Flooding stress is a major adverse condition during the emergence period of direct-seeded rice.This study investigated the use of wood vinegar as a seed soaking treatment to enhance rice seedling rates under flooding ...Flooding stress is a major adverse condition during the emergence period of direct-seeded rice.This study investigated the use of wood vinegar as a seed soaking treatment to enhance rice seedling rates under flooding stress,exploring both the methodology and physiological mechanisms involved.The optimal seed soaking concentration was determined through a gradient experiment,followed by a multi-cultivar validation test.The physiological mechanism of wood vinegar soaking on seedling emergence was analyzed by measuring the electrical conductivity of the flooding water,the changes in starch and soluble sugar contents in the grains and sprouts,and the dynamics ofα-amylase activity and antioxidant-related enzyme activities in the sprouts.The results showed that soaking rice seeds in a wood vinegar solution at a low concentration significantly enhanced the emergence of rice seedlings under flooding conditions,with a 100-fold dilution having the most pronounced effect,increasing seedling rates by 50.6%-60.0%.Further analysis indicated that wood vinegar treatment enhanced seedling establishment by inducing a significant increase inα-amylase activity,leading to a 74.9%-213.6%increase in soluble sugar content in the sprouts during 2-8 d after flooding stress compared with the control.Additionally,the treatment increased superoxide dismutase and peroxidase activities in the sprouts,mitigating lipid peroxidation of the cell membranes,and notably lower water electrical conductivity was observed in wood vinegar-treated seeds compared with the control.In conclusion,soaking rice seeds in a 100-fold diluted wood vinegar solution improves rice seedling rates under flooding stress by mitigating oxidative damage and maintaining energy supply.This approach is valuable for developing cost-effective seed treatment technologies and offering novel strategies to improve seedling rates and uniformity of direct-seeded rice under flooding conditions.展开更多
Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting...Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.展开更多
1.Introduction In recent years,intensifying climate extremes have triggered a sharp increase in global natural disasters,over 90%attributable to water-related hazards,particularly floods(Hirabayashi et al.,2013).Over ...1.Introduction In recent years,intensifying climate extremes have triggered a sharp increase in global natural disasters,over 90%attributable to water-related hazards,particularly floods(Hirabayashi et al.,2013).Over the past two decades,floods have inundated approximately 2.23 million km2 of land worldwide(Tellman et al.,2021),affecting over 250 million people and causing economic losses exceeding USD 651 billion(Devitt et al.,2023).Recent catastrophic floods in Pakistan,landslides in Indonesia,and dike breaches in China have intensified concerns over the effectiveness of current flood management strategies.展开更多
[Objective]Surface water flooding is caused by heavy rainfall,which has been the main type of flooding in many cities across the world.Real urban environments are highly complex,and there are numerous parameters influ...[Objective]Surface water flooding is caused by heavy rainfall,which has been the main type of flooding in many cities across the world.Real urban environments are highly complex,and there are numerous parameters influencing the rainfall-runoff processes,such as road width,orientation and building coverage.The main objective is to perform a parametric study concerning the rainfall-runoff processes in complex urban environments,in order to gain a better understanding of the impact of urban characteristics on the surface runoff.[Methods]Realistic urban layouts are generated by means of procedural modelling software,which parameterises the urban configurations using 11 independent variables,including the averaged street length,street orientation,street curvature,major street width,minor street width,park coverage,etc.A shock-capturing TVD MacCormack shallow water equations solver is used to undertake a large number of computational simulations regarding the rainfall-runoff processes over realistic urban layouts.The dominating urban parameters that influence the time of concentration is unveiled,which characterises the timescale of the flood formation.[Results]In order to generalise the research outcomes,the obtained hydrographs at the outlet of the catchment are normalised so that they are independent of the catchment area,slope or rainfall intensity.The dimensionless time of concentration is thus only the functions of 12 independent parameters,including 11 parameters that governing the urban layouts and the Manning roughness coefficient of the ground.A sensitivity analysis,based on the multiple linear regression method,is performed on the 2,994 simulation cases to quantify the influence of each parameter.[Conclusion]The results show that the ground roughness and the building coverage ratio are the two most important factors that influence the urban flood formation.Their influences on the dimensionless timescale of the urban catchments’response to rainfall are quantified by empirical formulae.The research findings can provide useful guidelines for the design of future flood-resilient urban environments and the improvement of existing drainage systems in cities.展开更多
In the context of climate change,the acceleration of the global water cycle has led to the emergence of abrupt transitions between drought and flood events,presenting a new challenge for flood and drought disaster mit...In the context of climate change,the acceleration of the global water cycle has led to the emergence of abrupt transitions between drought and flood events,presenting a new challenge for flood and drought disaster mitigation.Abrupt transitions between drought and flood refer to a phenomenon in which an extreme drought event quickly shifts to an extreme flood event,or vice versa,within a relatively short time span.This phenomenon disrupts the traditional spatiotemporal distribution patterns of water-related disasters,reflecting not only the extreme unevenness in the distribution of water resources but also the rapid alternation of the water cycle's evolution(He et al.,2016).Moreover,due to its suddenness,extremity,and complexity,it poses severe threats to human societies and ecosystems.Scientifically addressing abrupt transitions between drought and flood has thus become a new challenge in flood and drought disaster prevention.展开更多
