基于GIS/AHP集成的浙江省洪涝灾害风险评估

将GIS技术引入洪涝灾害风险评估,可以弥补传统方法评估结果空间化显示不足的缺点。本文针对浙江省洪涝灾害的发生特点,从危险性和易损性两方面选择了浙江省洪涝灾害的影响因素,包括降雨量、地形、河网密度、人口密度和耕地百分比等因子。运用GIS空间分析技术对各因子进行空间化,结合层次分析法（AHP）确定各影响因素的权重,进行浙江省洪涝灾害风险评估和制图,并基于SuperMap iObjects平台设计与开发了浙江省洪涝灾害风险评估系统。研究结果表明：浙江省发生洪涝灾害的风险普遍偏高,高风险区域位于浙北和浙东南的沿海地带,较高风险区域位于浙东、浙南和浙北地区及金衢盆地中间地区,中等风险区位于浙南的西面、浙北及浙西地区。本文分析结果可为浙江省洪涝灾害预防和管理提供决策依据。     

Spatial prediction of flood-susceptible areas using frequency ratio and maximum entropy models

Modelling the flood in watersheds and reducing the damages caused by this natural disaster is one of the primary objectives of watershed management. This study aims to investigate the application of the frequency ratio and maximum entropy models for flood susceptibility mapping in the Madarsoo watershed, Golestan Province, Iran. Based on the maximum entropy and frequency ratio methods as well as analysis of the relationship between the flood events belonging to training group and the factors affecting on the risk of flooding, the weight of classes of each factor was determined in a GIS environment. Finally, prediction map of flooding potential was validated using receiver operating characteristic (ROC) curve method. ROC curve estimated the area under the curve for frequency ratio and the maximum entropy models as 74.3% and 92.6%, respectively, indicating that the maximum entropy model led to better results for evaluating flooding potential in the study area.     

层次分析法支持下的道路洪涝灾害风险评价——以武夷山地区为例

本文首先采用基于多准则决策的层次分析评价法,根据自然灾害风险理论,将洪涝风险影响因子分为危险性和脆弱性两类,子准则层包括平均降雨量、汇流累积量、坡度、海拔和土地覆盖度、道路级别、地表产流能力7个因子,构建了道路洪涝灾害风险评价模型。然后以福建省武夷山地区为研究区,利用地形数据、气象数据及遥感影像提取土地覆盖类型数据,通过道路洪涝灾害风险评价模型,绘制了道路风险分区图。结果表明,中、高风险积水道路占比较高,主要集中在东部、西部和中南部地区。本文对道路洪涝灾害风险所进行的评价,可服务于洪涝灾害风险预警和应急救援规划。     

Landslide susceptibility mapping of a catchment area using frequency ratio,fuzzy logic and multivariate logistic regression approaches

Geospatial database creation for landslide susceptibility mapping is often an almost inhibitive activity. This has been the reason that for quite some time landslide susceptibility analysis was modelled on the basis of spatially related factors. This paper presents the use of frequency ratio, fuzzy logic and multivariate regression models for landslide susceptibility mapping on Cameron catchment area, Malaysia, using a Geographic Information System (GIS) and remote sensing data. Landslide locations were identified in the study area from the interpretation of aerial photographs, high resolution satellite images, inventory reports and field surveys. Topographical, geological data and satellite images were collected, processed, and constructed into a spatial database using GIS and image processing tools. There were nine factors considered for landslide susceptibility mapping and the frequency ratio coefficient for each factor was computed. The factors chosen that influence landslide occurrence were: topographic slope, topographic aspect, topographic curvature and distance from drainage, all from the topographic database; lithology and distance from lineament, taken from the geologic database; land cover from TM satellite image; the vegetation index value from Landsat satellite images; and precipitation distribution from meteorological data. Using these factors the fuzzy membership values were calculated. Then fuzzy operators were applied to the fuzzy membership values for landslide susceptibility mapping. Further, multivariate logistic regression model was applied for the landslide susceptibility. Finally, the results of the analyses were verified using the landslide location data and compared with the frequency ratio, fuzzy logic and multivariate logistic regression models. The validation results showed that the frequency ratio model (accuracy is 89%) is better in prediction than fuzzy logic (accuracy is 84%) and logistic regression (accuracy is 85%) models. Results show that, among the fuzzy operators, in the case with “gamma” operator (λ = 0.9) showed the best accuracy (84%) while the case with “or” operator showed the worst accuracy (69%).     

