Human–Environment Natural Disasters Interconnection in China: A Review

Multiple criteria and statistical sentiment analysis on flooding

The World Bank lists flooding as one of the main pressures on a community. Flooding can affect development prospects and potentially reverse decades of progress in the alleviation of poverty and in development. Flooding-related information usually involves many stakeholders, objects, and significant details. We examined linkage between the density of 506 flood management keywords from documents found via Google Search and 32 macroenvironment indicators for 76 countries, developing 506 neural networks models. These models show that flood management keywords are interconnected with the environmental, social, economic, political, and cultural dimensions of the examined countries. The models demonstrate that improvements in a country's sustainability and performance metrics are followed by an increase in flood keyword use. Microsoft Azure AI and ChatGPT were used to generate abstractive summaries for the 100 most dense and statistically significant flood management keywords and 28 macroenvironment indicators for the individual countries included in our analysis, and for the world. We reduced the number of flood keywords density variables from 506 to 100 by selecting only the most relevant ones in order to generate abstractive summaries. Our Web of Science Sentiment Analysis Articles Model and Map of the World demonstrates that nations with an unfavorable macroenvironment published disproportionately fewer papers on sentiment analysis. This research benefits stakeholders by providing comprehensive and holistic information and data analysis, focusing on evidence-based flooding information.

Ecological Vulnerability of Aquatic Ecosystems-A Review

The continuous increase of anthropogenic activities reinforces their stress on aquatic ecosystems (from continental to marine ecosystems) that are the most altered ecosystems on Earth. To evaluate the risk of ecosystem decline toward human alterations, the concept of "ecological vulnerability" was developed to help managers to prioritize conservation actions. Various definitions of vulnerability and its components were used, but this concept is often centered on the intersection of three components: sensitivity, exposition, and adaptive capacity. The aim of this study was through a review of the scientific literature of the last 10 years, first to assess the goals of the use of the concept of vulnerability in aquatic ecology: for which pressure (e.g., climate change, predation) and organism, on which level of organization (individuals, species, …). The second objective, was to address the methods developed to assess vulnerability: which components were considered, which metrics were used, the scoring process… Fish were the organisms the most frequently concerned and the number of publications decreased with the increasing complexity of biodiversity studied (from populations to multitrophic organizations). Climate change was the main stressor for which vulnerability was assessed. Vulnerability was commonly defined as being highly sensitive, highly exposed and lowly adaptable even if adaptive capacity was rarely addressed. This study showed an interest in the concept of vulnerability to protect aquatic ecosystems. Nonetheless, to better evaluate their risk of biodiversity loss, we should consider vulnerability at a higher level of organization and encompass the adaptive capacity of the biota.

Dynamic risk assessment of urban flood disasters based on functional area division-A case study in Shenzhen, China

The assessment of urban flood risk plays a vital role in disaster prevention and mitigation. This work aims to assess the dynamic risk of urban flood triggered by population movements through dividing urban functional zoning from the perspective of collective cognition. Firstly, the urban functional areas are identified using Points of Interest data and then the population movements mobile is detected based on functional areas using mobile signaling big data. Then, one-dimensional and two-dimensional hydrodynamic models are employed to simulate the 50-year flood scenario in Futian District, Shenzhen. Finally, a spatio-temporal dynamic assessment model for urban flood risk is constructed based on the extent of inundation, water depth, population density, and the disaster-bearing capacity of functional areas. The research findings are as follows: (1) Futian District's urban planning showcases harmonious integration of single-function and mixed-function areas. Utilizing the 50% perception standard efficiently identifies distinct functional types across diverse urban zones. The results are highly consistent with the actual situation. (2) During morning peak hours, the population exhibits a nuanced pattern of dispersal, concentration, and transition. Lunchtime witnesses multiple central clusters forming and gradually dispersing, while the evening peak witnesses population regrouping, covering broader geographical extents. Dynamic utilization of functional areas and mobile phone signaling data outperforms static population metrics, offering deeper insights into the complexities of human activity. (3) Between 12:00 and 13:00, lunchtime movements lead to a surge of 6 high-risk zones in the central area and 5 in the Meiling area. The dynamic flood risk assessment model, based on functional area delineation, effectively identifies disparities and fluctuations in flood risk across diverse functional areas during rainfall scenarios, ensuring heightened precision and accuracy in risk assessment.

