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Map of the two study areas with the location of the rain and river gauges, including location where temperature was also measured. The size and location of the EOBS grid cell is also shown for Ubaye. Salzach and Ubaye catchments differ in size and average annual precipitation, they also differ in flood seasonality as shown in previous flood hazard studies (Salzach: Stanzel et al. (2008), Ubaye: Ramesh (2013)). The Ubaye River generally experiences spring flood events, where warm rain amplifies elevated river levels due to snow melt (Ramesh, 2013). Summer flood events are more common for Salzach catchment, which includes the August 2002 flood event where the discharge was the highest in the previous 100 years (Ulbrich et al., 2003). More recently in June 2013, the Salzach catchment recorded high discharge after four days of high precipitation with high antecedent soil moisture (Blöschl et al., 2013).
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Flood type classification is an optimal tool to cluster floods with similar meteorological triggering conditions. Under climate change these flood types may change differently as well as new flood types develop. This paper presents a new methodology to classify flood types, particularly for use in climate change impact studies. A weather generator...
Contexts in source publication
Context 1
... highest measured discharge amount between 1970 and 2010 was in May 2008, and had similar values for the indicators as the flood Type 1a for Q25. The recorded 1- day precipitation was more than 40mm at the rain gauges in Figure 1, with above normal temperature. ...
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... change in frequency of each of the flood types was analyzed first (approach 1). Figure 9 and return period are in Table 5. Figure 10 shows the clustering of flood types based on the indicators DOY and 1-day rainfall, with the individual SI values in the supplementary material. Table 5. Cluster center values for the different discharge magnitudes for Salzach under the historical climate. ...
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... The most similar flood types from the Md projection retained the Type 1 and Type 2 events, although they occurred over a larger portion of the year (Figure 10a). The Mw projection future flood type characteristics had the largest contrast from the historical period (Figure 10b). ...
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... most similar flood types from the Md projection retained the Type 1 and Type 2 events, although they occurred over a larger portion of the year (Figure 10a). The Mw projection future flood type characteristics had the largest contrast from the historical period (Figure 10b). This was the only projection that did not use DOY in all of the flood type classifications (Table 5). ...
Context 5
... the Q25 flood events, three types were identified with the inclusion of the DOY indicator. For the two warmer projections, Wd and Ww, between two and four clusters were found based on 1-day precipitation, temperature, and DOY with higher 1-day totals (Figure 10c, d). Flood types 2a and 1 were similar to the flood types 2 and 1 from the historical period. ...
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... Thus, it is of great significance to improve the physical justification of TCMD by classifying flood types beforehand [3,7,28]. Classification of different FGMs is not only beneficial for the fulfilment of IID assumption in flood frequency analysis, but can help put flood events into a wider climate situation and analyze the future evolution of flood events [34][35][36]. ...
... FGMs can be classified into different types using either the meteorological triggers (e.g., different precipitation types and contribution of precipitation associated with a flood event, compared with snowmelt) or the basin characteristics (e.g., land-cover types, snow cover and soil moisture), since the interaction between meteorological factors and underlying surface dominates the formation of a flood event [7,34,[37][38][39][40][41][42]. Turkington et al. [34] highlighted the significance and applicability of classifying FGMs into different categories, and they generated a long sequence of hydrological data by combining the weather generator and a conceptual (Hydrologiska Byråns Vattenbalansavdelning) HBV model, and dividing flood types according to four meteorological indicators using the k-means clustering method. ...
... FGMs can be classified into different types using either the meteorological triggers (e.g., different precipitation types and contribution of precipitation associated with a flood event, compared with snowmelt) or the basin characteristics (e.g., land-cover types, snow cover and soil moisture), since the interaction between meteorological factors and underlying surface dominates the formation of a flood event [7,34,[37][38][39][40][41][42]. Turkington et al. [34] highlighted the significance and applicability of classifying FGMs into different categories, and they generated a long sequence of hydrological data by combining the weather generator and a conceptual (Hydrologiska Byråns Vattenbalansavdelning) HBV model, and dividing flood types according to four meteorological indicators using the k-means clustering method. However, as discussed by Turkington et al. [34], the selected indicators are likely to fail to capture FGMs such as snowmelt-induced flood. ...
