Spatiotemporal Characteristics and Driving Force Analysis of Flash Floods in Fujian Province

Xiong, Junnan; Quan, Pang; Fan, Chunkun; Cheng, Weiming; Ye, Chongchong; Zhao, Yunliang; He, Yuanrong; Cao, Yifan

doi:10.3390/ijgi9020133

Cited by 26 publications

(10 citation statements)

References 56 publications

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“…Such a model is the Geographical Detector (GeoDetector). Although GeoDetector has been tested in various studies of health, social and environmental sciences [20][21][22] over the last few years, its use in landslide-related research has been quite limited. Since it provides an effective way to identify and eliminate redundant variables, GeoDetector has been used in a few relevant studies [14,23,24] for factor selection purposes.…”

Section: Introductionmentioning

confidence: 99%

Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

Polykretis

Grillakis

Argyriou

et al. 2021

Land

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Over the last few years, landslides have occurred more and more frequently worldwide, causing severe effects on both natural and human environments. Given that landslide susceptibility (LS) assessments and mapping can spatially determine the potential for landslides in a region, it constitutes a basic step in effective risk management and disaster response. Nowadays, several LS models are available, with each one having its advantages and disadvantages. In order to enhance the benefits and overcome the weaknesses of individual modeling, the present study proposes a hybrid LS model based on the integration of two different statistical analysis models, the multivariate Geographical Detector (GeoDetector) and the bivariate information value (IV). In a GIS-based framework, the hybrid model named GeoDIV was tested to generate a reliable LS map for the vicinity of the Pinios artificial lake (Ilia, Greece), a Greek wetland. A landslide inventory of 60 past landslides and 14 conditioning (morphological, hydro-lithological and anthropogenic) factors was prepared to compose the spatial database. An LS map was derived from the GeoDIV model, presenting the different zones of potential landslides (probability) for the study area. This map was then validated by success and prediction rates—which translate to the accuracy and prediction ability of the model, respectively. The findings confirmed that hybrid modeling can outperform individual modeling, as the proposed GeoDIV model presented better validation results than the IV model.

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Section: Introductionmentioning

confidence: 99%

Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

Polykretis

Grillakis

Argyriou

et al. 2021

Land

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“…At the provincial scale, the spatiotemporal distribution and driving factors of HMPs were analysed in several specific region and time span. For instance, Xiong et al (2019Xiong et al ( , 2020 analysed the long period but the area they cover is limited to the Yunnan and Fujian provinces, respectively. focused on the driving factors of HMPs in Sichuan province during 1949-2015, and pointed out that human activities and precipitation showed the most importance in the spatiotemporal distribution of HMPs.…”

Section: Introductionmentioning

confidence: 99%

“…In this study, the spatiotemporal HMP series, which consists of the longest available time series in China, has been compiled to address the need for national HMP information based on the first national investigation held by the China Institute of Water Resources and Hydropower Research (Liu and Huang 2020;Liu et al 2018). Although some studies already exploited the same database we use here (Liu et al 2018;Xiong et al 2019Xiong et al , 2020, few of them has share some degree of commonality with the present contribution. More specifically, we analysed the spatial and temporal characteristics of HMPs for the whole Chinese territory, according to six geomorphological regions (geomorphic-regions): Eastern Plain (EP), South Eastern Mountain (SEM), North Central Plateau (NCP), North Western Basin (NWB), South Western Mountain (SWM), and Tibetan Plateau (TP).…”

Section: Introductionmentioning

confidence: 99%

Statistical spatiotemporal analysis of hydro-morphological processes in China during 1950–2015

Wang

Cheng

Lombardo

et al. 2021

Stoch Environ Res Risk Assess

Self Cite

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Hydro-morphological processes (HMPs) are some of the most destructive natural disasters. Understanding the spatiotemporal characteristics of HMPs across China is important for enabling better disaster estimation and prevention at the national scale. However, few studies have focused on the spatiotemporal HMP characterization under various geomorphic settings in China, which may shed light on their regional/national evolution. To bridge this research gap, we analysed the longest HMP time series available in China, which including 47,483 HMP records, to detect spatiotemporal patterns. Specifically, we run several tests namely, Mann-Kendall test, wavelet analysis, Monthly Frequency, and Index of Dispersion, with the objective of detecting the temporal evolution, trends, period, and clustering of HMPs in six geomorphological regions (geomorphic-regions): Eastern Plain, South Eastern Mountain (SEM), North Central Plateau, North Western Basin, South Western Mountain (SWM), and Tibetan Plateau. Our results show that in the last decades have been associated with a marked increase in HMPs and this should be accounted for, especially in those areas where we have retrieved high spatiotemporal clustering (e.g., SEM, SWM). Besides, the main periodicity of HMPs is approximately 12-25 years for most of China since the 1980s, which showed analogous patterns with precipitation anomalies. This study provides a preliminary reference for revealing the spatiotemporal characteristics of HMPs in the context of climate change; therefore, the information provided can be crucial to plan engineering applications with specific return period.

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“…Over 60,000 flash flood disasters have been reported in China since 1949, which resulted in great casualties and economic losses (Liu et al 2018). Flash floods have strong geographical differentiation, with their characteristics and disaster-causing factors varying from region to region (Hou et al 2020;Xiong et al 2020). Therefore, revealing the characteristics of flash floods in different regions is of great significance for preventing and mitigating hazards resulting from flash floods.…”

Section: Introductionmentioning

confidence: 99%

“…Using the geographical detector, the power of a single independent variable to explain the variation in a dependent variable as well as the power of the coupling of the spatial distributions of two independent variables to explain the variation in the dependent variable can be detected. Recent studies have used geographical detectors to analyze the driving forces of flash floods (Xiong et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Spatial–temporal characteristics and driving factors of flash floods in Shaanxi Province considering regional differentiation

Zhang

Luo

et al. 2021

Hydrology Research

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Flash floods show strong regional differentiation in spatial–temporal distribution and driving forces, thereby hindering their effective prevention and control. This study analyzed the spatiotemporal characteristics of flash floods in Shaanxi Province, China, differentiated among the northern Shaanxi (NS), Guanzhong (GZ), and southern Shaanxi (SS) regions based on the Mann–Kendall, Theil–Sen Median, and standard deviation ellipse methods. The main factors driving disasters and their interactions in each region were then identified within the three categories of precipitation factor (PPF), surface environment factor, and human activity factor (HAF) based on a geographical detector. Finally, the differences in flash flood characteristics among the NS, GZ, and SS regions were analyzed. The results showed that flash floods in Shaanxi Province are greatly affected by the PPF and the HAF, although the spatial–temporal characteristics and disaster-causing factors were significantly different in each region. The regions were ranked according to the number and growth trends of flash floods as follows: SS > GZ > NS. Furthermore, flash floods were affected by multiple factors, with the interaction between factors acting as a driving force of flash floods. The results of this study can provide a reference for the management of flash floods under regional differentiation.

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Spatiotemporal Characteristics and Driving Force Analysis of Flash Floods in Fujian Province

Cited by 26 publications

References 56 publications

Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece

Statistical spatiotemporal analysis of hydro-morphological processes in China during 1950–2015

Spatial–temporal characteristics and driving factors of flash floods in Shaanxi Province considering regional differentiation

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