Substantial increase in abrupt shifts between drought and flood events in China based on observations and model simulations

Drought-flood abrupt alternation (DFAA) refers to the rapid transformation between droughts and floods, posing serious threats to ecological security, food production, and human safety. Previous studies have insufficiently investigated DFAA events at large regional scales using high-resolution observations and model simulations. In this study, the standardized precipitation evapotranspiration index was used to construct the DFAA magnitude index, which considers the asymmetric effects of drought and flood alternations. Four types of DFAA events were then investigated using high-resolution station observations and NEX-GDDP-CMIP6 model simulations. The results showed that hotspot areas of drought-flood and flood-drought alternation events were mainly in the northern and eastern parts of China, while the hotspot areas of drought-flood-drought and flood-drought-flood alternation events were obviously smaller than those of drought-flood and flood-drought alternation events. Drought-flood, flood-drought, and drought-flood-drought alternation events showed significant upward trends at rates of 0.075, 0.057, and 0.051 events/decade, respectively, and these increases were attributed to significant increases in moderate, severe, and extreme events across China during 1981-2020. Generally, the total number of DFAA events above moderate grade in the northern, central, and some areas in the southern parts of China increased obviously (>50 %) during 2001-2020 compared to 1981-2000. NEX-GDDP-CMIP6 can reasonably represent the multi-year averages and long-term trends of precipitation, temperature, and DFAA events in China. Except for the flood-drought-flood alternation events, the other three types of DFAA events showed significant increasing trends in the future, with higher rates under the SSP585 scenario than under the SSP245 scenario (e.g., drought-flood alternation events at rates of 0.033 and 0.046 events/decade under SSP245 and SSP585, respectively, during 1981-2100). DFAA events above the moderate grade were predicted to increase significantly in both 2032-2065 and 2066-2099 compared to 1981-2014, especially in northern China for the 2066-2099 under the SSP585 scenario.

Impact of regional energy allocation distortion on carbon emission efficiency: Evidence from China

The free flow of energy cannot be fully achieved in China's energy market because of incomplete market-oriented reform, resulting in energy allocation distortion, which has hampered carbon emissions reduction. However, the extent of energy allocation distortion and its role in carbon emission efficiency remain unexplored. Therefore, this study aims to measure energy allocation distortion and investigate its impact on carbon emission efficiency. For this purpose, first, we derive energy allocation distortion based on a production function and carbon emission efficiency using a meta-frontier non-radial Malmquist index. To effectively address the endogeneity issue, we use a generalized method of moments model to estimate the impact of energy allocation distortion on carbon emission efficiency. Second, we further explore the distortionary mechanism of carbon emission efficiency associated with energy allocation and analyze the asymmetric effect of energy allocation distortion on carbon emission efficiency. The results show a certain degree of energy allocation distortion throughout the country, and disparity exists among different regions. The average value of carbon emission efficiency in the eastern region is 1.0286, well ahead of the national average, demonstrating better performance than other regions. Energy allocation distortion negatively affects carbon emission efficiency, with a 1% increase in energy allocation distortion leading to a 0.251% decrease in carbon emission efficiency. Technological progress, the structure of energy consumption, and industrial structure are important transmission channels through which energy allocation affects carbon emission efficiency. The study contributes to uncovering regional energy allocation distortion and its impacts on carbon emission efficiency and providing strategic policy recommendations for improving energy allocation efficiency.

