Detectability of rainfall characteristics over a mountain river basin in the Himalayan region from 2000 to 2015 using ground- and satellite-based products

Prajapati, Rajaram; Silwal, Priya; Duwal, Sudeep; Shrestha, Sandesh; Kafle, Aalok Sharma; Talchabhadel, Rocky; Kumar, Saurav

doi:10.1007/s00704-021-03820-9

Cited by 9 publications

(4 citation statements)

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“…PBIAS assesses the extent to which the simulated streamflow overestimates or underestimates the observations at the gauging station. Specifically, a positive PBIAS value indicates an overestimation relative to the gauging observations, while a negative PBIAS value indicates an underestimation [17].…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…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]. They are not limited by complex terrain and can provide consistent, wider spatial coverage and higher temporal resolution precipitation values compared with gauge-based datasets.…”

Section: Introductionmentioning

confidence: 99%

“…There are two primary approaches commonly used to evaluate SPP performance: (1) direct comparison of satellite-derived precipitation estimates with in situ precipitation data, and (2) capability assessment of SPPs to simulate streamflow through hydrological models [7,13]. Many researchers have used these approaches to assess various SPPs over different climate regions [3,13,16,17,24,[27][28][29][30][31][32][33]. The evaluation and validation of SPPs for a specific area may not apply to others owing to the heterogeneity of the terrain, climate, soil, and land cover [7].…”

Section: Introductionmentioning

confidence: 99%

“…Ref. [17] assessed the aptness of four SPPs, PERSIANNs, TMPA, CHIRPS, and Multi-Source Weighted-Ensemble Precipitation (MSWEP), in capturing rainfall attributes across mountainous Himalayan watersheds.…”

Section: Introductionmentioning

confidence: 99%

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Evaluation of Satellite-Derived Precipitation Products for Streamflow Simulation of a Mountainous Himalayan Watershed: A Study of Myagdi Khola in Kali Gandaki Basin, Nepal

Aryal,

Tran,

Kumar

et al. 2023

Remote Sensing

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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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Section: Evaluation Metricsmentioning

confidence: 99%