Flooding remains one of the most destructive natural disasters,posing significant risks to both human lives and infrastructure.In India,where a large area is susceptible to flood hazards,the importance of accurate flo...Flooding remains one of the most destructive natural disasters,posing significant risks to both human lives and infrastructure.In India,where a large area is susceptible to flood hazards,the importance of accurate flood frequency analysis(FFA)and flood susceptibility mapping cannot be overstated.This study focuses on the Haora River basin in Tripura,a region prone to frequent flooding due to a combination of natural and anthropogenic factors.This study evaluates the suitability of the Log-Pearson Type Ⅲ(LP-Ⅲ)and Gumbel Extreme Value-1(EV-1)distributions for estimating peak discharges and delineates floodsusceptible zones in the Haora River basin,Tripura.Using 40 years of peak discharge data(1984-2023),the LP-Ⅲ distribution was identified as the most appropriate model based on goodness-of-fit tests.Flood susceptibility mapping,integrating 16 thematic layers through the Analytical Hierarchy Process,identified 8%,64%,and 26%of the area as high,moderate,and low susceptibility zones,respectively,with a model success rate of 0.81.The findings highlight the need for improved flood management strategies,such as enhancing river capacity and constructing flood spill channels.These insights are critical for designing targeted flood mitigation measures in the Haora basin and other flood-prone regions.展开更多
Risk assessment is vital for humanities,especially in assessing natural and manmade hazards.Romblon,an archipelagic province in the Philippines,faces frequent typhoons and heavy rainfall,resulting in floods,with the M...Risk assessment is vital for humanities,especially in assessing natural and manmade hazards.Romblon,an archipelagic province in the Philippines,faces frequent typhoons and heavy rainfall,resulting in floods,with the Municipality of Santa Fe being particularly vulnerable to its severe damage.Thus,this research study intends to evaluate the flood risk of Santa Fe spatially using the fuzzy analytical hierarchy process(FAHP),taking into account data sourced fromvarious government agencies and online databases.GIS was utilized tomap flood-prone areas in the municipality.Hazard assessment factors included average annual rainfall,elevation,slope,soil type,and flood height.Distance to river,distance to road,types of building structure,mean age,gender ratio,and average annual incomewere considered parameters of vulnerability assessment.Exposure assessment considered land use,distance to evacuation facility,household number,and population density.Weights for each parameter were determined through pairwise comparison performed by experts.These weights were then incorporated into risk assessment estimation.The developed risk map identifies five high-risk barangays(small local government units).The study’s findings will enable local government units to establish flood mitigation programs,implement targeted mitigation measures,and formulate strategic response plans to lower risk and safeguard the residents of Santa Fe effectively.展开更多
Being caught in a flood is incredibly dangerous.Like many other natural disasters,floods can occur with little or no warning.Flash floods move quickly and have strong currents.They are known to rip(扯)trees out of the...Being caught in a flood is incredibly dangerous.Like many other natural disasters,floods can occur with little or no warning.Flash floods move quickly and have strong currents.They are known to rip(扯)trees out of the ground,destroy buildings and cause bridges to collapse.展开更多
Investing in disaster risk reduction is crucial for minimizing the impacts of disasters.However,little is known about the factors that influence changes in investment levels over time.This study aims to identify the k...Investing in disaster risk reduction is crucial for minimizing the impacts of disasters.However,little is known about the factors that influence changes in investment levels over time.This study aims to identify the key socio-economic drivers behind increases and decreases in flood protection investment in People’s Republic of China(PRC).Such information is crucial for policy makers to justify flood investments.By analyzing data on flood protection expenditures,economic losses from floods,and other relevant indicators from 1980 to 2020,the study evaluates the relationship between investment and disaster impacts through the lens of the flood investment cycle model.It was found that the country succeeded in reducing flood damage because of increasing investment in flood protection.The results indicate that changes in PRC’s flood protection investment have been driven by three major factors:the occurrence of major disasters,the fiscal situation,and shifts in government policies.Investment tended to increase following large-scale events,such as the 1998 Yangtze River Basin flood and the 2008 Wenchuan earthquake,which prompted policy changes and renewed focus on DRR measures.Fiscal constraints limited investment in the 1990s,but reforms and stimulus measures improved the financial situation,enabling increased spending on flood protection.PRC’s experience in steadily reducing flood damage through sustained investment and policy commitment offers valuable lessons for other developing countries facing similar challenges.展开更多
Flooding is a natural event often associated with floodplain areas,characterised by large,sudden and significant rises in river water levels that drastically alters the surrounding landscape.The research employs ArcGI...Flooding is a natural event often associated with floodplain areas,characterised by large,sudden and significant rises in river water levels that drastically alters the surrounding landscape.The research employs ArcGIS tools,multi-criteria evaluation techniques and theMaximumEntropy(MaxEnt)model to assess flood hazard zones.The key physical elements of slope,elevation,rainfall,drainage density,land use,and soil types have been integrated to identify areas vulnerable to flooding.Overlay analysis has been used to construct zones specifically designated for flood hazards.Additionally,pairwise comparison using Saaty’s scale was employed to calculate the Eigenvector weights for each physical factor.A comparison of AUC values is estimated to find the most effective method for delineating flood hazard zones.TheMaxEnt model achieved an Area Under Curve(AUC)of 0.978,outperforming the Analytical hierarchy Process(AHP)model with an AUC of 0.967.The higher AUC indicates that the MaxEnt model is better at distinguishing between positive and negative occurrences.This could lead to more reliable predictions of the flood hazard zones.Overall,the higher AUC of the MaxEnt model suggests greater reliability and robustness.展开更多
