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
Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery.However,conventional convolutional neural networks(CNNs)often struggle in complex flood s...Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery.However,conventional convolutional neural networks(CNNs)often struggle in complex flood scenarios involving reflections,occlusions,or indistinct boundaries due to limited contextual modeling.To address these challenges,we propose a hybrid flood segmentation framework that integrates a Vision Transformer(ViT)encoder with a U-Net decoder,enhanced by a novel Flood-Aware Refinement Block(FARB).The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms.We evaluate our model on a UAV-acquired flood imagery dataset,demonstrating that the proposed ViTUNet+FARB architecture outperforms existing CNN and Transformer-based models in terms of accuracy and mean Intersection over Union(mIoU).Detailed ablation studies further validate the contribution of each component,confirming that the FARB design significantly enhances segmentation quality.To its better performance and computational efficiency,the proposed framework is well-suited for flood monitoring and disaster response applications,particularly in resource-constrained environments.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
Soil microorganisms and labile soil organic carbon(SOC)fractions are essential factors affecting greenhouse gas(GHG)emissions in paddy fields.However,the effects of labile SOC fractions and microorganisms on GHG emiss...Soil microorganisms and labile soil organic carbon(SOC)fractions are essential factors affecting greenhouse gas(GHG)emissions in paddy fields.However,the effects of labile SOC fractions and microorganisms on GHG emissions from flooding to drying after organic fertilizer replacing for chemical fertilizer remain unclear.Here,a long-term experiment was conducted with four treatments:chemical fertilization only(control),organic fertilizer substituting 25%of chemical N fertilizer(NM1),50%of chemical N fertilizer(NM2),and NM2combined with crop straw(NMS).GHG emissions were monitored,and soil samples were collected to determine labile SOC fractions and microorganisms.Results revealed the GHG emissions in the NM2 significantly increased by 196.88%from flooding to drying,mainly due to the higher CO_(2) emissions.The GHG emissions per kg of C input in NMS was the lowest with the value of 9.17.From flooding to drying,organic fertilizer application significantly increased the readily oxidizable organic carbon(ROC)contents and C lability;the NM2 and NMS dramatically increased the SOC and non-readily oxidizable organic carbon(NROC).The bacterial communities showed significant differences among different treatments in the flooding,while the significant difference was only found between the NMS and other treatments in the drying.From flooding to drying,changing soil moisture conditions causes C fractions and microbial communities to jointly affect carbon emissions,and the NMS promoted carbon sequestration and mitigated GHG emissions.Our findings highlight the importance of the labile SOC fractions and microorganisms linked to GHG emissions in paddy fields.展开更多
The Taklimakan Desert,located in the heart of central Asia,covers approximately 330000 km^(2),making it China's largest desert and the world's second-largest shifting desert(Dong et al.,2024).With an average a...The Taklimakan Desert,located in the heart of central Asia,covers approximately 330000 km^(2),making it China's largest desert and the world's second-largest shifting desert(Dong et al.,2024).With an average annual precipitation of less than 100 mm and evaporation rates ranging from 2000 to 3000 mm(Yang et al.,2020),it is recognized as one of the driest regions on Earth,often referred to as the“sea of death”.展开更多
The Hengduan Mountains region(HMR)is one of the most densely distributed and severe flash flood disaster-prone areas in southwest China.It is also a key area for major engineering projects and beautiful countryside co...The Hengduan Mountains region(HMR)is one of the most densely distributed and severe flash flood disaster-prone areas in southwest China.It is also a key area for major engineering projects and beautiful countryside construction in Southwest China.However,previous studies have not systematically summarized the development characteristics and formation modes of flash flood disasters in the HMR,which limits the development of theoretical and technical system for flood control.In this study,we focused on the physical processes of flash flood disasters in the HMR,including generation,movement,and disaster formation,and clarified the dominant disaster-inducing conditions(multiple humid monsoon circulation,high potential energy and high heterogenous underlying surface)and disaster development characteristics(high spatio-temporal heterogeneity,highly concentrated energy,chain and cascading effects,and clustered occurrence)of flash floods in the HMR.Based on the entire processes of flash flood disasters,three major formation modes have been summarized:the runoff generation mode of vegetation-hydrology-soil coupling dominated by high hydraulic gradient in mountainous areas,strong flow-sediment coupling movement,and serious disaster losses due to high exposure of disaster bearing objects.Finally,based on the issues in previous research,four future research challenges for flash flood disaster in the HMR were proposed.Our study provides insights into disaster prevention and reduction research,including fundamental theoretical system,precise risk assessment of regional disasters,and accurate early warning and forecasting of flash floods.展开更多
