An approach to quality validation of large-scale data from the Chinese Flash Flood Survey and Evaluation (CFFSE)

Yuan, Xiaochen; Liu, Yesen; Huang, Yaohuan; Tian, Fuchang

doi:10.1007/s11069-017-2986-0

Cited by 21 publications

(18 citation statements)

References 17 publications

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“…Sichuan Province is located in southwestern China, between 97 • 21 E to 108 • 33 E and 26 • 03 N to 34 • 19 N. The area of the province is 486,000 km 2 , and the population was 82.62 million at the end of 2016. The altitude of Sichuan Province varies from east to west, increasing from 220 m to 7556 m, respectively, Additionally, the landform of the province is complex and includes the Sichuan Basin (SB); low-, middle-, and high-altitude areas; and high mountain areas [23].…”

Section: Study Areamentioning

confidence: 99%

“…VFC is calculated from normalized difference vegetation index (NDVI) grid data [31], as shown in Equation (2).…”

Section: Factor Acquisitionsmentioning

confidence: 99%

“…Statistics show that the proportions of deaths caused by flash floods to those caused by all flood disasters from 2004 to 2010 were 78%, 84%, 71%, 75%, 80%, 80%, and 90%, per year respectively, with 3887 people dead or missing in 2010 [1,2]. Notably, 560 million residents are scattered throughout hilly areas of China, where flash floods are likely to cause casualties during the rainy months.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Driving Force Analysis of the Temporal and Spatial Distribution of Flash Floods in Sichuan Province

et al. 2017

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Flash floods are important natural disasters in China that can result in casualties and property losses. In this paper, we present a quantitative approach to examine the driving factors of the spatiotemporal distribution of flash floods based on a geographical detector. The environmental background condition (elevation, slope, etc.), precipitation, and human activity factors, as well as changes in these factors, are investigated in Sichuan Province via a driving force analysis. The results show that heavy precipitation is the most important driver, with power of determinant (PD)

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Section: Study Areamentioning

confidence: 99%

“…VFC is calculated from normalized difference vegetation index (NDVI) grid data [31], as shown in Equation (2).…”

Section: Factor Acquisitionsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Driving Force Analysis of the Temporal and Spatial Distribution of Flash Floods in Sichuan Province

et al. 2017

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“…Gourley et al (2013) announced an assembled database on flash floods for the entire USA, which is available in multiple, common formats to a wide range of users. Yuan et al (2017) presented a quality validation approach that uses Anselin Local Moran's I (DM-Moran), which is based on a flash flood model and a spatial data mining algorithm. They used the DM-Moran model to validate the quality of the data from the Chinese Flash Flood Survey and Evaluation (CFFSE) project.…”

Section: Research Status Of Flash Flood Early-warning Indexmentioning

confidence: 99%

A review of advances in China’s flash flood early-warning system

et al. 2018

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This paper summarizes the main flash flood early-warning systems of America, Europe, Japan, and Taiwan China and discusses their advantages and disadvantages. The latest development in flash flood prevention is also presented. China's flash flood prevention system involves three stages. Herein, the warning methods and achievements in the first two stages are introduced in detail. Based on the worldwide experience of flash flood early-warning systems, the general research idea of the third stage is proposed from the viewpoint of requirements for flash flood prevention and construction progress of the next stage in China. Real-time dynamic warning systems can be applied to the early-warning platform at four levels (central level, provincial level, municipal level, and county level) . Through this, soil moisture, peak flow, and water level can be calculated in real-time using distributed hydrological models, and then flash flood warning indexes can be computed based on defined thresholds of runoff and water level. A compound warning index (CWI) can be applied to regions where rainfall and water level are measured by simple equipment. In this manner, flash-flood-related factors such as rainfall intensity and antecedent and cumulative rainfall depths can be determined using the CWI method. The proposed methodology for the third stage could support flash flood prevention measures in the 13th (2016)(2017)(2018)(2019)(2020). The research achievements will serve as a guidance for flash flood monitoring and warning as well as flood warning in medium and small rivers.

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“…The potential severity level is obtained through comparing the simulated peak flood discharge with the flood discharge threshold. This threshold is derived using the rational formula method [41] for estimating design storms (Equation (13)). …”

Section: Flash Flood Discharge Thresholdmentioning

confidence: 99%

Forecasting and Providing Warnings of Flash Floods for Ungauged Mountainous Areas Based on a Distributed Hydrological Model

Wang

Liu

Guo

et al. 2017

Water

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Flash floods occur in mountainous catchments with short response times, which are among the most devastating natural hazards in China. This paper intends to forecast and provide warnings of flash floods timely and precisely using the flash flood warning system, which is established by a new distributed hydrological model (the China flash flood hydrological model, CNFF-HM). Two ungauged mountainous regions, Shunchang and Zherong, are chosen as the study areas. The CNFF-HM is calibrated in five well-monitored catchments. The parameters for the ungauged regions are estimated by regionalization. River water stage data and reservoir water stage data from Shunchang, and reservoir water stage data from Zherong are used to validate the model. The model performs well and the average Nash-Sutcliffe efficiency (NSE) is above 0.8 for the five catchments. The validation shows the difference in the timing of flood peaks using the two types of water stage data is less than 1 h. The rising and declining trends of the floods correspond to the observed trends over the entire validation process. Furthermore, the flash flood warning system was effectively applied in flash flood event on 28 September 2016 in Zherong. Thus, the CNFF-HM with regionalization is effective in forecasting flash floods for ungauged mountainous regions.

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An approach to quality validation of large-scale data from the Chinese Flash Flood Survey and Evaluation (CFFSE)

Cited by 21 publications

References 17 publications

Driving Force Analysis of the Temporal and Spatial Distribution of Flash Floods in Sichuan Province

Driving Force Analysis of the Temporal and Spatial Distribution of Flash Floods in Sichuan Province

A review of advances in China’s flash flood early-warning system

Forecasting and Providing Warnings of Flash Floods for Ungauged Mountainous Areas Based on a Distributed Hydrological Model

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