Flooding has become an emerging global catastrophe,generating considerable damage to both infrastructures and lives.Despite the critical need for quantitative assessments of both flood damage and the effectiveness of ...Flooding has become an emerging global catastrophe,generating considerable damage to both infrastructures and lives.Despite the critical need for quantitative assessments of both flood damage and the effectiveness of flood mitigation measures,most existing studies have focused on isolated aspects of flood risk.Only a very limited number of studies have comprehensively integrated hazard mapping,hydrodynamic simulations,and economic damage estimations to evaluate the real-world impact and effectiveness of flood mitigation measures(FMMs).This study presents a multi-method approach to evaluate the performance of such established structural FMMs.Initially,hazard assessments for two selected case study areas,the Colombo Metropolitan Area in Sri Lanka and Auckland,New Zealand,two flood-prone cities with contrasting geographical contexts.Flood inundation mapping for the Madiwela South Diversion,Colombo,Sri Lanka,was performed using hydrodynamic modeling to demonstrate the reduction in flood inundation area and depth after the implementation of the measure,considering six(6)design return periods(RPs).Subsequently,tangible and intangible property damage estimations for“without FMMs”and“with FMMs”were evaluated to identify the benefit of responding to flood conditions,utilising a vulnerability-based economic analysis.In addition to damage estimations,the study adopts a novel approach by conducting an investment viability analysis to find the Benefit-to-Cost ratios and Net Present Value of nine(9)selected FMMs implemented by Sri Lanka Land Development Co-operation(SLLDC).The FMMs implemented by SLLDC were selected from Colombo,Sri Lanka.The quantified damage estimates revealed a reduction in flood damages ranging from 39%to 63%,alongside a decrease in flood inundation depths between 9%and 12%,and the results underscore the significant effectiveness of FMMs in managing urban flooding and minimising its impacts.This cross-disciplinary methodology enables a transferable framework for resilience-oriented urban planning in diverse hydrological and geographical contexts.展开更多
An effective warning system for flash floods along the upper River des Peres, a small urban stream in eastern Missouri, USA, is based on three enterprise-level, automated rain gauges.Because floods in this 25 km~2 bas...An effective warning system for flash floods along the upper River des Peres, a small urban stream in eastern Missouri, USA, is based on three enterprise-level, automated rain gauges.Because floods in this 25 km~2 basin develop rapidly and are commonly caused by small but intense thunderstorm cells, these rain gauges were necessarily deployed within the watershed, and immediate telemetry and processing of rainfall delivered in 5-minute intervals is required. Available data show that damaging floods in this area occur only 30 min to 3 h following the delivery of 38 mm of rainfall or more in a single hour. Water levels along this stream can rise more than 3 m/h. Since full deployment in Nov. 2021, our system has successfully predicted 3 significant floods with one false positive.展开更多
Among natural disasters,flash floods are the most destructive events,causing signif-icant damage to the economy and posing a serious threat to human life and property.Comprehensive risk assessment of these sudden floo...Among natural disasters,flash floods are the most destructive events,causing signif-icant damage to the economy and posing a serious threat to human life and property.Comprehensive risk assessment of these sudden floods is a key strategy to mitigate their impact.Accurate analysis of flash flood hazards can greatly enhance prevention efforts and inform critical decision-making processes,ultimately improving our ability to protect communities from these fast-onset disasters.This study analyzed the driving forces of flash flood disaster-causing factors in Heilongjiang Province.Meanwhile,nine different categories of variables affecting the occurrence of flash floods were selected,and the degree of influence of each driving factor on flash floods was quantitatively analyzed,and the driving force analysis of the driving factors of flash floods in Hei-longjiang Province was carried out by using the geographic probe model.This paper employs an uncertainty approach,utilizing a statistical-based interval weight deter-mination technique for evaluation indices and a two-dimensional information-based interval number sorting method.These methodologies are combined to construct a comprehensive flash flood risk assessment model.On this basis,the model was implemented in six regions within China's Heilongjiang province to evaluate and prioritize flash flood risks.The resulting risk ranking for these areas was as follows:Bayan≻Shuangcheng≻Boli≻Suibin≻Hailun≻Yian.The findings demonstrate that the interval number-based evaluation method effectively handles uncertainty,providing a more reliable risk grading system.This approach,by leveraging modern scientific advances and risk quantification techniques,is crucial for improving disaster management and mitigating flash flood impacts.展开更多
Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall ...Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.展开更多
Over the period of rainfall, urban green infrastructures(UGI) function like a sponge by absorbing surface runoff as sinks;however, they will shift to sources once their runoff reduction capacities are exceeded. This d...Over the period of rainfall, urban green infrastructures(UGI) function like a sponge by absorbing surface runoff as sinks;however, they will shift to sources once their runoff reduction capacities are exceeded. This dynamic of sink-source shifts, and its dependence on the vegetation structure, remain poorly understood, limiting the action of flood-resilient UGI strategies. This study employs MIKE SHE/11 model coupled with statistical analysis for such resolution. Across four scenarios ranging from light to heavy rainfall, we identified regime shifts in UGI system through the decreasing to increasing trends of sink fractions, typically occurring around 13–18 h after rainfall starts. Based on these regime shifts, we categorized the UGI system into vulnerable, reliable, and recoverable components, highlighting its heterogeneous performance. In addition, by examining the influence of vegetation structure on sink–source dynamics, we found that a higher probability of sinks under light rainfalls was associated with a greater leaf area index(LAI) and vegetation height standard deviation(VHSTD), while green volume(GV) and canopy height(CH) played a more prominent role under heavier rainfalls. Threshold effect analysis further revealed that, a high proportion of the recoverable parts met the thresholds of CH(82 %)and GV(85 %), whereas fewer reached the thresholds of LAI(15 %–19 %) and VHSTD(3 %–6 %). These findings underscore the importance of enhancing 3D vegetation configuration for UGI to adapt to flood impacts. Our study expects to provide actionable knowledge for understanding, quantification, and management of the runoff sink-source dynamics, informing UGI design and planning to achieve urban flood resilience.展开更多
