Journal of Flood Risk Management最新文献

Flood damage data are needed for various applications. Structural damage of buildings can reflect not only the economic damage but also the life-threatening condition of a building, which provide crucial information for disaster response and recovery. Since traditional on-site data collection shortly after a disaster is challenging, remote sensing data can be of great help, cover a wider area and be deployed earlier in time than on-site surveys. However, this has its challenges and limitations. We elucidate on that by presenting two case studies from flash floods in Germany. First, we assessed the reliability of an existing flood damage schema, which differentiates from minor (structural) damage to complete building collapse. We compared two on-site raters of the 2016 Braunsbach flood, reaching an excellent level of reliability. Second, we mapped structural building damage after the flood in the Ahr valley in 2021 using a textured 3D mesh and orthophotos. Here, we evaluated the remote sense-based damage mapping done by three raters. Although the heterogeneity of ratings using remote sensing data is larger than among on-site ratings, we consider it fit-for-purpose when compared with on-site mapping, especially for event documentation and as basis for financial damage estimation and less complex numerical modelling.

Coastal flooding is a growing hazard. Compound event characterization and uncertainty quantification are critical to accurate flood risk assessment. This study presents univariate, conditional, and joint probabilities for observed water levels, precipitation, and waves. Design events for 10- and 100-year marine water level and precipitation events are developed. A total water level formulation explicitly accounting for wave impacts is presented. Uncertainties associated with sampling method, copula selection, data record length, and utilized rainfall gauge are determined. Eight copulas are used to quantify multivariate uncertainty. Generally, copulas present similar results, except the BB5. Sampling method uncertainty was quantified using four sampling types; annual maximum, annual coinciding, wet season monthly maximum, and wet season monthly coinciding sampling. Annual coinciding sampling typically produced the lowest event magnitude estimates. Uncertainty associated with record length was explored by partitioning a 100-year record into various subsets. Withholding 30 years of observations (i.e., records of less than 70 years) resulted in substantial variability of both the 10- and 100-year return period estimates. Approximately equidistant rainfall gauges led to large event estimate differences, suggesting microclimatology and gauge selection play a key role in characterizing compound events. Generally, event estimate uncertainty was dominated by sampling method and rainfall gauge selection.

Identifying flood-prone areas is essential for preventing floods, reducing risks, and making informed decisions. A spatial database with 595 flood inventory and 13 flood predictors were used to implement five boosting algorithms: gradient boosting machine (GBM), extreme gradient boosting, categorical boosting, logit boost, and light gradient boosting machine (LGBM) to map flood susceptibility in Rathnapura while evaluating trained model's generalizing ability and assessing the feature importance in flood susceptibility mapping (FSM). The model performance was evaluated using the F1-score, kappa index, and area under curve (AUC) method. The findings revealed that all the models were effective in identifying the overall flood susceptibility trends while LightGBM model had superior results (F1-score = 0.907, Kappa value = 0.813 and AUC = 0.970), securing the top scores across all performance metrics compared to the other models (for testing dataset). Based on kappa evaluation, most of the models had finer performance (AUC _min = 0.737) while LightGBM had moderate performance for predictions beyond the training region. According to the results, regions with lower altitudes and topographic roughness values, moderate rainfall, and proximity to rivers are more susceptible to flooding. This framework can be adapted for rapid FSM in data-deficient regions.

Evaluation of the performance of flood models is a crucial step in the modeling process. Considering the limitations of single statistical metrics, such as uncertainty bounds, Nash Sutcliffe efficiency, Kling Gupta efficiency, and the coefficient of determination, which are widely used in the model evaluation, the inherent properties and sampling uncertainty in these metrics are demonstrated. A comprehensive evaluation is conducted using an ensemble of one-dimensional Hydrologic Engineering Center's River Analysis System (HEC-RAS) models, which account for the uncertainty associated with the channel roughness and upstream flow input, of six reaches located in Indiana and Texas of the United States. Specifically, the effects of different prior distributions of the uncertainty sources, multiple high-flow scenarios, and various types of measurement errors in observations on the evaluation metrics are investigated using bootstrapping. Results show that the model performances based on the uniform and normal priors are comparable. The statistical distributions of all the evaluation metrics in this study are significantly different under different high-flow scenarios, thus suggesting that the metrics should be treated as “random” variables due to both aleatory and epistemic uncertainties and conditioned on the specific flow periods of interest. Additionally, the white-noise error in observations has the least impact on the metrics.

