Success rate evaluation of severe storm phenomena and flash floods forecasting

This article focuses on proposal new methods to predict strong convective storms that can cause flash floods. Flash flood is determined by the interaction of a number of factors such as the very intense convective precipitation (torrential rainfall accompanied by hail and strong wind gusts), slow motion of convective storms and the soil saturation. These factors have been included in the Algorithm of Storm Prediction, whose prediction results are presented in the two outcome of this article. The result section contains an assessment of the success rate of predictions of convective precipitation and storm intensity, which is complemented by the evaluation of the prediction success rate of severe storm phenomena. Primarily, the goal of the algorithm is to provide predictive information about risk of flash floods that comprise all the above mentioned outputs. Secondarily, the orieintally overview of other forecast outputs is part of the second result section. © 2018, World Scientific and Engineering Academy and Society. All rights reserved

A review of applied methods in Europe for flood-frequency analysis in a changing environment

The report presents a review of methods used in Europe for trend analysis, climate change projections and non-stationary analysis of extreme precipitation and flood frequency. In addition, main findings of the analyses are presented, including a comparison of trend analysis results and climate change projections. Existing guidelines in Europe on design flood and design rainfall estimation that incorporate climate change are reviewed. The report concludes with a discussion of research needs on non-stationary frequency analysis for considering the effects of climate change and inclusion in design guidelines. Trend analyses are reported for 21 countries in Europe with results for extreme precipitation, extreme streamflow or both. A large number of national and regional trend studies have been carried out. Most studies are based on statistical methods applied to individual time series of extreme precipitation or extreme streamflow using the non-parametric Mann-Kendall trend test or regression analysis. Some studies have been reported that use field significance or regional consistency tests to analyse trends over larger areas. Some of the studies also include analysis of trend attribution. The studies reviewed indicate that there is some evidence of a general increase in extreme precipitation, whereas there are no clear indications of significant increasing trends at regional or national level of extreme streamflow. For some smaller regions increases in extreme streamflow are reported. Several studies from regions dominated by snowmelt-induced peak flows report decreases in extreme streamflow and earlier spring snowmelt peak flows. Climate change projections have been reported for 14 countries in Europe with results for extreme precipitation, extreme streamflow or both. The review shows various approaches for producing climate projections of extreme precipitation and flood frequency based on alternative climate forcing scenarios, climate projections from available global and regional climate models, methods for statistical downscaling and bias correction, and alternative hydrological models. A large number of the reported studies are based on an ensemble modelling approach that use several climate forcing scenarios and climate model projections in order to address the uncertainty on the projections of extreme precipitation and flood frequency. Some studies also include alternative statistical downscaling and bias correction methods and hydrological modelling approaches. Most studies reviewed indicate an increase in extreme precipitation under a future climate, which is consistent with the observed trend of extreme precipitation. Hydrological projections of peak flows and flood frequency show both positive and negative changes. Large increases in peak flows are reported for some catchments with rainfall-dominated peak flows, whereas a general decrease in flood magnitude and earlier spring floods are reported for catchments with snowmelt-dominated peak flows. The latter is consistent with the observed trends. The review of existing guidelines in Europe on design floods and design rainfalls shows that only few countries explicitly address climate change. These design guidelines are based on climate change adjustment factors to be applied to current design estimates and may depend on design return period and projection horizon. The review indicates a gap between the need for considering climate change impacts in design and actual published guidelines that incorporate climate change in extreme precipitation and flood frequency. Most of the studies reported are based on frequency analysis assuming stationary conditions in a certain time window (typically 30 years) representing current and future climate. There is a need for developing more consistent non-stationary frequency analysis methods that can account for the transient nature of a changing climate

Statistical, quantitative probability and nowcasting forecasting methods of severe convective storms