Multi-hazard assessment using machine learning and remote sensing in the North Central region of Vietnam

Natural hazards constitute a diverse category and are unevenly distributed in time and space. This hinders predictive efforts, leading to significant impacts on human life and economies. Multi-hazard prediction is vital for any natural hazard risk management plan. The main objective of this study was the development of a multi-hazard susceptibility mapping framework, by combining two natural hazards—flooding and landslides—in the North Central region of Vietnam. This was accomplished using support vector machines, random forest, and AdaBoost. The input data consisted of 4591 flood points, 1315 landslide points, and 13 conditioning factors, split into training (70%), and testing (30%) datasets. The accuracy of the models' predictions was evaluated using the statistical indices root mean square error, area under curve (AUC), mean absolute error, and coefficient of determination. All proposed models were good at predicting multi-hazard susceptibility, with AUC values over 0.95. Among them, the AUC value for the support vector machine model was 0.98 and 0.99 for landslide and flood, respectively. For the random forest model, these values were 0.98 and 0.98, and for AdaBoost, they were 0.99 and 0.99. The multi-hazard maps were built by combining the landslide and flood susceptibility maps. The results showed that approximately 60% of the study area was affected by landslides, 30% by flood, and 8% by both hazards. These results illustrate how North Central is one of the regions of Vietnam that is most severely affected by natural hazards, particularly flooding, and landslides. The proposed models adapt to evaluate multi-hazard susceptibility at different scales, although expert intervention is also required, to optimize the algorithms. Multi-hazard maps can provide a valuable point of reference for decision makers in sustainable land-use planning and infrastructure development in regions faced with multiple hazards, and to prevent and reduce more effectively the frequency of floods and landslides and their damage to human life and property.     

A novel approach to leveraging social media for rapid flood mapping: a case study of the 2015 South Carolina floods

Rapid flood mapping is critical for local authorities and emergency responders to identify areas in need of immediate attention. However, traditional data collection practices such as remote sensing and field surveying often fail to offer timely information during or right after a flooding event. Social media such as Twitter have emerged as a new data source for disaster management and flood mapping. Using the 2015 South Carolina floods as the study case, this paper introduces a novel approach to mapping the flood in near real time by leveraging Twitter data in geospatial processes. Specifically, in this study, we first analyzed the spatiotemporal patterns of flood-related tweets using quantitative methods to better understand how Twitter activity is related to flood phenomena. Then, a kernel-based flood mapping model was developed to map the flooding possibility for the study area based on the water height points derived from tweets and stream gauges. The identified patterns of Twitter activity were used to assign the weights of flood model parameters. The feasibility and accuracy of the model was evaluated by comparing the model output with official inundation maps. Results show that the proposed approach could provide a consistent and comparable estimation of the flood situation in near real time, which is essential for improving the situational awareness during a flooding event to support decision-making.     

无人机倾斜摄影三维模型在城市雨洪风险评估中的应用

研究城市雨洪风险问题,对提高城市洪涝灾害监测、预报的准确性,以及促进城市防洪决策制定具有重要的意义。鉴于高精度的城市三维模型可以提供丰富地理信息,便于准确分析淹没情况,本文针对当前城市洪涝模型对地形数据的高敏感性,且雨洪风险评估研究的准确性受限于地形数据精度的问题,提出利用无人机倾斜摄影测量技术重建高精度实景三维模型,并结合GIS的空间分析功能,以淹没深度为关键指标进行研究区的雨洪风险评估。通过提取不同重现期下研究区的淹没深度信息,进行可视化渲染实现三维淹没分析,可以直观地看到研究区的淹没情况,作为暴雨内涝风险管理依据,同时对城市规划布局有一定的参考价值。     