Assessing the urban road waterlogging risk to propose relative mitigation measures

Road waterlogging has become a significant issue in developed cities due to the rapid urbanization in China. It is necessary to accurately identify the risk of waterlogging in urban roads and propose appropriate mitigation measures. This study considered urban waterlogging as a landscape ecological process. The road waterlogging risk was simulated and estimated using the Minimum Cumulative Resistance model under natural drainage conditions. The results indicate that: 1) The Minimum Cumulative Resistance model effectively assesses the waterlogging risk for each road segment. The roads in and around the central city have relatively higher waterlogging risks. The overall length of high-risk roads is 918.7 km, accounting for 31.3 % of the total. 2) There are 448 potential runoff paths and 448 inflow sites. The city's center and its north and south sides are the primary locations of the high-risk runoff paths and the inflow sites. 3) Road waterlogging is significantly more affected by the land-use types of High density residential and Industrial under rainfall intensities of a-year, 2-year, 3-year, and 5-year return periods. And the effects of various land-use types on waterlogging vary with the rainfall intensity. Using landscape ecology theory to analyze the risk of road waterlogging is a novel method to address urban waterlogging issues. This approach provides a more accurate approach to identifying the urban waterlogging risks and can be applied to developed cities suffering from waterlogging to help decision-makers devise the most effective mitigation measures.

Designing Urban Green Infrastructures Using Open-Source Data—An Example in Çiğli, Izmir (Turkey)

The city of Izmir (Turkey) has experienced one of the most rapid and fastest urbanization processes in the last thirty years; more than 33 thousand hectares of agricultural and seminatural land have been transformed into urban areas, leading to a drastic reduction of biodiversity and hard deployments of the ecosystem service supply. In this perspective, the potential definition of methodologies to design multifunctional green infrastructures is extremely important to challenge the effects of climate change. The aim of this study is to propose an easy and replicable methodology to design a Green Infrastructure at the neighbourhood level in one of the most important districts of Izmir: Çiğli. To this end, we combined historical land-use change analysis (based on Urban Atlas, Copernicus Land Monitoring Service) with environmental and ecosystem mapping in a Geographic Information System environment (ESRI ArcMap 10.8.1) while creating a composite layer based on unweighted overlays of Imperviousness, Tree Cover Density, and Habitat Quality. Results were used to design the Green Infrastructure of Çiğli and suggest context-based strategies for urban adaptation, including Nature-Based Solutions for core, edge, and urban links.

Dynamic collaborative optimization for disaster relief supply chains under information ambiguity

Large-scale disasters occur worldwide, with a continuing surge in the frequency and severity of disruptive events. Researchers have developed several optimization models to address the critical challenges of disaster relief supply chains (e.g., emergency material reserving and scheduling inefficiencies). However, most developed algorithms are proven to have low fault tolerance, which makes it difficult for disaster relief supply chain managers to obtain optimal solutions and meet the emergency distribution requirements within a limited time frame. Considering the uncertainty and ambiguity of disaster relief information and using Interval Type-2 Fuzzy Set (IT2TFS), this paper presents a collaborative optimization model based on an integrative emergency material supplier evaluation framework. The optimal emergency material suppliers are first selected using a multi-attribute group decision-making ranking method. Multi-objective fuzzy optimization is then run in three emergency phases: early -, mid-, and late-disaster relief stages. Focusing on a massive flash flood disaster event in Yunnan Province as a case study, a comprehensive numerical analysis tests and validates the developed model. The results revealed that the proposed optimization method can optimize emergency material planning while ensuring that reserve material safety inventory is always maintained at a reasonable level. The presented method suggests a fuzzy interval to prevent emergency materials' safety inventory shortage and minimize continuous life/property losses in disaster-affected areas.

