Flash flood mitigation: recommendations for research and applications

Flash floods have the potential to damage infrastructure and buildings, and pose a considerable threat to human life. The short lag times associated with flash floods (typically a few hours) present a significant challenge to existing flood warning systems. These systems currently rely on rainfall and runoff measurements, as well as hydrological models. Consequently, they are of limited applicability in ungauged catchments. This is a critical issue given that climate change is intensifying extreme rainfall and thereby increasing the potential for flash flooding.This study investigates the detection of flash floods based solely on rainfall characteristics, thus eliminating dependency on runoff measurements and hydrological infrastructure. Using high-resolution radar rainfall data and 15-minute runoff observations, 1,330 extreme rainfall-runoff events are selected across 147 German catchments with an area of up to 100 km². These events are subsequently classified as either flash or non-flash floods using a rainfall-runoff-based classification scheme as a reference, with 103 of the selected events identified as flash floods. For each event, various space-time rainfall characteristics are quantified. A random forest model for flash flood detection is then trained using only the rainfall metrics and static catchment attributes. The main aim is to assess the potential for rainfall-driven flash flood detection without relying on runoff. In addition, the most relevant rainfall characteristics associated with flash floods are identified, thereby improving our understanding of the underlying drivers.In future work, the developed detection approach will be combined with real-time rainfall nowcasting to enable earlier prediction and warning of flash floods.

Flash floods account for a large proportion of flood-based fatalities, and they are becoming more frequent due to climate change. A global flash flood warning system therefore has the potential to be life saving.Standard approaches to flash flood forecasting – such as the Flash Flood Guidance (FFG) system in the US National Weather Service (NWS), or the European Runoff Index based on Climatology (ERIC) in the European Flood Awareness System (EFAS) – are utilizing recent weather and soil conditions, physiographic characteristics of basins, and weather forecasts, in order to produce a forecast for possible flash floods. These forecasts are not disseminated directly to the public. Instead, they are firstly refined by hydrologists that have intimate knowledge of the relevant basins and of previous flood events. This introduces a difficulty to scaling these methods worldwide, as the training of professional hydrologists in every region is costly and time consuming.Recent applications of Machine Learning (ML) to hydrology show that a learning system has the potential to train on data-rich basins and generalize to data-poor basins, with a skill that is comparable to state of the art hydrological models. In this work we attempt to build a ML model to produce daily flash flood forecasts, based on globally available weather reanalysis and physiographic characteristics from HydroATLAS. We discuss the model architecture, and evaluate it against NWS Flash Flood Warnings (FFW). While such models may not surpass the skill of a professional hydrologist, they have the potential to provide reasonable warnings in regions that do not currently have any such system in place.

Flash floods have become the nation's number one stormy weather killer in the 1970s. Deaths from flash floods are now approaching 200 each year compared to an average of less than 70 per year during the preceding 30-year period. Losses from flash floods are now nearly 10 times what they were in the 1940s. To combat these escalating losses the National Weather Service (NWS) is expanding its efforts to improve forecasting and warning of flash floods and to improve the public's response to flash flood threats. This paper summarizes the present and planned NWS operational flash flood warning and disaster preparedness programs.

National Oceanic and Atmospheric Administration's National Weather Service (NWS) flash flood warnings are issued by Weather Forecast Offices and are underpinned by information from the Flash Flood Guidance (FFG) system operated by the River Forecast Centers (RFCs). This study focuses on the quantitative evaluation and limitations of the FFG system using reported flash flood cases in 2010 and 2011. The flash flood reports were obtained from the NWS Storm Event database for the Arkansas‐Red Basin RFC (ABRFC). The current FFG system at the ABRFC provides gridded flash flood guidance (GFFG) system using the NWS Hydrology Laboratory‐Research Distributed Hydrologic Model to translate the upper zone soil moisture to estimates of Soil Conservation Service Curve Numbers. Comparisons of the GFFG and real‐time Multisensor Precipitation Estimator‐derived Quantitative Precipitation Estimate for the same duration and location were used to analyze the success of the system. Typically, the six‐hour duration was characterized by higher probability of detection values than the three‐hour duration, which highlights the difficulty of hydrologic process estimation for shorter time scales. The current system does not take into account physical characteristics such as land use, including irrigated agricultural farm and urban areas, hence, overly dry soil moisture estimates over these areas can lower the success rate of the GFFG product.

Quantifying uncertainty associated with flash flood warning or forecast systems is required to enable informed decision making by those responsible for operation and management of natural hazard protection systems. The current system used by the U.S. National Weather Service (NWS) to issue flash-flood warnings and watches over the Unites States is a purely deterministic system. The authors propose a simple approach to augment the Flash Flood Guidance System (FFGS) with uncertainty propagation components. The authors briefly discuss the main components of the system, propose changes to improve it, and allow accounting for several sources of uncertainty. They illustrate their discussion with examples of uncertainty quantification procedures for several small basins of the Illinois River basin in Oklahoma. As the current FFGS is tightly coupled with two technologies, that is, threshold-runoff mapping and the Sacramento Soil Moisture Accounting Hydrologic Model, the authors discuss both as sources of uncertainty. To quantify and propagate those sources of uncertainty throughout the system, they develop a simple version of the Sacramento model and use Monte Carlo simulation to study several uncertainty scenarios. The results point out the significance of the stream characteristics such as top width and the hydraulic depth on the overall uncertainty of the Flash Flood Guidance System. They also show that the overall flash flood guidance uncertainty is higher under drier initial soil moisture conditions. The results presented herein, although limited, are a necessary first step toward the development of probabilistic operational flash flood guidance forecast-response systems.

