Pakistan is home to three of the world's largest mountain ranges in the Upper Indus Basin (UIB), where the majority of Pakistan's water resources are located: the Himalayan, Karakorum, and Hindu-Kush. This work estimated the (snow+glacier) and rainfall runoff from one of the major tributaries, the Gilgit River, nestled within the UIB of Pakistan. The snowmelt runoff model (SRM) derived by the cryospheric data from the MODIS (moderate resolution imaging spectroradiometer) was employed to predict the daily discharges of the Gilgit. The SRM was successfully calibrated, and the simulation was undertaken from 2005 to 2010, with a coefficient of model efficiency ranging from 0.84-0.94. The average contributions of (snow+glacier) and rainfall to the stream flows of the Gilgit from 2001-10 were 78.35% and 21.65%, respectively, derived from the SRM. The representative concentration pathways (RCP) 4.5 and 8.5 scenarios of the Intergovernmental Panel on Climate Change (IPCC) AR5 were used to investigate the effects of the changes in temperature on climate of the Gilgit catchment. Under the RCP 4.5 scenario, the air temperature
Abstract:The performance evaluation of satellite-based precipitation products at local and regional scales is crucial for modification in satellite-based precipitation retrieval algorithms, as well as for the provision of guidance during the selection of substitute precipitation data. This study evaluated the performances of three Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA) products (3B42V6, 3B42RT and 3B42V7) with a reference to rain gauge observations in the Hunza River basin, northern Pakistan. Multi-spatial (pixel and basin) and temporal (daily, monthly, seasonal and annual) resolutions were considered for performance evaluation of TMPA products. Results revealed that the spatial pattern of observed precipitation over the basin was adequately captured by 3B42V7 but misplaced by 3B42V6 and 3B42RT. All TMPA products were unable to capture the intense precipitation events. On the daily time scale, the performance of TMPA products was very poor over both spatial scales. 3B42V6 underestimated the precipitation (31.25% and 44.27% on pixel and basin scales, respectively). By contrast, 3B42RT significantly overestimated the precipitation (47.91% and 38.62% on pixel and basin scales, respectively), while 3B42V7 showed overestimation (17.30%) on pixel scale and slight underestimation (6.24%) on the basin scale. On the seasonal scale, TMPA products showed significant biases with observed precipitation data. We found that the TMPA products performed relatively better on monthly and annual time scales and overall performance of 3B42V7 product was better than that of 3B42V6 and 3B42RT. The bias in 3B42V7 was improved by 85.90% compared with 3B42V6 and by 116.16% compared with 3B42RT. Thus, it is concluded that the TMPA products were unreliable to capture the intense precipitation events and retain high errors on daily and seasonal scales. Therefore, caution should be considered while using these precipitation estimates as a substitute data in hydrology, meteorology and climatology studies in Hunza River basin. However, due to the reasonable performance of monthly and annual 3B42V7 estimates, these can be used as an acceptable substitute for applications in the region.
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