Designing with Ecosystem Modelling: The Sponge District Application in İzmir, Turkey

Climate change is threatening coastal cities affected by multiple hazards worldwide. Due to the increase in extreme weather events and the low capacity of cities to adapt to these odds, losses, exposure, and fatalities arise with the occurred events. Besides, traditional hazard mapping and risk planning techniques often fall behind when facing a climate crisis since extreme changes in quantity, frequency, and distribution of meteorological phenomena are observed. Specific and localized flood vulnerability mitigation strategies need to be developed, particularly for settlements on coastal and sloppy areas with a high risk of stream accumulation during rainfall days. This paper uses the urban flood risk mitigation of Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) generated within the Natural Capital Project to determine İzmir’s cloudburst vulnerability level. The city has experienced several cloudbursts in the last couple of months which inundated densely populated areas. The soil’s hydrologic conductibility and the land use/land cover (LULC) map were required as main inputs. The LULC map was produced on the basis of surface imperviousness. The model calculates the run-off volume and how much is retained by soil and vegetation. Outputs were used to concretely apply the sponge district concept while designing performance-based ecosystem solutions. In the light of the findings, the new performance-based design demonstrated how digital ecosystem modelling could support the urban design decision-making process.

Risks to the Health of Russian Population from Floods and Droughts in 2010‒2020: A Scoping Review

Climate change and natural disasters caused by hydrological, meteorological, and climatic causes have a significant and increasing direct and indirect impact on human health, leading to increased mortality and morbidity. Russia is a country that suffers from frequent climatic and weather disasters. This is mainly due to its vast territory, complex geographical and ecological environment, and widely varying climatic conditions. This review provides information on climatological and hydrological extremes in Russia in 2010–2020, floods and droughts, and their impact on the health and well-being of the country’s population. A literature search was conducted using electronic databases Web of Science, Pubmed, Science Direct, Scopus, and e-Library, focusing on peer-reviewed journal articles published in English and in Russian from 2010 to 2021. Four conceptual categories were used: “floods”, “droughts”, “human health”, and “Russia”. It is concluded that while most hazardous weather events cannot be completely avoided, many health impacts can potentially be prevented. The recommended measures include early warning systems and public health preparedness and response measures, building climate resilient health systems and other management structures.

Comparative Study of Convolutional Neural Network and Conventional Machine Learning Methods for Landslide Susceptibility Mapping

Landslide susceptibility mapping (LSM) is a useful tool to estimate the probability of landslide occurrence, providing a scientific basis for natural hazards prevention, land use planning, and economic development in landslide-prone areas. To date, a large number of machine learning methods have been applied to LSM, and recently the advanced convolutional neural network (CNN) has been gradually adopted to enhance the prediction accuracy of LSM. The objective of this study is to introduce a CNN-based model in LSM and systematically compare its overall performance with the conventional machine learning models of random forest, logistic regression, and support vector machine. Herein, we selected Zhangzha Town in Sichuan Province, China, and Lantau Island in Hong Kong, China, as the study areas. Each landslide inventory and corresponding predisposing factors were stacked to form spatial datasets for LSM. The receiver operating characteristic analysis, area under the curve (AUC), and several statistical metrics, such as accuracy, root mean square error, Kappa coefficient, sensitivity, and specificity, were used to evaluate the performance of the models. Finally, the trained models were calculated, and the landslide susceptibility zones were mapped. Results suggest that both CNN and conventional machine learning-based models have a satisfactory performance. The CNN-based model exhibits an excellent prediction capability and achieves the highest performance but also significantly reduces the salt-of-pepper effect, which indicates its great potential for application to LSM.

Identifying and Mapping the Responses of Ecosystem Services to Land Use Change in Rapidly Urbanizing Regions: A Case Study in Foshan City, China

Rapid urbanization has degraded some important ecosystem services and threatens socioeconomic sustainability. Although many studies have focused on the effect of urbanization on ecosystem services, the effect and its threshold have not been well-identified spatially. In this study, we propose a research framework by integrating nighttime light data, the InVEST (Integrated Valuation of Environmental Service and Tradeoffs) model, and a spatial response index to characterize the response of ecosystem services to rapid urbanization. We considered Foshan City as a case study to explore the effect of rapid urbanization on ecosystem services during 2000–2018. Our results showed that rapid urbanization resulted in a 49.13% reduction in agricultural production and a 10.13% reduction in habitat quality. The spatial response index of agricultural production, habitat quality, soil retention, water yield, and carbon sequestration were 14.25%, 2.94%, 0.04%, 0.78%, and 0.07%, respectively. We found that developing urban areas had the highest spatial response index, indicating that this area was the crucial area for future land management. We consider that our research framework can help identify the key areas affected by rapid urbanization. Visualizing the spatial response index and extracting the threshold for different levels of urbanization will be conducive to sustainable urban management and planning.

