Parameter estimation and uncertainty analysis of the Spatial Agro Hydro Salinity Model (SAHYSMOD) in the semi-arid climate of Rechna Doab,Pakistan |
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| Affiliation: | 1. Department of Bioresource Engineering, Faculty of Agricultural and Environmental Sciences, Macdonald Campus, McGill University, Canada;2. School of Engineering, University of Basilicata, Via dell’Ateneo Lucano n.10, Potenza 85100, Italy;1. Department of Plant Biotechnology and Bioinformatics, Ghent University, 9052 Ghent, Belgium;2. Center for Plant Systems Biology, VIB, 9052 Ghent, Belgium;1. School of Medicine, Stanford University, Stanford, California, USA;2. Department of Dermatology, Stanford University Medical Center, Stanford, California, USA;3. Institute for Computational Health Sciences, University of California San Francisco, San Francisco, California, USA;1. Chinese-Israeli International Center for Research and Training in Agriculture, China Agricultural University, Beijing 100083, PR China;2. Center for Agricultural Water Research, China Agricultural University, Beijing 100083, PR China;3. MARETEC, Instituto Superior Técnico, Universidade de Lisboa, Av. Rovisco Pais n°. 1, 1049-001 Lisboa, Portugal;1. State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing, 100084, China;2. Bayannur Institute of Water Conservancy Research, Bayannur, Inner Mongolia, 015000, China;3. Centre for Agricultural Water Research in China, China Agricultural University, Beijing, 100083, China |
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| 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. |
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| Keywords: | SAHYSMOD Generalized Likelihood Uncertainty Estimation Groundwater Sensitivity analysis Parameter estimation Monte Carlo Equifinality |
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