Appraising the Potential of Using Satellite‐Based Rainfall Estimates for Evaluating Extreme Precipitation: A Case Study of August 2014 Event Across the West Rapti River Basin, Nepal

Talchabhadel, Rocky; Nakagawa, Hajime; Kawaike, Kenji; Yamanoi, Kazuki; Musumari, Herman; Adhikari, Tika B.; Prajapati, Rajaram

doi:10.1029/2020ea001518

Cited by 8 publications

(3 citation statements)

References 34 publications

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“…In contrast, precipitation observations serve as the primary input data required for SM modeling. However, all other products were inappropriate with respect to spatial and temporal variation of precipitation over the study area, with similar outcomes found in the previous findings of certain studies [37][38][39]. The temporal estimations of SM2Rain were in better agreement with gauge estimation, which is consistent with the findings of a particular study [10,24].…”

Section: Discussionsupporting

confidence: 89%

Evaluation of Soil Moisture-Based Satellite Precipitation Products over Semi-Arid Climatic Region

et al. 2022

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The ground validation of satellite-based precipitation products (SPPs) is very important for their hydroclimatic application. This study evaluated the performance assessment of four soil moisture-based SPPs (SM2Rain, SM2Rain- ASCAT, SM2Rain-CCI, and GPM-SM2Rain). All data of SPPs were compared with 64 weather stations in Pakistan from January 2005 to December 2020. All SPPs estimations were evaluated on daily, monthly, seasonal, and yearly scales, over the whole spatial domain, and at point-to-pixel scale. Widely used evaluation indices (root mean square error (RMSE), correlation coefficient (CC), bias, and relative bias (rBias)) along with categorical indices (false alarm ratio (FAR), probability of detection (POD), success ratio (SR), and critical success index (CSI) were evaluated for performance analysis. The results of our study signposted that: (1) On a monthly scale, all SPPs estimations were in better agreement with gauge estimations as compared to daily scales. Moreover, SM2Rain and GPM-SM2Rain products accurately traced the spatio-temporal variability with CC >0.7 and rBIAS within the acceptable range (±10) of the whole country. (2) On a seasonal scale (spring, summer, winter, and autumn), GPM-SM2Rain performed more satisfactorily as compared to all other SPPs. (3) All SPPs performed better at capturing light precipitation events, as indicated by the Probability Density Function (PDF); however, in the summer season, all SPPs displayed considerable over/underestimates with respect to PDF (%). Moreover, GPM-SM2RAIN beat all other SPPs in terms of probability of detection. Consequently, we suggest the daily and monthly use of GPM-SM2Rain and SM2Rain for hydro climate applications in a semi-arid climate zone (Pakistan).

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Section: Discussionsupporting

confidence: 89%

Evaluation of Soil Moisture-Based Satellite Precipitation Products over Semi-Arid Climatic Region

et al. 2022

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“…Merapi area is IMERG-L. This result is also supported by Talchabhadel et al (2021) who examined the ability of several rain satellites to evaluate extreme rainfall in Nepal and found IMERG-LATE to be the best.…”

Section: Discussionsupporting

confidence: 56%

Analysis of Extreme Rainfall in the Mt. Merapi Area

Anita Yuliana,

Joko Sujono,

Karlina

2023

jcef

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The slopes of Mount Merapi (Mt. Merapi) are an area prone to hydrological disasters due to elevation and orography. Hydrological disasters that have the potential to occur include floods, erosion, landslides, and drought which are closely related to extreme rainfall. Spatial and temporal variability of rainfall in mountainous areas requires rainfall data that can represent rainfall events. Therefore, this research aims to obtain the reliability of satellite rainfall data in the extreme rainfall indices. The CHIRPS, GPM-IMERG FINAL (IMERG-F) and GPM-IMERG LATE (IMERG-L) will be used in the reliability analysis of satellite-based rainfall compared to observed rainfall station. To validate satellite rain data, statistical criteria are utilized with parameters such as Correlation Coefficient (R), Root Mean Squared Error (RMSE), and Relative Bias (RB). Satellite-based rainfall estimates have a weak to moderate correlation (0.19 – 0.55), the RMSE value is relatively good (12.18 – 31.35 mm) and the observed bias tends to underestimate the estimated values. The capabilities of the IMERG-F, IMERG-L and CHIRPS satellites as alternative rainfall data in the Mt. Merapi area are quite good where IMERG-L has the best performance in capturing rainfall above 50 mm (R50mm), Consecutive Dry Days (CDD) indices, max 1–day and 5-day precipitation (Rx1day and Rx5day). The potential for extreme rainfall that is most prone to trigger lava floods occurs in the western region of Mt. Merapi at Ngandong Station (Sta. Ngandong). In this region, there is a high occurrence of extreme rainfall events. For instance, there were 501 instances of R50mm with an intensity of 77 mm day-1, Total Precipitation (PRCPTOT) reaches 3385 mm, Rx5day reaches 393 mm, and Consecutive Wet Days (CWD) lasts for 30 days. The results of this analysis can assist in climate understanding and modeling of extreme rainfall relevant to the region and support water resource management and disaster risk mitigation.