Quantitative analysis of artificial dam failure effects on debris flows - A case study of the Zhouqu '8.8' debris flow in northwestern China

Artificial dams are one of the most common hydraulic structures for mitigating debris flow disasters in alpine valley regions. However, performance alteration and failure after successive debris flows can lead to dam failure, releasing large amounts of materials within a very short time; moreover, the contribution of artificial dam failures to debris flows is poorly understood. This study quantitatively analyzed the artificial dam failure effects based on the numerical simulations of the Zhouqu '8.8' debris flow, with three scenarios: all nine dams failed (S1); no dams were ever built (S2); all nine dams remained intact (S3). The results showed that artificial dam failures had a significant amplifying effect on the magnitude of a debris flow. The maximum velocity and flow depth decreased by 20% and 11.2% if all the dams did not collapse; comparison of S1 and S2 showed that discharge and velocity at the front of the debris flow increased by 54.6% and 89%, the bulk density and yield stress increased by 3.3% and 5.7%, due to artificial dam failures. This could increase the destructive capacity of a debris flow and the possibility of a river blockage. A single artificial dam failure could locally amplify the magnitude of debris flow. Overall, on the catchment scale, the magnitude of a debris flow was dominated by topography and channel geometry, which can reduce the amplification effect of dam failures at locations where the channel was curved. However, where the channel was straight and flat, the flow velocity and discharge increased cumulatively by 3 m/s and 637 m³/s due to cascading failure. In addition, a comprehensive scheme combining ecological and engineering measures to mitigate debris flow disasters is discussed. This quantitative study is important and urgent needed to understand the amplification effect of dam failures and to implement debris flow mitigation in alpine valley regions.

Does high risk mean high loss: Evidence from flood disaster in southern China

Southern China has suffered from flood disasters for over sixty years, which results in tremendous socio-economic loss. With the development of economy and the improvement of disaster reduction, both the exposure and potential loss of flood disaster are increasing. However, previous studies only focus on risk assessment, few has examined the comparison of potential risk and the actual losses caused by it. To this end, a method combing entropy weight and TOPSIS based on flood data (2008 to 2018) in China's national and provincial disaster database is applied to analysis flood risk and resulting loss in southern China. By using disaster system dimensions of hazard, exposure and vulnerability, the effect of natural, economic and social factors on flood risk are also examined. Results indicate that: (1) flood risk in southern China is relatively low from 2008 to 2014 and becomes severe since 2016; (2) the resulting losses of flood disasters in southern China are optimistic during most of the selected years in the study period; (3) flood risk is not always in line with the resulting loss; and (4) flood disasters in southern China are categorized into high-risk and low-loss situation, low-risk and high-loss situation, and the situation with the same level of risk and loss. To the best of our knowledge, this is the first study to assess southern China on a regional scale from both temporal and spatial perspectives, and has compensated for the lack of comparative research on flood risk and the resulting loss. In practice, our findings can protrude the priorities of flood prevention both in flood-prone areas and specific measures, which is conducive to improve the efficiency of resource allocation.

Shrinking Tonlé Sap and the recent intensification of sand mining in the Cambodian Mekong River

