The production of flood hazard assessment maps is an important component of flood risk assessment. This study analyses flood hazard using flood mark data. The chosen case study is the 2013 flood ...event in Quang Nam, Vietnam. The impacts of this event included 17 deaths, 230 injuries, 91,739 flooded properties, 11,530 ha of submerged and damaged agricultural land, 85,080 animals killed and widespread damage to roads, canals, dykes and embankments. The flood mark data include flood depth and flood duration. Analytic hierarchy process method is used to assess the criteria and sub-criteria of the flood hazard. The weights of criteria and sub-criteria are generated based on the judgements of decision-makers using this method. This assessment is combined into a single map using weighted linear combination, integrated with GIS to produce a flood hazard map. Previous research has usually not considered flood duration in flood hazard assessment maps. This factor has a rather strong influence on the livelihood of local communities in Quang Nam, with most agricultural land within the floodplain. A more comprehensive flood hazard assessment mapping process, with the additional consideration of flood duration, can make a significant contribution to flood risk management activities in Vietnam.
Flood risk and its reduction in China Kundzewicz, ZW; Su, Buda; Wang, Yanjun ...
Advances in water resources,
August 2019, 2019-08-00, 20190801, Letnik:
130
Journal Article
Recenzirano
•Floods in China often cause annual loss in excess of 10 billion US$.•Flood risk has grown in many places in China and is likely to grow further in the future.•Floods of a given return period in the ...reference interval are projected to become more frequent in much of China.•There is a strong link between climate variability and abundant humidity in China.
Despite massive flood protection efforts in China, undertaken since the ancient times, disastrous floods continue to plague the country. In this paper, we discuss changes in flood hazard and flood risk in China. First, we review published results (including our own works) on change detection in observed records of intense precipitation, high river flow and flood damage in China. We provide information on essential features of extreme floods in last decades – floods on large rivers, urban floods, and flash floods. Next, we review available projections for the future (including our own results), related to intense precipitation, high river flow and flood damage in China. We try to interpret the difference in flood hazard projections obtained in various publications. Since the spread of river flood hazard projections is large, projections have to be interpreted with caution, because of the impact on decisions related to climate change adaptation, flood risk reduction, and water resources management. We review flood risk reduction strategies in China, focusing on the present situation and division of responsibilities. China has embarked upon an ambitious and vigorous task to improve flood preparedness, by both structural (“hard”) defences, such as: dikes, dams and flood control reservoirs, and diversions, as well as non-structural (“soft”) measures: spatial planning and zoning; watershed management (source control), flood forecasting and warning systems; and awareness raising. The strategy of flood mitigation includes flood retention and urban water management to alleviate the burden of flash and urban flooding.
Floods are one of nature's most destructive disasters because of the immense damage to land, buildings, and human fatalities. It is difficult to forecast the areas that are vulnerable to flash ...flooding due to the dynamic and complex nature of the flash floods. Therefore, earlier identification of flash flood susceptible sites can be performed using advanced machine learning models for managing flood disasters. In this study, we applied and assessed two new hybrid ensemble models, namely Dagging and Random Subspace (RS) coupled with Artificial Neural Network (ANN), Random Forest (RF), and Support Vector Machine (SVM) which are the other three state-of-the-art machine learning models for modelling flood susceptibility maps at the Teesta River basin, the northern region of Bangladesh. The application of these models includes twelve flood influencing factors with 413 current and former flooding points, which were transferred in a GIS environment. The information gain ratio, the multicollinearity diagnostics tests were employed to determine the association between the occurrences and flood influential factors. For the validation and the comparison of these models, for the ability to predict the statistical appraisal measures such as Freidman, Wilcoxon signed-rank, and t-paired tests and Receiver Operating Characteristic Curve (ROC) were employed. The value of the Area Under the Curve (AUC) of ROC was above 0.80 for all models. For flood susceptibility modelling, the Dagging model performs superior, followed by RF, the ANN, the SVM, and the RS, then the several benchmark models. The approach and solution-oriented outcomes outlined in this paper will assist state and local authorities as well as policy makers in reducing flood-related threats and will also assist in the implementation of effective mitigation strategies to mitigate future damage.
