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Lian J, Li J, Xu K, Bin L. The impact of tropical cyclones and water conservancy projects on island's flash floods. ENVIRONMENTAL SCIENCE AND POLLUTION RESEARCH INTERNATIONAL 2024; 31:23162-23177. [PMID: 38418780 DOI: 10.1007/s11356-024-32613-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/19/2023] [Accepted: 02/19/2024] [Indexed: 03/02/2024]
Abstract
The analysis of the influencing factors of flash floods, one of the most destructive natural disasters, is the basis of scientific disaster prevention and mitigation. There is little research considering the influence of tropical cyclones (TCs) and water conservancy projects on flash floods, which cannot be ignored in the island areas where flash floods often occur due to the complex influence of various factors. In this study, under the pressure-state-response framework (PSR framework), the factors affecting the distribution of flash floods on Hainan Island, China, from 1970 to 2010 were quantitatively analyzed by using the geographical detector method. By dividing the time period, give full play to the advantages of the PSR framework and show the evolution process of various factors. Different from inland areas, extreme precipitation and tropical cyclones play a major role in the spatial distribution of flash floods on Hainan Island, China, and the driving force of tropical cyclones is 1.1 times that of extreme precipitation on average. Medium-sized reservoirs play the greatest role in the prevention of flash floods on Hainan Island, and their driving forces reach 0.38 times of extreme precipitation on average, followed by large-sized reservoirs and small-sized reservoirs. Large-sized reservoirs are limited in quantity and have limited effectiveness in preventing flash floods on Hainan Island. Therefore, in the forecasting and risk management of flash flood in the island area, more attention should be paid to the impact of extreme precipitation and TCs, and the role of medium-sized reservoir should be fully exerted.
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Affiliation(s)
- Jijian Lian
- State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin, 300350, China
- School of Civil Engineering, Tianjin University, Tianjin, 300350, China
| | - Jinxuan Li
- State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin, 300350, China
- School of Civil Engineering, Tianjin University, Tianjin, 300350, China
| | - Kui Xu
- State Key Laboratory of Hydraulic Engineering Intelligent Construction and Operation, Tianjin University, Tianjin, 300350, China.
- School of Civil Engineering, Tianjin University, Tianjin, 300350, China.
| | - Lingling Bin
- School of Geographic and Environmental Sciences, Tianjin Normal University, Tianjin, 300387, China
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Ikhumhen HO, Fang Q, Lu S, Meilana L, Raimundo Lopes ND. Investigating socio-ecological vulnerability to climate change via remote sensing and a data-driven ranking algorithm. JOURNAL OF ENVIRONMENTAL MANAGEMENT 2023; 347:119254. [PMID: 37806274 DOI: 10.1016/j.jenvman.2023.119254] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 08/02/2023] [Accepted: 10/02/2023] [Indexed: 10/10/2023]
Abstract
The necessity for extensive historical data, variables, and weight determination still presents challenges and complexity, notwithstanding the growth in research on socio-ecological vulnerability to climate change. In order to fill in these gaps, this study used China's Fujian Province as a case study to propose a unique strategic approach for studying socio-ecological vulnerability to climate change from 2000 to 2020 by utilizing remote sensing and the framework of the Intergovernmental Panel on Climate Change. In a GIS scenario, this method employs a comprehensive framework with a wide variety of indicators and a data-driven ranking algorithm. The findings of this study revealed a moderate degree of socio-ecological vulnerability throughout the coast, with significant regional heterogeneity in its spatial distribution. Furthermore, throughout the course of the two-decade, the highly vulnerable zones expanded by 6.04%, outpacing the low-risk areas by 1116 km2 (61.41%) and 2066 km2 (123.39%), respectively, with the majority of the increase taking place in Fuzhou and Ningde. These changes in vulnerability were shown to be principally influenced by changes in vegetation, precipitation, GDP, and land use (LULC). The major influence of precipitation was highlighted further in the spatial autocorrelation analysis, which demonstrated a close correlation between growing socio-ecological vulnerability and increased precipitation. To conclude, this study's methodology differs from other socio-ecological vulnerability studies in that it is flexible and self-sufficient, offering users a choice of weight application. It also gives a more useful, accurate, and suggestive model to enable decision-makers or stakeholders build strategies or ideas for constructing more resilient coastal systems.
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Affiliation(s)
- Harrison Odion Ikhumhen
- Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Fujian, 361102, China
| | - Qinhua Fang
- Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Fujian, 361102, China; Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, 361102, China; Xiamen Key Laboratory of Urban Sea Ecological Conservation and Restoration (USER), Xiamen University, 361102, China; Coastal and Ocean Management Institute, Xiamen University, 361102, China.