The risk of flooding has become more significant in many parts of the world due to climate change and increased urbanization. Flood has devastating effects on infrastructure, and communities, causing damage to property and loss of life. Simulation of flood extent in a particular area is done by using various mathematical models, hydrologic-hydraulic models, and datasets. Flood modeling using hydraulic-hydrological models has many errors due to the lack of hydraulic-hydrologic data and insufficient statistical period length. This study demonstrates the fact that the geomorphological index (GI) method, which is based on the digital elevation model and requires little hydraulic-hydrologic data, is an effective method for flood modeling. Flood zoning based on GI was performed within the Kashafroud basin with 25, 100, and 200-year return periods by using geomorphic flood area (GFA) plugin in QGIS software. The true positive rates were 0.985, 0.989, and 0.992, respectively, which showed the high accuracy of flood zoning based on the GI method. Here proposed method showed that using the GFA plugin offers a good way for the flood risk assessment in a basin with the lack of measured data as an alternative to the hydraulic-hydrological methods.

Woodlands can reduce downstream flooding, but it is not well known how the extent and distribution of woodland affects reductions in peak flow. We used the spatially distributed TOPMODEL to simulate peak flow during a 1 in 50 year storm event for a range of broadleaf woodland scenarios across a 2.6 km² catchment in Northern England. Woodland reduced peak flow by 2.6%–15.3% depending on the extent and spatial distribution of woodland cover. Cross slope and riparian woodland resulted in larger reductions in peak flow, 4.9% and 3.3% for a 10-percentage point increase in woodland cover respectively, compared to a 2.7% reduction for woodland randomly located across the catchment. Our results demonstrate that increased woodland cover can reduce peak flows during a large storm event and suggest that targeted placement of woodland can maximise the effectiveness of natural flood management interventions.

Flood events can cause extensive damage to physical infrastructure, pose risks to human life, and necessitate the reoccupation and rehabilitation of affected areas. A key parameter for flood vulnerability assessment is the first floor height (FFH), which also plays an important role in setting insurance premiums. Traditional methods for FFH estimation rely on ground surveys and site inspections, yet these approaches are both time-consuming and labor-intensive. In this study, we propose an alternative approach based on measurements derived from Google Street View (GSV) images and Deep Learning (DL). We employ the YOLOv5s algorithm, which belongs to a family of compound-scaled object detection models trained on the COCO dataset, for the detection of crucial building elements such as the Front Door (FD), stairs, and overall building extent. Additionally, we utilized the YOLOv5s algorithm to identify basement windows and assess the existence of basements. To validate our methodology, we conducted tests in both the Greater Toronto Area (GTA) and the state of Virginia in the United States. The results demonstrate an achievement of RMSE and Bias values of 81 cm and −50 cm for GTA, and 95 cm and −20 cm for the Virginia region, respectively.

Accurate estimation of design floods is necessary for developing effective flood-management strategies. Climate change (CC) studies on floods generally consider alterations in mean runoff using ensembles compared to a base period. In this study, we examined the plausibility and implications of applying individual climate model-generated flows versus their ensembles to estimate peak floods (magnitude and timing of occurrence), using Budhigandaki River Basin of Nepal as a case study. Annual maximum one-day floods were derived for four future climate scenario projections (cold-dry, cold-wet, warm-wet, and warm-dry) from simulated daily flow series. Future floods of six return periods estimated for the individual climate scenarios were compared with their “Ensemble” (combiner for the ensemble series is the arithmetic mean of daily floods), “Average,” and ‘Baseline.” Results showed that magnitudes of the flood peaks are such that those estimated using “Ensemble” < “Average” < individual series. We conclude that ensemble series should not be used for flood estimation because of the averaging effect. Designers should consider at the least the “Average” instead of the “Ensemble” series while designing climate-resilient flood structures. Furthermore, the occurrences of flood peaks are likely to be confined within the monsoon season for the “Ensemble” but spread out in the other months for the individual climate scenarios. This could have direct implications on the availability and mobilization of resources as well as the need for a year-round operational early warning system for flood risk management.