Accurate estimates of flood peaks, corresponding volumes, and hydrographs are required to design safe and cost‐effective hydraulic structures. In this paper, we propose a statistical approach for the ...estimation of the design variables peak and volume by constructing synthetic design hydrographs for different flood types such as flash‐floods, short‐rain floods, long‐rain floods, and rain‐on‐snow floods. Our approach relies on the fitting of probability density functions to observed flood hydrographs of a certain flood type and accounts for the dependence between peak discharge and flood volume. It makes use of the statistical information contained in the data and retains the process information of the flood type. The method was tested based on data from 39 mesoscale catchments in Switzerland and provides catchment specific and flood type specific synthetic design hydrographs for all of these catchments. We demonstrate that flood type specific synthetic design hydrographs are meaningful in flood‐risk management when combined with knowledge on the seasonality and the frequency of different flood types.
Key Points
Construction of synthetic design hydrographs using frequency and process‐based knowledge
Flood types have a specific behavior regarding event shapes and the dependence between flood peaks and volumes
Flood type specific design hydrographs can aid flood‐risk management
Flash flood is one of the most common natural hazards affecting many mountainous areas. Previous studies explored flash flood susceptibility models; however, there is still a lack of case studies in ...the transport sector. This paper aimed to develop advanced hybrid machine learning (ML) algorithms for flash flood susceptibility modeling and mapping using data from the road network National Highway 6 in Hoa Binh province, Vietnam. A single ML model of reduced error pruning trees (REPT) and four hybrid ML models of Decorate-REPT, AdaBoostM1-REPT, Bagging-REPT, and MultiBoostAB-REPT were applied to develop flash flood susceptibility maps. Field surveys were conducted about the flash flood locations on the 115-km route length of the National Highway 6 in 2017, 2018, and 2019 flood events. This study used 88 flash flood locations and 14 flood conditioning factors to construct and validate the proposed models. Statistical metrics, including sensitivity, specificity, accuracy, root mean square error, and area under the receiver operating characteristic curve, were applied to evaluate the models’ performance and accuracy. The DCREPT model showed the best performance (AUC = 0.988) among the training models and had the highest prediction accuracy (AUC = 0.991) among the testing models. We found that 12,572 ha (Decorate-REPT), 9564 ha (AdaBoostM1-REPT), 11,954 ha (Bagging-REPT), 14,432 ha (MultiBoostAB-REPT), and 17,660 ha (REPT) of the 3-km buffer area of the highway are in the high- and very high-flash-flood-susceptibility areas. The proposed methodology could be potentially generalized to other transportation routes in mountainous areas to generate flash flood susceptibility prediction maps.
Flash flood early warning is a very effective way to reduce casualties induced by rainstorm flash flood in mountainous area. The forecasting of flash flooding remains challenging because of the short ...response time and inaccurate warning threshold. So far, the flash flood disaster defenses often adopt the critical rainfall amounts inducing the peak discharge or water level to establish an early warning threshold in China. However, the runoff peak discharge depends on rainfall patterns including rainfall intensity and accumulation, result in the critical rainfall threshold has a significant uncertainty. To reduce this uncertainty, herein we present a dual-threshold method for flash flood early warning with consideration of rainfall patterns based on above two-rainfall metrics. Moreover, applying this new method in the flash flood disasters occurred in the Zhongdu river basin, Sichuan province of China to evaluate the early warning reliability. Firstly, five most likely rainfall patterns of this basin were determined according to the timing of rain peak in historical rainfall events, and then, we determined the critical rainfall thresholds under different rainfall patterns and soil moisture conditions. The result showed that the rainfall thresholds uncertainty caused by rainfall pattern is more pronounced than soil moisture. Next, using the cumulative rainfall depth and maximum rainfall intensity corresponding to disaster discharge in different flood processes to establish the dual-thresholds. We found the dual-threshold method comprehensively considers the impacts of soil moisture, rainfall temporal distribution and flood rising property, which can achieve early warning for the four protected objects along the Zhongdu River, with an average lead duration of 46.2 min. Compared with the other three single-threshold methods, the critical rainfall and the critical rainstorm curve methods frequently created false or missing warnings, making it difficult to achieve the effect of early warning. Although reliability of flood water level rising rate method is high, the lead time is relatively short and only lasts for a few minutes in some cases. As a result, the new proposed dual-threshold method, accounting for both the reliability and long lead time, can be a potential candidate for the flash flood disaster early warning.
In this paper, the catastrophic flash flood event which occurred in the western part of Attica (Greece) in November 2017 is reconstructed. The flood event hit the town of Mandra, causing 24 ...fatalities and huge damages in the properties and the infrastructure. The flood hydrograph was derived using the two-dimensional hydrodynamic model FLOW-R2D. Attention was drawn on the uncertainties of the model output due to the uncertainty of the estimated parameters such as infiltration, friction and the uncertainty of input data. Due to the computational burden related to the model, a global sensitivity analysis based on Morris method was performed. Then, a Monte Carlo-based uncertainty analysis was performed on the two most influential factors. It was concluded that even the results of the physically based hydrodynamic models are characterised by uncertainties. However, the capability of the hydrodynamic models to describe in detail the dynamics of the overland flow is the main advantage of these models against the conventional hydrological models. It is concluded that the rational use of physically based models for analysing complex storm phenomena with high variable spatial and temporal distribution can lead to a more accurate range of magnitudes of flood peak.
