Analysis of flash flood disaster characteristics in China from 2011 to 2015

He, Bin; Huang, Xianlong; Ma, Ming; Chang, Qingrui; Tu, Yong; Li, Qing; Zhang, Ke; Hong, Yang

doi:10.1007/s11069-017-3052-7

Cited by 108 publications

(56 citation statements)

References 20 publications

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“…Flood disaster is one of the common natural disaster, which often causes loss of life and property [1,2]. The risk of extreme floods in large basins of China has been substantially reduced owing to the improvement of flood forecasting skills in recent years [3]. However, flash flood forecasting and prevention of the small and medium catchments (SMCs) remains an urgent problem [4,5].…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Sun

Yao

et al. 2020

Water

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The Weather Research and Forecasting (WRF)-Hydro model as a physical-based, fully-distributed, multi-parameterization modeling system easy to couple with numerical weather prediction model, has potential for operational flood forecasting in the small and medium catchments (SMCs). However, this model requires many input forcings, which makes it difficult to use it for the SMCs without adequate observed forcings. The Global Land Data Assimilation System (GLDAS), the WRF outputs and the ideal forcings generated by the WRF-Hydro model can provide all forcings required in the model for these SMCs. In this study, seven forcing scenarios were designed based on the products of GLDAS, WRF and ideal forcings, as well as the observed and merged rainfalls to assess the performance of the WRF-Hydro model for flood simulation. The model was applied to the Chenhe catchment, a typical SMC located in the Midwestern China. The flood prediction capability of the WRF-Hydro model was also compared to that of widely used Xinanjiang model. The results show that the three forcing scenarios, including the GLDAS forcings with observed rainfall, the WRF forcings with observed rainfall and GLDAS forcings with GLDAS-merged rainfall, are optimal input forcings for the WRF-Hydro model. Their mean root mean square errors (RMSE) are 0.18, 0.18 and 0.17 mm/h, respectively. The performance of the WRF-Hydro model driven by these three scenarios is generally comparable to that of the Xinanjiang model (RMSE = 0.17 mm/h).

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Section: Introductionmentioning

confidence: 99%

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Sun

Yao

et al. 2020

Water

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“…Barredo () reported that nearly 40% of flood‐related casualties in Europe during the period from 1950 to 2006 were triggered by flash floods. The devastating flash flood that occurred in Zhouqu County, Gansu Province, China, in 2010 caused 1,765 casualties (He et al, ). The Wenchuan earthquake‐stricken region in China was characterized by high‐relief topography, complex geological conditions, and abundant loose solid materials in the hillslopes, where flash floods or debris flows often occurred under heavy rainfall conditions in recent years (Zhou & Tang, ).…”

Section: Introductionmentioning

confidence: 99%

Real‐time monitoring and estimation of the discharge of flash floods in a steep mountain catchment

Zhang

Cui

Yin

et al. 2019

Hydrological Processes

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Synchronously and accurately estimating the flood discharges and dynamic changes in the fluid density is essential for hydraulic analysis and forecasting of flash floods, as well as for risk assessment. However, such information is rare for steep mountain catchments, especially in regions that are hotspots for earthquakes. Therefore, six hydrological monitoring sites were established in the main stream and tributaries of the 78.3-km 2 Longxi River catchment, an affected region of the Wenchuan earthquake region in China. Direct real-time monitoring equipment was installed to measure the flow depths, velocities, and fluid total pressures of the flood hydrographs. On the basis of field measurements, real-time mean cross-sectional velocities during the flood hydrographs could be derived from easily obtainable parameters: crosssectional maximum velocities and the calibrated dimensionless parameter K h . Realtime discharges were determined on the basis of a noncontact method to establish the effective rating curves of this mountainous stream, ranging from 1.46 to 386.34 m 3 /s with the root mean square errors of ≤10.22 m 3 /s. Compared with the traditional point-velocity method and empirical Manning's formula, the proposed noncontact method was reliable and safe for monitoring whole flood hydrographs. Additionally, the real-time fluid density during the flood hydrographs was calculated on the basis of the direct monitoring parameters for fluid total pressures and water depths. During the flood hydrograph, transient flow behaviour with higher fluid density generally occurred downstream during the flood peak periods when the flow was in the supercritical flow regime. The observed behaviour greatly increased the threat of damage to infrastructure and human life near the river. Thus, it is important to accurately estimate flood discharge and identify for fluid densities so that people at risk from an impending flash flood are given reliable, advanced warning. K E Y W O R D S discharge estimation, flash floods, flow regime, fluid density, real-time monitoring, steep mountain catchment

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“…Another age group, which literature is paying a little less attention to, are children and/or very young people. On one hand, there are several features they have in common with older people, such as being regarded as highly vulnerable (Cutter et al, ; He et al ) and in need of others' support (Birkmann et al, ; Fatemi et al, ; Tragaki et al, ). On the other hand, there are major differences, such as their physical condition, which is considered to be less heavily affected by natural hazards (Fekete, ; Steinführer & Kuhlicke, ), and their lack of experiences, which often leads to a lack of natural hazard risk awareness (Grothmann & Reusswig ; Kellens et al, ; Knocke & Kolivras ).…”

Section: The Linkages Between Demographic and Natural Hazards Researchmentioning

confidence: 99%

How demographic developments determine the management of hydrometeorological hazard risks in rural communities: The linkages between demographic and natural hazards research

Clar

2019

WIREs Water

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The management of hydrometeorological hazard risks increasingly builds on the notion that the main constituents—hazard risk and vulnerability—have to be understood as dynamic processes. Natural hazards and their impacts are influenced by changes in land use and impacts of climate change, whereas the vulnerability of communities is, inter alia, determined by changes in their demographic composition. Structurally weak rural regions have to face these challenges, while their capacities to manage hydrometeorological risks are affected by major demographic developments, such as population decline and demographic aging (especially in Europe). Nonetheless, academic research regarding this issue is still in its infancy. Research on the relationship between hydrometeorological hazards and long‐term demographic change often focuses on one side of the coin: impacts of hazards on the demographic composition of communities. The impacts of demographic change onto communities' ability to prepare for, respond to and recover from natural hazards appear as disregarded link. This article gives an overview over linkages by reviewing literature at the intersection of demographic and hydrological hazard research. The review of more than 90 publications provides insights into how particular demographic factors (socioeconomic status; migration, residency, and mobility; gender and gender relations; education and knowledge; and religion and beliefs) determine the management of hydrometeorological hazard risks. Furthermore, it identifies publications which consider long‐term developments. The overview shows that we lack a thorough connection between demographic change and hydrometeorological hazard risk management, which would be crucial for in‐depth studies as well as a more dynamic understanding of respective linkages. This article is categorized under: Science of Water > Water Extremes Engineering Water > Planning Water

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Analysis of flash flood disaster characteristics in China from 2011 to 2015

Cited by 108 publications

References 20 publications

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Evaluation of Flood Prediction Capability of the WRF-Hydro Model Based on Multiple Forcing Scenarios

Real‐time monitoring and estimation of the discharge of flash floods in a steep mountain catchment

How demographic developments determine the management of hydrometeorological hazard risks in rural communities: The linkages between demographic and natural hazards research

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