The historicity of China's first state-level government(the Xia Dynasty),during which a Great Flood is claimed to have swept through the core of northern China,remains a well-known yet unresolved issue.Archaeologi...The historicity of China's first state-level government(the Xia Dynasty),during which a Great Flood is claimed to have swept through the core of northern China,remains a well-known yet unresolved issue.Archaeologists hypothesize a connection between the legendary events of the Xia Dynasty and archaeological discoveries in the Central China Plains cultural area,encompassing late Neolithic and Bronze Age cultures such as Henan's Longshan,Xinzhai,Erlitou,and Erligang.The authenticity of these speculations has been challenging to substantiate due to the lack of systematic evidence for the Great Flood in the middle to lower Yellow River(YR)Basin.In this paper,we present high-resolution hydrological environmental proxy data,sedimentological remains,and paleontological evidence from the central North China Plain.Our findings provide isochronous evidence of the termination of a hundred-year-long flood period dated to approximately 2080±216 BC,consistent with the observations from lower YR flood plain and marginal marine sediments.These findings both spatially and temporally overlap with the framework of the Great Flood described in the Chinese classics.The alignment between the geoscientific and archaeological evidence and the information in the Chinese classics provides robust confirmation that the Great Flood occurred in the middle to lower YR region during the late Longshan era.展开更多
Polymer flooding is an important means of improving oil recovery and is widely used in Daqing,Xinjiang,and Shengli oilfields,China.Different from conventional injection media such as water and gas,viscoelastic polymer...Polymer flooding is an important means of improving oil recovery and is widely used in Daqing,Xinjiang,and Shengli oilfields,China.Different from conventional injection media such as water and gas,viscoelastic polymer solutions exhibit non-Newtonian and nonlinear flow behavior including shear thinning and shear thickening,polymer convection,diffusion,adsorption,retention,inaccessible pore volume,and reduced effective permeability.However,available well test model of polymer flooding wells generally simplifies these characteristics on pressure transient response,which may lead to inaccurate results.This work proposes a novel two-phase numerical well test model to better describe the polymer viscoelasticity and nonlinear flow behavior.Different influence factors that related to near-well blockage during polymer flooding process,including the degree of blockage(inner zone permeability),the extent of blockage(composite radius),and polymer flooding front radius are explored to investigate these impacts on bottom hole pressure responses.Results show that polymer viscoelasticity has a significant impact on the transitional flow segment of type curves,and the effects of near-well formation blockage and polymer concentration distribution on well test curves are very similar.Thus,to accurately interpret the degree of near-well blockage in injection wells,it is essential to first eliminate the influence of polymer viscoelasticity.Finally,a field case is comprehensively analyzed and discussed to illustrate the applicability of the proposed model.展开更多
MIKE Flood模型在城市洪涝分析管理中已得到广泛应用,软件模块丰富,功能齐全,对城市洪涝风险分析提供了理论依据和技术支持。文章主要根据某建设项目所在排水区域的地形、排水管网、周边河道等基础资料,利用MIKE FLOOD软件进行建模,耦...MIKE Flood模型在城市洪涝分析管理中已得到广泛应用,软件模块丰富,功能齐全,对城市洪涝风险分析提供了理论依据和技术支持。文章主要根据某建设项目所在排水区域的地形、排水管网、周边河道等基础资料,利用MIKE FLOOD软件进行建模,耦合计算区域设计工况下洪涝水位、淹没水深、淹没范围,并提出相应工作建议,为城市防灾减灾工作提供支持。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
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
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 the National Research Foundation of Korea(NRF)grant funded by theKorea government(MSIT)(No.RS-2024-00405278)partially supported by the Jeju Industry-University Convergence District Project for Promoting Industry-Campus Cooperationfunded by the Ministry of Trade,Industry and Energy(MOTIE,Korea)[Project Name:Jeju Industry-University Convergence District Project for Promoting Industry-Campus Cooperation/Project Number:P0029950].