The fundamental assumption of flood frequency analysis is that flood samples are generated by the same flood generation mechanism (FGM). However, flood events are usually triggered by the interaction of meteorological factors and watershed properties, which results in different FMGs. To solve this problem, researchers have put forward traditional two-component mixture distributions (TCMD-T) without clearly linking each component distribution to an explicit FGM. In order to improve the physical meaning of mixture distributions in seasonal snow-covered areas, the ratio of rainfall to flood volume (referred to as rainfall–flood ratio, RF) method was used to classify distinct FGMs. Thus, the weighting coefficient of each component distribution was determined in advance in the rainfall–flood ratio based TCMD (TCMD-RF). TCMD-RF model was applied to 34 basins in Norway. The results showed that flood types can be clearly divided into rain-on-snow-induced flood, snowmelt-induced flood and rainfall-induced flood. Moreover, the design flood and associated uncertainties were also estimated. It is found that TCMD-RF model can reduce the uncertainties of design flood by 20% compared with TCMD-T. The superiority of TCMD-RF is attributed to its clear classification of FGMs, thus determining the weighting coefficients without optimization and simplifying the parameter estimation procedure of mixture distributions.
... Hydrological classification of flood events based on hydrometeorological forcing within catchments (for example, rainfall and snowmelt) and catchment states (for example, soil moisture) is one of the most commonly used methods for flood typology 54 . It can be further categorized into two approaches: the decision tree 21,22,24,25 and the statistical clustering algorithm 55,56 . In this study, we combined these two approaches to classify flood types by first constructing a decision tree to define the criteria for each flood type, this tree consisting of decision attributes and the attribute thresholds ( Supplementary Fig. 4 and Table 1). ...
An intensified hydrological cycle with global warming is expected to increase the intensity and frequency of extreme precipitation events. However, whether and to what extent the enhanced extreme precipitation translates into changes in river floods remains controversial. Here we demonstrate that previously reported unapparent or even negative responses of river flood discharge (defined as annual maximum discharge) to extreme precipitation increases are largely caused by mixing the signals of floods with different generating mechanisms. Stratifying by flood type, we show a positive response of rainstorm-induced floods to extreme precipitation increases. However, this response is almost entirely offset by concurrent decreases in snow-related floods, leading to an overall unapparent change in total global floods in both historical observations and future climate projections. Our findings highlight an increasing rainstorm-induced flood risk under warming and the importance of distinguishing flood-generating mechanisms in assessing flood changes and associated social-economic and environmental risks.
... Flooding origins depend on the water source and on the reasons and processes causing the water level to rise including spatial patterns and characteristics of flood seasonality (warm or cold periods etc.) [22,[34][35][36]. In particular, in terms of origin and drivers, riverine floods can be triggered and developed by hydrometeorological conditions through precipitation, temperature, evaporation, snow accumulating and melting processes, and high soil moisture [11,16,21,22,35,36]; coastal floodsby high tides, combined with low atmospheric pressures and strong winds inducing a storm surging [16][17][18][19]37]. ...
... Flooding origins depend on the water source and on the reasons and processes causing the water level to rise including spatial patterns and characteristics of flood seasonality (warm or cold periods etc.) [22,[34][35][36]. In particular, in terms of origin and drivers, riverine floods can be triggered and developed by hydrometeorological conditions through precipitation, temperature, evaporation, snow accumulating and melting processes, and high soil moisture [11,16,21,22,35,36]; coastal floodsby high tides, combined with low atmospheric pressures and strong winds inducing a storm surging [16][17][18][19]37]. There are also many unusual flood cases [2], including groundwater flooding caused by high seepage through permeable, river-connected alluvial aquifers [38][39][40], tsunamis flooding [41], floods because of dam disasters [42][43][44], dike and levee breaches [45,46], floods caused by landslide dam collapses [47] and glacial lake outburst floods [48], backwater floods [49,50], debris flows and mudflows floods [51,52], etc. ...
