Conceptual snowmelt runoff hydrological models involve uncertainties originating from various sources. For example, uncertainties related to the structure of hydrological models, robustness, parameters, calibration approaches, and input and output datasets may lead to considerably uncertain hydrological predictions. Quantifying such uncertainties is an essential engineering and scientific endeavor. In this study, the newly developed Weather Research and Forecasting Model Hydrological Glacier (WRF-Hydro/Glacier) modeling framework was applied over the snow-fed Astore catchment in the Upper Indus Basin; the hydrological processes were simulated more effectively, while demonstrating their uncertainties as well as the overall model robustness. Three precipitation datasets were used for calibrating and validating the WRF-Hydro/Glacier. The model parameters with different meteorological forcings were robust for simulating the hydrological processes in Astore. Furthermore, the uncertainty contributed by the optimized parameters and precipitation inputs was segregated to simulate the daily streamflow, snow-cover area (SCA), and evapotranspiration (ET). For streamflow, the optimized parameters were the primary source of uncertainty; for SCA and ET, the optimized parameters were a major source of uncertainty in spring and summer, whereas input precipitation was the key uncertainty in fall and winter. Overall, the robustness of the WRF-Hydro/Glacier was demonstrated using multiple optimized parameters from satellite-based precipitation datasets, while also demonstrating the uncertainties in the prediction of hydrological processes in Astore.
|Journal||Journal of Hydrology|
|Publication status||Published - 2022 Nov|
Bibliographical noteFunding Information:
This work was supported by the Basic Science Research Program through the National Research Foundation of Korea, funded by the Ministry of Science, ICT & Future Planning (2020R1A2C2007670), the Technology Advancement Research Program through the Korea Agency for Infrastructure Technology Advancement (KAIA), funded by the Ministry of Land, Infrastructure, and Transport (22CTAP-C163540-02), and the Korea Environmental Industry & Technology Institute, funded by the Ministry of Environment (2022003640002). We also acknowledge the Water and Power Development Authority (WAPDA) of Pakistan and Pakistan Meteorological Department (PMD) for providing the streamflow and meteorological datasets.
© 2022 Elsevier B.V.
All Science Journal Classification (ASJC) codes
- Water Science and Technology