文摘Recent advances in deep learning have significantly improved flood detection and segmentation from aerial and satellite imagery.However,conventional convolutional neural networks(CNNs)often struggle in complex flood scenarios involving reflections,occlusions,or indistinct boundaries due to limited contextual modeling.To address these challenges,we propose a hybrid flood segmentation framework that integrates a Vision Transformer(ViT)encoder with a U-Net decoder,enhanced by a novel Flood-Aware Refinement Block(FARB).The FARB module improves boundary delineation and suppresses noise by combining residual smoothing with spatial-channel attention mechanisms.We evaluate our model on a UAV-acquired flood imagery dataset,demonstrating that the proposed ViTUNet+FARB architecture outperforms existing CNN and Transformer-based models in terms of accuracy and mean Intersection over Union(mIoU).Detailed ablation studies further validate the contribution of each component,confirming that the FARB design significantly enhances segmentation quality.To its better performance and computational efficiency,the proposed framework is well-suited for flood monitoring and disaster response applications,particularly in resource-constrained environments.
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the support of the National Natural Science Foundation of China(No.42107247)the National Key Research and Development Project(No.2022YFD1901605)+1 种基金the Natural Science Foundation of Sichuan Province(Nos.2025YFHZ0142 and 2024NSFSC0800)the Tobacco Science Foundation of Sichuan Province(No.SCYC202407)。
文摘Soil microorganisms and labile soil organic carbon(SOC)fractions are essential factors affecting greenhouse gas(GHG)emissions in paddy fields.However,the effects of labile SOC fractions and microorganisms on GHG emissions from flooding to drying after organic fertilizer replacing for chemical fertilizer remain unclear.Here,a long-term experiment was conducted with four treatments:chemical fertilization only(control),organic fertilizer substituting 25%of chemical N fertilizer(NM1),50%of chemical N fertilizer(NM2),and NM2combined with crop straw(NMS).GHG emissions were monitored,and soil samples were collected to determine labile SOC fractions and microorganisms.Results revealed the GHG emissions in the NM2 significantly increased by 196.88%from flooding to drying,mainly due to the higher CO_(2) emissions.The GHG emissions per kg of C input in NMS was the lowest with the value of 9.17.From flooding to drying,organic fertilizer application significantly increased the readily oxidizable organic carbon(ROC)contents and C lability;the NM2 and NMS dramatically increased the SOC and non-readily oxidizable organic carbon(NROC).The bacterial communities showed significant differences among different treatments in the flooding,while the significant difference was only found between the NMS and other treatments in the drying.From flooding to drying,changing soil moisture conditions causes C fractions and microbial communities to jointly affect carbon emissions,and the NMS promoted carbon sequestration and mitigated GHG emissions.Our findings highlight the importance of the labile SOC fractions and microorganisms linked to GHG emissions in paddy fields.
基金supported by the National Natural Science Foundation of China(No.42072211)the National Natural Science Foundation of China(No.42401048)the Third Xinjiang Scientific Expedition and Research Program(No.2021xjkk0302)。
文摘The Taklimakan Desert,located in the heart of central Asia,covers approximately 330000 km^(2),making it China's largest desert and the world's second-largest shifting desert(Dong et al.,2024).With an average annual precipitation of less than 100 mm and evaporation rates ranging from 2000 to 3000 mm(Yang et al.,2020),it is recognized as one of the driest regions on Earth,often referred to as the“sea of death”.
