基于SWMM和LISFLOOD-FP的城市内涝耦合模型研究

以柳州市箭盘山流域为例,构建SWMM一维管道模型与LISFLOOD-FP二维地面模型并将其耦合,基于实测降雨“20180818”24 h暴雨资料,将得到的暴雨内涝淹没水深和淹没面积与该场次降雨情况下记录淹没点的范围相比较,验证耦合模型具有较好的适用性。进而基于耦合模型,对柳州市箭盘山流域2、5、10、20年一遇下设计降雨进行模拟,得到不同重现期下研究区的溢流节点、淹没水深和淹没面积,并于ARCGIS平台将结果可视化。结果表明,重现期由2年上升到20年过程中,溢流节点比例从9.03%增加至25.99%,溢流面积从0.473 km^(2)增加至2.114 km^(2);重点淹没区域分布在屏山大道、炮团路、西江路和东堤路。

基于MIKE FLOOD模型的平原水网地区内涝治理效果模拟研究

为科学评估平原水网地区内涝治理效果,以昆山市淀山湖镇为研究对象,基于MIKE FLOOD模型,耦合一维河网、二维地形和一维管网模拟了平原水网地区城镇内涝情况。采用实测降雨数据对模型进行了率定和验证,并模拟了2年一遇、5年一遇、10年一遇和20年一遇4种重现期降雨条件下现状和治理后的最大淹没水深。模拟结果表明:20年一遇降雨条件下内涝范围变化明显,其余3种重现期降雨条件下内涝范围变化不明显;对于降雨重现期较小条件下的城市内涝治理,应优先考虑管网改造,而针对短历时的强暴雨事件,应优先考虑海绵城市设施建设改造;为了应对不同重现期、短历时降雨造成的城市内涝风险,应在管网改造的基础上配合海绵城市设施建设改造。

基于MIKE FLOOD耦合模型模拟的城市内涝对道路安全风险的影响评估

城市内涝风险评估对预防洪涝灾害有重要作用,但现有研究很少针对道路安全风险评估。以重庆市秀山县为研究对象,通过构建MIKE FLOOD耦合模型对道路安全风险进行评估。结果表明:地面积水深度主要集中在0.05~0.15 m,不同重现期(P=1%~20%)降雨下的水深在空间分布上大体呈一致状态,积水时间主要集中在60~90 min,积水流速主要大于0.8 m/s。其中,老城区部分区域的基础设施安全风险以极高风险(水深>0.4 m)为主,其余区域为轻微风险(水深<0.5 m)为主,而行人安全风险以Ⅲ级(危险性指数1.25~2)为主,其余区域则以Ⅰ级(危险性指数<0.75)为主。大部分路段的交通运行状况以畅通等级(保留系数>0.7)为主,但部分路段遇暴雨时中断(保留系数=0)。研究结果可为城市内涝发生前的交通管控和通行预警提供科学依据。

Climate change drives flooding risk increases in the Yellow River Basin

The Yellow River Basin(YRB)has experienced severe floods and continuous riverbed elevation throughout history.Global climate change has been suggested to be driving a worldwide increase in flooding risk.However,owing to insufficient evidence,the quantitative correlation between flooding and climate change remains illdefined.We present a long time series of maximum flood discharge in the YRB dating back to 1843 compiled from historical documents and instrument measurements.Variations in yearly maximum flood discharge show distinct periods:a dramatic decreasing period from 1843 to 1950,and an oscillating gentle decreasing from 1950 to 2021,with the latter period also showing increasing more extreme floods.A Mann-Kendall test analysis suggests that the latter period can be further split into two distinct sub-periods:an oscillating gentle decreasing period from 1950 to 2000,and a clear recent increasing period from 2000 to 2021.We further predict that climate change will cause an ongoing remarkable increase in future flooding risk and an∼44.4 billion US dollars loss of floods in the YRB in 2100.