This paper deals with the comparison of the statistical, quantitative and nowcasting method of prediction of convective precipitation and the risk of flood floods, which are the main outputs calculated by the Algorithm of Storm Prediction. The evaluation of the success of these outputs was carried out on the basis of verified 63 thunderstorms and three floods that affected the Zlín Region between 2015 and 2017. The first part of the article focuses on the description and evaluation of the predictive outputs of the quantitative prediction of the probability of the occurrence and the intensity of convective precipitation computed from NWP models. At the same time, these outcomes are compared with the outputs of the statistical and nowcasting predictions of convective precipitation. The statistical prediction of convective precipitation is calculated on the selection of the predicted and historical situation from the statistics database. The nowcasting prediction works with the outputs of the MMR50 X-band meteorological radar of the Zlín Region. The second part explores the use of track storms for statistical prediction, which is intended as an indicative and complementary forecast for the method of quantitative prediction of precipitation. The conclusion of the two chapters is a comparison of the success of the predicted outputs of methods, which can be used and put into practice in particular for the prediction of convective precipitation and the risk of floods for purposes of warning and meteorological services and crisis management. © 2018, World Scientific and Engineering Academy and Society. All rights reserved

Enhancing community resilience in arid regions: A smart framework for flash flood risk assessment

This paper presents a novel framework for smart integrated risk management in arid regions. The framework combines flash flood modelling, statistical methods, artificial intelligence (AI), geographic evaluations, risk analysis, and decision-making modules to enhance community resilience. Flash flood is simulated by using Watershed Modelling System (WMS). Statistical methods are also used to trim outlier data from physical systems and climatic data. Furthermore, three AI methods, including Support Vector Machine (SVM), Artificial Neural Network (ANN), and Nearest Neighbours Classification (NNC), are used to predict and classify flash flood occurrences. Geographic Information System (GIS) is also utilised to assess potential risks in vulnerable regions, together with Failure Mode and Effects Analysis (FMEA) and Hazard and Operability Study (HAZOP) methods. The decision-making module employs the Classic Delphi technique to classify the appropriate solutions for flood risk control. The methodology is demonstrated by its application to the real case study of the Khosf region in Iran, which suffers from both drought and severe floods simultaneously, exacerbated by recent climate changes. The results show high Coefficient of determination (R2) scores for the three AI methods, with SVM at 0.88, ANN at 0.79, and NNC at 0.89. FMEA results indicate that over 50% of scenarios are at high flood risk, while HAZOP indicates 30% of scenarios with the same risk rate. Additionally, peak flows of over 24 m3/s are considered flood occurrences that can cause financial damage in all scenarios and risk techniques of the case study. Finally, our research findings indicate a practical decision support system that is compatible with sustainable development concepts and can enhance community resilience in arid regions

Dynamic pluvial flash flooding hazard forecast using weather radar data

Pluvial flash floods are among the most dangerous weather-triggered disasters, usually affecting watersheds smaller than 100 km(2), with a short time to peak discharge (from a few minutes to a few hours) after causative rainfall. Several warning systems in the world try to use this time lag to predict the location, extent, intensity, and time of flash flooding. They are based on numerical hydrological models processing data collected by on-ground monitoring networks, weather radars, and precipitation nowcasting. However, there may be areas covered by weather radar data, in which the network of ground-based precipitation stations is not sufficiently developed or does not even exist (e.g., in an area covered by portable weather radar). We developed a method usable for designing an early warning system based on a different philosophy for such a situation. This method uses weather radar data as a 2D signal carrying information on the current precipitation distribution over the monitored area, and data on the watershed and drainage network in the area. The method transforms (concentrates) the 2D signal on precipitation distribution into a 1D signal carrying information on potential runoff distribution along the drainage network. For sections of watercourses where a significant increase in potential runoff can be expected (i.e., a significant increase of the 1D signal strength is detected), a warning against imminent flash floods can be possibly issued. The whole curve of the potential runoff development is not essential for issuing the alarm, but only the significant leading edge of the 1D signal is important. The advantage of this procedure is that results are obtained quickly and independent of any on-ground monitoring system; the disadvantage is that it does not provide the exact time of the onset of a flash flooding or its extent and intensity. The generated alert only warns that there is a higher flash flooding hazard in a specific section of the watercourse in the coming hours. The forecast is presented as a dynamic map of the flash flooding hazard distribution along the segments of watercourses. Relaying this hazard to segments of watercourses permits a substantial reduction in false alarms issued to not-endangered municipalities, which lie in safe areas far away from the watercourses. The method was tested at the local level (pluvial flash floods in two small regions of the Czech Republic) and the national level for rainfall episodes covering large areas in the Czech Republic. The conclusion was that the method is applicable at both levels. The results were compared mainly with data related to the Fire and Rescue Service interventions during floods. Finally, the increase in the reliability of hazard prediction using the information on soil saturation is demonstrated. The method is applicable in any region covered by a weather radar (e.g., a portable one), even if there are undeveloped networks of rain and hydrometric gauge stations. Further improvement could be achieved by processing more extended time series and using computational intelligence methods for classifying the degree of flash flooding hazard on individual sections of the watercourse network.Web of Science1315art. no. 294