Many urban areas experience both fluvial and pluvial floods, because locations next to rivers are preferred settlement areas and the predominantly sealed urban surface prevents infiltration and ...facilitates surface inundation. The latter problem is enhanced in cities with insufficient or non-existent sewer systems. While there are a number of approaches to analyse either a fluvial or pluvial flood hazard, studies of a combined fluvial and pluvial flood hazard are hardly available. Thus this study aims to analyse a fluvial and a pluvial flood hazard individually, but also to develop a method for the analysis of a combined pluvial and fluvial flood hazard. This combined fluvial–pluvial flood hazard analysis is performed taking Can Tho city, the largest city in the Vietnamese part of the Mekong Delta, as an example. In this tropical environment the annual monsoon triggered floods of the Mekong River, which can coincide with heavy local convective precipitation events, causing both fluvial and pluvial flooding at the same time. The fluvial flood hazard was estimated with a copula-based bivariate extreme value statistic for the gauge Kratie at the upper boundary of the Mekong Delta and a large-scale hydrodynamic model of the Mekong Delta. This provided the boundaries for 2-dimensional hydrodynamic inundation simulation for Can Tho city. The pluvial hazard was estimated by a peak-over-threshold frequency estimation based on local rain gauge data and a stochastic rainstorm generator. Inundation for all flood scenarios was simulated by a 2-dimensional hydrodynamic model implemented on a Graphics Processing Unit (GPU) for time-efficient flood propagation modelling. The combined fluvial–pluvial flood scenarios were derived by adding rainstorms to the fluvial flood events during the highest fluvial water levels. The probabilities of occurrence of the combined events were determined assuming independence of the two flood types and taking the seasonality and probability of coincidence into account. All hazards – fluvial, pluvial and combined – were accompanied by an uncertainty estimation taking into account the natural variability of the flood events. This resulted in probabilistic flood hazard maps showing the maximum inundation depths for a selected set of probabilities of occurrence, with maps showing the expectation (median) and the uncertainty by percentile maps. The results are critically discussed and their usage in flood risk management are outlined.
Regional information on stream discharge is needed in order to improve flood estimates based on the limited data availability. Regional flood estimation is fundamental for designing hydraulic ...structures and managing flood plains and water resource projects. It is essential for estimating flood risks during recurrent periods due to suitable distributions. Regional flood frequency analysis is crucial for evaluating design flows in ungauged basins, and can complement existing time series in gauged sites and transfer them to ungauged catchments. Hence, this study aims to perform a regional flood frequency analysis of the Genale–Dawa River Basin of Ethiopia using the index flood and L-moments approach for sustainable water resource management. Three homogeneous hydrological regions were defined and delineated based on homogeneity tests from data of 16 stream-gauged sites, named Region-A, Region-B, and Region-C. The discordancy index of regional data for L-moment statistics was identified using MATLAB. All regions showed promising results of L-moment statistics with discordance measures (discordance index less than 3) and homogeneity tests (combined coefficient of variation (CC) less than 0.3). L-moment ratio diagrams were used to select best fit probability distributions for areas. Generalized extreme value, log-Pearson type III, and generalized Pareto distributions were identified as suitable distributions for Region-A, Region-B, and Region-C, respectively, for accurately modeling flood flow in the basin. Regional flood frequency curves were constructed, and peak flood was predicted for different return periods. Statistical analysis of the gauged sites revealed an acceptable method of regionalization of the basin. This study confirms that the robustness of the regional L-moments algorithm depends on particular criteria used to measure the performance of estimators. The identified regions should be tested with other physical catchment features to enhance flood quantile estimates at gauged and ungauged sites. Henceforth, this study’s findings can be further extended into flood hazard, risk, and inundation mapping of identified regions of the study area. Furthermore, this study’s approach can be used as a reference for similar investigations of other river basins.
Mitigating the adverse impacts caused by increasing flood risks in urban coastal communities requires effective flood prediction for prompt action. Typically, physics‐based 1‐D pipe/2‐D overland flow ...models are used to simulate urban pluvial flooding. Because these models require significant computational resources and have long run times, they are often unsuitable for real‐time flood prediction at a street scale. This study explores the potential of a machine learning method, Random Forest (RF), to serve as a surrogate model for urban flood predictions. The surrogate model was trained to relate topographic and environmental features to hourly water depths simulated by a high‐resolution 1‐D/2‐D physics‐based model at 16,914 road segments in the coastal city of Norfolk, Virginia, USA. Two training scenarios for the RF model were explored: (i) training on only the most flood‐prone street segments in the study area and (ii) training on all 16,914 street segments in the study area. The RF model yielded high predictive skill, especially for the scenario when the model was trained on only the most flood‐prone streets. The results also showed that the surrogate model reduced the computational run time of the physics‐based model by a factor of 3,000, making real‐time decision support more feasible compared to using the full physics‐based model. We concluded that machine learning surrogate models strategically trained on high‐resolution and high‐fidelity physics‐based models have the potential to significantly advance the ability to support decision making in real‐time flood management within urban communities.
