Flood Risk Assessment in Urban Areas of Southern Taiwan

Efficiency evaluation of low impact development practices on urban flood risk

Urban flood risk assessment delivers invaluable information regarding flood management as well as preventing the associated risks in urban areas. The present study prepares a flood risk map and evaluate the practices of low-impact development (LID) intended to decrease the flood risk in Shiraz Municipal District 4, Fars province, Iran. So, this study investigate flood vulnerability using MCDM models and some indices, including population density, building age, socio-economic conditions, floor area ratio, literacy, the elderly population, and the number of building floors to. Then, the map of thematic layers affecting the urban flood hazard, including annual mean rainfall, land use, elevation, slope percentage, curve number, distance from channel, depth of groundwater, and channel density, was prepared in GIS. After conducting a multicollinearity test, data mining models were used to create the urban flood hazard map, and the urban flood risk map was produced using ArcGIS 10.8. The evaluation of vulnerability models was shown through the use of Boolean logic that TOPSIS and VIKOR models were effective in identifying urban flooding vulnerable areas. Data mining models were also evaluated using ROC and precision-recall curves, indicating the accuracy of the RF model. The importance of input variables was measured using Shapley value, which showed that curve number, land use, and elevation were more important in flood hazard modeling. According to the results, 37.8 percent of the area falls into high and very high categories in terms of flooding risk. The study used a stormwater management model (SWMM) to simulate node flooding and provide management scenarios for rainfall events with a return period ranging from 2 to 50 years and five rainstorm events. The use of LID practices in flood management was found to be effective for rainfall events with a return period of less than 10 years, particularly for two-year events. However, the effectiveness of LID practices decreases with an increase in the return period. By applying a combined approach to a region covering approximately 10 percent of the total area of Shiraz Municipal District 4, a reduction of 2-22.8 percent in node flooding was achieved. The analysis of data mining and MCDM models with a physical model revealed that more than 60% of flooded nodes were classified as "high" and "very high" risk categories in the RF-VIKOR and RF-TOPSIS risk models.

A systematic review of the flood vulnerability using geographic information system

The world has faced many disasters in recent years, but flood impacts have gained immense importance and attention due to their adverse effects. More than half of global flood destruction and damages occur in the Asia region, which causes losses of life, damage infrastructure, and creates panic conditions among the communities. To provide a better understanding of flood hazard management, flood vulnerability assessment is the primary objective. In this case, vulnerability is the central construct in flood analysis and assessment. Many researchers have defined different approaches and methods to understand vulnerability assessment and how geographic information systems assess the flood vulnerability and their associated risk. Geographic information systems track and predict the disaster trend and mitigate the risk and damages. This study systematically reviews the methodologies used to measure floods and their vulnerabilities by integrating geographic information system. Articles on flood vulnerability from 2010 to 2020 were selected and reviewed. Through the systematic review methodology of five research engines, the researchers discovered a difference in flood vulnerability assessment tools and techniques that can be bridged by integrating high-resolution data with a multidimensional vulnerability methodology. The study reviewed several vulnerability components and directly examined the shortcomings in flood vulnerability approaches at different levels. The research contributed that the indicator-based approach gives a better understanding of vulnerability assessment. The geographic information system provides an effective environment for mapping and precise analysis to mitigate the flood disaster.

Assessment of Flood Risk Map under Climate Change RCP8.5 Scenarios in Taiwan

Climate change has exerted a significant global impact in recent years, and extreme weather-related hazards and incidents have become the new normal. For Taiwan in particular, the corresponding increase in disaster risk threatens not only the environment but also the lives, safety, and property of people. This highlights the need to develop a methodology for mapping disaster risk under climate change and delineating those regions that are potentially high-risk areas requiring adaptation to a changing climate in the future. This study provides a framework of flood risk map assessment under the RCP8.5 scenario by using different spatial scales to integrate the projection climate data of high resolution, inundation potential maps, and indicator-based approach at the end of the 21st century in Taiwan. The reference period was 1979–2003, and the future projection period was 2075–2099. High-resolution climate data developed by dynamic downscaling of the MRI-JMA-AGCM model was used to assess extreme rainfall events. The flood risk maps were constructed using two different spatial scales: the township level and the 5 km × 5 km grid. As to hazard-vulnerability(H-V) maps, users can overlay maps of their choice—such as those for land use distribution, district planning, agricultural crop distribution, or industrial distribution. Mapping flood risk under climate change can support better informed decision-making and policy-making processes in planning and preparing to intervene and control flood risks. The elderly population distribution is applied as an exposure indicator in order to guide advance preparation of evacuation plans for high-risk areas. This study found that higher risk areas are distributed mainly in northern and southern parts of Taiwan and the hazard indicators significantly increase in the northern, north-eastern, and southern regions under the RCP8.5 scenario. Moreover, the near-riparian and coastal townships of central and southern Taiwan have higher vulnerability levels. Approximately 14% of townships have a higher risk level of flooding disaster and another 3% of townships will become higher risk. For higher-risk townships, adaptation measures or strategies are suggested to prioritize improving flood preparation and protecting people and property. Such a flood risk map can be a communication tool to effectively inform decision- makers, citizens, and stakeholders about the variability of flood risk under climate change. Such maps enable decision-makers and national spatial planners to compare the relative flood risk of individual townships countrywide in order to determine and prioritize risk adaptation areas for planning spatial development policies.

Evaluation of Debris Flows for Flood Plain Estimation in a Small Ungauged Tropical Watershed for Hurricane Otto

The variability of climate, increase in population, and lack of territorial plans in Costa Rica have caused intense disasters with human and economic losses. In 2016, Hurricane Otto hit the country’s northern area, leaving substantial damages, including landslides, debris flows, and flooding. The present study evaluated different scenarios to estimate flooded areas for Newtonian (clean water), and non-Newtonian flows with volumetric sediment concentrations (Cv) of 0.3, 0.45, 0.55, and 0.65 using Hydro-Estimator (HE), rain gauge station, and the 100-year return period event. HEC–HMS modeled the rainfall products, and FLO-2D modeled the hydrographs and Cv combinations. The simulation results were evaluated with continuous statistics, contingency table, Nash Sutcliffe Efficiency, measure of fit (F), and mean absolute differences (E) in the floodplains. Flow depths, velocities, and hazard intensities were obtained in the floodplain. The debris flood was validated with field data and classified with a Cv of 0.45, presenting lower MAE and RMSE. Results indicated no significant differences in flood depths between hydrological scenarios with clean-water simulations with a difference of 8.38% in the peak flow. The flood plain generated with HE rainfall and clear-water condition presented similar results compared to the rain gauge input source. Additionally, hydraulic results with HE and Cv of 0.45 presented E and F values similar to the simulation of Cv of 0.3, demonstrating that the HE bias did not influence the determination of the floodplain depth and extent. A mean bias factor can be applied to a sub-daily temporal resolution to enhance HE rain rate quantifications and floodplain determination.