Forecasting of flash floods by Algorithm of Storm Prediction

This article focuses on the forecasting of flash floods using the Algorithm of Storm Prediction as a new tool to predict convective precipitation, severe phenomena and the risk of flash floods. The first part of the article contains information on methods for predicting dangerous severe phenomena. This algorithm uses mainly data from numerical weather prediction models (NWP models), database of historic weather events and relief characteristics describing the influence of orography on the initiation of atmospheric convection. The result section includes verification of predicted algorithm outputs, selected NWP models and warnings of CHMI and ESTOFEX on three events related to the floods that hit the Zlín Region between years of 2015-2017. The main result is a report with prediction outputs of the algorithm visualized in maps for the territory of municipalities with extended competence and their regions. The outputs of the algorithm will be used primarily to increase the effectiveness of preventive measures against flash floods not only by the Fire Rescue Service of Czech Republic but also by the flood and crisis management authorities. © 2018 The Authors, published by EDP Sciences.A2.4 - ICT [LO1303

Causative classification of river flood events

A wide variety of processes controls the time of occurrence, duration, extent, and severity of river floods. Classifying flood events by their causative processes may assist in enhancing the accuracy of local and regional flood frequency estimates and support the detection and interpretation of any changes in flood occurrence and magnitudes. This paper provides a critical review of existing causative classifications of instrumental and preinstrumental series of flood events, discusses their validity and applications, and identifies opportunities for moving toward more comprehensive approaches. So far no unified definition of causative mechanisms of flood events exists. Existing frameworks for classification of instrumental and preinstrumental series of flood events adopt different perspectives: hydroclimatic (large‐scale circulation patterns and atmospheric state at the time of the event), hydrological (catchment scale precipitation patterns and antecedent catchment state), and hydrograph‐based (indirectly considering generating mechanisms through their effects on hydrograph characteristics). All of these approaches intend to capture the flood generating mechanisms and are useful for characterizing the flood processes at various spatial and temporal scales. However, uncertainty analyses with respect to indicators, classification methods, and data to assess the robustness of the classification are rarely performed which limits the transferability across different geographic regions. It is argued that more rigorous testing is needed. There are opportunities for extending classification methods to include indicators of space–time dynamics of rainfall, antecedent wetness, and routing effects, which will make the classification schemes even more useful for understanding and estimating floods

Risk Mapping of Flood Hazards in New Member States

In 2003 the Joint Research Centre conducted a survey of mapping practices in eleven (11) new Member States (Bulgaria, Cyprus, Czech Republic, Estonia, Hungary, Latvia, Lithuania, Poland, Romania, Slovakia and Slovenia) for eight (8) major natural and technological hazards such as floods, forest fires, storms, landslides, earthquakes, industrial installations, transport of dangerous goods and contaminated lands. This activity was funded as part of the project entitled "Management of Natural and Technological Risks". One fundamental project objective was to examine the existing situation in each of the surveyed countries, and compare different mapping methodologies in order to define guidelines for establishing compatible risk mapping systems, in particular multi-hazard risk mapping. This report describes the results of the flood section of the risk mapping activity. Responses to the survey provide important information about the current status of flood hazards and risk mapping in different countries and advantages and obstacles to developing a common methodology for multi-hazard risk mapping including this hazard in each country.JRC.G.7-Traceability and vulnerability assessmen