This paper explores some aspects relating to retrospective predicting the confirmed monetary losses caused by the disastrous floods of 1980, 1986, and 1998 in the Tisza River basin within the Transcarpathian region of Ukraine. The research was based on two time series – the losses because of past floods and the maxima water discharges gauged at the hydrological station near the village of Vylok, Vynohradiv district. The main aim of the research was to make out whether it had been the possibility to predict the losses due to those floods in advance.In solving the task, there was revealed and modelled the dependence of the risk of losses due to the floods in Transcarpathia on the maximum water discharges of the Tisza River gauged at the “Vylok” hydrological station. Predicting was based on the hypothesis of the stationary random process for maximum water discharges, which allowed using an empirical distribution function of a random variable regarding flood water discharges assessing the risk of flood losses.Retrospective predicting of the losses caused by the floods of 1980, 1986, and 1998 was carried out by means of a combined situational-inductive predictive modelling method (CSIPMM), being an original author’s development. The method relates to predicting the behaviour of complex dynamic systems based on monitoring findings presented as time series data reflecting evolutions of a resulting (dependent) variable and an explaining (independent) variable (predictor). The method uses extrapolation-regression type models. According to this method, the prediction task is performed in two stages. The first stage realises the retrospective situational modelling task aiming to obtain a set of simple regressions (situational models) built on data of sample time series. The situational models are accepted to be adequate or relevant ones only within certain periods of time determined as situations. In the second stage, based on the generalization (on an ensemble) of the obtained retrospective situational models, inductive “levels” models are built, which reflect the behaviour of a controlled parameter of the system or process (a resulting variable) at several fixed values of a predictor in time. The inductive models are used in extrapolative predicting situational models belonging to future periods (situations).In total, three predictions were made: (1) taking into account the annual maximum flood discharges from 1954 to 1979 (before the flood of 1980); (2) the same from 1954 to 1985 (before the flood of 1986); (3) the same from 1954 to 1997 (before the flood of 1998). The study found that there had been a possibility to predict the confirmed monetary losses inflicted by the flood of 1986 and 1998 (relative predicting errors of 7.2-8.7% and 6.0-12.8% depending on the prediction options).
... An earlier definition [16]) extends this temporal limit to 12 h. It is worth mentioning that similar precipitation patterns, but different hydrological antecedent conditions, may influence flood occurrence and severity, as well as catchment response during weather events that appear similar [17] (iii) early warning capacity. Classification criteria based on early warning capacity deviate from the scientific definition, which is linked to the dynamics of the phenomenon. ...
Central Italy is characterized by complex orography. The territorial response to heavy precipitation may activate different processes in terms of hydrogeological hazards. Floods, flash floods, and wet mass movements are the main ground effects triggered by heavy or persistent rainfall. The main aim of this work is to present a unique tool that is based on a distributed hydrological model, able to predict different rainfall-induced phenomena, and essential for the civil protection early warning activity. The Cetemps Hydrological Model is applied to the detection of hydrologically stressed areas over a spatial domain covering the central part of Italy during a weather event that occurred in 2014. The validation of three hydrological stress indices is proposed over a geographical area of approximately 64,500 km2 that includes catchments of varying size and physiography. The indices were used to identify areas subject to floods, flash floods, or landslides. Main results showed very high accuracies (~90%) for all proposed indices, with flood false alarms growing downstream to larger basins, but very close to zero in most cases. The three indices can give complementary information about the predominant phenomenon and are able to distinguish fluvial floods from pluvial floods. Nevertheless, the results were influenced by the presence of artificial reservoirs that regulated flood wave propagation, therefore, indices timing slightly worsen downstream in larger basins.
... Xu and Peng (2015) took the precipitation distribution, time variance of precipitation intensity and some hydrology characteristics as the classification indicators to cluster floods, and used a rough set to identify rules for flood forecast. Turkington et al. (2016) first generated by a weather generator and a conceptual rainfall-runoff model, and then used temperature and precipitation indicators to achieve flood classification in two basin of Austria. The methods above are all designed by enough information for extracting indicators. ...
... Different from previous models using indicators extracted from geography, hydrology, meteorology, and human activities (Ren et al. 2010;Turkington et al. 2016;Xu and Peng 2015), we propose a new simple model to cluster historic floods by the occurrence and development of floods. The similarity of the occurrence and development of each flood has been described as the similarity of the simulated model for each flood in the study. ...
The classification and identification can increase the prediction accuracy effectively due to the complexity and regularity of flood formation. However, it is difficult to extract the influence indicators, especially in data-sparse basins. This research proposes a framework for flood classification and dynamic flood forecast identification in data-sparse basins. The framework starts from a new perspective for flood classification and introduces the concept of forgetting mechanism for flood identification. In the framework, the Data-Based Mechanistic (DBM) forecasting model, a data-driven model with a physically mechanistic interpretation, has been selected as the basic simulated model; then a flood classification model based on DBM and the process of flood occurrence and development has been built to classify floods and generate the corresponding sub-cluster models, and the similarity of the process of flood occurrence and development for each flood is described as the similarity of the simulated model trained for each flood; the forgetting mechanism, which can eliminate the out-of-date data gradually to reduce the influence of the misleading information, is coupled with the deterministic coefficient to identify one of the sub-models for the dynamic flood forecast. The framework has been tested in Shihuiyao Basin, Northeastern China. Results show that the average deterministic coefficients of the proposed framework are 0.87 and 0.86, which are 0.05 and 0.16 higher than those without classification and identification (0.82 and 0.70). The established framework provides a new idea for flood classification and identification, which has the advantages of ease of use, good generality, and low data requirements.
