Urban fluvial flood modelling using a two‐dimensional diffusion‐wave treatment,part 1: mesh resolution effects

High‐resolution data obtained from airborne remote sensing is increasing opportunities for representation of small‐scale structural elements (e.g. walls, buildings) in complex floodplain systems using two‐dimensional (2D) models of flood inundation. At the same time, 2D inundation models have been developed and shown to provide good predictions of flood inundation extent, with respect to both full solution of the depth‐averaged Navier–Stokes equations and simplified diffusion‐wave models. However, these models have yet to be applied extensively to urban areas. This paper applies a 2D raster‐based diffusion‐wave model to determine patterns of fluvial flood inundation in urban areas using high‐resolution topographic data and explores the effects of spatial resolution upon estimated inundation extent and flow routing process. Model response shows that even relatively small changes in model resolution have considerable effects on the predicted inundation extent and the timing of flood inundation. Timing sensitivity would be expected, given the relatively poor representation of inertial processes in a diffusion‐wave model. Sensitivity to inundation extent is more surprising, but is associated with: (1) the smoothing effect of mesh coarsening upon input topographical data; (2) poorer representation of both cell blockage and surface routing processes as the mesh is coarsened, where the flow routing is especially complex; and (3) the effects of (1) and (2) upon water levels and velocities, which in turn determine which parts of the floodplain the flow can actually travel to. It is shown that the combined effects of wetting and roughness parameters can compensate in part for a coarser mesh resolution. However, the coarser the resolution, the poorer the ability to control the inundation process, as these parameters not only affect the speed, but also the direction of wetting. Thus, high‐resolution data will need to be coupled to a more sophisticated representation of the inundation process in order to obtain effective predictions of flood inundation extent. This is explored in a companion paper. Copyright © 2005 John Wiley & Sons, Ltd.     

2015年广东开汛前后旱涝急转特征及成因分析

利用1961—2015年广东86站3—5月逐日降水资料、NCEP/NCAR再分析资料及NOAA的Hysplit后向轨迹模式资料和海温资料,分析了2015年广东开汛前后旱涝异常特征,旱涝急转前后的大气环流和水汽条件转变及成因。(1) 2015年5月第1候广东发生了严重的旱涝急转事件,旱涝急转前全省严重干旱,旱涝急转后广东大部地区降水异常偏多。(2) 旱涝急转前后,500 hPa中高纬度由“两槽两脊”转为“两脊一槽”和“两槽一脊”,副高持续加强,广东地区高层辐散和低层辐合加强,地面由冷空气控制广东转为冷暖空气在广东交汇;水汽输送通道由北部湾和长江中下游地区转为南海南部和中南半岛南部海面,广东低层水汽净流入增加。 (3) 黑潮区、北太平洋中部、Ni?o3、Ni?o4区及印度洋和南海的海温异常增暖造成了旱涝急转前后大气环流和水汽的转变。     

基于Asar与TM图像的洪水淹没范围提取

在洪水灾害监测、灾情评估中,洪水淹没范围是最重要的信息之一。洪水灾害中获取的雷达图像能够提取水体的范围,但很难区分出原有水体和洪灾中淹没区范围。基于这个问题,本文提出利用目前功能最为强大的星载雷达系统Asar在灾中得到雷达图像和灾前或者灾后的TM图像复合,得到灾中与非洪灾时期水体的变化范围,即获得洪水淹没范围的方法。试验结果表明,利用这种方法可以快速、准确地提取洪水淹没范围。     

Quantifying the failure probability of a canal levee

Polders in the Netherlands are protected from flooding by flood defence systems along main water bodies such as rivers, lakes or the sea. Inside polders, canal levees provide protection from smaller water bodies. Canal levees are mainly earthen levees along drainage canals that drain excess water from polders to the main water bodies. The water levels in these canals are regulated. During the last decades, probabilistic approaches have been developed to quantify the probability of failure of flood defences along the main water bodies. This paper proposes several extensions to this method to quantify the probability of failure of canal levees. These extensions include a method to account for (i) water-level regulation in canals, (ii) the effect of maintenance dredging on the geohydrological response of the canal levee and (iii) survival of loads in the past. The results of a case study demonstrate that the proposed approach is capable of quantifying the probability of failure of canal levees and is useful for exploring the relative benefit of risk mitigating measures for canal levees.     

