Improving the Reliability of Numerical Groundwater Modeling in a Data-Sparse Region
AbstractIn data-sparse areas, due to the lack of hydrogeological data, numerical groundwater models have some uncertainties. In this paper, a nested model and a multi-index calibration method are used to improve the reliability of a numerical groundwater model in a data-sparse region, the Nalinggele River catchment in the Qaidam Basin. Referencing this key study area, a regional three-dimensional groundwater flow model is developed in a relatively complete hydrogeological unit. A complex set of calibration indices, including groundwater fitting errors, dynamic groundwater trends, spring discharges, overflow zone location, and groundwater budget status, are proposed to calibrate the regional numerical groundwater model in the Nalinggele alluvial–proluvial fan. Constrained by regional groundwater modeling results, a local-scale groundwater model is developed, and the hydrogeological parameters are investigated to improve modeling accuracy and reliability in this data-sparse region. View Full-Text
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Du, X.; Lu, X.; Hou, J.; Ye, X. Improving the Reliability of Numerical Groundwater Modeling in a Data-Sparse Region. Water 2018, 10, 289.
Du X, Lu X, Hou J, Ye X. Improving the Reliability of Numerical Groundwater Modeling in a Data-Sparse Region. Water. 2018; 10(3):289.Chicago/Turabian Style
Du, Xinqiang; Lu, Xiangqin; Hou, Jiawei; Ye, Xueyan. 2018. "Improving the Reliability of Numerical Groundwater Modeling in a Data-Sparse Region." Water 10, no. 3: 289.
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