... Such a system will subsequently aid policy and decisionmakers in developing resilience-guided risk management strategies, accounting for the four attributes of resilience. Classification and data driven models require a sufficient number of observations in a dataset to allow for meaningful classification and clustering [23]. While this necessitates the accessibility to a large volume of high quality data, there are also alternative ways to account for missing data within an employable dataset. ...
... There have been numerous ANN techniques developed to date, each of which may befit a specific application (e.g., self-organizing maps, recurrent neural networks, and feed-forward back-propagation neural networks). However, ANN is more commonly employed in predictive algorithms [54,56,57] and pattern recognition applications [23,36,55,58]. For the study presented herein, SOM was utilized using the Deep Learning Toolbox in MATLAB, where the Kohonen rule was adopted [55,59]. ...
Climate change and the development of urban centers within flood-prone areas have significantly increased flood-related disasters worldwide. However, most flood risk categorization and prediction efforts have been focused on the hydrologic features of flood hazards, often not considering subsequent long-term losses and recovery trajectories (i.e., community’s flood resilience). In this study, a two-stage Machine Learning (ML)-based framework is developed to accurately categorize and predict communities’ flood resilience and their response to future flood hazards. This framework is a step towards developing comprehensive, proactive flood disaster management planning to further ensure functioning urban centers and mitigate the risk of future catastrophic flood events. In this framework, resilience indices are synthesized considering resilience goals (i.e., robustness and rapidity) using unsupervised ML, coupled with climate information, to develop a supervised ML prediction algorithm. To showcase the utility of the framework, it was applied on historical flood disaster records collected by the US National Weather Services. These disaster records were subsequently used to develop the resilience indices, which were then coupled with the associated historical climate data, resulting in high-accuracy predictions and, thus, utility in flood resilience management studies. To further demonstrate the utilization of the framework, a spatial analysis was developed to quantify communities’ flood resilience and vulnerability across the selected spatial domain. The framework presented in this study is employable in climate studies and patio-temporal vulnerability identification. Such a framework can also empower decision makers to develop effective data-driven climate resilience strategies.
... According to the flood generation mechanism, floods can be classified into long-and short-rain floods [11,12]. A key for building resilience to short-rain floods is to anticipate in a timely way the event, in order to gain time for better preparedness. ...
Worldwide, machine learning (ML) is increasingly being used for developing flood early warning systems (FEWSs). However, previous studies have not focused on establishing a methodology for determining the most efficient ML technique. We assessed FEWSs with three river states, No-alert, Pre-alert and Alert for flooding, for lead times between 1 to 12 h using the most common ML techniques, such as multi-layer perceptron (MLP), logistic regression (LR), K-nearest neighbors (KNN), naive Bayes (NB), and random forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as a case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1 h and 12 h cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. The proposed methodology for selecting the optimal ML technique for a FEWS can be extrapolated to other case studies. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of society of floods.
... According to the flood generation mechanism can floods be classified into long-and short-rain floods [11], [12]. A key for building resilience to floods is to anticipate timely to the event, as to gain time for better preparedness. ...
Flood Early Warning Systems (FEWSs) using Machine Learning (ML) has gained worldwide popularity. However, determining the most efficient ML technique is still a bottleneck. We assessed FEWSs with three river states, No-alert, Pre-alert, and Alert for flooding, for lead times between 1 to 12 hours using the most common ML techniques, such as Multi-Layer Perceptron (MLP), Logistic Regression (LR), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Random Forest (RF). The Tomebamba catchment in the tropical Andes of Ecuador was selected as case study. For all lead times, MLP models achieve the highest performance followed by LR, with f1-macro (log-loss) scores of 0.82 (0.09) and 0.46 (0.20) for the 1- and 12-hour cases, respectively. The ranking was highly variable for the remaining ML techniques. According to the g-mean, LR models correctly forecast and show more stability at all states, while the MLP models perform better in the Pre-alert and Alert states. Future efforts are recommended to enhance the input data representation and develop communication applications to boost the awareness of the society for floods.
