High effectiveness of GRACE data in daily-scale flood modeling: case study in the Xijiang River Basin, China

Xiong, Jinghua; Wang, Zhaoli; Wu, Xushu; Yin, Jiabo; Wang, Jun; Gong, Qi

doi:10.1007/s11069-022-05312-z

Cited by 5 publications

(6 citation statements)

References 74 publications

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“…Extreme precipitation has sudden characteristics, and its signals are reflected in the high-frequency signals of GRACE (Xiong et al, 2022;Gouweleeuw et al, 2018). Seasonal and trend decomposition using loess (STL) (Robert et al, 1990) is a filtering process as well as a general and robust time series decomposition and forecasting method used to decompose time series variables into seasonal, trend and remainder components for further forecasting.…”

Section: Seasonal and Trend Decomposition Using Loess (Stl)mentioning

confidence: 99%

“…Gouweleeuw et al (2018) used a daily solution based on GRACE TWS and daily river runoff data to assess major flood events in the Ganges-Brahmaputra Delta and confirmed the method's potential for gravity-based large-scale flood monitoring. Xiong et al (2022) used daily downscaled GRACE data to detect short-duration and high-intensity floods. They found that there was a strong correlation between the high-frequency components of GRACE TWS and runoff.…”

Section: Introductionmentioning

confidence: 99%

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Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data

Zhang

Wang

2023

Earth Syst. Sci. Data

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Abstract. A complete global flood event record would aid researchers to analyze the distribution of global floods and, thus, better formulate and manage disaster prevention and reduction policies. This study used Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days globally between 1 April 2002 and 31 August 2016; these results were then further compared and validated with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that GRACE-based flood days could cover 81 % of the flood events in the DFO database, 87 % of flood events extracted by MODIS and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection greater than or equal to 0.5 reached 62 % among 261 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. Finally, we provided flood day products with a 1∘ spatial resolution covering the range between 60∘ S and 60∘ N from 1 April 2002 to 31 August 2016; these products can be obtained from https://doi.org/10.5281/zenodo.6831384 (Zhang et al., 2022b). Thus, this research contributes a data foundation for the mechanistic analysis and attribution of global flood events.

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Section: Seasonal and Trend Decomposition Using Loess (Stl)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data

Zhang

Wang

2023

Earth Syst. Sci. Data

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show abstract

“…Extreme precipitation has sudden characteristics, and its signals are reflected in the high-frequency signals of GRACE 165 (Xiong et al, 2022;Gouweleeuw et al, 2018). Seasonal and trend decomposition using loess (STL) (Robert et al, 1990) is a filtering process as well as a general and robust time series decomposition and forecasting method used to decompose time series variables to obtain season, trend and remainder information for further forecasting.…”

Section: Seasonal and Trend Decomposition Using Loess (Stl)mentioning

confidence: 99%

“…In this study, the STL function in the R language "stats" package was used to process all grid point time series corresponding to the GRACE TWS period (Apr. 1st, 2002-Aug. 31st, 2016, where the parameter "t.window" was set to 31 days based on the methods of Gouweleeuw and Xiong et al (Xiong et al, 2022;Gouweleeuw et al, 2018) and "s.window" was set according to the period of the time series dataset itself. The period was determined using a Fourier transform to convert to the frequency domain to obtain the frequency (i.e., period) corresponding to the maximum amplitude.…”

Section: Seasonal and Trend Decomposition Using Loess (Stl)mentioning

confidence: 99%

“…Ben T. Gouweleeuw et al (Gouweleeuw et al, 2018) used a daily solution based on GRACE TWS and daily river runoff data to assess major flood events in the Ganges-Brahmaputra Delta and confirmed the method's potential for gravity-based large-85 scale flood monitoring. Jinghua Xiong et al (Xiong et al, 2022) used daily downscaled GRACE data to detect short-duration and high-intensity floods. They found that there is a strong correlation between the high-frequency components of GRACE TWS and runoff.…”

mentioning

confidence: 99%

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Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation

Zhang

Li²,

Wang³

2022

Preprint

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Abstract. A complete global flood event record helps researchers analyse the distribution law of global floods and better formulate and manage disaster prevention and reduction policies. This study used GRACE terrestrial water storage and precipitation data combined with high-frequency filtering, anomaly detection and flood potential index methods to successfully extract historical flood days quasi-globally between Apr. 1st, 2002, and Aug. 31st, 2016, and further compared and validated the results with Dartmouth Flood Observatory (DFO) data, Global Runoff Data Centre (GRDC) discharge data, news reports and social media data. The results showed that the floods extracted in this study can cover 81 % of the flood events in the DFO database and supplement many additional flood events not recorded by the DFO. Moreover, the probability of detection exceeding 0.5 reached 62 % in level-4 river basins compared to flood events derived from the GRDC discharge data. These detection capabilities and detection results are both good. We finally provided flood day products with 1° spatial resolution covering the range of 60° S–60° N from Apr. 1st, 2002, to Aug. 31st, 2016. This research provides a data foundation for the mechanistic analysis and attribution of global flood events.

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Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review

Nourani,

Jabbarian Paknezhad,

et al. 2024

Groundwater for Sustainable Development

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High effectiveness of GRACE data in daily-scale flood modeling: case study in the Xijiang River Basin, China

Cited by 5 publications

References 74 publications

Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data

Flood detection using Gravity Recovery and Climate Experiment (GRACE) terrestrial water storage and extreme precipitation data

Flood Detection Using GRACE Terrestrial Water Storage and Extreme Precipitation

Application of the machine learning methods for GRACE data based groundwater modeling, a systematic review

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