This paper investigates the impacts of extensive riverbed mining in the Lower Mekong on the water level, extent and volume of the Tonlé Sap Lake, the largest freshwater lake in Southeast Asia. Our results indicate that the lake's volume has decreased from 1980 to 2018 (p-value = 0.016), with water levels at Phnom Penh Port and Phnom Penh Bassac showing decreasing trends since 1980 (p-values <0.0001). However, discharge at Phnom Penh Bassac (1960-2002) presented an insignificant trend (p-value = 0.147), indicating that riverbed incision due to extensive sand mining in Phnom Penh has affected the Mekong's water levels more than basin-scale climatic factors. Similarly, the modulation of a limited portion of water by upstream dams is unlikely to have caused dramatic inundation variation along the Lower Mekong River around Tonlé Sap. A hysteretic relationship between water levels at Prek Kdam and Tonlé Sap indicates that Tonlé Sap's water level is largely controlled by Tonlé Sap River and the Mekong, and declining water levels at Prek Kdam due to extensive sand mining in Phnom Penh is directly related to the shrinking of the lake. Although there are three main inflows into Tonlé Sap (from the Mekong, local tributaries and direct precipitation), the Mekong's contribution is the largest; tributary discharge and rainfall did not display any significant trend over the investigated period as well. Additionally, the analysis of high-resolution images revealed a recent intensification of riverbed mining, with Phnom Penh being a mining hotspot in the Lower Mekong. Considering its economic and ecological importance, the shrinking of Tonlé Sap would have grave repercussions for the region. Since sand demand is unlikely to weaken in the foreseeable future, these new insights can potentially help inform regulatory frameworks in ensuring sustainable sand extraction rates.

Development of hydro-social-economic-environmental sustainability index (HSEESI) in integrated water resources management

Sustainable development is a grand challenge of the present century, with tremendous direct and indirect implications for a range of social, economic, and environmental factors. This research proposes a water-centric framework for evaluating "relative" sustainability of the status quo in a country via a new "hydro-social-economic-environmental sustainability index" (HSEESI). We test this framework across 35 countries of American continent using national-scale surveyed data for the 2005-2017 periods. HSEESI possesses four dimensions, namely economic, social development, knowledge and technology, and health sanitation and environment, and 12 related indicators for characterizing these dimensions. Based on the developed HSEESI scores, we assess the linkages between water resources and social-economic-environmental systems at the country level, using single and hybrid-artificial intelligence-gene expression programming (GEP) methods. The former method involves all the indicators, while the latter focuses only on the most effective indicators. Further, we aggregate these analyses at three spatial scales, including American continent, North American countries, and South American countries. Our analyses show that both methods lead to approximate similar results, but the latter is preferred for larger scales as it is more cost effective. Overall, results indicate that the status of water resources in North America is relatively sustainable, whereas in South America, it is relatively unsustainable. Importantly, social development, health sanitation, and environmental dimensions, in both North and South American continents, seem to have a relatively unsustainable status, indicating that water resources systems may not have enough capacity to meet the needs of those dimensions. At the country level, our analyses show that water resources systems of Uruguay, Guyana, and Venezuela may face the highest relative unsustainability, across economic, social development, and health sanitation and environment dimensions. The approach and the framework developed in this study can be applied in other regions around the world and with a more detailed representation of intra-country sustainability issues. It can inform managers and policymakers for sustainable planning and developing water resources projects across scales.

Performance-Based Planning to Reduce Flooding Vulnerability Insights from the Case of Turin (North-West Italy)

Climate change impacts urban areas with greater frequency and exposes continental cities located on floodplains to extreme cloudbursts events. This scenario requires developing specific flooding vulnerability mitigation strategies that improve local knowledge of flood-prone areas at the urban scale and supersede the traditional hazard approach based on the classification of riverine buffers. Moreover, decision-makers need to adopt performance-based strategies for contrasting climate changes and increasing the resilience of the system. This research develops the recent Flooding Risk Mitigation model of InVEST (Integrated Evaluation of Ecosystem Services and Trade-off), where cloudburst vulnerability results from the soil’s hydrological conductivity. It is based on the assumption that during cloudburst events, all saturated soils have the potential for flooding, regardless of the distance to rivers or channels, causing damage and, in the worst cases, victims. The model’s output gives the run-off retention index evaluated in the catchment area of Turin (Italy) and its neighborhoods. We evaluated the outcome to gain specific insight into potential land use adaptation strategies. The index is the first experimental biophysical assessment developed in this area, and it could prove useful in the revision process of the general town plan underway.

