Meta-Analysis and Visualization of the Literature on Early Identification of Flash Floods

Yang, Zhengli; Yuan, Xinyue; Liu, Chao; Nie, Ruihua; Liu, Tiegang; Dai, Xiaoai; Ma, Lei; Tang, Min; Xu, Yangsheng; Lü, Heng

doi:10.3390/rs14143313

Cited by 7 publications

(3 citation statements)

References 122 publications

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“…However, this is only an application analysis of the two technologies rather than a comprehensive review of early identification technologies. In 2022 (Yang et al, 2022), we began to focus on the research of early identification technology of flash floods, roughly sorted out the development process of this field by combining meta-analysis with visual analysis, initially established the knowledge framework for the research and provided future research directions. The prevention and control of most traditional flash flood disasters focus solely on the role of rainstorms in triggering floods but do not consider the factor that local sediment deposition will raise the water level, resulting in insufficient accuracy in the early identification of flash flood disasters (Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

Yang,

Lu,

Song

et al. 2024

Preprint

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Abstract. Flash flood disasters are one of the major natural disasters in the world, and rapid and accurate early identification of flash flood disasters is the key to preventing and controlling them. In recent years, computer and spatial information technology development has promoted the advancement of early identification technology for flash floods. However, previous research has mainly focused on the impact of "water" and neglected the impact of "sediment" deposition on the rise of water levels. To gain a more comprehensive understanding of flash floods and improve the accuracy of early identification, this article first uses bibliometric methods to review the spatiotemporal distribution, internal relationships, and research hotspots of literature in this field over the past 42 years. Then, the research practice of considering the impact of sediment on the early identification of flash floods was introduced from three aspects: mechanism, model, and uncertainty. Finally, the existing problems in current research were discussed, and future research directions were proposed. The research results have shown that the number of publications in this field has been increasing yearly and will continue to increase, but the cooperation between authors is not close. The coupling effect between sediment replenishment and floods cannot be ignored. Taking into account multiple uncertainties can greatly improve recognition accuracy. This study can provide a panoramic literature perspective and practical research experience for relevant researchers and decision-makers and support further improving flash flood prevention and control capabilities.

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Section: Introductionmentioning

confidence: 99%

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

Yang,

Lu,

Song

et al. 2024

Preprint

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“…These examples show how disastrous the increasing temperatures and combined rain and snowmelt floods can be [20]. There is a potential for even higher temperatures, more intense snowmelt and an increase in RoS events in coming years [21], and this development is more pronounced at higher latitudes [22,23], leading to more frequent and severe floods, snow avalanches [24,25], and landslides [26,27]. Hence, it is important to analyze these combined rain and snowmelt-induced flash floods.…”

Section: Introductionmentioning

confidence: 99%

Modelling Flash Floods Driven by Rain-on-Snow Events Using Rain-on-Grid Technique in the Hydrodynamic Model TELEMAC-2D

Godara,

Bruland,

Alfredsen

2023

Water

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Due to the changing climate, flash floods have been increasing recently and are expected to further increase in the future. Flash floods caused by heavy rainfall with snowmelt contribution due to sudden rises in temperature or rain-on-snow events have become common in autumn and winter in Norway. These events have caused widespread damage, closure of roads and bridges, and landslides, leading to evacuations in the affected areas. Hence, it is important to analyze such events. In this study, the rain-on-grid technique in the TELEMAC-2D hydrodynamic model was used for runoff modelling and routing using input of snowmelt, and precipitation partitioned on snow and rain was calculated via the hydrological model HBV. The results show the importance of including snowmelt for distributed runoff generation and how the rain-on-grid technique enables extracting flow hydrographs anywhere in the catchment. It is also possible to extract the flow velocities and water depth at each time step, revealing the critical locations in the catchment in terms of flooding and shear stresses. The rain-on-grid model works particularly well for single peak events, but the results indicate the need for a time-varying curve number for multiple peak flood events or the implementation of another infiltration model.

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“…The have been collected 175 documents in the United States had carried out the most research in this field, followed by India by 77 documents, and then 71 documents Italy (Aronica et al, 2012;De Marchi et al, 2012;Forte et al, 2005;Heredia-Calderon et al, 1999, Miglietta et al, 2008. China ranked fourth (70 documents), and France ranked fifth, with 64 published documents (Yang et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

Generation of a flood susceptibility map of evenly weighted conditioning factors for Hungary

Sarkadi¹,

Pirkhoffer²,

Lóczy³

et al. 2022

Geographica Pannonica

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Over the past decades, in the mountainous, hilly and/or urban areas of Hungary several high-intensity storms were followed by severe flash flooding and other hydrologic consequences. The overall aim of this paper was to upgrade the national flash flood susceptibility map of Hungary first published by Czigány et al. (2011). One elementary watershed level (FFSIws) and three settlement level flash flood susceptibility maps (FFSIs) were constructed using 13 environmental factors that influence flash flood generation. FFSI maps were verified by 2,677 documented flash flood events. In total, 5,458 watersheds were delineated. Almost exactly 10% of all delineated watersheds were included into the category of extreme susceptibility. While the number of the mean-based FFSIs demonstrated a normal quasi-Gaussian distribution with very low percentages in the quintile of low and extreme categories, the maximum-based FFSIs overemphasized the proportion of settlements of high and extreme susceptibility. These two categories combined accounted for more than 50% of all settlements. The highest accuracy at 59.02% for class 5 (highest susceptibility) was found for the majority based FFSIs. The current map has been improved compared to the former one in terms of (i) a higher number of conditional factors considered, (ii) higher resolution, (iii) being settlement-based and (iv) a higher number of events used for verification.

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Meta-Analysis and Visualization of the Literature on Early Identification of Flash Floods

Cited by 7 publications

References 122 publications

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

The Early Identification of Flash Flood Disasters: Mechanism, Model and Uncertainty

Modelling Flash Floods Driven by Rain-on-Snow Events Using Rain-on-Grid Technique in the Hydrodynamic Model TELEMAC-2D

Generation of a flood susceptibility map of evenly weighted conditioning factors for Hungary

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