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•Ensemble machine learning algorithms were applied for flood susceptibility modelling.•14%-20% areas to the total study area were predicted as high flood susceptibility zones.•Dagging model appeared as best model (AUC-0.871 and 0.873 for training and testing data).•Friedman test and Wilcoxon signed-rank test were used for comparing the flood susceptible models.
Flood, is a constant phenomenon, especially in semi-arid and flood plain regions are one of the most destructive natural hazards endangering a people's life, property, and physical and economic ...well-being. This paper focuses on hydraulic modelling using the HEC-RAS model in combination with the Watershed Modeling System (WMS) tools and compares the results to the Flood Hazard Index (FHI) method using GIS in the Seyad Basin situated in the southwestern region of Morocco with an area of 1512.85 km2. The goal of the study is to evaluate flood hazards in the Seyad Basin in which the cities of Taghjijt, Aday, Amtoudi, and Tagriante are located. The HEC-RAS approach combines the hydraulic surface water flow model and the digital terrain model data. This combination allows the mapping of the flood zones by using the WMS tools. This approach predicts flood occurrence probability for different times and determines the intensity of the flood (depth and velocity of floodwater) using the existing hydrological data.
On the other hand, the Flood Hazard Index method (FHI) presents a multi-criteria index to assess areas prone to flood hazards, using six physical parameters: permeability, slope, distance from rivers, land use, drainage density, and flow accumulation. A weight is calculated from the analytic hierarchy process method and applied to each parameter. HEC-RAS method allows the mapping of a flood with a flood water surface profile that shows the flood depth for Annual Exceedance Probability (AEP). At the same time, FHI permits establishing flood hazard levels without showing the water depth. In both approaches, six simulations were performed with the return periods of 10, 20, 50, 100, 200, and 500 years. The simulation revealed that the most susceptible areas to flooding are the areas along the Wadi Seyad.
•Flood hazard mapping of the Seyad basin, Morocco, is done in this study.•HEC-RAS/WMS and Flood Hazard Index methods were utilized for flood zonation.•A total of six environmental parameters were integrated with this study.•31.12% of the total study area was found to be highly susceptible to floods.•Comparison between the HEC-RAS/WMS and Flood Hazard Index methods in Seyad basin floods was deciphered through this paper.
Bangladesh is a country of natural disasters and climatic hazards, which frequently affect its inhabitants’ lives and livelihoods. Among the various risks and disasters, floods are the most frequent ...hazard that makes
haor
households vulnerable. Therefore, this study was undertaken to estimate livelihood vulnerability to flooding within the flood-prone
haor
ecosystem in Bangladesh. Primary data were collected from 100
haor
households each from Kishoreganj, Netrokona, and Sunamganj districts (
N
= 300) by applying a multistage random sampling technique. Data were collected through face-to-face interviews using a pretested structured questionnaire. The Livelihood Vulnerability Index (LVI) and the Intergovernmental Panel on Climate Change (IPCC) framework of vulnerability were applied to compare vulnerabilities among the selected
haor
-based communities. The empirical results revealed that
haor
households in Sunamganj district were more vulnerable to flood hazard and natural disaster in terms of food, water, and health than households in the other two districts. Taking into account the major components of the LVI, the IPCC framework of vulnerability indicated that households in Sunamganj district were the most vulnerable due to their lowest adaptive capacity and highest sensitivity and exposure. These findings enable policymakers to formulate and implement effective strategies and programs to minimize vulnerability and enhance resilience by improving the livelihoods of the vulnerable
haor
households of Bangladesh, especially those in Sunamganj district.
•Global flood hazard trends were tested using the Global Runoff Data Centre database.•More stations had significant decreasing than increasing trends for most analyses.•Sampling variability plays an ...important role on trend detection findings.•Catchment size had significant effects on trend results.•Neither presence of dams nor changes in forest cover had a large effect on findings.