运用社交网络分析淹水空间信息之关联性

随着社交网络的普遍发展,大量的讯息透过智能手机发布在个人的微博或其他社交网站。台湾地区的社交网站以脸书(Facebook)的使用量最大,平均每天有近千万笔的讯息量,大多数的讯息多以食衣住行或个人讯息为主,但从本研究所撷取自2010年至2015年的数据中显示,公众在社交网站所分享的信息中具有降雨、淹水或相关灾情的讯息,而这些讯息具有极高比例的正确性。由于社交网站无法提供私人讯息,故本研究将从社交信息中,以地点为单位撷取大量的数据信息再辅以语意关键词萃取出有关可作为淹水预判的讯息数据。为检核资料的可性度,本研究透过历史台风数据FLO-2D仿真重建淹水之空间信息进行检核。从研究比对分析中发现,经萃取后的公众信息其与灾害的关联性及正确性相当显着,故透过社交网站中大量的非结构讯息,透过语意及空间的转换,可萃取转化为防灾信息,对广域的都市治理而言,此一讯息将可作为预判区域淹水或防救灾情报之有效参考。     

Flood inundation modeling using MIKE FLOOD and remote sensing data

A coupled 1D-2D hydrodynamic model, MIKE FLOOD was used to simulate the flood inundation extent and flooding depth in the delta region of Mahanadi River basin in India. Initially, the 1D model MIKE 11 was calibrated using river water level and discharge data of various gauging sites for the monsoon period (June to October) of the year 2002. Subsequently, the calibrated set up was validated using both discharge and water level data for the same period of the year 2001. The performance of calibration and validation results of MIKE 11 were evaluated using different performance indices. A bathymetry of the study area with a spatial resolution of 90m was prepared from SRTM DEM and provided as an input to the 2D model, MIKE 21. MIKE 11 and MIKE 21 models were then coupled using lateral links to form the MIKE FLOOD model set up for simulating the two dimensional flood inundations in the study area. Flood inundation is simulated for the year 2001 and the maximum flood inundation extent simulated by the model was compared with the corresponding actual inundated area obtained from IRS-1D WiFS image.     

Mapping floods due to Hurricane Sandy using NPP VIIRS and ATMS data and geotagged Flickr imagery

In this study, we present an approach to estimate the extent of large-scale coastal floods caused by Hurricane Sandy using passive optical and microwave remote sensing data. The approach estimates the water fraction from coarse-resolution VIIRS and ATMS data through mixed-pixel linear decomposition. Based on the water fraction difference, using the physical characteristics of water inundation in a basin, the flood map derived from the coarse-resolution VIIRS and ATMS measurements was extrapolated to a higher spatial resolution of 30 m using topographic information. It is found that flood map derived from VIIRS shows less inundated area than the Federal Emergency Management Agency (FEMA) flood map and the ground observations. The bias was mainly caused by the time difference in observations. This is because VIIRS can only detect flood under clear conditions, while we can only find some clear-sky data around the New York area on 4 November 2012, when most flooding water already receded. Meanwhile, microwave measurements can penetrate through clouds and sense surface water bodies under clear-or-cloudy conditions. We therefore developed a new method to derive flood maps from passive microwave ATMS observations. To evaluate the flood mapping method, the corresponding ground observations and the FEMA storm surge flooding (SSF) products are used. The results show there was good agreement between our ATMS and the FEMA SSF flood areas, with a correlation of 0.95. Furthermore, we compared our results to geotagged Flickr contributions reporting flooding, and found that 95% of these Flickr reports were distributed within the ATMS-derived flood area, supporting the argument that such crowd-generated content can be valuable for remote sensing operations. Overall, the methodology presented in this paper was able to produce high-quality and high-resolution flood maps over large-scale coastal areas.     