Nature-based solutions(NBS)involve the sustainable maintenance,management,and restoration of natural or modified ecosystems.Flooding is a major problem in Phnom Penh,Cambodia,and has significant social and economic ra...Nature-based solutions(NBS)involve the sustainable maintenance,management,and restoration of natural or modified ecosystems.Flooding is a major problem in Phnom Penh,Cambodia,and has significant social and economic ramifications.This study tries to suggest creative solutions that support human welfare and biodiversity while simultaneously resolving social problems by adopting NBS.An online survey using convenience and snowball sampling was conducted to assess the openness of Phnom Penh residents to adopting NBS for flood mitigation in their homes or buildings.The survey investigated perceptions of NBS effectiveness based on previous knowledge and flood risk perception.Results revealed a strong correlation between perceived efficacy and willingness to adopt NBS.Specifically,flood risk perception and prior knowledge significantly influenced the perceived effectiveness of NBS.Key findings indicate that high installation and maintenance costs,lack of awareness,limited space,cultural factors,and perceived ineffectiveness are primary barriers to NBS adoption.Additionally,specific regional factors contribute to reluctance in certain areas of Phnom Penh.To overcome these barriers,the study recommends that the Cambodian government and other stakeholders invest in public education campaigns to raise awareness about the benefits of NBS.Financial incentives and subsidies should be provided to reduce the economic burden on residents.Furthermore,integrating NBS into urban planning and infrastructure development is crucial to enhance community resilience against floods.展开更多
Quantification of river flood risks is a prerequisite for floodplain management and development.The lower Yellow River(LYR)is characterized by a complex channel–floodplain system,which is prone to flooding but inhabi...Quantification of river flood risks is a prerequisite for floodplain management and development.The lower Yellow River(LYR)is characterized by a complex channel–floodplain system,which is prone to flooding but inhabits a large population on the floodplains.Many floodplain management modes have been presented,but implementation effects of these management modes have not been evaluated correctly.An integrated model was first proposed to evaluate the flood risks to people’s life and property,covering an improved module of two-dimensional(2D)morphodynamic processes and a module of flood risk evaluation for people,buildings and crops on the floodplains.Two simulation cases were then conducted to validate the model accuracy,including the hyperconcentrated flood event and dike-breach induced flood event occurring in the LYR.Finally,the integrated model was applied to key floodplains in the LYR,and the effects of different floodplain management modes were quantified on the risks to people’s life and property under an extreme flood event.Results indicate that:①Satisfactory accuracy was achieved in the simulation of these two flood events.The maximum sediment concentration was just underestimated by 9%,and the simulated inundation depth agreed well with the field record;②severe inundation was predicted to occur in most domains under the current topography(SchemeⅠ),which would be alleviated after implementing different floodplain management modes,with the area in slight inundation degree accounting for a large proportion under the mode of“construction of protection embankment”(SchemeⅡ)and the area in medium inundation degree occupying a high ratio under the mode of“floodplain partition harnessing”(SchemeⅢ);and③compared with SchemeⅠ,the high-risk area for people’s life and property would reduce by 21%–49%under SchemeⅡ,and by 35%–93%under SchemeⅢ.展开更多
The flood hazard management is one of the major challenges in the floodplain regions worldwide.With the rise in population growth and the spread of infrastructural development,the level of risk has increased over time...The flood hazard management is one of the major challenges in the floodplain regions worldwide.With the rise in population growth and the spread of infrastructural development,the level of risk has increased over time.Therefore,the prediction of flood susceptible area is a key challenge for the adoption of management plans.Flood susceptibility modeling is technically a common work,but it is still a very tough job to validate flood susceptible models in a very rigorous and scientific manner.Therefore,the present work in the Atreyee River Basin of India and Bangladesh was planned to establish artificial neural network(ANN),radial basis function(RBF),random forest(RF)and their ensemble-based flood susceptibility models.The flood susceptible models were constructed based on nine flood conditioning parameters.The flood susceptibility models were validated in a conventional way using the receiver operating curve(ROC).To validate the flood-susceptible models,a two dimensional(2D)hydraulic flood simulation model was developed.Also,the index of flood vulnerability model was developed and applied for validating the flood susceptible models,which was a very unique way to validate the predictive models.Friedman test and Wilcoxon Signed rank test were employed to compare the generated flood susceptible models.Results showed that 11.95%-12.99%of the entire basin area(10188.4 km^(2))comes under very high flood-susceptible zones.Accuracy evaluation results have shown that the performance of ensemble flood susceptible models outperforms other standalone machine learning models.The flood simulation model and IFV model were also spatially adjusted with the flood susceptibility models.Therefore,the present study recommended for the ensemble flood susceptibility prediction and IFV based validation along with conventional ways.展开更多
基金supported by the National Key Research and Development Program of China(Grant No.2023YFD2301300)the National Rice Industry Technology System,China(Grant No.CARS-01).
