Prediction of the flooding area of the northeastern Caspian Sea from satellite images

Studying the dynamic changes in the coastline of the northeastern Caspian Sea is significant since the level of the Caspian is unstable,and the coastline change can cause enormous damage to the ecology,economy,and population of the coastal part of Kazakhstan.In this work,we use remote sensing and Geographic Information System(GIS)technologies to study the changes in the coastline of the northeastern Caspian Sea and predict the extent of flooding with increasing water levels.The proposed methodology for creating dynamic maps can be used to monitor the coastline and forecast the extent of flooding in the area.As a result of this work,the main factors affecting changes in the coastline were identified.After analyzing the water level data from 1988 to 2019,it was revealed that the rise in water level was observed from 1980 to 1995.The maximum sea level rise was recorded at-26.04 m.After that,the sea level began to fall,and between 1996 and 2009,there were no significant changes;the water level fluctuated with an average of-27.18 m.Then,a map of the water level dynamics in the Caspian Sea from 1988 to 2019 was compiled.According to the dynamics map,water level rise and significant coastal retreat were revealed,especially in the northern part of the Caspian Sea and the northern and southern parts of Sora Kaydak.The method for predicting the estimated flooding area was described.As a result,based on a single map,the flooding area of the northeast coast was predicted.A comparative analysis of Landsat and SRTM data is presented.

Flooding and its relationship with land cover change, population growth, and road density

Bangladesh experiences frequent hydro-climatic disasters such as flooding.These disasters are believed to be associated with land use changes and climate variability.However,identifying the factors that lead to flooding is challenging.This study mapped flood susceptibility in the northeast region of Bangladesh using Bayesian regularization back propagation(BRBP)neural network,classification and regression trees(CART),a statistical model(STM)using the evidence belief function(EBF),and their ensemble models(EMs)for three time periods(2000,2014,and 2017).The accuracy of machine learning algorithms(MLAs),STM,and EMs were assessed by considering the area under the curve—receiver operating characteristic(AUC-ROC).Evaluation of the accuracy levels of the aforementioned algorithms revealed that EM4(BRBP-CART-EBF)outperformed(AUC>90%)standalone and other ensemble models for the three time periods analyzed.Furthermore,this study investigated the relationships among land cover change(LCC),population growth(PG),road density(RD),and relative change of flooding(RCF)areas for the period between 2000 and 2017.The results showed that areas with very high susceptibility to flooding increased by 19.72%between 2000 and 2017,while the PG rate increased by 51.68%over the same period.The Pearson correlation coefficient for RCF and RD was calculated to be 0.496.These findings highlight the significant association between floods and causative factors.The study findings could be valuable to policymakers and resource managers as they can lead to improvements in flood management and reduction in flood damage and risks.

Assessing Multidimensional Vulnerability of Rural Areas to Flooding:An Index‑Based Approach

Vulnerability assessment is essential for understanding and launching effective flood risk reduction strategies.This study aimed to examine the vulnerability of flood-prone rural communities in southern Punjab,Pakistan to external shocks.The concept of vulnerability encompasses a range of dimensions,including physical,social,institutional,environmental,economic,and attitudinal.Using a composite index method,indices were developed for each dimension and combined to create a multidimensional measure of vulnerability.A sample of 365 communities was selected using the Yamane sampling technique,and data were collected through a questionnaire containing 65 indicators across all dimensions.Descriptive statistics and ANOVA tests were used to analyze the data.The results show that communities near the Chenab River had higher attitudinal and institutional vulnerability compared to other communities.High attitudinal vulnerabilities were as sociated with poorly perceived flood risks and low preparedness measures,whereas institutional vulnerabilities were driven by conventional flood protection strategies,lack of institutional trust,and lack of flood risk awareness.This research provides insights into the various components of vulnerability in flood-prone rural communities in Pakistan and demonstrates a useful methodology that can be applied to other disasters at different spatial scales.

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.

基于Flood Area模型的邢台市小马河流域暴雨洪涝灾害风险分布特征

利用邢台市小马河流域26个区域站2014—2019年及周边7个国家站1980—2019年的逐日降水资料,基于泰森多边形法和MuDFiT软件计算不同重现期面雨量。运行Flood Area模型对该流域不同重现期下的淹没范围进行模拟,基于此叠加承灾体数据,得到各承灾体受损率。为验证模拟结果的准确性,选取2016年7月19—20日强降雨过程对百年一遇暴雨洪涝灾害风险分布特征进行验证,结果与模拟淹没范围基本一致。研究表明随着重现期不断增大,洪水逐渐向中下游地区推进,淹没范围不断扩大加深。当淹没深度在0.1~0.3 m及1.0 m以上时,林地受损率最高,淹没深度小于0.1 m时,林地受损率最低。人口、GDP、城镇用地、耕地、草地等其他承灾体淹没深度在0.1 m以下时受损率最高,在0.1~0.3 m时受损率次之,在1.0 m以上时受损率居中,在0.3~0.5 m时受损率较低,淹没深度位于0.5~1.0 m时受损率最低。

