Beyond Flood Preparedness: Effects of Experience, Trust, and Perceived Risk on Preparation Intentions and Financial Risk-Taking in China

Flooding, already the most damaging type of natural disaster in China, is expected to become increasingly costly around the world. However, few studies have examined residents’ flood-preparedness intentions and the effect of flood experience and other variables on general financial risk-taking. This study explored the effects of Chinese residents’ previous flood experiences, trust in public flood protection, and flood-risk perception on flood-preparedness intentions and attitudes towards financial risk-taking in general. Study 1 surveyed residents in a flooded area (n = 241) and a non-flooded area (n = 248); Study 2 surveyed a non-flooded area (n = 1599). The relations between the variables were tested through structural-equation modelling (SEM). Overall, the two studies found that residents’ flood experiences, trust in public protection, and flood-risk perception not only predicted their flood preparedness but also their financial risk aversion. This study highlights the importance of residents’ trust in public flood protection for flood risk management and communication, especially for those who have not yet experienced flooding.

Hurricane Flood Hazard Assessment for the Archipelago of San Andres, Providencia and Santa Catalina, Colombia

Despite the low occurrence of tropical cyclones at the archipelago of San Andres, Providencia, and Santa Catalina (Colombia), Hurricane Iota in 2020 made evident the area vulnerability to tropical cyclones as major hazards by obliterating 56.4 % of housing, partially destroying the remaining houses in Providencia. We investigated the hurricane storm surge inundation in the archipelago by forcing hydrodynamic models with synthetic tropical cyclones and hypothetical hurricanes. The storm surge from synthetic events allowed identifying the strongest surges using the probability distribution, enabling the generation of hurricane storm surge flood maps for 100 and 500 year return periods. This analysis suggested that the east of San Andres and Providencia are the more likely areas to be flooded from hurricanes storm surges. The hypothetical events were used to force the hydrodynamic model to create worst-case flood scenario maps, useful for contingency and development planning. Additionally, Hurricane Iota flood levels were calculated using 2D and 1D models. The 2D model included storm surge (SS), SS with astronomical tides (AT), and SS with AT and wave setup (WS), resulting in a total flooded area (percentage related to Providencia’s total area) of 67.05 ha (3.25 %), 65.23 ha (3.16 %), and 76.68 ha (3.68%), respectively. While Hurricane Iota occurred during low tide, the WS contributed 14.93 % (11.45 ha) of the total flooded area in Providencia. The 1D approximation showed that during the storm peak in the eastern of the island, the contribution of AT, SS, and wave runup to the maximum sea water level was −3.01%, 46.36%, and 56.55 %, respectively. This finding provides evidence of the water level underestimation in insular environments when modeling SS without wave contributions. The maximum SS derived from Iota was 1.25 m at the east of Providencia, which according to this study has an associated return period of 3,234 years. The methodology proposed in this study can be applied to other coastal zones and may include the effect of climate change on hurricane storm surges and sea-level rise. Results from this study are useful for emergency managers, government, coastal communities, and policymakers as civil protection measures.

Optimization of Evacuation and Walking-Home Routes from Osaka City After a Nankai Megathrust Earthquake Using Road Network Big Data

When a disaster such as a large earthquake occurs, the resulting breakdown in public transportation leaves urban areas with many people who are struggling to return home. With people from various surrounding areas gathered in the city, unusually heavy congestion may occur on the roads when the commuters start to return home all at once on foot. In this chapter, it is assumed that a large earthquake caused by the Nankai Trough occurs at 2 p.m. on a weekday in Osaka City, where there are many commuters. We then assume a scenario in which evacuation from a resulting tsunami is carried out in the flooded area and people return home on foot in the other areas. At this time, evacuation and returning-home routes with the shortest possible travel times are obtained by solving the evacuation planning problem. However, the road network big data for Osaka City make such optimization difficult. Therefore, we propose methods for simplifying the large network while keeping those properties necessary for solving the optimization problem and then recovering the network. The obtained routes are then verified by large-scale pedestrian simulation, and the effect of the optimization is verified.