基金The Second Tibetan Plateau Scientific Expedition and Research Program,No.2019QZKK0903-02National Key R&D Program of China,No.2022YFC3002902National Natural Science Foundation of China,No.42201086。
文摘The Hengduan Mountains region(HMR)is one of the most densely distributed and severe flash flood disaster-prone areas in southwest China.It is also a key area for major engineering projects and beautiful countryside construction in Southwest China.However,previous studies have not systematically summarized the development characteristics and formation modes of flash flood disasters in the HMR,which limits the development of theoretical and technical system for flood control.In this study,we focused on the physical processes of flash flood disasters in the HMR,including generation,movement,and disaster formation,and clarified the dominant disaster-inducing conditions(multiple humid monsoon circulation,high potential energy and high heterogenous underlying surface)and disaster development characteristics(high spatio-temporal heterogeneity,highly concentrated energy,chain and cascading effects,and clustered occurrence)of flash floods in the HMR.Based on the entire processes of flash flood disasters,three major formation modes have been summarized:the runoff generation mode of vegetation-hydrology-soil coupling dominated by high hydraulic gradient in mountainous areas,strong flow-sediment coupling movement,and serious disaster losses due to high exposure of disaster bearing objects.Finally,based on the issues in previous research,four future research challenges for flash flood disaster in the HMR were proposed.Our study provides insights into disaster prevention and reduction research,including fundamental theoretical system,precise risk assessment of regional disasters,and accurate early warning and forecasting of flash floods.
基金Strategic Priority Research Program of the Chinese Academy of Sciences,No.XDB26000000National Natural Science Foundation of China,No.41888101,No.42072209Geological Survey Projects of China,No.DD20189629,No.DD20190370。
文摘The historicity of China's first state-level government(the Xia Dynasty),during which a Great Flood is claimed to have swept through the core of northern China,remains a well-known yet unresolved issue.Archaeologists hypothesize a connection between the legendary events of the Xia Dynasty and archaeological discoveries in the Central China Plains cultural area,encompassing late Neolithic and Bronze Age cultures such as Henan's Longshan,Xinzhai,Erlitou,and Erligang.The authenticity of these speculations has been challenging to substantiate due to the lack of systematic evidence for the Great Flood in the middle to lower Yellow River(YR)Basin.In this paper,we present high-resolution hydrological environmental proxy data,sedimentological remains,and paleontological evidence from the central North China Plain.Our findings provide isochronous evidence of the termination of a hundred-year-long flood period dated to approximately 2080±216 BC,consistent with the observations from lower YR flood plain and marginal marine sediments.These findings both spatially and temporally overlap with the framework of the Great Flood described in the Chinese classics.The alignment between the geoscientific and archaeological evidence and the information in the Chinese classics provides robust confirmation that the Great Flood occurred in the middle to lower YR region during the late Longshan era.
基金supported by the National Natural Science Foundation of China(52104049)the Young Elite Scientist Sponsorship Program by Beijing Association for Science and Technology(BYESS2023262)。
文摘Polymer flooding is an important means of improving oil recovery and is widely used in Daqing,Xinjiang,and Shengli oilfields,China.Different from conventional injection media such as water and gas,viscoelastic polymer solutions exhibit non-Newtonian and nonlinear flow behavior including shear thinning and shear thickening,polymer convection,diffusion,adsorption,retention,inaccessible pore volume,and reduced effective permeability.However,available well test model of polymer flooding wells generally simplifies these characteristics on pressure transient response,which may lead to inaccurate results.This work proposes a novel two-phase numerical well test model to better describe the polymer viscoelasticity and nonlinear flow behavior.Different influence factors that related to near-well blockage during polymer flooding process,including the degree of blockage(inner zone permeability),the extent of blockage(composite radius),and polymer flooding front radius are explored to investigate these impacts on bottom hole pressure responses.Results show that polymer viscoelasticity has a significant impact on the transitional flow segment of type curves,and the effects of near-well formation blockage and polymer concentration distribution on well test curves are very similar.Thus,to accurately interpret the degree of near-well blockage in injection wells,it is essential to first eliminate the influence of polymer viscoelasticity.Finally,a field case is comprehensively analyzed and discussed to illustrate the applicability of the proposed model.
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.
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.