Research on Evaluation of Meteorological Disaster Governance Capabilities in Mainland China Based on Generalized λ-Shapley Choquet Integral

According to the United Nations report, climate disasters have intensified in the past 20 years, and China has the largest number of disasters in the world. So the study of meteorological disaster governance capacities is critically important for China. We designed a meteorological disaster governance capacity evaluation system to calculate the evaluation values by using the generalized λ-Shapley Choquet integral, a method that considers the interaction between indicators. We used various official statistical yearbooks and internal data of China Meteorological Administration (CMA) and weight intervals set by meteorologists for each level of indicators to calculate the evaluation values of meteorological disaster governance capacity in mainland provinces, from 2014 to 2018. We compared them with other methods (entropy weight method, Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS), and Analytic Hierarchy Process (AHP)), and the results showed that the results calculated by the designed interaction method provided in this paper are more stable and differentiated. The results show that provincial meteorological disaster governance capacities in Mainland China are characterized by uneven development and a pro-slight polarization phenomenon. This leads to policy recommendations: Provinces should strengthen the construction of meteorological disaster information; provinces with outstanding capacity must strengthen the experience sharing with provinces with lower capacity.

Flood risk influenced by the compound effect of storm surge and rainfall under climate change for low-lying coastal areas

Under climate change, compound flooding has resulted in severe disasters in coastal areas around the world. In this study, an integrated framework is proposed to determine the range of compound flood risk without the requirement of joint probability analysis between storm surge and rainfall. In the framework, the flood risks are analyzed under four extreme scenarios with/without the compound effect of storm surge and rainfall in the past and the future. From the end of the 20th century to the middle of the 21st century, the worst scenario shows that the flood area significantly increases by 92% for the low-lying coastal areas in southwest Taiwan under the compound effect of storm surge and rainfall if they are fully correlated. In the most optimistic scenario, the flood area slightly increases by 15% without compound effect (only storm surge is considered). To coastal flooding, the synchronization of storm surge and rainfall contributes much more than the climate-induced amplification of individual factors. When storm surge and rainfall happen at the same time, the extent and duration of flooding increase simultaneously under the influence of pluvial and surge-induced flooding. Risk analysis shows an obvious increase of risk level for villages originally at low risks, which require integrated countermeasures against the consequence brought by compound flooding in the future. The framework can be applied in other low-lying coastal areas to quantify the potential impacts on human and environment caused by compound flooding under climate change.

Modeling Flood Peak Discharge Caused by Overtopping Failure of a Landslide Dam

Overtopping failure often occurs in landslide dams, resulting in the formation of strong destructive floods. As an important hydraulic parameter to describe floods, the peak discharge often determines the downstream disaster degree. Based on 67 groups of landslide dam overtopping failure cases all over the world, this paper constructs the calculation model for peak discharge of landslide dam failure. The model considers the influence of dam erodibility, breach shape, dam shape and reservoir capacity on the peak discharge. Finally, the model is compared with the existing models. The results show that the new model has a higher accuracy than the existing models and the simulation accuracy of the two outburst peak discharges of Baige dammed lake in Jinsha River (10 October 2018 and 3 November 2018) is higher (the relative error is 0.73% and 6.68%, respectively), because the model in this study considers more parameters (the breach shape, the landslide dam erodibility) than the existing models. The research results can provide an important reference for formulating accurate and effective disaster prevention and mitigation measures for such disasters.

An Overview of Flood Risk Analysis Methods

Scientific papers present a wide range of methods of flood analysis and forecasting. Floods are a phenomenon with significant socio-economic implications, for which many researchers try to identify the most appropriate methodologies to analyze their temporal and spatial development. This research aims to create an overview of flood analysis and forecasting methods. The study is based on the need to select and group papers into well-defined methodological categories. The article provides an overview of recent developments in the analysis of flood methodologies and shows current research directions based on this overview. The study was performed taking into account the information included in the Web of Science Core Collection, which brought together 1326 articles. The research concludes with a discussion on the relevance, ease of application, and usefulness of the methodologies.