Flood Impact Assessment Using HEC-RAS and GIS Techniques Dinder River, Southeast Sudan

Dinder River is largest tributary of the Blue Nile. It is seasonal river that flows from June to November and reaches its high peak in September. Frequently, the water level exceeds the normal height causing over bank flow and consequently floods. The floods generally ring about losses properties and crops close to river banks. This study is attempts to figure out the river flow behavior and find out the aerial extent of inundated lands in four flooding seasons. The investigated area is located in Sennar State, SE Sudan. Discharge data collected over the period from 2015 to 2018 and Digital Elevation Model (DEM) have been used to model the River flow regime, while land cover data was used to determine the affected LU/LC types in the area. HEC-RAS software was used to create 2D unsteady flow model in order to simulate Dinder River flooded area in four seasons. The largest flooded area extent in each season was used as input in GIS environment for further spatial analysis. Statistical computation for the affected area and consequent analysis revealed that: the affected urban area in 2018 was around 28.152km2, in 2017 was 29.205 km2, in 2016 was 16.531km2, and in 2015 was 10.422km2. Similar calculations were carried out for the other LU/LC types. According to the present study, the year 2017 witnessed the largest extent of flooding in the area.

A practical methodology to perform global sensitivity analysis for 2D hydrodynamic computationally intensive simulations

Sensitivity analysis is a commonly used technique in hydrological modeling for different purposes, including identifying the influential parameters and ranking them. This paper proposes a simplified sensitivity analysis approach by applying the Taguchi design and the ANOVA technique to 2D hydrodynamic flood simulations, which are computationally intensive. This approach offers an effective and practical way to rank the influencing parameters, quantify the contribution of each parameter to the variability of the outputs, and investigate the possible interaction between the input parameters. A number of 2D flood simulations have been carried out using the proposed combinations by Taguchi (L27 and L9 orthogonal arrays) to investigate the influence of four key input parameters, namely mesh size, runoff coefficient, roughness coefficient, and precipitation intensity. The results indicate that the methodology is adequate for sensitivity analysis, and that the precipitation intensity is the dominant parameter. Furthermore, the model calibration based on local variables (cross-sectional water level) can be inaccurate to simulate global variables (flooded area).

Spatial-temporal Patterns and Risk Factors for Human Leptospirosis in Thailand, 2012-2018

Leptospirosis is a globally important zoonotic disease. The disease is particularly important in tropical and subtropical countries. Infections in humans can be caused by exposure to infected animals or contaminated soil or water, which are suitable for Leptospira. To explore the cluster area, the Global Moran’s I index was calculated for incidences per 100,000 population at the province level during 2012–2018, using the monthly and annual data. The high-risk and low-risk provinces were identified using the local indicators of spatial association (LISA). The risk factors for leptospirosis were evaluated using a generalized linear mixed model (GLMM) with zero-inflation. We also added spatial and temporal correlation terms to take into account the spatial and temporal structures. The Global Moran’s I index showed significant positive values. It did not demonstrate a random distribution throughout the period of study. The high-risk provinces were almost all in the lower north-east and south parts of Thailand. For yearly reported cases, the significant risk factors from the final best-fitted model were population density, elevation, and primary rice arable areas. Interestingly, our study showed that leptospirosis cases were associated with large areas of rice production but were less prevalent in areas of high rice productivity. For monthly reported cases, the model using temperature range was found to be a better fit than using percentage of flooded area. The significant risk factors from the model using temperature range were temporal correlation, average soil moisture, normalized difference vegetation index, and temperature range. Temperature range, which has strongly negative correlation to percentage of flooded area was a significant risk factor for monthly data. Flood exposure controls should be used to reduce the risk of leptospirosis infection. These results could be used to develop a leptospirosis warning system to support public health organizations in Thailand.