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“…Omranian et al [22] investigated and analyzed the spatiotemporal performance of IMERG-Final for precipitation monitoring during hurricane events, and the findings indicate that the general performance of IMERG-Final is satisfactory in detecting the spatial variability for this heavy rainfall event. Talchabhadel et al [23] evaluated the performance of different satellite rainfall products in capturing extreme precipitation events in the West Rapti River (WRR) basin of Nepal in August 2014. Aiming at the Beijing extreme heavy rainfall event of 21 July 2012, Huang et al [24] reported the performance of the TMPA precipitation products (3B42RT and 3B42V7) in this extreme precipitation event.…”

Section: Introductionmentioning

confidence: 99%

Research on the Monitoring Ability of Fengyun-Based Quantitative Precipitation Estimates for Capturing Heavy Precipitation: A Case Study of the “7·20” Rainstorm in Henan Province, China

Yong

Shen

2023

Remote Sensing

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One of the important tasks of the Chinese geostationary and meteorological satellite Fengyun-2 (FY2) series is to provide quantitative precipitation estimates (QPE) with high spatiotemporal resolutions for East Asia. To analyze the monitoring capabilities of FY2-based QPEs in extreme rainfall events, this study comprehensively evaluated and compared the performances of FY-2G and FY-2H QPEs for the “7.20” rainstorm in Henan province, China from 17 July 2021 to 22 July 2021. Three continuous metrics and three categorical metrics were adopted to assess the accuracies of FY-2G and FY-2H QPEs, referenced by gauge observations from 116 meteorological stations. The results show that the FY-2G QPE has lower BIAS (−9.64% for FY-2G, −46.22% for FY-2H) and RMSE (5.83 mm/h for FY-2G, 8.4 mm/h for FY-2H) and higher CC (0.57 for FY-2G, 0.24 for FY-2H) than FY-2H QPE in this rainstorm event. Moreover, the FY-2G QPE is not only more consistent with the ground reference with respect to the rainfall amount, but also has higher detecting capability in the “7.20” rainstorm event when compared with the FY-2H QPE. The FY-2G QPE presented a higher capability to correctly capture the precipitation event for the “7.20” rainstorm because of higher POD (probability of detection) and CSI (critical success index) relative to FY-2H QPE, especially in complex topography. From the spatial distribution of precipitation amount, the FY-2G QPE captured the rainstorm center of extreme precipitation more accurately relative to the latest FY-2H product. On the other hand, the previous generation of FY-2G QPE was closer to the continuous rainfall process and precipitation duration with ground observations than the latest FY-2H QPE. Therefore, the precipitation retrieval algorithm of FY-2H QPE still had room to improve. It is necessary to introduce error correction algorithms, especially in complex topography for rainstorm events.

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Appraising the Potential of Using Satellite‐Based Rainfall Estimates for Evaluating Extreme Precipitation: A Case Study of August 2014 Event Across the West Rapti River Basin, Nepal

Cited by 8 publications

References 34 publications

Evaluation of Soil Moisture-Based Satellite Precipitation Products over Semi-Arid Climatic Region

Evaluation of Soil Moisture-Based Satellite Precipitation Products over Semi-Arid Climatic Region

Analysis of Extreme Rainfall in the Mt. Merapi Area

Research on the Monitoring Ability of Fengyun-Based Quantitative Precipitation Estimates for Capturing Heavy Precipitation: A Case Study of the “7·20” Rainstorm in Henan Province, China

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