Using Integrated Hydrological Models to Assess the Impacts of Climate Change on Discharges and Extreme Flood Events in the Upper Yangtze River Basin

Amongst the impacts of climate change, those arising from extreme hydrological events are expected to cause the greatest impacts. To assess the changes in temperature and precipitation and their impacts on the discharge in the upper Yangtze Basin from pre-industrial to the end of 21st century, four hydrological models were integrated with four global climate models. Results indicated that mean discharge was simulated to increase slightly for all hydrological models forced by all global climate models during 1771–1800 and 1871–1900 relative to the 1971–2000 reference period, whereas the change directions in mean discharge were not consistent among the four global climate models during 2070–2099, with increases from HadGEM2-ES and MIROC5, and decreases from GFDL-ESM2M and IPSL-CM5A-LR. Additionally, our results indicated that decreases in precipitation may always result in the decrease in mean discharge, but increases in precipitation did not always lead to increases in discharge due to high temperature rise. The changes in extreme flood events with different return intervals were also explored. These extreme events were projected to become more intense and frequent in the future, which could have potential devastating impacts on the society and ecosystem in this region.

Spatial Prediction of Future Flood Risk: An Approach to the Effects of Climate Change

Preparation of a flood probability map serves as the first step in a flood management program. This research develops a probability flood map for floods resulting from climate change in the future. Two models of Flexible Discrimination Analysis (FDA) and Artificial Neural Network (ANN) were used. Two optimistic (RCP2.6) and pessimistic (RCP8.5) climate change scenarios were considered for mapping future rainfall. Moreover, to produce probability flood occurrence maps, 263 locations of past flood events were used as dependent variables. The number of 13 factors conditioning floods was taken as independent variables in modeling. Of the total 263 flood locations, 80% (210 locations) and 20% (53 locations) were considered model training and validation. The Receiver Operating Characteristic (ROC) curve and other statistical criteria were used to validate the models. Based on assessments of the validated models, FDA, with a ROC-AUC = 0.918, standard error (SE = 0.038), and an accuracy of 0.86% compared to the ANN model with a ROC-AUC = 0.897, has the highest accuracy in preparing the flood probability map in the study area. The modeling results also showed that the factors of distance from the River, altitude, slope, and rainfall have the greatest impact on floods in the study area. Both models’ future flood susceptibility maps showed that the highest area is related to the very low class. The lowest area is related to the high class.

Lesson Learned from Catastrophic Floods in Western Japan in 2018: Sustainable Perspective Analysis

Natural hazards have a significant impact on the sustainable development of human society. This paper reports on the catastrophic floods in western Japan in 2018. Continuous rainfall resulted in catastrophic floods, leading to 212 deaths, damage to more than 2000 houses and 619 geological disasters in 31 prefectures. The causes and contributing factors of these catastrophic floods are analyzed. The analysis of the causes of typical natural hazards provides an important lesson for hazard prevention and management. To adapt to climate change and prevent natural hazards in the future, the preliminary investigation and sustainable perspective analysis in this paper suggest the importance of the construction of a spongy city and the establishment of an early warning system with the help of information science and artificial intelligence technologies (ISAIT); we also highlight the urgent need to improve and strengthen the management of infrastructure.

Comparative Analysis on Urban Flood Countermeasures Based on Life Cycle Thinking: A Comparison between Enhancing of Drainage Capacity Project and Sponge City

The recent increase in rainstorm waterlogging disasters has acutely threatened sustainable urban development in China. Traditional strategy to solve this problem is drainage capacity enhancing projects, which aims at enlarging the discharge of water. Recently, there is a new countermeasure emerged in Chinese cities: ‘Sponge City’, which aims at enlarging the absorption of water by increasing the curves of urban land. This article endeavours to make a comparison between these two countermeasures by building a framework to design and analyze the private or social costs of two projects which have the same rainwater control capacity. Finally, we have come to the conclusion that only considering initial cost, Sponge City unit cost is much more than drainage capacity enhancing project unit cost; considering external cost, Sponge City is not only competitive from an economical perspective, from an environmental perspective Sponge City is also competitive.