文摘Flooding stress is a major adverse condition during the emergence period of direct-seeded rice.This study investigated the use of wood vinegar as a seed soaking treatment to enhance rice seedling rates under flooding stress,exploring both the methodology and physiological mechanisms involved.The optimal seed soaking concentration was determined through a gradient experiment,followed by a multi-cultivar validation test.The physiological mechanism of wood vinegar soaking on seedling emergence was analyzed by measuring the electrical conductivity of the flooding water,the changes in starch and soluble sugar contents in the grains and sprouts,and the dynamics ofα-amylase activity and antioxidant-related enzyme activities in the sprouts.The results showed that soaking rice seeds in a wood vinegar solution at a low concentration significantly enhanced the emergence of rice seedlings under flooding conditions,with a 100-fold dilution having the most pronounced effect,increasing seedling rates by 50.6%-60.0%.Further analysis indicated that wood vinegar treatment enhanced seedling establishment by inducing a significant increase inα-amylase activity,leading to a 74.9%-213.6%increase in soluble sugar content in the sprouts during 2-8 d after flooding stress compared with the control.Additionally,the treatment increased superoxide dismutase and peroxidase activities in the sprouts,mitigating lipid peroxidation of the cell membranes,and notably lower water electrical conductivity was observed in wood vinegar-treated seeds compared with the control.In conclusion,soaking rice seeds in a 100-fold diluted wood vinegar solution improves rice seedling rates under flooding stress by mitigating oxidative damage and maintaining energy supply.This approach is valuable for developing cost-effective seed treatment technologies and offering novel strategies to improve seedling rates and uniformity of direct-seeded rice under flooding conditions.
基金National Key Research and Development Program of China,No.2023YFC3006704National Natural Science Foundation of China,No.42171047CAS-CSIRO Partnership Joint Project of 2024,No.177GJHZ2023097MI。
文摘Accurate prediction of flood events is important for flood control and risk management.Machine learning techniques contributed greatly to advances in flood predictions,and existing studies mainly focused on predicting flood resource variables using single or hybrid machine learning techniques.However,class-based flood predictions have rarely been investigated,which can aid in quickly diagnosing comprehensive flood characteristics and proposing targeted management strategies.This study proposed a prediction approach of flood regime metrics and event classes coupling machine learning algorithms with clustering-deduced membership degrees.Five algorithms were adopted for this exploration.Results showed that the class membership degrees accurately determined event classes with class hit rates up to 100%,compared with the four classes clustered from nine regime metrics.The nonlinear algorithms(Multiple Linear Regression,Random Forest,and least squares-Support Vector Machine)outperformed the linear techniques(Multiple Linear Regression and Stepwise Regression)in predicting flood regime metrics.The proposed approach well predicted flood event classes with average class hit rates of 66.0%-85.4%and 47.2%-76.0%in calibration and validation periods,respectively,particularly for the slow and late flood events.The predictive capability of the proposed prediction approach for flood regime metrics and classes was considerably stronger than that of hydrological modeling approach.
基金supported by National Key Research and Development Program of China(Grants No.2022YFF0802401 and 2023YFF0806900)China Postdoctoral Science Foundation(Grants No.2023M743456,GZB20230740,and 2024T170908).
文摘1.Introduction In recent years,intensifying climate extremes have triggered a sharp increase in global natural disasters,over 90%attributable to water-related hazards,particularly floods(Hirabayashi et al.,2013).Over the past two decades,floods have inundated approximately 2.23 million km2 of land worldwide(Tellman et al.,2021),affecting over 250 million people and causing economic losses exceeding USD 651 billion(Devitt et al.,2023).Recent catastrophic floods in Pakistan,landslides in Indonesia,and dike breaches in China have intensified concerns over the effectiveness of current flood management strategies.
文摘[Objective]Surface water flooding is caused by heavy rainfall,which has been the main type of flooding in many cities across the world.Real urban environments are highly complex,and there are numerous parameters influencing the rainfall-runoff processes,such as road width,orientation and building coverage.The main objective is to perform a parametric study concerning the rainfall-runoff processes in complex urban environments,in order to gain a better understanding of the impact of urban characteristics on the surface runoff.[Methods]Realistic urban layouts are generated by means of procedural modelling software,which parameterises the urban configurations using 11 independent variables,including the averaged street length,street orientation,street curvature,major street width,minor street width,park coverage,etc.A shock-capturing TVD MacCormack shallow water equations solver is used to undertake a large number of computational simulations regarding the rainfall-runoff processes over realistic urban layouts.The dominating urban parameters that influence the time of concentration is unveiled,which characterises the timescale of the flood formation.[Results]In order to generalise the research outcomes,the obtained hydrographs at the outlet of the catchment are normalised so that they are independent of the catchment area,slope or rainfall intensity.The dimensionless time of concentration is thus only the functions of 12 independent parameters,including 11 parameters that governing the urban layouts and the Manning roughness coefficient of the ground.A sensitivity analysis,based on the multiple linear regression method,is performed on the 2,994 simulation cases to quantify the influence of each parameter.[Conclusion]The results show that the ground roughness and the building coverage ratio are the two most important factors that influence the urban flood formation.Their influences on the dimensionless timescale of the urban catchments’response to rainfall are quantified by empirical formulae.The research findings can provide useful guidelines for the design of future flood-resilient urban environments and the improvement of existing drainage systems in cities.
基金supported by the National Key Research and Development Program of China(Grant No.2023YFC3209800)the National Natural Science Foundation of China(Grant No.52279011).
文摘In the context of climate change,the acceleration of the global water cycle has led to the emergence of abrupt transitions between drought and flood events,presenting a new challenge for flood and drought disaster mitigation.Abrupt transitions between drought and flood refer to a phenomenon in which an extreme drought event quickly shifts to an extreme flood event,or vice versa,within a relatively short time span.This phenomenon disrupts the traditional spatiotemporal distribution patterns of water-related disasters,reflecting not only the extreme unevenness in the distribution of water resources but also the rapid alternation of the water cycle's evolution(He et al.,2016).Moreover,due to its suddenness,extremity,and complexity,it poses severe threats to human societies and ecosystems.Scientifically addressing abrupt transitions between drought and flood has thus become a new challenge in flood and drought disaster prevention.
