Parameter estimation and uncertainty analysis of the Spatial Agro Hydro Salinity Model (SAHYSMOD) in the semi-arid climate of Rechna Doab, Pakistan. (August 2017)

• Record Type: Journal Article
• Authors: Inam, Azhar; Adamowski, Jan; Prasher, Shiv; Albano, Raffaele
• Is Part Of: Environmental modelling & software. Volume 94(2017)
• Page Start: 186
• Page End: 211
• Publication Date: 2017-08
• Subjects: SAHYSMOD -- Generalized Likelihood Uncertainty Estimation -- Groundwater -- Sensitivity analysis -- Parameter estimation -- Monte Carlo -- Equifinality; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2017.04.002
• Languages: English
• ISSNs: 1364-8152

Abstract

Abstract: Manual calibration of distributed models with many unknown parameters can result in problems of equifinality and high uncertainty. In this study, the Generalized Likelihood Uncertainty Estimation (GLUE) technique was used to address these issues through uncertainty and sensitivity analysis of a distributed watershed scale model (SAHYSMOD) for predicting changes in the groundwater levels of the Rechna Doab basin, Pakistan. The study proposes and then describes a stepwise methodology for SAHYSMOD uncertainty analysis that has not been explored in any study before. One thousand input data files created through Monte Carlo simulations were classified as behavior and non-behavior sets using threshold likelihood values. The model was calibrated (1983–1988) and validated (1998–2003) through satisfactory agreement between simulated and observed data. Acceptable values were observed in the statistical performance indices. Approximately 70% of the observed groundwater level values fell within uncertainty bounds. Groundwater pumping (Gw) and hydraulic conductivity (Kaq) were found to be highly sensitive parameters affecting groundwater recharge. Highlights: SAHYSMOD was explored in a data limited environment. GLUE was used for uncertainty estimation of SAHYSMOD. The proposed model was tested in the Rechna Doab watershed in Pakistan. Results indicate that the proposed model is useful for groundwater level modeling.