| | - Shanlong Lu
- Key Laboratory of Digital Earth Science, Aerospace Information Research Institute, Chinese Academy ofSciences, 9 Dengzhuang South Road, Haidian District, Beijing, 100094, China
| | - Lusita Meilana
- Key Laboratory of Ministry of Education for Coastal Wetland Ecosystems, College of the Environment and Ecology, Xiamen University, Fujian, 361102, China; Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Xiamen University, 361102, China; Xiamen Key Laboratory of Urban Sea Ecological Conservation and Restoration (USER), Xiamen University, 361102, China; Coastal and Ocean Management Institute, Xiamen University, 361102, China
| | - Namir Domingos Raimundo Lopes
- School of Energy and Environmental Engineering College, University of Science and Technology Beijing, Beijing, 100083, China
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Wu J, Luo J, Zhang H, Qin S, Yu M. Projections of land use change and habitat quality assessment by coupling climate change and development patterns. THE SCIENCE OF THE TOTAL ENVIRONMENT 2022; 847:157491. [PMID: 35870584 DOI: 10.1016/j.scitotenv.2022.157491] [Citation(s) in RCA: 11] [Impact Index Per Article: 5.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/09/2022] [Revised: 06/27/2022] [Accepted: 07/14/2022] [Indexed: 06/15/2023]
Abstract
Exploring future land use changes and assessing the habitat quality remains a challenging topic for watershed ecological sustainability. However, most studies ignore the effects of coupled climate change and development patterns. In this study, a framework for assessing habitat quality under the influence of future land use change is constructed based on exploring the driving forces of land use change factors and integrating the system dynamics (SD) model, future land use simulation (FLUS) model and InVest model. The framework enables the projection of land use change and the assessment of habitat quality in the context of future climate change and different development strategies. Applying the framework to the Weihe River Basin, the main driving forces of land-use change in the Weihe River Basin were identified based on geographical detectors, and habitat quality assessment was realized for the Weihe River Basin under the coupled scenarios of three typical shared socioeconomic pathways and future development patterns (SSP126-EP, SSP245-ND, SSP585-EG). The results show that 1) population, precipitation, and temperature are the major driving factors for land use change. 2) The coupling model of SD and FLUS can effectively simulate the future trend of land use change, the relative error is within 2 %, and the overall accuracy is 93.58 %. 3) Significant differences in habitat quality as a result of modifications in land use patterns in different contexts. Affected by ecological protection, the habitat quality in SSP126-EP was significantly better than that in SSP245-ND and SSP585-EG. This research can provide references for future watershed ecological management decisions.
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Affiliation(s)
- Jingyan Wu
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
| | - Jungang Luo
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China.
| | - Han Zhang
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
| | - Shuang Qin
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
| | - Mengjie Yu
- State Key Laboratory of Eco-hydraulics in Northwest Arid Region, Xi'an University of Technology, Xi'an, Shaanxi 710048, China
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Spatiotemporal Characteristics Analysis and Driving Forces Assessment of Flash Floods in Altay. WATER 2022. [DOI: 10.3390/w14030331] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/23/2022]
Abstract
Flash floods are devastating natural disasters worldwide. Understanding their spatiotemporal distributions and driving factors is essential for identifying high risk areas and predicting hydrological conditions. In this study, several methods were used to analyze the changing patterns and driving factors of flash floods in the Altay region. Results indicate that the number of flash floods each year increased in 1980–2015, with two sudden change points (1996 and 2008), and April, June, and July presented the highest frequency of events. Habahe and Jeminay were known to have high flash flood incidences; however, currently, Altay City, Fuhai, Fuyun, and Qinghe are most affected. In terms of driving force analysis, precipitation and altitude performance have a key impact on flash flood occurrence in this settlement compared to other subregions, with a high percentage increase in the mean squared error value of 39, 37, 37, 37, and 33 for 10 min precipitation in a 20-year return period, elevation, 60 min precipitation in a 20-year return period, 6 h precipitation in a 20-year return period, and 24 h precipitation in a 20-year return period, respectively. The study results provide insights into spatial–temporal dynamics of flash floods and a scientific basis for policymakers to set improvement targets in specific areas.
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Integrating Multivariate (GeoDetector) and Bivariate (IV) Statistics for Hybrid Landslide Susceptibility Modeling: A Case of the Vicinity of Pinios Artificial Lake, Ilia, Greece. LAND 2021. [DOI: 10.3390/land10090973] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
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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Flash Flood Susceptibility Assessment Based on Geodetector, Certainty Factor, and Logistic Regression Analyses in Fujian Province, China. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION 2020. [DOI: 10.3390/ijgi9120748] [Citation(s) in RCA: 15] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Flash floods are one of the most frequent natural disasters in Fujian Province, China, and they seriously threaten the safety of infrastructure, natural ecosystems, and human life. Thus, recognition of possible flash flood locations and exploitation of more precise flash flood susceptibility maps are crucial to appropriate flash flood management in Fujian. Based on this objective, in this study, we developed a new method of flash flood susceptibility assessment. First, we utilized double standards, including the Pearson correlation coefficient (PCC) and Geodetector to screen the assessment indicator. Second, in order to consider the weight of each classification of indicator and the weights of the indicators simultaneously, we used the ensemble model of the certainty factor (CF) and logistic regression (LR) to establish a frame for the flash flood susceptibility assessment. Ultimately, we used this ensemble model (CF-LR), the standalone CF model, and the standalone LR model to prepare flash flood susceptibility maps for Fujian Province and compared their prediction performance. The results revealed the following. (1) Land use, topographic relief, and 24 h precipitation (H24_100) within a 100-year return period were the three main factors causing flash floods in Fujian Province. (2) The area under the curve (AUC) results showed that the CF-LR model had the best precision in terms of both the success rate (0.860) and the prediction rate (0.882). (3) The assessment results of all three models showed that between 22.27% and 29.35% of the study area have high and very high susceptibility levels, and these areas are mainly located in the east, south, and southeast coastal areas, and the north and west low mountain areas. The results of this study provide a scientific basis and support for flash flood prevention in Fujian Province. The proposed susceptibility assessment framework may also be helpful for other natural disaster susceptibility analyses.
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