Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components
AbstractA large number of local and global databases for soil, land use, crops, and climate are now available from different sources, which often differ, even when addressing the same spatial and temporal resolutions. As the correct database is unknown, their impact on estimating water resource components (WRC) has mostly been ignored. Here, we study the uncertainty stemming from the use of multiple databases and their impacts on WRC estimates such as blue water and soil water for the Karkheh River Basin (KRB) in Iran. Four climate databases and two land use maps were used to build multiple configurations of the KRB model using the soil and water assessment tool (SWAT), which were similarly calibrated against monthly river discharges. We classified the configurations based on their calibration performances and estimated WRC for each one. The results showed significant differences in WRC estimates, even in models of the same class i.e., with similar performance after calibration. We concluded that a non-negligible level of uncertainty stems from the availability of different sources of input data. As the use of any one database among several produces questionable outputs, it is prudent for modelers to pay more attention to the selection of input data. View Full-Text
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Kamali, B.; Abbaspour, K.C.; Yang, H. Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components. Water 2017, 9, 709.
Kamali B, Abbaspour KC, Yang H. Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components. Water. 2017; 9(9):709.Chicago/Turabian Style
Kamali, Bahareh; Abbaspour, Karim C.; Yang, Hong. 2017. "Assessing the Uncertainty of Multiple Input Datasets in the Prediction of Water Resource Components." Water 9, no. 9: 709.
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