Abstract
This paper investigates the performance of gridded rainfall datasets for precipitation detection and streamflow simulations in Indiaʼs Tungabhadra river basin. Sixteen precipitation datasets categorized under gauge-based, satellite-only, reanalysis, and gauge-adjusted datasets were compared statistically against the gridded Indian Meteorological Dataset (IMD) employing two categorical and three continuous statistical metrics. Further, the precipitation datasets’ performance in simulating streamflow was assessed by using the Soil and Water Assessment Tool (SWAT) hydrological model. Based on the statistical metrics, Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation (APHRODITE) furnished very good results in terms of detecting rainfall, followed by Climate Hazards Group Infrared Precipitation (CHIRP), National Centres for Environmental Prediction-Climate Forecast System Reanalysis (NCEP CFSR), Tropical Rainfall Measurement Mission (TRMM) 3B42 v7, Global Satellite Mapping of Precipitation Gauge Reanalysis v6 (GSMaP_Gauge_RNL), and Multisource Weighted Ensemble Precipitation (MSWEP) datasets which had good-to-moderate performances at a monthly time step. From the hydrological simulations, TRMM 3B42 v7, CHIRP, CHIRPS 0.05°, and GSMaP_Gauge_RNL v6 produced very good results with a high degree of correlation to observed streamflow, while Soil Moisture 2 Rain-Climate Change Initiative (SM2RAIN-CCI) dataset exhibited poor performance. From the extreme flow event analysis, it was observed that CHIRP, TRMM 3B42 v7, Global Precipitation Climatology Centre v7 (GPCC), and APHRODITE datasets captured more peak flow events and hence can be further implemented for extreme event analysis. Overall, we found that TRMM 3B42 v7, CHIRP, and CHIRPS 0.05° datasets performed better than other datasets and can be used for hydrological modeling and climate change studies in similar topographic and climatic watersheds in India.
Highlights
Precipitation is an intrinsic component of the hydrological cycle
Whether measured directly through rain gauge stations or measured from different satellite sensors, it plays a crucial role in water resources management, climatic research, and disaster management studies. ough in situ ground-based precipitation datasets provide highly accurate results, the unavailability of data and sparse and uneven distribution of gauges over unpopulated areas makes it challenging to use them for global applications
Several studies were conducted by comparing different satellite precipitation products (SPPs) with gauge-based or radar-based datasets in terms of statistical metrics evaluation or hydrological modeling to predict SPPs ability and efficiency in detecting rainfall accurately
Summary
Precipitation is an intrinsic component of the hydrological cycle. Whether measured directly through rain gauge stations or measured from different satellite sensors, it plays a crucial role in water resources management, climatic research, and disaster management studies. ough in situ ground-based precipitation datasets provide highly accurate results, the unavailability of data and sparse and uneven distribution of gauges over unpopulated areas makes it challenging to use them for global applications. Most of the studies have not considered reanalysis products during their evaluation or have not recalibrated each rainfall dataset, missing to differentiate the in situ corrected and uncorrected dataset efficiencies [15, 21,22,23,24] Studies that executed both statistical and hydrological comparisons revealed that the precipitation datasets that prove effective in statistical comparison might not exhibit the same accuracy while performing hydrological simulations [9,10,11]. Very few studies tested the efficiency of more precipitation datasets; these studies evaluated the SPPs performance using lumped hydrological models, not considering the intrinsic spatial behavior of river basin characteristics that are averaged over the subbasins [17, 25, 26]. The few studies that implemented semidistributed/distributed models have not accommodated and tested the efficiency of multiple/more number of SPPs [18, 27,28,29,30]. is provoked us to test numerous SPPs efficiency using a semidistributed hydrological model in the current study
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