A decadal historical satellite data analysis for flood hazard evaluation: A case study of Bihar (North India)

Flood is one of the major recurrent natural disasters faced by the state of Bihar in north India. In the present study the authors assess the severity of flood hazard in Bihar, using 128 decadal historical satellite datasets acquired during different flood magnitudes during 1998 to 2010. The satellite‐based observations have been analysed in conjunction with the hydrological data, for assessing the frequency of inundation, severity of flood hazard and cropped land under flood hazard. This study assesses the spatial distribution of flooding and creation of systematic flood hazard database, which can be analysed from a spatial dimension in GIS. It is observed that about 24.56 lakh ha of the state's area and about 15.85 lakh ha of the cropped area are vulnerable to flood hazard. North Bihar is more vulnerable to flooding; 8 of the 10 areas identified as worst flood‐affected districts lie in this region.     

2001-2012年新疆融雪型洪水时空分布特征

利用2001-2012年新疆区域内发生的融雪型洪水资料,分析研究了近12 a新疆融雪型洪水时空分布特征.结果表明:新疆融雪型洪水与前一年10月至当年3月新疆全站总累计降水量大小相关,降水量大的年份,对应的融雪型洪水发生次数也多;冬末至夏季融雪型洪水在北疆地区基本上是从西向东、从南向北的先后顺序出现,而在南疆地区的融雪洪水基本上是从西向东、从北向南先后顺序出现.新疆融雪型洪水主要集中出现在春夏季,其中,北疆地区在3月,南疆地区在7月发生较多;伊犁河谷、昌吉、阿勒泰、和田等地区及青河、乌鲁木齐、阿克陶、民丰等市县是新疆融雪型洪水的高发区.     

四川省山洪地质灾害气象预警系统的设计与实现

四川省是一个山区较多的省份,尤其是环绕四川盆地的山脉和川西高原,海拔高、坡度陡;并且四川省降雨不均匀,这些因素导致四川省是一个饱受山洪地质灾害侵害的省份。因此,为了对四川省山洪地质灾害的发生环境和影响因素进行量化分析,提高防灾减灾水平,降低人身财产损失,建立四川省山洪地质灾害气象预警系统是非常有必要的。本研究基于SuperMap GIS平台和Web技术建立山洪地质灾害气象预警系统,使用SuperMapGIS平台对山洪地质灾害等相关地理信息数据进行管理和分析,利用Web技术对山洪地质灾害信息进行发布,将GIS技术的空间分析能力与Web技术的易用性、高扩展性、交互性和广泛用户群体性相结合。本文首先对四川省的降雨量、地表地形、地质构造、土地利用等各种可能引发山洪地质灾害的因素进行了综合分析,然后建立了山洪地质灾害预警模型,最后在预警模型的基础上构建了四川省山洪地质灾害气象预警系统。在系统可视化的界面中,客户端可以通过选择日期作为输入参数传送到服务器端,服务器端通过模型分析得到所指定日期的山洪地质灾害危险等级信息,为地质灾害的预警与防治提供一定的参考。     

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.     

Hydrological considerations for dam siting in arid regions: a Saudi Arabian study

Abstract

In any dam siting study in arid regions, rainfall records, runoff measurements and their greatest magnitudes are very important. Unfortunately, the data are scarce and, therefore, empirical approaches and charts obtained from similar regions in other parts of the world are necessary for complete applications. The lack of observed data presents the major problem for runoff modelling in arid regions. These regions have characteristically high rainfall intensity and consequent flash floods with large amounts of sediments. Occurrence of rainfall is sporadic, both temporally and spatially, which makes the interpretation of the rainfall-runoff relationship quite difficult. Flood estimations play a significant role in dam siting from the point of view of water availability. This paper presents the basic calculations of floods and sediment amounts that are necessary in dam siting and construction in an arid area by considering the southwestern part of the Kingdom of Saudi Arabia.     

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Abstract

A reliable flood warning system depends on efficient and accurate forecasting technology. A systematic investigation of three common types of artificial neural networks (ANNs) for multi-step-ahead (MSA) flood forecasting is presented. The operating mechanisms and principles of the three types of MSA neural networks are explored: multi-input multi-output (MIMO), multi-input single-output (MISO) and serial-propagated structure. The most commonly used multi-layer feed-forward networks with conjugate gradient algorithm are adopted for application. Rainfall—runoff data sets from two watersheds in Taiwan are used separately to investigate the effectiveness and stability of the neural networks for MSA flood forecasting. The results indicate consistently that, even though the MIMO is the most common architecture presented in ANNs, it is less accurate because its multi-objectives (predicted many time steps) must be optimized simultaneously. Both MISO and serial-propagated neural networks are capable of performing accurate short-term (one- or two-step-ahead) forecasting. For long-term (more than two steps) forecasts, only the serial-propagated neural network could provide satisfactory results in both watersheds. The results suggest that the serial-propagated structure can help in improving the accuracy of MSA flood forecasts.