Abstract:Precipitation in a mountainous region is highly variable due to the complex terrain. Satellite-based precipitation estimates are potential alternatives to gauge measurements in these regions, as these typical measurements are not available or are scarce in high elevation areas. However, the accuracy of these gridded precipitation datasets need to be addressed before further usage. In this study, an evaluation of the spatial precipitation pattern in satellite-based precipitation products is provided, including … Show more
“…Summer monsoons and westerlies are Nepal's two dominant weather systems [70]. Most of the annual precipitation in Nepal falls during the monsoon period (June-September), and the rest falls during the pre-monsoon (March-May), post-monsoon (October-November), and winter (December-February) periods [14,70]. Due to the topographical differences, Nepal's mountainous regions receive less rainfall than other regions.…”
Section: Discussionmentioning
confidence: 99%
“…Precipitation data are essential for hydrological modeling [5,7,8], used in hydraulic studies [9], environmental studies [10][11][12], and climate change investigations [5]. Obtaining accurate and reliable precipitation data in remote and rugged areas, such as mountainous regions, is particularly challenging due to the high spatial and temporal variability of data, the sparse and irregular distribution of rain gauge networks, the inadequate spatial representation of in situ measurements, and the failure of climate stations due to natural disasters [7,13,14]. Satellite products have gained popularity for estimating or measuring precipitation and simulating streamflow in recent decades [6,8,[15][16][17].…”
Section: Introductionmentioning
confidence: 99%
“…The use of satellite-derived precipitation products (SPPs) is promising for supporting improved water resources management in Nepal, particularly the Himalayan region. The Himalayas make up almost 85% of Nepal's terrain, and the water reservoirs are the main source of several rivers providing water to millions of people residing in the downstream areas [14,21,22]. Due to Nepal's complex geography, the summer monsoon system governs most of the country's precipitation [14,23,24].…”
Section: Introductionmentioning
confidence: 99%
“…The Himalayas make up almost 85% of Nepal's terrain, and the water reservoirs are the main source of several rivers providing water to millions of people residing in the downstream areas [14,21,22]. Due to Nepal's complex geography, the summer monsoon system governs most of the country's precipitation [14,23,24]. The number of rain gauge-based stations in most mountain areas, particularly in high-elevation areas of Nepal, is significantly lower compared with low-elevation areas.…”
Section: Introductionmentioning
confidence: 99%
“…Ref. [14] evaluated the spatial patterns in satellite-only and gauge-calibrated precipitation products and compared them with 387-gauge measurements in Nepal. Ref.…”
This study assesses four Satellite-derived Precipitation Products (SPPs) that are corrected and validated against gauge data such as Soil Moisture to Rain—Advanced SCATterometer V1.5 (SM2RAIN-ASCAT), Multi-Source Weighted-Ensemble Precipitation V2.8 (MSWEP), Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM Final run V6 (GPM IMERGF), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). We evaluate the performance of these SPPs in Nepal’s Myagdi Khola watershed, located in the Kali Gandaki River basin, for the period 2009–2019. The SPPs are evaluated by validating the gridded precipitation products using the hydrological model, Soil and Water Assessment Tool (SWAT). The results of this study show that the SM2RAIN-ASCAT and GPM IMERGF performed better than MSWEP and CHIRPS in accurately simulating daily and monthly streamflow. GPM IMERGF and SM2RAIN-ASCAT are found to be the better-performing models, with higher NSE values (0.63 and 0.61, respectively) compared with CHIRPS and MSWEP (0.45 and 0.41, respectively) after calibrating the model with monthly data. Moreover, SM2RAIN-ASCAT demonstrated the best performance in simulating daily and monthly streamflow, with NSE values of 0.57 and 0.63, respectively, after validation. This study’s findings support the use of satellite-derived precipitation datasets as inputs for hydrological models to address the hydrological complexities of mountainous watersheds.
“…Summer monsoons and westerlies are Nepal's two dominant weather systems [70]. Most of the annual precipitation in Nepal falls during the monsoon period (June-September), and the rest falls during the pre-monsoon (March-May), post-monsoon (October-November), and winter (December-February) periods [14,70]. Due to the topographical differences, Nepal's mountainous regions receive less rainfall than other regions.…”
Section: Discussionmentioning
confidence: 99%
“…Precipitation data are essential for hydrological modeling [5,7,8], used in hydraulic studies [9], environmental studies [10][11][12], and climate change investigations [5]. Obtaining accurate and reliable precipitation data in remote and rugged areas, such as mountainous regions, is particularly challenging due to the high spatial and temporal variability of data, the sparse and irregular distribution of rain gauge networks, the inadequate spatial representation of in situ measurements, and the failure of climate stations due to natural disasters [7,13,14]. Satellite products have gained popularity for estimating or measuring precipitation and simulating streamflow in recent decades [6,8,[15][16][17].…”
Section: Introductionmentioning
confidence: 99%
“…The use of satellite-derived precipitation products (SPPs) is promising for supporting improved water resources management in Nepal, particularly the Himalayan region. The Himalayas make up almost 85% of Nepal's terrain, and the water reservoirs are the main source of several rivers providing water to millions of people residing in the downstream areas [14,21,22]. Due to Nepal's complex geography, the summer monsoon system governs most of the country's precipitation [14,23,24].…”
Section: Introductionmentioning
confidence: 99%
“…The Himalayas make up almost 85% of Nepal's terrain, and the water reservoirs are the main source of several rivers providing water to millions of people residing in the downstream areas [14,21,22]. Due to Nepal's complex geography, the summer monsoon system governs most of the country's precipitation [14,23,24]. The number of rain gauge-based stations in most mountain areas, particularly in high-elevation areas of Nepal, is significantly lower compared with low-elevation areas.…”
Section: Introductionmentioning
confidence: 99%
“…Ref. [14] evaluated the spatial patterns in satellite-only and gauge-calibrated precipitation products and compared them with 387-gauge measurements in Nepal. Ref.…”
This study assesses four Satellite-derived Precipitation Products (SPPs) that are corrected and validated against gauge data such as Soil Moisture to Rain—Advanced SCATterometer V1.5 (SM2RAIN-ASCAT), Multi-Source Weighted-Ensemble Precipitation V2.8 (MSWEP), Global Precipitation Measurement Integrated Multi-satellitE Retrievals for GPM Final run V6 (GPM IMERGF), and Climate Hazards Group InfraRed Precipitation with Station (CHIRPS). We evaluate the performance of these SPPs in Nepal’s Myagdi Khola watershed, located in the Kali Gandaki River basin, for the period 2009–2019. The SPPs are evaluated by validating the gridded precipitation products using the hydrological model, Soil and Water Assessment Tool (SWAT). The results of this study show that the SM2RAIN-ASCAT and GPM IMERGF performed better than MSWEP and CHIRPS in accurately simulating daily and monthly streamflow. GPM IMERGF and SM2RAIN-ASCAT are found to be the better-performing models, with higher NSE values (0.63 and 0.61, respectively) compared with CHIRPS and MSWEP (0.45 and 0.41, respectively) after calibrating the model with monthly data. Moreover, SM2RAIN-ASCAT demonstrated the best performance in simulating daily and monthly streamflow, with NSE values of 0.57 and 0.63, respectively, after validation. This study’s findings support the use of satellite-derived precipitation datasets as inputs for hydrological models to address the hydrological complexities of mountainous watersheds.
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