文摘Flooding remains one of the most destructive natural disasters,posing significant risks to both human lives and infrastructure.In India,where a large area is susceptible to flood hazards,the importance of accurate flood frequency analysis(FFA)and flood susceptibility mapping cannot be overstated.This study focuses on the Haora River basin in Tripura,a region prone to frequent flooding due to a combination of natural and anthropogenic factors.This study evaluates the suitability of the Log-Pearson Type Ⅲ(LP-Ⅲ)and Gumbel Extreme Value-1(EV-1)distributions for estimating peak discharges and delineates floodsusceptible zones in the Haora River basin,Tripura.Using 40 years of peak discharge data(1984-2023),the LP-Ⅲ distribution was identified as the most appropriate model based on goodness-of-fit tests.Flood susceptibility mapping,integrating 16 thematic layers through the Analytical Hierarchy Process,identified 8%,64%,and 26%of the area as high,moderate,and low susceptibility zones,respectively,with a model success rate of 0.81.The findings highlight the need for improved flood management strategies,such as enhancing river capacity and constructing flood spill channels.These insights are critical for designing targeted flood mitigation measures in the Haora basin and other flood-prone regions.
文摘Risk assessment is vital for humanities,especially in assessing natural and manmade hazards.Romblon,an archipelagic province in the Philippines,faces frequent typhoons and heavy rainfall,resulting in floods,with the Municipality of Santa Fe being particularly vulnerable to its severe damage.Thus,this research study intends to evaluate the flood risk of Santa Fe spatially using the fuzzy analytical hierarchy process(FAHP),taking into account data sourced fromvarious government agencies and online databases.GIS was utilized tomap flood-prone areas in the municipality.Hazard assessment factors included average annual rainfall,elevation,slope,soil type,and flood height.Distance to river,distance to road,types of building structure,mean age,gender ratio,and average annual incomewere considered parameters of vulnerability assessment.Exposure assessment considered land use,distance to evacuation facility,household number,and population density.Weights for each parameter were determined through pairwise comparison performed by experts.These weights were then incorporated into risk assessment estimation.The developed risk map identifies five high-risk barangays(small local government units).The study’s findings will enable local government units to establish flood mitigation programs,implement targeted mitigation measures,and formulate strategic response plans to lower risk and safeguard the residents of Santa Fe effectively.
文摘Being caught in a flood is incredibly dangerous.Like many other natural disasters,floods can occur with little or no warning.Flash floods move quickly and have strong currents.They are known to rip(扯)trees out of the ground,destroy buildings and cause bridges to collapse.
文摘Investing in disaster risk reduction is crucial for minimizing the impacts of disasters.However,little is known about the factors that influence changes in investment levels over time.This study aims to identify the key socio-economic drivers behind increases and decreases in flood protection investment in People’s Republic of China(PRC).Such information is crucial for policy makers to justify flood investments.By analyzing data on flood protection expenditures,economic losses from floods,and other relevant indicators from 1980 to 2020,the study evaluates the relationship between investment and disaster impacts through the lens of the flood investment cycle model.It was found that the country succeeded in reducing flood damage because of increasing investment in flood protection.The results indicate that changes in PRC’s flood protection investment have been driven by three major factors:the occurrence of major disasters,the fiscal situation,and shifts in government policies.Investment tended to increase following large-scale events,such as the 1998 Yangtze River Basin flood and the 2008 Wenchuan earthquake,which prompted policy changes and renewed focus on DRR measures.Fiscal constraints limited investment in the 1990s,but reforms and stimulus measures improved the financial situation,enabling increased spending on flood protection.PRC’s experience in steadily reducing flood damage through sustained investment and policy commitment offers valuable lessons for other developing countries facing similar challenges.
基金Lab facilities are supported by DST-FIST.The DST-FIST program,under the Department of Science and Technology,Government of India,provides financial assistance through the‘Fund for Improvement of S&T Infrastructure in Universities and Higher Educational Institutions’(FIST)scheme.
文摘Flooding is a natural event often associated with floodplain areas,characterised by large,sudden and significant rises in river water levels that drastically alters the surrounding landscape.The research employs ArcGIS tools,multi-criteria evaluation techniques and theMaximumEntropy(MaxEnt)model to assess flood hazard zones.The key physical elements of slope,elevation,rainfall,drainage density,land use,and soil types have been integrated to identify areas vulnerable to flooding.Overlay analysis has been used to construct zones specifically designated for flood hazards.Additionally,pairwise comparison using Saaty’s scale was employed to calculate the Eigenvector weights for each physical factor.A comparison of AUC values is estimated to find the most effective method for delineating flood hazard zones.TheMaxEnt model achieved an Area Under Curve(AUC)of 0.978,outperforming the Analytical hierarchy Process(AHP)model with an AUC of 0.967.The higher AUC indicates that the MaxEnt model is better at distinguishing between positive and negative occurrences.This could lead to more reliable predictions of the flood hazard zones.Overall,the higher AUC of the MaxEnt model suggests greater reliability and robustness.