Accuracy of The Level of Critical Water Catchment Area for Flood Mitigation Around Bengkulu City, Indonesia

Disaster mitigation activities require the availability of a potentially flooded area (PFA) map. One of the causes of flooding is the criticality of water catchment areas; the higher the criticality level, the higher the flooding potential. This study aims to determine the accuracy of the model for determining the PFA around Bengkulu City, which was derived from the Level of Critical Water Catchment Area (LCWCA) model developed by the Ministry of Forestry. After obtaining the LCWCA Map, another analysis was performed in order to obtain the PFA Map. Furthermore, the overlaying was carried out with the Existing Flood Map in such a way that the level of accuracy is known. The threshold values from Justice are used to justify the level of accuracy in three categories, namely Good (> 85%), Moderate (70 - 85%), and Poor (<70%). The results showed that in the eight sub-watersheds around the city of Bengkulu, there were two sub-watersheds with reasonable accuracy (> 85%), which means that there was > 85% overlap between areas on the Potentially Flooded Area Map as a result of the analysis of The LCWCA with the area on the Existing Flood Map. There are three sub-watersheds with Moderate accuracy (70 - 85%) and three sub-watersheds with Poor accuracy (<70%)

The Influence of the Instantaneous Collapse of Tailings Pond on Downstream Facilities

To evaluate the evolutionary processes guiding the formation of the tailings-water mixtures produced by the instantaneous collapse of tailings ponds and the influence of these on downstream facilities, a 2D simulation model with reasonable boundary and working conditions derived from actual engineering practice was built in this study, and the relationship between dam-break elevation and impact on downstream facilities was also analyzed to determine the relevant mechanism of influence. Computational results indicated that lowering the dam-break elevation caused the maximum velocity and flooding depth, along with the flooded area at monitoring points, to gradually increase. The occurrence times of maximum velocity and flooding depth were also gradually moved forward as the breaking elevation was reduced; this effect is directly related to the increase in the total potential energy at the lower break elevations. Further simulations of sand-prevent dams with different heights located downstream from a tailings pond were carried out to identify methods for mitigating the impact of dam failure. The results revealed that increasing the height of the sand-prevent dam reduced the production of tailings mixtures. Based on the results, the construction of a sand-prevent dam with a crest elevation equal to that of the starter dam was recommended.

The Story of a Steep River: Causes and Effects of the Flash Flood on 24 July 2017 in Western Norway

Flash floods can cause great geomorphological changes in ephemeral fluvial systems and result in particularly severe damages for the unprepared population exposed to it. The flash flood in the Storelva river in Utvik (western Norway) on 24 July 2017 was witnessed and documented. This study assessed the causes and effects of the 2017 flood and provides valuable information for the calibration and validation of future modelling studies. The flooded area at peak discharge, maximum wetted and dry areas during the entire event, critical points and main flow paths were reconstructed using on-site and post-event (i) visual documentation, such as photographs and videos, and (ii) aerial surveying, such as orthophotographs and laser scanning, of the lowermost reach. The steep longitudinal slope together with the loose material forming the valley and riverbed contributed to a large amount of sediment transport during this extreme event. Steep rivers such as the Storelva river have very short response times to extreme hydrologic conditions, which calls for exhaustive monitoring and data collection in case of future events, as well as modelling tools that can emulate the hydro-morphodynamics observed during events such as the 2017 flash flood.

Variation of Uncertainty of Drainage Density in Flood Hazard Mapping Assessment with Coupled 1D-2D Hydrodynamics Model

Coupled 1D-2D hydrodynamic models are widely utilized in flood hazard mapping. Researchers have explored several uncertainties in flood hazard mapping, but have not addressed the uncertainty of drainage density. Drainage density is equal to total length of the drainage divided by the catchment area. The model sets denser the tributary drainages for higher drainage density values. This study uses a designed case and a real case, Yanshuixi Drainage in Tainan, Taiwan, to assess the uncertainty of drainage density in flood hazard mapping. Analytical results indicate that under the same return period rainfall, reduction in tributary drainages in a model (indicating a lower drainage density) results in an underestimate of the flooded area in tributary drainages. This underestimate causes higher peak discharges and total volume of discharges in the drainages, leading to flooding in certain downstream reaches, thereby overestimating the flooded area. The uncertainty of drainage density decreases with increased rainfall. We suggest that modeling flood hazard mapping with low return period rainfalls requires tributary drainages. For extreme rainfall events, a lower drainage density could be selected, but the drainage density of local key areas should be raised.