A statistics-based automated flood event separation

Fischer, Svenja; Schumann, Andreas; Bühler, Philipp

doi:10.1016/j.hydroa.2020.100070

Cited by 16 publications

(15 citation statements)

References 30 publications

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“…To verify the reliability of the extracted results, this paper used the global discharge data products released by GRDC and the statistics-based automated flood event extraction (FloodR) method to extract possible flood events. FloodR is a statistics-based flood event separation method proposed by Fischer et al (2021). It can automatically separate flood events using a univariate daily discharge time series, and it includes additional tool for manually checking and correcting the separation results quickly, allowing expert knowledge to be easily incorporated.…”

Section: Flood Event Extraction Based On Daily Discharge Datamentioning

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: Flood Event Extraction Based On Daily Discharge Datamentioning

confidence: 99%

“…(3) the sum of the increasing discharges is similar to the sum of the recession of the flood event (Fischer et al, 2021). FloodR can also automatically handle missing data and perform flood separation in segments according to the missing data before finally merging them.…”

Section: Flood Event Extraction Based On Daily Discharge Datamentioning

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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“…A nonparametric algorithm suggested by Tarasova et al ( 2018) is adopted to identify runoff events in this study, which has been widely applied in many different basins over the world because of its advantages in identifying flood events (Fischer et al, 2021;Giani et al, 2022;Lu et al, 2020;Winter et al, 2022). The brief procedure of this algorithm is described as follows: (1) picking out local minima within nonoverlapping 5 d windows with respect to the entire streamflow time series; (2) examining the extracted series of minima with the goal of finding turning points, all of which are usually defined as the points that are at least 1.11 times smaller than their neighboring minima;…”

Section: Flood Event Selectionmentioning

confidence: 99%

Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Xie

Xu²,

Yu³

et al. 2022

Hydrol. Earth Syst. Sci.

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Abstract. Gravity Recovery and Climate Experiment (GRACE) and its successor GRACE Follow-on (GRACE-FO) satellite provide terrestrial water storage anomaly (TWSA) estimates globally that can be used to monitor flood in various regions at monthly intervals. However, the coarse temporal resolution of GRACE and GRACE-FO satellite data has been limiting their applications at finer temporal scales. In this study, TWSA estimates have been reconstructed and then temporally downscaled into daily values based on three different learning-based models, namely a multi-layer perceptron (MLP) model, a long-short term memory (LSTM) model and a multiple linear regression (MLR) model. Furthermore, a new index incorporating temporally downscaled TWSA estimates combined with daily average precipitation anomalies is proposed to monitor the severe flood events at sub-monthly timescales for the Yangtze River basin (YRB), China. The results indicated that (1) the MLP model shows the best performance in reconstructing the monthly TWSA with root mean square error (RMSE) = 10.9 mm per month and Nash–Sutcliffe efficiency (NSE) = 0.89 during the validation period; (2) the MLP model can be useful in temporally downscaling monthly TWSA estimates into daily values; (3) the proposed normalized daily flood potential index (NDFPI) facilitates robust and reliable characterization of severe flood events at sub-monthly timescales; (4) the flood events can be monitored by the proposed NDFPI earlier than traditional streamflow observations with respect to the YRB and its individual subbasins. All these findings can provide new opportunities for applying GRACE and GRACE-FO satellite data to investigations of sub-monthly signals and have important implications for flood hazard prevention and mitigation in the study region.

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“…The main characteristics for all considered catchments are listed in Table 1 Daily series of discharges were obtained from the Global Runoff Data Center (GRDC). To separate flood events, we used a variance threshold equal to the mean of all 3-days variances of daily discharges plus 25% of the standard deviation of all 3-days variances (Fischer et al, 2021). This way, significant increases in the daily discharge values were identified and the corresponding direct runoff was separated for each flood event such that the baseflow components at the beginning and end of it were approximately identical.…”

Section: Datamentioning

confidence: 99%

“…Only floods in the joint observation period of all gauges (from 1936 to 2013 for the Danube Basin, respectively, 1931 to 2013 for the Rhine Basin) were considered. Both small and large floods were included in the data set to obtain a large data sample for flood type classification, while small variations in discharge were excluded by the separation algorithm of Fischer et al (2021) assuming that they are natural variability of flow. For the Danube Basin, we obtained in average 5.7 events per year with a mean duration of 9 days and, for the Rhine Basin, 5.4 events per year with a mean duration of 15 days.…”

Section: Datamentioning

confidence: 99%

Multivariate Flood Frequency Analysis in Large River Basins Considering Tributary Impacts and Flood Types

Fischer

Schumann

2021

Water Resources Research

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Flood events are characterized by the nature of runoff-generating precipitation events and distributions of runoff volumes among tributaries • A statistical simulation generates peak-volume pairs based on flood types at confluences, consistently for upstream and downstream gauges • Vine copulas are used to estimate most probable combinations of flood types and superposition for downstream events of given return periods

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A statistics-based automated flood event separation

Cited by 16 publications

References 30 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

Monitoring the extreme flood events in the Yangtze River basin based on GRACE and GRACE-FO satellite data

Multivariate Flood Frequency Analysis in Large River Basins Considering Tributary Impacts and Flood Types

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