时序Sentinel-1A数据支持的长江中下游汛情动态监测

合成孔径雷达（SAR）因其对地观测全天候、全天时优势,成为多云多雨天气限制下洪水动态监测中不可或缺的数据来源之一。由于GEE（Google Earth Engine）云计算平台的兴起和短重访Sentinel-1数据的可获取性,洪水监测与灾害评估目前正面向动态化、广域化快速发展。顾及洪水淹没区土地覆盖变化的复杂性和发生时间的不确定性,基于时序Sentinel-1A卫星数据提出了针对大尺度范围、连续长期的汛情自动检测及动态监测方法。该方法首先,利用图像二值化分割时序SAR数据实现水体时空分布粗制图,逐像素计算时间序列中被识别为水体候选点的频率。然后,利用Sentinel-2光学影像对精度较粗的初期SAR水体提取结果进行校正,得到精细的水体分布图。最后,针对不同频率区间的淹没特点,采用差异化的时序异常检测策略识别淹没范围：对低频覆水区利用欧氏距离检测时序断点,以提取扰动强度大、淹没时间短的洪涝灾害区;对高频覆水区利用标准分数（Z-Score）检测时序断点,以提取季节性水体覆盖区。在GEE平台上利用该方法,实现了2020-05—10长江中下游地区全域洪水淹没范围时空信息的自动、快速、有效监测,揭示了不同区域汛情发展模式的差异性。本文提出的洪水快速监测方法对大尺度下的汛情动态监测、灾害定量评估和快速预警响应具有重要的现实意义。     

基于GIS/RS的洪涝灾害承灾极限遥感估算方法

利用ＧＩＳ和遥感技术相结合的方式进行洪涝灾害灾情调查和损失评估,可以采用一种新的方法──洪涝灾害承灾极限估算法。洪涝灾害承灾极限是区域稳定性的一种表述方式,而灾害条件下区域的稳定性取决于人－地系统中致灾因中的Ｅ｛ｋ｝和承灾因子的Ｅ｛ｋ｝及致灾因子和承灾因中之间的耦合关系Ｅ｛ｋ,ｋ｝。本文从原理和方法上对此作了阐述,根据承灾极限计算原理,在ＤＥＭ数据的支持下,利用洪水期实时图像和正常水位时期遥感图像提取的信息,可以进行洪涝灾害的灾情评估。     

基于潮汐变化的潮滩淹没模型构建

潮汐的变化带来海岸线的不断演变,位于潮间带的潮滩在潮汐作用下,会产生周期性的淹没与干出。从研究潮汐变化模型与潮滩地形模型入手,对两种模型进行运算、融合,构建出基于潮汐变化的潮滩淹没模型。实验表明,此模型可以很好满足潮滩淹没三维可视化仿真的需要。     

中国城市洪涝实时预报研究:现状与挑战

全球气候变化和快速城市化打破了城市降水—汇水—排水原有的平衡,加剧了中国城市洪涝问题,造成了巨大的生命和财产损失。因此,亟须探索精确、高效的城市洪涝预报方法,提高城市防洪抗灾能力,降低灾害损失影响。然而,城市气象水文过程的复杂性使得城市洪涝实时预报研究面临诸多挑战。本文梳理了我国城市洪涝频发的原因,总结了国内城市洪涝实时预报研究在数据和模型方面的进展,指出了当前研究面临的问题和挑战,并对未来的发展趋势进行了展望,以期为我国城市防洪减灾研究和工作提供参考依据。     

Flood prediction and assessment of vulnerability risk in the southern coasts of the Caspian Sea