This study investigates the presence of trends in annual maximum daily streamflow data from the Global Runoff Data Centre database, which holds records of 9213 stations across the globe. The records were divided into three reference datasets representing different compromises between spatial coverage and minimum record length, followed by further filtering based on continent, Köppen-Weiger climate classification, presence of dams, forest cover changes and catchment size. Trends were evaluated using the Mann-Kendall nonparametric trend test at the 10% significance level, combined with a field significance test. The analysis found substantial differences between reference datasets in terms of the specific stations that exhibited significant increasing or decreasing trends, showing the need for careful construction of statistical methods. The results were more consistent at the continental scale, with decreasing trends for a large number of stations in western North America and the data-covered regions of Australia, and increasing trends in parts of Europe, eastern North America, parts of South America and southern Africa. Interestingly, neither the presence of dams nor changes in forest cover had a large effect on the trend results, but the catchment size was important, as catchments exhibiting increasing (decreasing) trends tended to be smaller (larger). Finally, there were more stations with significant decreasing trends than significant increasing trends across all the datasets analysed, indicating that limited evidence exists for the hypothesis that flood hazard is increasing when averaged across the data-covered regions of the globe.
•We derive global flood hazard maps at 30′′ resolution for several return periods•The mapping procedure is based on a hydrological and hydraulic modelling chain•Maps are evaluated using official and ...satellite-derived flood extent maps
Nowadays, the development of high-resolution flood hazard models have become feasible at continental and global scale, and their application in developing countries and data-scarce regions can be extremely helpful to increase preparedness of population and reduce catastrophic impacts.
The present work describes the development of a novel procedure for global flood hazard mapping, based on the most recent advances in large scale flood modelling. We derive a long-term dataset of daily river discharges from the hydrological simulations of the Global Flood Awareness System (GloFAS). Streamflow data is downscaled on a high resolution river network and processed to provide the input for local flood inundation simulations, performed with a two-dimensional hydrodynamic model. All flood-prone areas identified along the river network are then merged to create continental flood hazard maps for different return periods at 30′′ resolution. We evaluate the performance of our methodology in several river basins across the globe by comparing simulated flood maps with both official hazard maps and a mosaic of flooded areas detected from satellite images. The evaluation procedure also includes comparisons with the results of other large scale flood models. We further investigate the sensitivity of the flood modelling framework to several parameters and modelling approaches and identify strengths, limitations and possible improvements of the methodology.
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•This study provides a detailed flood risk map for a Basin in north-east of Iran.•A flood risk index introduced based on SWAT to identify flood-prone area.•Seven different factors ...were used and their weights sensitivity is analyzed.•About 40% the total area of study area is under very high or high flood risk.•97% of past floods have occurred in moderate to very high flood risk locations.
Flood is a major natural hazard with extremely large impact on social-ecological systems. Therefore, developing reliable and efficient tools to identify areas vulnerable to potential flooding is vital for water managers, engineers and decision makers. Moreover, being able to accurately classify the level of hazard is a step forward towards more efficient flood hazard mapping. This study presents a multi-criteria index approach to classify potential flood hazards at the river basin scale. The presented methodology was implemented in the Mashhad Plain basin in North-east Iran, where flood has been a major issue in the last few decades. In the present study, seven factors, selected based on their greater influence towards flooding, were identified and extracted from the basic thematic layers to be used to generate a five-class Flood Hazard Index (FHI) map. The Soil and Water Assessment Tool (SWAT) was used to develop a runoff coefficient map, which was found to be the most influential factor. A sensitivity analysis was performed and the results incorporated to generate a modified Flood Hazard Index (mFHI) map. The accuracy of the proposed method was evaluated against the well-documented flood records in the last 42 years at the study area. The results showed that, for both FHI and mFHI maps, more than 97% of historical flood events have occurred in moderate to very high flood hazard areas. This demonstrates that incorporating hydrological model (such as SWAT) and multi-criteria analysis introduces a robust methodology to generate comprehensive potential flood hazard maps. Moreover, the proposed modified methodology can be used to identify high potential flood hazard zones and work towards more efficient flood management and mitigation strategies.
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