Real-time flood forecasting of Huai River with flood diversion and retarding areas

A combination of the rainfall-runoff module of the Xin’anjiang model, the Muskingum routing method, the water stage simulating hydrologic method, the diffusion wave nonlinear water stage method, and the real-time error correction method is applied to the real-time flood forecasting and regulation of the Huai River with flood diversion and retarding areas. The Xin’anjiang model is used to forecast the flood discharge hydrograph of the upstream and tributary. The flood routing of the main channel and flood diversion areas is based on the Muskingum method. The water stage of the downstream boundary condition is calculated with the water stage simulating hydrologic method and the water stages of each cross section are calculated from downstream to upstream with the diffusion wave nonlinear water stage method. The input flood discharge hydrograph from the main channel to the flood diversion area is estimated with the fixed split ratio of the main channel discharge. The flood flow inside the flood retarding area is calculated as a reservoir with the water balance method. The faded-memory forgetting factor least square of error series is used as the real-time error correction method for forecasting discharge and water stage. As an example, the combined models were applied to flood forecasting and regulation of the upper reaches of the Huai River above Lutaizi during the 2007 flood season. The forecast achieves a high accuracy and the results show that the combined models provide a scientific way of flood forecasting and regulation for a complex watershed with flood diversion and retarding areas.

A GIS-based method for flooded area calculation and damage evaluation

Using geographic information system to study flooded area and damage evaluation has been a hotspot in environmental disaster research for years. In this paper, a model for flooded area calculation and damage evaluation is presented. Flooding is divided into two types: ‘soruce flood’ and ‘non-source flood’. The source-flood area calculation is based on seed spread algorithm. The flood damage evaluation is calculated by overlaying the flooded ara range with thematic maps and relating the results to other social and economic data. To raise the operational efficiency of the model, a skipping approach is used to speed seed spread algorithm and all thematic maps are converted to raster format before overlay analysis. The accuracy of flooded area calculation and damage evaluation is mainly dependent upon the resolution and precision of the digital elevation model (DEM) data, upon the accuracy of registering all raster layers, and upon the quality of economic information. This model has been successfully used in the Zhejiang Province Comprehensive Water Management Information System developed by the authors. The applications show that this model is especially useful for most counties of China and other developing countries.

Visualization and Damage Assessment for Flooded Area

A practical method for visualizing flood area and evaluating damage is presented, which consists of two technical approaches: self\|programming and adapting commercial GIS platforms. The low\|cost and easy\|to\|use GIS\|Based model developed by self\|programming can meet current requirements of most local authorities, especially in developing countries. In this model, two cases, non\|source flood and source flood, are distinguished and the Seed\|spread algorithm suitable for source\|flood is discussed; The flood damage is assessed by overlaying the flood area range with thematic maps and other related social and economic data. and all thematic maps are converted to raster format before overlay analysis. Two measures are taken to improve the operation efficiency of speed seed\|spread algorithm. The accuracy of the model mainly depends on the resolution and precision of the DEM data, and the accuracy of registering all raster layers and the quality of attribute data.

“21·7”郑州极端强降水过程在贵阳市的推演

基于降水实况资料、高度场和风场再分析资料,对比分析了郑州“21·7”降水与贵州省“14·7”降水的成因,并利用Flood Area淹没模型对假定发生在贵阳的“21·7”强降水过程进行过程推演。结果表明:贵州省1 h、3 h、6 h、24 h极端降水量的空间分布特征基本相同,均具有自西北向东或向南递增的分布规律。西太平洋副热带高压、台风、高空槽、低涡切变均是郑州“21·7”和贵州“14·7”降水过程的重要影响系统,但系统的位置却大不相同,并且地形抬升作用对“21·7”降水过程至关重要。若郑州“21·7”降水发生在贵阳,贵阳淹没深度>2.4 m的区域将集中出现在云岩区和南明区的交界处、白云区中部、乌当区南部、花溪区北部,占贵阳市总面积的6.8%。对选定区域进行淹没模拟显示,随着降水量的增大,选定区域内淹没深度>2.4 m的增幅网格数呈现先增多后减少的变化趋势,而淹没深度>2.4 m的网格总数变化呈现前后增加缓慢、中间快速增加的变化特征。选定区域内各时次的淹没深度>2.4 m的网格总数和增幅网格数分别与各时效累积降水量和逐小时降水量呈明显的正相关。