... e., through rapidity evaluation). Numerous flood categorizations have been suggested [26][27][28][29], most notably that by the Federal Emergency Management Agency (FEMA), to facilitate the assessment of flood damages. However, all such categorizations focused on the hazard properties (i.e., categorizing floods based on magnitude, duration, and degree of severity), and to a much lesser extent, on the consequence/risk, without considering a key community resilience goalrapidity (i.e., the time taken to recovery from both short-and long-term impacts). ...
... Several types of ANN have been developed to date (e.g., feed-forward back-propagation neural network, recurrent neural network, convolution neural network, and self-organizing map), each of which is suitable for specific applications. ANN has been widely employed to develop predictive models (e.g., [53,55,56]), and to solve complex pattern recognition problems (e.g., [29,36,54,57]). It is noteworthy that pattern recognition (e.g., cluster analysis) problems are most often challenging due to the unstructured nature of the data employed, and particularly when the variables associated with the data are interdependent. As such, ANN-based clustering is preferred over other techniques (e.g., model-based and K-means clustering) as it typically converges to a global, rather than a local, optimal solution [53,54,58]. ...
Coupled with climate change, the expansive developments of urban areas are causing a significant increase in flood-related disasters worldwide. However, most flood risk analysis and categorization efforts have been focused on the hydrologic features of flood hazards (e.g., inundation depth, extent, and duration), rarely considering the resulting long-term losses and recovery time (i.e., the community’s flood resilience). This paper aims at developing a data-driven community flood resilience categorization framework that can be utilized for the development of realistic disaster management strategies and proactive risk mitigation measures to better protect urban centers from future catastrophic flood events. This approach considers key resilience metrics such as the robustness of the exposed community and its recovery rapidity. Such categorization that focuses on two resilience goals, namely resourcefulness and redundancy, can empower decision makers to learn from past events and guide future resilience strategies. To demonstrate the applicability of the developed framework, a data-driven framework was applied on historical mainland flood disaster records collected by the US National Weather Services between 1996 and 2019. Descriptive analysis was conducted to identify the features of this dataset as well as the interdependence between the different variables considered. To further demonstrate the utilization of the developed framework, a spatial analysis was conducted to quantify community flood resilience across different counties within the affected states. Beyond the work presented in this paper, the developed framework lays the foundation to adopt data driven approaches for disasters prediction to guide proactive risk mitigation measures and develop community resilience management insights.
... A flood can be classified into two types: major and minor, according to the extent, depth or the duration of inundation (Hundecha et al., 2017). Further, according to the nature and the source of flooding, different types of floods are there (Turkington et al., 2016). Among them, riverine floods, in which a river overflows due to continuous heavy rain for several days, are the most common type of flood experienced by Sri Lankans (Rubinato et al., 2019;Turkington et al., 2016). ...
... Further, according to the nature and the source of flooding, different types of floods are there (Turkington et al., 2016). Among them, riverine floods, in which a river overflows due to continuous heavy rain for several days, are the most common type of flood experienced by Sri Lankans (Rubinato et al., 2019;Turkington et al., 2016). Localized floods, generally known as urban floods, occur as a result of unplanned urbanization. ...
Gampaha district is one of the areas that suffer from frequent flash floods in Sri Lanka. Since flash floods occur unexpectedly, and with minimal warning, preparedness is essential to minimize losses during such a disaster. Planning the evacuation during a flood is a complex process; therefore, it needs focused consideration on several factors. The main objective of this paper is to propose a Geographical Information Systems (GIS) based approach to plan the evacuation process during a situation where there is a flash flood in the Gampaha Divisional Secretariat Division (DSD), with the intent to reduce negative consequences. The study has considered seven criteria: elevation, accessibility, land-use, availability of buildings, presence of water features, rainfall, and population density, in selecting locations for evacuation centers. These data were analyzed with the tools and models available in the GIS software package. As a first step, the flood inundation map was created using elevation and rainfall data. Evacuation centers were then identified outside of the inundated area. Finally, after field verification, 7 potential locations (Bandaranayake Vidyalaya, Bandarawatta Parakrama Vidyalaya, Sri Sumangalaramaya, Madegama Sri Sunandaaramaya, Sri Wajiraghanaramaya, St. Jude Church Idigolla, and Holy Cross College) were selected by considering the capacities such as elevation (above 15m from the Mean Sea Level), accessibility (within 200m from main roads), ownership (public only), and the number of people accommodated. The results of this study will be very helpful for the government, non-government organizations, and the victims to take immediate actions during a flash flood event in the study area.
