, Department of Chemical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal;LSRE - Laboratory of Separation and Reaction Engineering - Associate Laboratory - LSRE/LCM, Department of Chemical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal;LSRE - Laboratory of Separation and Reaction Engineering - Associate Laboratory - LSRE/LCM, Department of Chemical Engineering, Faculty of Engineering of the University of Porto, Porto, Portugal

  • Venue:
  • Environmental Modelling & Software
  • Year:
  • 2014

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Abstract

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 to help provide guidance regarding allocation of limited data collection and model parameterization resources for modelers working in any data and resource limited environment. The watershed-scale hydrology and water quality simulation model, Hydrologic Simulation Program - FORTRAN (HSPF), was used to predict the hydrology of Lis River basin in Portugal. The model was calibrated for a 5-year period 1985-1989 and validated for a 4-year period 2003-2006. Agreement between simulated and observed streamflow data was satisfactory considering the performance measures such as Nash-Sutcliffe efficiency (E), deviation runoff (Dv) and coefficient of determination (R^2). The Generalized Likelihood Uncertainty Estimation (GLUE) method was used to establish uncertainty bounds for the simulated flow using the Nash-Sutcliffe 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.