Flood Velocity Prediction Using Deep Learning Approach

Floods are one of the most serious natural disasters that can cause huge societal and economic losses.Extensive research has been conducted on topics like flood monitoring,prediction,and loss estimation.In these research fields,flood velocity plays a crucial role and is an important factor that influences the reliability of the outcomes.Traditional methods rely on physical models for flood simulation and prediction and could generate accurate results but often take a long time.Deep learning technology has recently shown significant potential in the same field,especially in terms of efficiency,helping to overcome the time-consuming associated with traditional methods.This study explores the potential of deep learning models in predicting flood velocity.More specifically,we use a Multi-Layer Perceptron(MLP)model,a specific type of Artificial Neural Networks(ANNs),to predict the velocity in the test area of the Lundesokna River in Norway with diverse terrain conditions.Geographic data and flood velocity simulated based on the physical hydraulic model are used in the study for the pre-training,optimization,and testing of the MLP model.Our experiment indicates that the MLP model has the potential to predict flood velocity in diverse terrain conditions of the river with acceptable accuracy against simulated velocity results but with a significant decrease in training time and testing time.Meanwhile,we discuss the limitations for the improvement in future work.

Real-time Rescue Target Detection Based on UAV Imagery for Flood Emergency Response

Timely acquisition of rescue target information is critical for emergency response after a flood disaster.Unmanned Aerial Vehicles(UAVs)equipped with remote sensing capabilities offer distinct advantages,including high-resolution imagery and exceptional mobility,making them well suited for monitoring flood extent and identifying rescue targets during floods.However,there are some challenges in interpreting rescue information in real time from flood images captured by UAVs,such as the complexity of the scenarios of UAV images,the lack of flood rescue target detection datasets and the limited real-time processing capabilities of the airborne on-board platform.Thus,we propose a real-time rescue target detection method for UAVs that is capable of efficiently delineating flood extent and identifying rescue targets(i.e.,pedestrians and vehicles trapped by floods).The proposed method achieves real-time rescue information extraction for UAV platforms by lightweight processing and fusion of flood extent extraction model and target detection model.The flood inundation range is extracted by the proposed method in real time and detects targets such as people and vehicles to be rescued based on this layer.Our experimental results demonstrate that the Intersection over Union(IoU)for flood water extraction reaches an impressive 80%,and the IoU for real-time flood water extraction stands at a commendable 76.4%.The information on flood stricken targets extracted by this method in real time can be used for flood emergency rescue.

Spatial diversion and coordination of flood water for an urban flood control project in Suzhou, China

Suzhou City,located in the Yangtze River Delta in China,is prone to flooding due to a complex combination of natural factors,including its monsoon climate,low elevation,and tidally influenced position,as well as intensive human activities.The Large Encirclement Flood Control Project(LEFCP)was launched to cope with serious floods in the urban area.This project changed the spatiotemporal pattern of flood processes and caused spatial diversion of floods from the urban area to the outskirts of the city.Therefore,this study developed a distributed flood simulation model in order to understand this transition of flood processes.The results revealed that the LEFCP effectively protected the urban areas from floods,but the present scheduling schemes resulted in the spatial diversion of floods to the outskirts of the city.With rainstorm frequencies of 10.0%to 0.5%,the water level differences between two representative water level stations(Miduqiao(MDQ)and Fengqiao(FQ))located inside and outside the LEFCP area,ranged from 0.75 m to 0.24 m and from 1.80 m to 1.58 m,respectively.In addition,the flood safety margin at MDQ and the duration with the water level exceeding the warning water level at FQ ranged from 0.95 m to 0.43 m and from 4 h to 22 h,respectively.Rational scheduling schemes for the hydraulic facilities of the LEFCP in extreme precipitation cases were developed ac-cording to food simulations under seven scheduling scenarios.This helps to regulate the spatial flood diversion caused by the LEFCP during extreme precipitation.