Mathematical model for flood routing in Jingjiang River and Dongting Lake network

The main stream of the Yangtze River, Dongting Lake, and the river network in the Jingjiang reach of the Yangtze River constitute a complex water system. This paper develops a one-dimensional （l-D） mathematical model for flood routing in the river network Of the Jingjiang River and Dongting Lake using the explicit finite volume method. Based on observed data during the flood periods in 1996 and 1998, the model was calibrated and validated, and the results show that the model is effective and has high accuracy. In addition, the one-dimensional mathematical model for the river network and the horizontal two-dimensional （2-D） mathematical model for the Jingjiang flood diversion area were coupled to simulate the flood process in the Jingjiang River, Dongting Lake, and the Jingjiang flood diversion area. The calculated results of the coupled model are consistent with the practical processes. Meanwhile, the results show that the flood diversion has significant effects on the decrease of the peak water level at the Shashi and Chenjiawan hydrological stations near the flood diversion gates, and the effect is more obvious in the downstream than in the upstream.

Catchment-based Imperviousness Metrics Impacts on Floods in Niushou River Basin,Nanjing City,East China

Concerns regarding urbanization impacts on floods gradually moved from end-of-pipe solutions, based on open channel hydraulics improvement, to imperviousness ratio limiting and then to land use control and to integrated planning at local and large scale levels. The Niushou River basin is one of the fastest urbanizing areas in Nanjing City, East China, however, the high urban land percentage has leaded to series of flooding events. The paper aims to reveal the impact of imperviousness ratio, patterns and drainage system on flooding areas based on the unit of catchment and Storm Water Management Model(SWMM). The following conclusions were reached. 1) The ratio or spatial characteristics of the impervious surface affected the runoff volumes and associated floods areas. Despite the well-established drainage system, the high imperviousness ratio, particularly clustered pattern in locations such as hydrological sensitive zones aggravated the flooding tension across the basin. 2) The poor drainage hydraulic efficiency in local areas, and the lack of integral processes of infiltration, yield, storage and discharge in local catchment and larger basin are also significant factors. 3) The Niushou River basin development should improve the drainage transformations from a single local, short-term drainage process into integral, elastic processes of infiltration, yield, storage, and discharge.

ANALYSIS OF CHANGING FLOOD CHARACTERISTICS IN SOUTH CHINA DURING THE RECENT 50 YEARS

Based on China’s monthly precipitation data from 1950 to 2000 and by using the Z-index, 4 categories of flood were estimated. Variation and change of flood in South China were analyzed in terms of percentage areas of flood. This study reveals that flood areas in South China had a slightly decreasing trend in the latest 50 years. During the winter half year, however, it displayed an increasing trend, especially since the 1990’s. It is also found that flood areas decreased during the summer half year from April to September, but increased during summer, especially since the 1990’s. In the annually first season of precipitation, the flood area has a decreasing trend, but it has a strongly increasing trend in the annually second season. The gradual wet trend during the winter-half year results in wetter climate condition for South China, which will be more favorable for spreading some of the epidemic pathogenic bacterium, crop diseases and insect pests.

Rainfall-runoff simulation and flood forecasting for Huaihe Basin

The main purpose of this study was to forecast the inflow to Hongze Lake using the Xin＇anjiang rainfall-runoff model. The upper area of Hongze Lake in the Huaihe Basin was divided into 23 sub-basins, including the surface of Hongze Lake. The influence of reservoirs and gates on flood forecasting was considered in a practical and simple way. With a one-day time step, the linear and non-linear Muskingum method was used for channel flood routing, and the least-square regression model was used for real-time correction in flood forecasting. Representative historical data were collected for the model calibration. The hydrological model parameters for each sub-basin were calibrated individually, so the parameters of the Xin＇anjiang model were different for different sub-basins. This flood forecasting system was used in the real-time simulation of the large flood in 2005 and the results are satisfactory when compared with measured data from the flood.

Flood Risk Analysis and Flood Potential Losses Assessment

The heavy floods in the Taihu Basin showed increasing trend in recent years. In this work, a typical area in the northern Taihu Basin was selected for flood risk analysis and potential flood losses assessment. Human activities have strong impact on the study area’s flood situation (as affected by the polders built, deforestation, population increase, urbanization, etc.), and have made water level higher, flood duration shorter, and flood peaks sharper. Five years of different flood return periods [(1970), 5 (1962), 10 (1987), 20 (1954), 50 (1991)] were used to calculate the potential flood risk area and its losses. The potential flood risk map, economic losses, and flood-impacted population were also calculated. The study’s main conclusions are: 1) Human activities have strongly changed the natural flood situation in the study area, increasing runoff and flooding; 2) The flood risk area is closely related with the precipitation center; 3) Polder construction has successfully protected land from flood, shortened the flood duration, and elevated water level in rivers outside the polders; 4) Economic and social development have caused flood losses to increase in recent years.