文摘Flooding has become an emerging global catastrophe,generating considerable damage to both infrastructures and lives.Despite the critical need for quantitative assessments of both flood damage and the effectiveness of flood mitigation measures,most existing studies have focused on isolated aspects of flood risk.Only a very limited number of studies have comprehensively integrated hazard mapping,hydrodynamic simulations,and economic damage estimations to evaluate the real-world impact and effectiveness of flood mitigation measures(FMMs).This study presents a multi-method approach to evaluate the performance of such established structural FMMs.Initially,hazard assessments for two selected case study areas,the Colombo Metropolitan Area in Sri Lanka and Auckland,New Zealand,two flood-prone cities with contrasting geographical contexts.Flood inundation mapping for the Madiwela South Diversion,Colombo,Sri Lanka,was performed using hydrodynamic modeling to demonstrate the reduction in flood inundation area and depth after the implementation of the measure,considering six(6)design return periods(RPs).Subsequently,tangible and intangible property damage estimations for“without FMMs”and“with FMMs”were evaluated to identify the benefit of responding to flood conditions,utilising a vulnerability-based economic analysis.In addition to damage estimations,the study adopts a novel approach by conducting an investment viability analysis to find the Benefit-to-Cost ratios and Net Present Value of nine(9)selected FMMs implemented by Sri Lanka Land Development Co-operation(SLLDC).The FMMs implemented by SLLDC were selected from Colombo,Sri Lanka.The quantified damage estimates revealed a reduction in flood damages ranging from 39%to 63%,alongside a decrease in flood inundation depths between 9%and 12%,and the results underscore the significant effectiveness of FMMs in managing urban flooding and minimising its impacts.This cross-disciplinary methodology enables a transferable framework for resilience-oriented urban planning in diverse hydrological and geographical contexts.
文摘An effective warning system for flash floods along the upper River des Peres, a small urban stream in eastern Missouri, USA, is based on three enterprise-level, automated rain gauges.Because floods in this 25 km~2 basin develop rapidly and are commonly caused by small but intense thunderstorm cells, these rain gauges were necessarily deployed within the watershed, and immediate telemetry and processing of rainfall delivered in 5-minute intervals is required. Available data show that damaging floods in this area occur only 30 min to 3 h following the delivery of 38 mm of rainfall or more in a single hour. Water levels along this stream can rise more than 3 m/h. Since full deployment in Nov. 2021, our system has successfully predicted 3 significant floods with one false positive.
基金Basic Scientific Research Expense Project of IWHR-Extreme rainstorm development trends and prediction techniques,Grant/Award Number:JZ0145B142024National Natural Science Foundation of China,Grant/Award Number:42271095。
文摘Among natural disasters,flash floods are the most destructive events,causing signif-icant damage to the economy and posing a serious threat to human life and property.Comprehensive risk assessment of these sudden floods is a key strategy to mitigate their impact.Accurate analysis of flash flood hazards can greatly enhance prevention efforts and inform critical decision-making processes,ultimately improving our ability to protect communities from these fast-onset disasters.This study analyzed the driving forces of flash flood disaster-causing factors in Heilongjiang Province.Meanwhile,nine different categories of variables affecting the occurrence of flash floods were selected,and the degree of influence of each driving factor on flash floods was quantitatively analyzed,and the driving force analysis of the driving factors of flash floods in Hei-longjiang Province was carried out by using the geographic probe model.This paper employs an uncertainty approach,utilizing a statistical-based interval weight deter-mination technique for evaluation indices and a two-dimensional information-based interval number sorting method.These methodologies are combined to construct a comprehensive flash flood risk assessment model.On this basis,the model was implemented in six regions within China's Heilongjiang province to evaluate and prioritize flash flood risks.The resulting risk ranking for these areas was as follows:Bayan≻Shuangcheng≻Boli≻Suibin≻Hailun≻Yian.The findings demonstrate that the interval number-based evaluation method effectively handles uncertainty,providing a more reliable risk grading system.This approach,by leveraging modern scientific advances and risk quantification techniques,is crucial for improving disaster management and mitigating flash flood impacts.
文摘Floods and storm surges pose significant threats to coastal regions worldwide,demanding timely and accurate early warning systems(EWS)for disaster preparedness.Traditional numerical and statistical methods often fall short in capturing complex,nonlinear,and real-time environmental dynamics.In recent years,machine learning(ML)and deep learning(DL)techniques have emerged as promising alternatives for enhancing the accuracy,speed,and scalability of EWS.This review critically evaluates the evolution of ML models—such as Artificial Neural Networks(ANN),Convolutional Neural Networks(CNN),and Long Short-Term Memory(LSTM)—in coastal flood prediction,highlighting their architectures,data requirements,performance metrics,and implementation challenges.A unique contribution of this work is the synthesis of real-time deployment challenges including latency,edge-cloud tradeoffs,and policy-level integration,areas often overlooked in prior literature.Furthermore,the review presents a comparative framework of model performance across different geographic and hydrologic settings,offering actionable insights for researchers and practitioners.Limitations of current AI-driven models,such as interpretability,data scarcity,and generalization across regions,are discussed in detail.Finally,the paper outlines future research directions including hybrid modelling,transfer learning,explainable AI,and policy-aware alert systems.By bridging technical performance and operational feasibility,this review aims to guide the development of next-generation intelligent EWS for resilient and adaptive coastal management.
基金supported by the National Key R&D Program of China(Grant No.2022YFF1303102)the Global Engagement for Strategic Partnership project of Nanjing University,the China Scholarship Council(Grant No.202406190182)+1 种基金the Swedish Research Council(VR,Grant No.2022–04672)the Kunshan Water Bureau for supporting this study through the project cooperation.