Experimental study of the effects of a multistage pore-throat structure on the seepage characteristics of sandstones in the Beibuwan Basin:Insights into the flooding mode

To investigate the relationship between grain sizes, seepage capacity, and oil-displacement efficiency in the Liushagang Formation of the Beibuwan Basin, this study identifies the multistage pore-throat structure as a crucial factor through a comparison of oil displacement in microscopic pore-throat experiments. The two-phase flow evaluation method based on the Li-Horne model is utilized to effectively characterize and quantify the seepage characteristics of different reservoirs, closely relating them to the distribution of microscopic pores and throats. It is observed that conglomerate sandstones at different stages exhibit significant heterogeneity and noticeable differences in seepage capacity, highlighting the crucial role played by certain large pore throats in determining seepage capacity and oil displacement efficiency. Furthermore, it was found that the displacement effects of conglomeratic sandstones with strong heterogeneity were inferior to those of conventional homogeneous sandstone, as evidenced by multiple displacement experiments conducted on core samples with varying granularities and flooding systems. Subsequently, core-based experiments on associated gas flooding after water flooding were conducted to address the challenge of achieving satisfactory results in a single displacement mode for reservoirs with significant heterogeneity. The results indicate that the oil recovery rates for associated gas flooding after water flooding increased by 7.3%-16.4% compared with water flooding alone at a gas-oil ratio of approximately 7000 m^(3)/m^(3). Therefore, considering the advantages of gas flooding in terms of seepage capacity, oil exchange ratio, and the potential for two-phase production, gas flooding is recommended as an energy supplement mode for homogeneous reservoirs in the presence of sufficient gas source and appropriate tectonic angle. On the other hand, associated gas flooding after water flooding is suggested to achieve a more favorable development effect compared to a single mode of energy supplementation for strongly heterogeneous sandstone reservoirs.

Flood Risk Mapping of the Benin Municipalities at the Intersection of the Coastal Sedimentary Zone and the Crystalline Surface

Climate change and population growth have led to the increase and/or intensification of flooding becoming a major issue. The objective of this study is to visualize flooding risk of municipalities at the intersection of the coastal sedimentary zone and the crystalline surface. The methodology adopted is based on geomatic approach, which involves documentary research, processing and assisted classification using remote sensing images and multi-criteria analysis of the Geographic Information System (GIS). Flooding risk is very high at 8.85% in Djidja, Toffo, Zè and Bonou municipalities. In other municipalities such as Agbangnizoun, Abomey, Bohicon, Za-Kpota and Cove, it is high of 46.85%. To the Southeast of the study area, it is located on the eastern and western banks of Oueme Valley. The medium risk represents 26.35% and is located in the municipalities of Ouinhi and Adjohoun. The other municipalities have a low rate of 17.95%. Risk modeling has made it possible to access the various levels of rising water that can cause flooding. Land-use planning decisions can be influenced by the results of this study.

Mapping of Flood Risk Zones Using Multi-Criteria Approach and Radar a Case Study of Ala and Akure-Ofosu Communities, Ondo State, Nigeria

Floods are among the worst natural catastrophes, devastating homes, businesses, public buildings, farms, and crops. Studies show that it’s not the flood itself that’s deadly but people’s vulnerability. This study investigates the Ala and Akure-Ofosu flood-prone zones;identifies elements that cause flooding in the study area;classifies each criterion by its effect;develops a flood risk map;estimates flood damage using Sentinel-1A SAR data;compares AHP results. Literature study and GIS-computer database georeferenced fieldwork data. Photos from the 2020 Sentinel 2A satellite have been organized. Built-up area, cropland, rock, the body of water, and forest Land use and cover, slope, rainfall, soil, Euclidean River Distance, and flow accumulation were mapped. These variables were integrated into a Multi-Criteria Analysis (MCA) using GIS tools, resulting in the creation of a flood risk map that categorizes the region into five risk zones: 5% of the area is identified as high-risk, 21% as low-risk, and 74% as moderate-risk. Copernicus SAR data from before and after the flood were processed on Google Earth Engine to map flood extent and ensured that the MCA map accurately reflected flood-prone areas. Periodic review, real-time flood susceptibility monitoring, early warning, and quick damage assessment are suggested to avoid flood danger and other environmental problems.

