Extreme precipitation is a primary driver of flood hazards causing damage to humans and infrastructure. Analysis of extreme precipitation without considering temporally compounding events underestimates the severity of the hazard. Especially mountainous regions such as the Hindu Kush Himalayan region are highly vulnerable to multiday extreme precipitation events. However, analysis of such events and their time distribution patterns are still unknown, and a comprehensive assessment is needed. To this, the concept of event‐based extreme precipitation, which accounts for the preceding and succeeding precipitation, is employed for the Upper Indus basin using three reference gridded datasets, namely, Indian Meteorological Department (IMD), Multi‐Source Weighted Ensemble Precipitation (MSWEP) and Himalayan Adaption, Water and Resilience (HI‐AWARE). Four different types of time distribution patterns were identified based on the occurrence of extreme precipitation during an event. We identified that the time distribution pattern with the peak on the right side is predominant among the four types. Subsequently, trend analysis on the characteristics, namely amount, frequency, duration and concentration ratio for all four events, display negative trends with IMD and MSWEP datasets, whereas HI‐AWARE displays positive trends in the northwestern part of the catchment. Further, the 100‐year return level of the amount of multiday extreme precipitation is computed along with the traditional method of single‐day extremes considering nonstationarity. The differences between the return values obtained using the traditional method and the event‐based extreme precipitation concept were distinct and substantial (>50 mm for three datasets). The findings clearly show that the analysis of multiday events is much more essential than single‐day extreme events, particularly for events like floods. Moreover, the results of the study were found to be conflicting among reference datasets, demonstrating the importance of identifying the suitability of reference datasets in extreme event analysis.
<p>Hydrological models are simplified mathematical representations of various hydrological processes and their interactions in a catchment. They are widely employed to simulate hydrological responses under diverse scenarios of climate, land use, land cover, and agricultural and soil management practices, which are helpful for planning water resources and management at the catchment scale. The parameters of the hydrological models are often optimised by calibrating them such that the observed and simulated streamflow match closely. Reliable prediction of hydrological variables of interest in ungauged or poorly gauged basins and addressing the uncertainty associated with the prediction is a challenging task as it is very difficult to calibrate the models due to the unavailability of measured hydrological responses. Escalating research interest in predicting hydrological fluxes at ungauged or poorly gauged catchments has been witnessed recently using distributed modelling, advanced scientific methods, and the availability of high-resolution satellite-based and reanalysis datasets used in model calibration. Additionally, the ability of remote sensing data sources to consider spatial variability is a further benefit in calibrating hydrological models, which lowers the level of uncertainty in the outputs. This proposed study focuses on calibrating 3 layered Variable Infiltration Capacity (VIC) model with soil moisture, and evapotranspiration obtained from different remote sensed and reanalysis data sets in the Upper Indus basin of the Hindukush Himalayan region. As the Upper Indus basin has limited meteorological stations and no gauging stations in the Indian mainland, the current study has much scope to quantify the water resources using different remote sensing/reanalysis datasets. The results expected from this study are to find the suitable variable and reanalysis product for the calibration of the VIC model and the uncertainty associated with various remote sensing and reanalysis products.</p>
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