Abstract

The southern part of the Caspian Sea shoreline in Iran with a length of 813 km has different topographic conditions. Owing to sea fluctuation, these zones have various dimensions in different times. During the last years, the Caspian Sea experienced enormous destructive rises. The historical information and tidal gauge measurements showed different ranges of sea rise from ?30 m to ?22 m from the mean sea level. On the other hand, the probable flooding zone is related to slope gradient of coasts. To help the determination of the probable flooding area owing to sea level rises, the coastal zones can be modelled using geographic information system (GIS) environment as vulnerability risk rates. These rates would be useful for making decisions in coastal management programs. This study examined different scenarios of sea rise to determine hazard-flooding rates in the coastal cities of the Mazandaran province and classified them based on vulnerability risk rates. The 1:2000 scale topographic maps of the coastal zones were prepared to extract topographic information and construct the coastal digital elevation model. With the presumption of half-metre sea rise scenarios, the digital elevation models classified eight scenarios from ?26 to ?22 m. The flooding areas in each scenario computed for 11 cities respectively. The vulnerability risk rate in each rise scenario was computed by dividing the flooded area of each scenario to city area. The results showed that in the first four scenarios, from ?26 to ?24 m, the Behshahr, Joibar, Neka and Babolsar cites would be more vulnerable than other cites. Moreover, for the second four scenarios from ?24 to ?22 m sea level rise scenario, only the coastal area of Chalous city would be vulnerable. It was also observed that the coastal region of Behshahr would be critical in total scenarios. Further studies would be necessary to complete this assessment by considering social-economic and land use information to estimate the exact hazardous and vulnerable zones.     

Flood susceptibility mapping using frequency ratio and weights-of-evidence models in the Golastan Province,Iran

Flood is one of the most devastating natural disasters with socio-economic and environmental consequences. Thus, comprehensive flood management is essential to reduce the flood effects on human lives and livelihoods. The main goal of this study was to investigate the application of the frequency ratio (FR) and weights-of-evidence (WofE) models for flood susceptibility mapping in the Golestan Province, Iran. At first, a flood inventory map was prepared using Iranian Water Resources Department and extensive field surveys. In total, 144 flood locations were identified in the study area. Of these, 101 (70%) floods were randomly selected as training data and the remaining 43 (30%) cases were used for the validation purposes. In the next step, flood conditioning factors such as lithology, land-use, distance from rivers, soil texture, slope angle, slope aspect, plan curvature, topographic wetness index (TWI) and altitude were prepared from the spatial database. Subsequently, the receiver operating characteristic (ROC) curves were drawn for produced flood susceptibility maps and the area under the curves (AUCs) was computed. The final results indicated that the FR (AUC = 76.47%) and WofE (AUC = 74.74%) models have almost similar and reasonable results. Therefore, these flood susceptibility maps can be useful for researchers and planner in flood mitigation strategies.     

Multi-source data fusion and modeling to assess and communicate complex flood dynamics to support decision-making for downstream areas of dams: The 2011 hurricane irene and schoharie creek floods,NY

In light of climate and land use change, stakeholders around the world are interested in assessing historic and likely future flood dynamics and flood extents for decision-making in watersheds with dams as well as limited availability of stream gages and costly technical resources. This research evaluates an assessment and communication approach of combining GIS, hydraulic modeling based on latest remote sensing and topographic imagery by comparing the results to an actual flood event and available stream gages. On August 28th 2011, floods caused by Hurricane Irene swept through a large rural area in New York State, leaving thousands of people homeless, devastating towns and cities. Damage was widespread though the estimated and actual floods inundation and associated return period were still unclear since the flooding was artificially increased by flood water release due to fear of a dam break. This research uses the stream section right below the dam between two stream gages North Blenheim and Breakabeen along Schoharie Creek as a case study site to validate the approach. The data fusion approach uses a GIS, commonly available data sources, the hydraulic model HEC-RAS as well as airborne LiDAR data that were collected two days after the flood event (Aug 30, 2011). The aerial imagery of the airborne survey depicts a low flow event as well as the evidence of the record flood such as debris and other signs of damage to validate the hydrologic simulation results with the available stream gauges. Model results were also compared to the official Federal Emergency Management Agency (FEMA) flood scenarios to determine the actual flood return period of the event. The dynamic of the flood levels was then used to visualize the flood and the actual loss of the Old Blenheim Bridge using Google Sketchup. Integration of multi-source data, cross-validation and visualization provides new ways to utilize pre- and post-event remote sensing imagery and hydrologic models to better understand and communicate the complex spatial-temporal dynamics, return periods and potential/actual consequences to decision-makers and the local population.     

