Evaluation of GSMaP Daily Rainfall Satellite Data for Flood Monitoring: Case Study—Kyushu Japan

Setiawati, Martiwi Diah; Miura, Fusanori

doi:10.4236/gep.2016.412008

Cited by 22 publications

(12 citation statements)

References 23 publications

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“…The validation of rainfall estimates from satellite data is essential for assessing the accuracy of estimation algorithms and for providing feedback to algorithm developers to improve their methods. A number of studies have evaluated satellite-derived rainfall information for areas of interest by comparing it to rain-gauge rainfall data (Dinku et al, 2010;Sapiano, 2010;Scheel et al, 2011;Setiawati 2016;Giarno et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Validation of Satellite Daily Rainfall Estimates Over Indonesia

Fatkhuroyan

Wati²,

Sukmana³

et al. 2018

For. Geo.

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The aim of this research is to evaluate the accuracy of Global Satellite Mapping of Rainfall (GSMaP) data by referencing daily rain-gauged rainfall measurements across the Indonesian Maritime Continent. We compare the daily rainfall data from GSMaP Moving Kalman Filter (MVK) to readings from 152 rain-gauge observation stations across Indonesia from March 2014 to December 2017. The results show that the mean square error (RMSE) is more accurate in the dry season. The highest proportion correct (PC) value is obtained for Bali-NTT, while the highest probability of detection (POD) and false alarm ratio (FAR) values are obtained for Kalimantan. GSMaP-MVK data is over-estimated compared to observations in Indonesia, with the mean accuracy for daily rainfall estimation being 85.47% in 2014, 85.74% in 2015, 82.73 in 2016, and 82.59% in 2017. Abstrak. Curah hujan adalah faktor terpenting dari siklus air dan energi bumi. Tujuan dari penelitian ini adalah untuk mengevaluasi akurasi dari GSMaP (Global Satellite Mapping of hujan dan memiliki nilai RMSE terbaik di musim kemarau. Selain itu, nilai tertinggi Proportion over-estimasi terhadap hasil pengamatan di Indonesia dengan akurasi rata-rata untuk prakiraan Kata kunci: GSMaP, curah hujan, rain-gauge.

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Section: Introductionmentioning

confidence: 99%

Validation of Satellite Daily Rainfall Estimates Over Indonesia

Fatkhuroyan

Wati²,

Sukmana³

et al. 2018

For. Geo.

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“…Among the available global precipitation data, GSMaP_MVK performs relatively well in east Asia. However, according to Setiawati and Miura [26], GSMaP_MVK underestimated rainy season precipitation in Kyushu Island of Japan by 46.78%, as compared with HSS of only 0.67. Therefore, according to existing literatures, MSWEP has obviously higher ability to identify precipitation events than TRMM 3B42 and other precipitation datasets at the daily time scale, which fully demonstrated the advantages of MSWEP.…”

Section: Discussionmentioning

confidence: 96%

Precipitation Characteristic Analysis of the Zhoushan Archipelago: From the View of MSWEP and Rainfall Merging

Xuan

Wang

et al. 2020

Water

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Based on the long series of gauge rainfall data from 1979 to 2015, the performance of Multi-Source Weighted-Ensemble Precipitation (MSWEP) precipitation dataset in the Zhoushan Archipelago and its surrounding sea area in Southeast China was evaluated from a variety of perspectives, and then the Cressman scheme was used to merge MSWEP with surface gauge measurements. It was found that at the spatial scale of 0.1° × 0.1°, MSWEP correctly detected most of the daily rainfall events in the study area. The surface precipitation was generally underestimated, with a relative deviation no more than 10%, but there was a fairly high miss reporting on heavy precipitation. The performance of MSWEP is also obviously characterized with seasonal fluctuation. Compared with the gauge records interpolation results, the accuracy statistics of rainfall dataset generated by merging MSWEP with gauge observations is improved to a certain degree. Especially its comprehensive identification ability of the dry and wet state for daily precipitation has been obviously raised. In addition, the merged data has the mixed characteristics of rain gauge observations and MSWEP in spatial structure. This paper has deepened the understanding of the performance of MSWEP in islands and sea areas, and also strengthened the understanding of the marginal effect of merging gauge data with MSWEP, even other global precipitation datasets.

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“…) satellite precipitation products (https://sharaku.eorc. jaxa.jp/GSMaP_crest/html/data.html) [45]. GSMaP is one of the most important satellite precipitation products of the new generation of GPM programs.…”

Section: Satellite Precipitation Datamentioning

confidence: 99%

Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

Wang

Chen

et al. 2020

Remote Sensing

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Global Satellite Mapping of Precipitation (GSMaP), Climate Hazards Group InfraRed Preconception with Station data (CHIRPS), Tropical Rain Measurement Mission Multisatellite Precipitation Analysis (TRMM 3B42 V7) and Rainfall Estimation from Soil Moisture Observations (SM2RAIN) are satellite precipitation products with high applicability, but their applicability in hydrological research in arid mountainous areas is not clear. Based on precipitation and runoff data, this study evaluated the applicability of each product to hydrological research in a typical mountainous basin (the Qaraqash River basin) in an arid region by using two methods: a statistical index and a hydrological model (Soil and Water Assessment Tool, SWAT). Simulation results were evaluated by Nash efficiency coefficient (NS), relative error (PBIAS) and determination coefficient (R2). The results show that: (1) The spatial distributions of precipitation estimated by these four products in the Qaraqash River basin are significantly different, and the multi-year average annual precipitation of GSMaP is 97.11 mm, which is the closest to the weather station interpolation results. (2) On the annual and monthly scales, GSMaP has the highest correlation (R ≥ 0.82) with the observed precipitation and the smallest relative error (BIAS < 6%). On the seasonal scale, the inversion accuracy of GSMaP in spring, summer and autumn is significantly higher than other products. In winter, all four sets of products perform poorly in estimating the actual precipitation. (3) Monthly runoff simulations based on SM2RAIN and GSMaP show good fitting (R2 > 0.6). In daily runoff simulation, GSMaP has the greatest ability to reproduce runoff changes. The study provides a reference for the optimization of precipitation image data and hydrological simulation in data-scarce areas.

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Evaluation of GSMaP Daily Rainfall Satellite Data for Flood Monitoring: Case Study—Kyushu Japan

Cited by 22 publications

References 23 publications

Validation of Satellite Daily Rainfall Estimates Over Indonesia

Validation of Satellite Daily Rainfall Estimates Over Indonesia

Precipitation Characteristic Analysis of the Zhoushan Archipelago: From the View of MSWEP and Rainfall Merging

Applicability Evaluation of Multisource Satellite Precipitation Data for Hydrological Research in Arid Mountainous Areas

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