Experimental investigation into effects of the natural polymer and nanoclay particles on the EOR performance of chemical flooding in carbonate reservoirs

This paper aims to investigate the tragacanth gum potential as a natural polymer combined with natural clay mineral(montmorillonite,kaolinite,and illite)nanoparticles(NPs)to form NP-polymer suspension for enhanced oil recovery(EOR)in carbonate reservoirs.Thermal gravimetric analysis(TGA)tests were conducted initially in order to evaluate the properties of tragacanth gum.Subsequently,scanning electron microscopy(SEM)and energy-dispersive X-ray(EDX)tests were used to detect the structure of clay particles.In various scenarios,the effects of natural NPs and polymer on the wettability alteration,interfacial tension(IFT)reduction,viscosity improvement,and oil recovery were investigated through contact angle system,ring method,Anton Paar viscometer,and core flooding tests,respectively.The entire experiment was conducted at 25,50,and 75℃,respectively.According to the experimental results,the clay minerals alone did not have a significant effect on viscosity,but the addition of minerals to the polymer solution leads to the viscosity enhancement remarkably,resulting mobility ratio improvement.Among clay NPs,the combination of natural polymer and kaolinite results in increased viscosity at all temperatures.Considerable wettability alteration was also observed in the case of natural polymer and illite NPs.Illite in combination with natural polymer showed an ability in reducing IFT.Finally,the results of displacement experiments revealed that the combination of natural polymer and kaolinite could be the best option for EOR due to its substantial ability to improve the recovery factor.

基于FloodArea模型的呼和浩特市城区大青山南麓山洪致灾临界面雨量研究

以呼和浩特市城区北部大青山南麓山洪沟流域为研究区域,利用流域内气象观测站逐小时降水数据、数字高程模型数据、土地利用数据等资料,针对1998年7月12日和2020年8月1日暴雨时段,采用FloodArea淹没模型进行洪水动态淹没模拟。结果表明:1998年7月12日暴雨过程的FloodArea水动力模型模拟结果与实际情况较为吻合,且9条山洪沟的洪水淹没深度与累计9 h滑动面雨量的相关程度最高,构建了面雨量与淹没深度的回归关系方程,基于隐患点3个风险等级,确定了不同等级下的临界面雨量,以红山口沟为例,分别为69.9 mm(1级)、39.3 mm(2级)、17.2 mm(3级)。2020年8月1日暴雨过程的模型模拟结果基本与山洪过程一致。

Potential morphological responses of an artificial beach to a flood in extreme events: field observation and numerical modelling

Conch Island is a typical artificial island at the Tanghe Estuary in Bohai Sea,China.To improve natural environment and boost local tourism,beach nourishment will be applied to its north-western shore.The projected beach is landward and opposite to the Jinmeng Bay Beach.Nowadays,with climate changes,frequent heavy rainfalls in Hebei Province rise flood hazards at the Tanghe Estuary.Under this circumstance,potential influences on the projected beach of a flood are investigated for sustainable managements.A multi-coupled model is established and based on the data from field observations,where wave model,flow model and multifraction sediment transport model are included.In addition,the impacts on the projected beach of different components in extreme events are discussed,including the spring tides,storm winds,storm waves,and sediment inputs.The numerical results indicate the following result.(1)Artificial islands protect the coasts from erosion by obstructing landward waves,but rise the deposition risks along the target shore.(2)Flood brings massive sediment inputs and leads to scours at the estuary,but the currents with high sediment concentration contribute to the accretions along the target shore.(3)The projected beach mitigates flood actions and reduces the maximum mean sediment concentration along the target shore by 20%.(4)The storm winds restrict the flood and decrease the maximum mean sediment concentration by 21%.With the combined actions of storm winds and waves,the maximum value further declines by 38%.(5)A quadratic polynomial relationship between the deposition depths and the maximum sediment inputs with flood is established for estimations on the potential morphological changes after the flood process in extreme events.For the uncertainty of estuarine floods,continuous monitoring on local hydrodynamic variations and sediment characteristics at Tanghe Estuary is necessary.