Investigating flash floods potential areas using ASCAT and TRMM satellites in the Western Cape Province,South Africa

The aim of the study was to evaluate flash flood potential areas in the Western Cape Province of South Africa, by integrating remote sensing products of high rainfall intensity, antecedent soil moisture and topographic wetness index (TWI). Rainfall has high spatial and temporal variability, thus needs to be quantified at an area in real time from remote sensing techniques unlike from sparsely distributed, point gauge network measurements. Western Cape Province has high spatial variation in topography which results in major differences in received rainfall within areas not far from each other. Although high rainfall was considered as the major cause of flash flood, also other contributing factors such as topography and antecedent soil moisture were considered. Areas of high flash flood potential were found to be associated with high rainfall, antecedent precipitation and TWI. Although TRMM 3B42 was found to have better rainfall intensity accuracy, the product is not available in near real time but rather at a rolling archive of three months; therefore, Multi- sensor precipitation estimate rainfall estimates available in near real time are opted for flash flood events. Advanced Scatterometer (ASCAT) soil moisture observations were found to have a reasonable r value of 0.58 and relatively low MAE of 3.8 when validated with in situ soil moisture measurements. The results of this study underscore the importance of ASCAT and TRMM satellite datasets in mapping areas at risk of flooding.     

Flood risk analysis in the Kosi river basin, north Bihar using multi-parametric approach of Analytical Hierarchy Process (AHP)

The Kosi river in north Bihar plains, eastern India presents a challenge in terms of long and recurring flood hazard. Despite a long history of flood control management in the basin for more than 5 decades, the river continues to bring a lot of misery through extensive flooding. This paper revisits the flooding problem in the Kosi river basin and presents an in-depth analysis of flood hydrology. We integrate the hydrological analysis with a GIS-based flood risk mapping in parts of the basin. Typical hydrological characteristics of the Kosi river include very high discharge variability, and high sediment flux from an uplifting hinterland. Annual peak discharges often exceed the mean annual flood and the low-lying tracts of the alluvial plains are extensively inundated year after year. Our flood risk analysis follows a multi-parametric approach using Analytical Hierarchy Process (AHP) and integrates geomorphological, land cover, topographic and social (population density) parameters to propose a Flood Risk Index (FRI). The flood risk map is validated with long-term inundation maps and offers a cost-effective solution for planning mitigation measures in flood-prone areas.     

Assessment of Coastal Erosion Vulnerability around Midnapur-Balasore Coast, Eastern India using Integrated Remote Sensing and GIS Techniques

Digha coastal region in the northeastern part of the Bay of Bengal is potentially vulnerable to erosional hazard. The present study assessed the coastal erosion vulnerability along this 65 km long coastal stretch located between Rasulpur (Midnapur) and Subarnarekha (Balasore) estuarine complex, which had been subjected to anthropogenic intervention. Multi-resolution Landsat satellite imagery were used for shoreline change study from 1972 to 2010. During this period, accretion was recorded updrift of artificial structures, viz, seawall, groin, pylons and jetties; while, extensive erosion was recorded in downdrift areas of these structures. Assessment was subsequently divided into four categories ranging from “high erosion” to “accretion”. Data from several sources were compiled to map landuse and human activities in the coastal zone. This map was divided into four categories, ranging from “very high capital” to “no capital” landuse. Population density map of the surrounding coastal villages was generated using census data, and divided into four categories ranging from “high density area” to “very low density area”. Subsequently, coastal erosion vulnerability was assessed by combining coastal retreat with landuse type and population density in this study area using simple vector algebraic technique. Zones of vulnerability of different magnitude (viz., very high, high, moderate, and low) have been identified. Furthermore, calculation of “imminent collapse zone (ICZ)” shows that maximum values are around artificial structures and anthropogenic activities. The coastal erosion vulnerability map prepared from this study can be used for proper planning and management of this coastal region.