A new multi-source remote sensing image sample dataset with high resolution for flood area extraction:GF-FloodNet

Deep learning algorithms show good prospects for remote sensingflood monitoring.They mostly rely on huge amounts of labeled data.However,there is a lack of available labeled data in actual needs.In this paper,we propose a high-resolution multi-source remote sensing dataset forflood area extraction:GF-FloodNet.GF-FloodNet contains 13388 samples from Gaofen-3(GF-3)and Gaofen-2(GF-2)images.We use a multi-level sample selection and interactive annotation strategy based on active learning to construct it.Compare with otherflood-related datasets,GF-FloodNet not only has a spatial resolution of up to 1.5 m and provides pixel-level labels,but also consists of multi-source remote sensing data.We thoroughly validate and evaluate the dataset using several deep learning models,including quantitative analysis,qualitative analysis,and validation on large-scale remote sensing data in real scenes.Experimental results reveal that GF-FloodNet has significant advantages by multi-source data.It can support different deep learning models for training to extractflood areas.There should be a potential optimal boundary for model training in any deep learning dataset.The boundary seems close to 4824 samples in GF-FloodNet.We provide GF-FloodNet at https://www.kaggle.com/datasets/pengliuair/gf-floodnet and https://pan.baidu.com/s/1vdUCGNAfFwG5UjZ9RLLFMQ?pwd=8v6o.

Disastrous Rains Downpours and flooding cause more problems for the area hie hard by last year’s earthquake

At least six people were killed on July 25 when a rockslide smashed into a bridge in Wenchuan County,ithe epicenter of last

Flood Duration Estimation Based on Multisensor,Multitemporal Remote Sensing:The Sardoba Reservoir Flood

Single-sensor monitoring of flood events at high spatial and temporal resolutions is difficult because of the lack of data owing to instrument defects,cloud contamination,imaging geometry.However,combining multisensor data provides an impressive solution to this problem.In this study,11 synthetic aperture radar(SAR)images and 13 optical images were collected from the Google Earth Engine(GEE)platform during the Sardoba Reservoir flood event to constitute a time series dataset.Threshold-based and indices-based methods were used for SAR and optical data,respectively,to extract the water extent.The final sequential flood water maps were obtained by fusing the results from multisensor time series imagery.Experiments show that,when compare with the Global Surface Water Dynamic(GSWD)dataset,the overall accuracy and Kappa coefficient of the water body extent extracted by our methods range from 98.8%to 99.1%and 0.839 to 0.900,respectively.The flooded extent and area increased sharply to a maximum between May 1 and May 4,and then experienced a sustained decline over time.The flood lasted for more than a month in the lowland areas in the north,indicating that the northern region is severely affected.Land cover changes could be detected using the temporal spectrum analysis,which indicated that detailed temporal information benefiting from the multisensor data is highly important for time series analyses.

Prediction accuracy of reservoir break flood simulation model using finite volume method and UAV

Methods for producing high-resolution digital topographic maps using an unmanned aerial vehicle(UAV),and 3D fluid dynamics simulation to estimate the flooded areas caused by a collapsed reservoir,were proposed in this paper.The UAV flight path for photographing damaged areas was divided into two sections considering the drone flight time and overlapping range of the images in the x-and y-directions.The metadata taken by the drone were transferred into world coordinates by tracking the key features of the photographs of nearby areas using a 3D rotation matrix.The point cloud data with a 3D space were extracted from the registered images,and a digital surface map(DSM)was produced using a point cloud classification geometric mapping technique.To amend the serious elevation errors caused by natural or artificial obstacles,a kriging interpolation method was used to reproduce the DSM.A transient computational simulation that considers both the complex geometric topology and hydrodynamic energy of flowing water was conducted using FLOW-3D software to deal with an renormalization group(RNG)turbulence model.The flooded areas calculated through visual reading using images taken by the UAV were compared with the 3D simulation results for verification.The flooded areas estimated through the simulation were approximately 18.3%larger than those found by visual reading.Turbulent flows were mainly observed in obstacles or curved areas of the stream,and the differences in the water depth could be further increased.However,the villagers confirmed that the flooded areas were much greater than what was seen through the visual reading.Therefore,the combination of UAV surveying and the 3D simulation method based on the RNG turbulence model is recommended to accurately estimate flooded areas,and it will support an administrative policy aimed at minimizing the economic costs of damage caused by future reservoir collapses.