Pressure transient characteristics of non-uniform conductivity fractured wells in viscoelasticity polymer flooding based on oil-water two-phase flow

Polymer flooding in fractured wells has been extensively applied in oilfields to enhance oil recovery.In contrast to water,polymer solution exhibits non-Newtonian and nonlinear behavior such as effects of shear thinning and shear thickening,polymer convection,diffusion,adsorption retention,inaccessible pore volume and reduced effective permeability.Meanwhile,the flux density and fracture conductivity along the hydraulic fracture are generally non-uniform due to the effects of pressure distribution,formation damage,and proppant breakage.In this paper,we present an oil-water two-phase flow model that captures these complex non-Newtonian and nonlinear behavior,and non-uniform fracture characteristics in fractured polymer flooding.The hydraulic fracture is firstly divided into two parts:high-conductivity fracture near the wellbore and low-conductivity fracture in the far-wellbore section.A hybrid grid system,including perpendicular bisection(PEBI)and Cartesian grid,is applied to discrete the partial differential flow equations,and the local grid refinement method is applied in the near-wellbore region to accurately calculate the pressure distribution and shear rate of polymer solution.The combination of polymer behavior characterizations and numerical flow simulations are applied,resulting in the calculation for the distribution of water saturation,polymer concentration and reservoir pressure.Compared with the polymer flooding well with uniform fracture conductivity,this non-uniform fracture conductivity model exhibits the larger pressure difference,and the shorter bilinear flow period due to the decrease of fracture flow ability in the far-wellbore section.The field case of the fall-off test demonstrates that the proposed method characterizes fracture characteristics more accurately,and yields fracture half-lengths that better match engineering reality,enabling a quantitative segmented characterization of the near-wellbore section with high fracture conductivity and the far-wellbore section with low fracture conductivity.The novelty of this paper is the analysis of pressure performances caused by the fracture dynamics and polymer rheology,as well as an analysis method that derives formation and fracture parameters based on the pressure and its derivative curves.

Influence of pore structure heterogeneity on channeling channels during hot water flooding in heavy oil reservoir based on CT scanning

Hot water flooding is an effective way to develop heavy oil reservoirs.However,local channeling channels may form,possibly leading to a low thermal utilization efficiency and high water cut in the reservoir.The pore structure heterogeneity is an important factor in forming these channels.This study proposes a method that mixes quartz sand with different particle sizes to prepare weakly heterogeneous and strongly heterogeneous models through which hot water flooding experiments are conducted.During the experiments,computer tomography(CT)scanning identifies the pore structure and micro remaining oil saturation distribution to analyze the influence of the pore structure heterogeneity on the channeling channels.The oil saturation reduction and average pore size are divided into three levels to quantitatively describe the relationship between the channeling channel distribution and pore structure heterogeneity.The zone where oil saturation reduction exceeds 20%is defined as a channeling channel.The scanning area is divided into 180 equally sized zones based on the CT scanning images,and threedimensional(3D)distributions of the channeling channels are developed.Four micro remaining oil distribution patterns are proposed,and the morphology characteristics of micro remaining oil inside and outside the channeling channels are analyzed.The results show that hot water flooding is more balanced in the weakly heterogeneous model,and the oil saturation decreases by more than 20%in most zones without narrow channeling channels forming.In the strongly heterogeneous model,hot water flooding is unbalanced,and three narrow channeling channels of different lengths form.In the weakly heterogeneous model,the oil saturation reduction is greater in zones with larger pores.The distribution range of the average pore size is larger in the strongly heterogeneous model.The network remaining oil inside the channeling channels is less than outside the channeling channels,and the hot water converts the network remaining oil into cluster,film,and droplet remaining oil.