文摘Over the period of rainfall, urban green infrastructures(UGI) function like a sponge by absorbing surface runoff as sinks;however, they will shift to sources once their runoff reduction capacities are exceeded. This dynamic of sink-source shifts, and its dependence on the vegetation structure, remain poorly understood, limiting the action of flood-resilient UGI strategies. This study employs MIKE SHE/11 model coupled with statistical analysis for such resolution. Across four scenarios ranging from light to heavy rainfall, we identified regime shifts in UGI system through the decreasing to increasing trends of sink fractions, typically occurring around 13–18 h after rainfall starts. Based on these regime shifts, we categorized the UGI system into vulnerable, reliable, and recoverable components, highlighting its heterogeneous performance. In addition, by examining the influence of vegetation structure on sink–source dynamics, we found that a higher probability of sinks under light rainfalls was associated with a greater leaf area index(LAI) and vegetation height standard deviation(VHSTD), while green volume(GV) and canopy height(CH) played a more prominent role under heavier rainfalls. Threshold effect analysis further revealed that, a high proportion of the recoverable parts met the thresholds of CH(82 %)and GV(85 %), whereas fewer reached the thresholds of LAI(15 %–19 %) and VHSTD(3 %–6 %). These findings underscore the importance of enhancing 3D vegetation configuration for UGI to adapt to flood impacts. Our study expects to provide actionable knowledge for understanding, quantification, and management of the runoff sink-source dynamics, informing UGI design and planning to achieve urban flood resilience.
文摘Nature-based solutions(NBS)involve the sustainable maintenance,management,and restoration of natural or modified ecosystems.Flooding is a major problem in Phnom Penh,Cambodia,and has significant social and economic ramifications.This study tries to suggest creative solutions that support human welfare and biodiversity while simultaneously resolving social problems by adopting NBS.An online survey using convenience and snowball sampling was conducted to assess the openness of Phnom Penh residents to adopting NBS for flood mitigation in their homes or buildings.The survey investigated perceptions of NBS effectiveness based on previous knowledge and flood risk perception.Results revealed a strong correlation between perceived efficacy and willingness to adopt NBS.Specifically,flood risk perception and prior knowledge significantly influenced the perceived effectiveness of NBS.Key findings indicate that high installation and maintenance costs,lack of awareness,limited space,cultural factors,and perceived ineffectiveness are primary barriers to NBS adoption.Additionally,specific regional factors contribute to reluctance in certain areas of Phnom Penh.To overcome these barriers,the study recommends that the Cambodian government and other stakeholders invest in public education campaigns to raise awareness about the benefits of NBS.Financial incentives and subsidies should be provided to reduce the economic burden on residents.Furthermore,integrating NBS into urban planning and infrastructure development is crucial to enhance community resilience against floods.
基金supported by the National Natural Science Foundation of China(U2243238)the Program of the National Key Research and Development Plan(2023YFC3209304).
文摘Quantification of river flood risks is a prerequisite for floodplain management and development.The lower Yellow River(LYR)is characterized by a complex channel–floodplain system,which is prone to flooding but inhabits a large population on the floodplains.Many floodplain management modes have been presented,but implementation effects of these management modes have not been evaluated correctly.An integrated model was first proposed to evaluate the flood risks to people’s life and property,covering an improved module of two-dimensional(2D)morphodynamic processes and a module of flood risk evaluation for people,buildings and crops on the floodplains.Two simulation cases were then conducted to validate the model accuracy,including the hyperconcentrated flood event and dike-breach induced flood event occurring in the LYR.Finally,the integrated model was applied to key floodplains in the LYR,and the effects of different floodplain management modes were quantified on the risks to people’s life and property under an extreme flood event.Results indicate that:①Satisfactory accuracy was achieved in the simulation of these two flood events.The maximum sediment concentration was just underestimated by 9%,and the simulated inundation depth agreed well with the field record;②severe inundation was predicted to occur in most domains under the current topography(SchemeⅠ),which would be alleviated after implementing different floodplain management modes,with the area in slight inundation degree accounting for a large proportion under the mode of“construction of protection embankment”(SchemeⅡ)and the area in medium inundation degree occupying a high ratio under the mode of“floodplain partition harnessing”(SchemeⅢ);and③compared with SchemeⅠ,the high-risk area for people’s life and property would reduce by 21%–49%under SchemeⅡ,and by 35%–93%under SchemeⅢ.
文摘The flood hazard management is one of the major challenges in the floodplain regions worldwide.With the rise in population growth and the spread of infrastructural development,the level of risk has increased over time.Therefore,the prediction of flood susceptible area is a key challenge for the adoption of management plans.Flood susceptibility modeling is technically a common work,but it is still a very tough job to validate flood susceptible models in a very rigorous and scientific manner.Therefore,the present work in the Atreyee River Basin of India and Bangladesh was planned to establish artificial neural network(ANN),radial basis function(RBF),random forest(RF)and their ensemble-based flood susceptibility models.The flood susceptible models were constructed based on nine flood conditioning parameters.The flood susceptibility models were validated in a conventional way using the receiver operating curve(ROC).To validate the flood-susceptible models,a two dimensional(2D)hydraulic flood simulation model was developed.Also,the index of flood vulnerability model was developed and applied for validating the flood susceptible models,which was a very unique way to validate the predictive models.Friedman test and Wilcoxon Signed rank test were employed to compare the generated flood susceptible models.Results showed that 11.95%-12.99%of the entire basin area(10188.4 km^(2))comes under very high flood-susceptible zones.Accuracy evaluation results have shown that the performance of ensemble flood susceptible models outperforms other standalone machine learning models.The flood simulation model and IFV model were also spatially adjusted with the flood susceptibility models.Therefore,the present study recommended for the ensemble flood susceptibility prediction and IFV based validation along with conventional ways.