Key Points
Surrogate machine learning models were trained for flood prediction using a high‐resolution and high‐fidelity physics‐based model
The surrogate model accurately emulated flooding depth and duration on streets simulated by the physics‐based model
A 3,000 times speedup was achieved with the surrogate model compared to the physics‐based model, making it attractive for real‐time decision support
Flood hazards are common in Bhutan as a result of torrential rainfall. Historical flooding events also point to flooding during the main monsoon season of the year, which has had a huge impact in ...many parts of the country. To account for climate change patterns in flood hazards in Bhutan, 116 historical flood events between 1968 and 2020 for 20 districts were retrieved and reviewed. The preliminary review revealed that the frequency of flood occurrence has increased by three times in recent years. In this study, seven flood vulnerability (FV) indicators were considered. Five are the attributes of historical floods, classified into a number of incidents for flood events, fatalities, affected population, and infrastructure damages including economic losses. Additionally, the highest annual rainfall and existence of a flood map were other two indicators considered. Using historical data, flood hazard and impact zonation were performed. The analytic hierarchy process (AHP) method was employed to derive a multi-criteria decision model. This resulted in priority ranking of the seven FV indicators, broadly classified into social, physical/economic, and environmental. Thereafter, an indicator-based weighted method was used to develop the district flood vulnerability index (DFVI) map of Bhutan. The DFVI map should help researchers understand the flood vulnerability scenarios in Bhutan and use these to mediate flood hazard and risk management. According to the study, FVI is very high in Chhukha, Punakha, Sarpang, and Trashigang districts, and the index ranges between 0.75 to 1.0.
Risk-based approaches have been increasingly accepted and operationalized in flood risk management during recent decades. For instance, commercial flood risk models are used by the insurance industry ...to assess potential losses, establish the pricing of policies and determine reinsurance needs. Despite considerable progress in the development of loss estimation tools since the 1980s, loss estimates still reflect high uncertainties and disparities that often lead to questioning their quality. This requires an assessment of the validity and robustness of loss models as it affects prioritization and investment decision in flood risk management as well as regulatory requirements and business decisions in the insurance industry. Hence, more effort is needed to quantify uncertainties and undertake validations. Due to a lack of detailed and reliable flood loss data, first order validations are difficult to accomplish, so that model comparisons in terms of benchmarking are essential. It is checked if the models are informed by existing data and knowledge and if the assumptions made in the models are aligned with the existing knowledge. When this alignment is confirmed through validation or benchmarking exercises, the user gains confidence in the models. Before these benchmarking exercises are feasible, however, a cohesive survey of existing knowledge needs to be undertaken. With that aim, this work presents a review of flood loss-or flood vulnerability-relationships collected from the public domain and some professional sources. Our survey analyses 61 sources consisting of publications or software packages, of which 47 are reviewed in detail. This exercise results in probably the most complete review of flood loss models to date containing nearly a thousand vulnerability functions. These functions are highly heterogeneous and only about half of the loss models are found to be accompanied by explicit validation at the time of their proposal. This paper exemplarily presents an approach for a quantitative comparison of disparate models via the reduction to the joint input variables of all models. Harmonization of models for benchmarking and comparison requires profound insight into the model structures, mechanisms and underlying assumptions. Possibilities and challenges are discussed that exist in model harmonization and the application of the inventory in a benchmarking framework.
Short‐term weather forecasts have the potential to improve reservoir operations for both flood control and water supply objectives, especially in regions currently relying on fixed seasonal flood ...pools to mitigate risk. The successful development of forecast‐based policies should integrate uncertainty from modern forecast products to create unambiguous rules that can be tested on out‐of‐sample periods. This study investigates the potential for such operating policies to improve water supply efficiency while maintaining flood protection, combining state‐of‐the‐art weather hindcasts with downstream conjunctive use to transfer surplus flood releases to groundwater storage. Because available weather hindcasts are relatively short (10–20 years), we propose a novel statistical framework to develop synthetic forecasts over longer periods of the historical record. Operating rules are trained with a recently developed policy search framework in which decision rules are structured as binary trees. Policies are developed for a range of scenarios with varying forecast skill and conjunctive use capacity, using Folsom Reservoir, California, as a case study. Results suggest that the combination of conjunctive use and short‐term weather forecasts can substantially improve both water supply and flood control objectives by allowing storage to remain high until forecasts trigger a release. Further, increased conjunctive use capacity reduces the importance of forecast skill, since surface storage can be moved to groundwater during the flood season without losing water supply. This analysis serves the development of forecast‐based operating policies for large, multipurpose reservoirs in California and other regions with similar flood hydroclimatology.
Key Points
Short‐term forecasts can help balance water supply and flood control objectives in areas dominated by synoptic‐scale flood hydroclimatology
The combined use of forecasts and conjunctive use provides complementary benefits for water supply/flood control tradeoffs
Forecast uncertainty of state‐of‐the‐art numerical weather prediction models does not preclude forecast use in reservoir management
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