Response Mechanisms to Flooding Stress in Mulberry Revealed by Multi-Omics Analysis

Abiotic stress,including flooding,seriously affects the normal growth and development of plants.Mulberry(Morus alba),a species known for its flood resistance,is cultivated worldwide for economic purposes.The transcriptomic analysis has identified numerous differentially expressed genes(DEGs)involved in submergence tolerance in mulberry plants.However,a comprehensive analyses of metabolite types and changes under flooding stress in mulberry remain unreported.A non-targeted metabolomic analysis utilizing liquid chromatographytandem mass spectrometry(LC-MS/MS)was conducted to further investigate the effects of flooding stress on mulberry.A total of 1,169 metabolites were identified,with 331 differentially accumulated metabolites(DAMs)exhibiting up-regulation in response to flooding stress and 314 displaying down-regulation.Pathway enrichment analysis identified significant modifications in many metabolic pathways due to flooding stress,including amino acid biosynthesis and metabolism and flavonoid biosynthesis.DAMs and DEGs are significantly enriched in the Kyoto Encyclopedia of Genes and Genomes(KEGG)pathways for amino acid,phenylpropanoid and flavonoid synthesis.Furthermore,metabolites such as methyl jasmonate,sucrose,and D-mannose 6-phosphate accumulated in mulberry leaves post-flooding stress.Therefore,genes and metabolites associated with these KEGG pathways are likely to exert a significant influence on mulberry flood tolerance.This study makes a substantial contribution to the comprehension of the underlying mechanisms implicated in the adaptation of mulberry plants to submergence.

Flood Risk Assessment in the Lower Valley of Ouémé, Benin

In response to the increased frequency of flood events in recent years, it has become crucial to enhance preparedness and anticipation through precise flood risk assessments. To this end, this study aims to produce updated and precise flood risk maps for the Lower Valley of Ouémé River Basin, located in the South of Benin. The methodology used consisted of a combination of geographical information systems (GIS) and multi-criteria analysis, including Analytical Hierarchy Process (AHP) methods to define and quantify criteria for flood risk assessment. Seven hydro-geomorphological indicators (elevation, rainfall, slope, distance from rivers, flow accumulation, soil type, and drainage density), four socio-economic vulnerability indicators (female population density, literacy rate, poverty index, and road network density), and two exposure indicators (population density and land use) were integrated to generate risk maps. The results indicate that approximately 21.5% of the Lower Valley is under high and very high flood risk, mainly in the south between Dangbo, So-Ava, and Aguégués. The study findings align with the historical flood pattern in the region, which confirms the suitability of the used method. The novelty of this work lies in its comprehensive approach, the incorporation of AHP for weighting factors, and the use of remote sensing data, GIS technology, and spatial analysis techniques which adds precision to the mapping process. This work advances the scientific understanding of flood risk assessment and offers practical insights and solutions for flood-prone regions. The detailed flood risk indicator maps obtained stand out from previous studies and provide valuable information for effective flood risk management and mitigation efforts in the Lower Valley of Ouémé.

Occurrence of Extreme Rainfall and Flood Risks in Yopougon, Abidjan, Southeast Côte d’Ivoire from 1971 to 2022

Yopougon, located in the western part of the Autonomous District of Abidjan, is the most heavily populated municipality in Côte d’Ivoire. However, this area is prone to floods and landslides during the rainy season. The study aims to assess recent flood risks in the municipality of Yopougon of the Autonomous District of Abidjan. To achieve this objective, the study analyzed two types of data: daily rainfall from 1971 to 2022 and parameters derived from a Numerical Field and Altitude Model (NFAM). The study examined six rainfall parameters using statistical analysis and combined land use maps obtained from the NFAM of Yopougon. The results indicated that, in 67% of cases, extreme rainfall occurred mainly between week 3 of May and week 1 of July. The peak of extreme rainfall was observed in week 2 of June with 15% of cases. These are critical periods of flood risks in the Autonomous District of Abidjan, especially in Yopougon. In addition, there was variability of rainfall parameters in the Autonomous District of Abidjan. This was characterized by a drop of annual and seasonal rainfall, and an increase of numbers of rainy days. Flood risks in Yopougon are, therefore, due to the regular occurrence of rainy events. Recent floods in Yopougon were caused by normal rains ranging from 55 millimeters (mm) to 153 mm with a return period of less than five years. Abnormal heavy rains of a case study on June 20-21, 2022 in Yopougon were detected by outputs global climate models. Areas of very high risk of flood covered 18% of Yopougon, while 31% were at high risk. Climate information from this study can assist authorities to take in advance adaptation and management measures.

The Impacts of Flood and Local Communities’ Coping Strategies along the River Gambia

Flood disasters as Climate change hazards are common in developing countries, particularly in communities along the river Gambia. Local communities, for instance, had their local coping strategies that enabled them to stay in their communities even amid these ordeals, and climate change disaster threats. This work strives to understand flood impacts and the local peoples’ adaptation or coping strategies along the River Gambia basin. A community-based cross-sectional research study of 422 research participants of which 294 are males (69.7%) and females 128 (30.3%), and a focus group discussion of 10 groups which comprised 5 female groups and 5 male groups respectively found that 98.6% of the households experienced floods in their community, and 70.6% experienced flood in their houses, 2.1% have impending flood information and 88.4% do not know evacuation centres. The majority of the households had some local coping strategies, but they acknowledged their insufficient effectiveness. The result also shows that the impact of floods on farmlands, roads, buildings, and livestock was greatly felt. Coping strategies such as sandbags, raised elevations, contour bonds, dikes, and buildings on highlands were all found to be common mechanisms the local people used. The study opines that floods affect communities, but the effects vary depending on individual assets.

Hot spot tracking of flood remote sensing research over the past 22 years:abibliometric analysis using CiteSpace

Background,aim,and scope In the context of climate change,extreme precipitation and resulting flooding events are becoming increasingly severe.Remote sensing technologies are advantageous for monitoring such disasters due to their wide observation range,periodic revisit capabilities,and continuous spatial coverage.These tools enable real-time and quantitative assessment of flood inundation.Over the past 20 years,the field of remote sensing for floods has seen significant advancements.Understanding the evolution of research hotspots within this field can offer valuable insights for future research directions.Materials and methods This study systematically analyzes the development and hotspot evolution in the field of flood remote sensing,both domestically and internationally during 2000—2021.Data from CNKI(China National Knowledge Infrastructure)and WOS(Web of Science)databases are utilized for this analysis.Results(1)A total of 1693 articles have been published in this field,showing a stable growth trend post-2008.Significant contributors include the Chinese Academy of Sciences,Beijing Normal University,Wuhan University,the Italian National Research Council,and National Aeronautics and Space Administration.(2)High-frequency keywords from 2000 to 2021 include“remote sensing”“flood”“model”“classification”“GIS”“climate change”“area”,and“MODIS”.(3)The most prominent keywords were“GIS”(8.65),“surface water”(7.16),“remote sensing”(7.07),“machine learning”(6.52),and“sentinel-2”(5.86).(4)Thirteen cluster labels were identified through clustering,divided into three phases:2000—2009(initial exploratory stage),2010—2014(period of rapid development),and 2015—2021(steady development of remote sensing for floods and related disasters).Discussion The field exhibits strong phase-based development,with research focuses shifting over time.From 2000 to 2009,emphasis was on remote sensing image application and flood model development.From 2010 to 2014,the focus shifted to accurate interpretation of remote sensing images,multispectral image applications,and long time series detection.From 2015 to 2021,research concentrated on steady development,leveraging large datasets and advanced data processing techniques,including improvements in water body indices,big data fusion,deep learning,and drone monitoring.Early on,SAR data,known for its all-weather capability,was crucial for rapid flood hazard extraction and flood hydrological models.With the rise of high-quality optical satellites,optical remote sensing has become more prevalent,though algorithm accuracy and efficiency for water body index methods still require improvement.Conclusions Data sources and methodologies have evolved from early reliance on radar data to the current exploration of optical image fusion and multi-source data integration.Algorithms now increasingly employ deep learning,super image elements,and object-oriented methods to enhance flood identification accuracy.Recent studies focus on spatial and temporal changes in flooding,risk identification,and early warning for climate change-related flooding,including glacial melting and lake outbursts.Recommendations and perspectives To enhance monitoring accuracy and timeliness,UAV technology should be further utilized.Strengthening multi-source data fusion and assimilation is crucial,as is analyzing long-term flood disaster sequences to better understand their mechanisms.