Floods are one of the most devastating natural hazards around the world, and flash floods are extremely dangerous to property and human lives. This study reviews the current flash flood forecasting in China and proposes a new methodology to develop a flash flood potential index (FFPI) based on GIS to further improve China's flash flood guidance system and national weather forecasting services. We collected datasets required to calculate the FFPI and tested a series of schemes to determine the method to calculate the FFPI over China. The result indicates that the calculated China's FFPI distribution can represent the risk of flash flood well, especially in Central and Southeast China. It is also implied that the southwest and western complex terrains are more susceptible to rain storms. Then we chose Yunnan Province as a test-bed and also yielded good results which can offer reference to the current China's flash flood forecasting system. Further research can be placed on incorporation of soil moisture and rainfall frequency components into the FFPI calculation method for dynamic warning and forecasting of flash floods.

Simulating flash flood hydrographs and behavior metrics across China: Implications for flash flood management

One of the costliest natural hazards around the globe is flash floods, resulting from localized intense convective precipitation over short periods of time. Since intense convective rainfall (especially over the continents) is well correlated with lightning activity in these storms, a European Union FP6 FLASH project was realized from 2006 to 2010, focusing on using lightning observations to better understand and predict convective storms that result in flash floods. As part of the project, 23 case studies of flash floods in the Mediterranean region were examined. For the analysis of these storms, lightning data were used together with rainfall estimates in order to understand the storms’ development and electrification processes. In addition, these case studies were simulated using mesoscale meteorological models to better understand the local and synoptic conditions leading to such intense and damaging storms. As part of this project, tools for short-term predictions (nowcasts) of intense convection across the Mediterranean and Europe, and long-term forecasts (a few days) of the likelihood of intense convection, were developed and employed. The project also focused on educational outreach through a special Web site http://flashproject.org supplying real-time lightning observations, real-time experimental nowcasts, medium-range weather forecasts and educational materials. While flash floods and intense thunderstorms cannot be prevented, long-range regional lightning networks can supply valuable data, in real time, for warning the public, end-users and stakeholders of imminent intense rainfall and possible flash floods.

Flash flood is an annual occurrence in Uttarakhand especially during the Monsoon season. Over the recent years, the amount of rainfall in this region has risen, thereby posing even greater threats and challenges. Almora, one of its 13 districts, experienced one of the most damaging flash-floods during the 2010 Monsoon season. Thus, there is an urgent need to strategize the flash flood protection and mitigation measures. Implementing the flood protection steps often involves very long planning horizons of 30 years or more, because each phase is time-consuming to implement or entail significant investment, or both. The flash flood issue can be addressed by diverting more attention on high-threat areas that can be identified spatially through thematic layer preparation and Analytic Hierarchy Process (AHP) analysis based on parameters that directly influence it further combining them using flood risk index to generate flood risk zones. Successively, responsive guidelines and standards at every scale of planning hierarchy, from macro to micro level, are proposed to reduce the impact during a disaster and minimize those events. Certain guidelines are developed for high alert zones giving them more emphasis, as they hold maximum chances of future occurrence of a disaster event. ¹

Jordan experienced flash floods that resulted in numerous fatalities and injuries. This research focuses on identifying the Wadi Mujib’s flash flood potential zones and evaluating their potential magnitude. In this work, hydrological models were developed by integrating GIS settings with HEC-HMS software (V. 4.11). The hydrological model for Wadi Mujib is simulated in this research by means of the Soil Conservation Service (curve number method) while using rainfall data from 1970 to 2022. The results show that the optimum curve number values (CN) were 78.5 at normal antecedent moisture content. Additionally, in order to aid in the decision-making process for flash flood warnings, a flash flood potential index (FFPI) was also introduced based on four main physiographic parameters (slope, land use, plant cover, and soil texture) ranging from 1 to 10. The accumulative chart’s FFPI threshold, which indicates the areas with the highest potential for flash floods, was set at 95% or above. The FFPI threshold was chosen using the accumulative chart of FFPI, which shows that the FFPM threshold value is 7 and covers 13.39% of the study area.

Using statistical functions and hydro-hydraulic models to develop human vulnerability curves for flash floods: The flash flood of the Taitou catchment (China) in 2016

Egypt is located in a semi-arid region and the annual average precipitation is 12 mm/year which ranges from 0 mm/year in the desert areas to 200 mm/year in the coastal areas. Countries located in semi-arid regions such as Egypt have alluvial (wadi) systems that formed during the fluvial time of the Tertiary and Quaternary Periods. These wadis suffer from a flash flood, consequent to heavy precipitations. Two major factors are responsible for flood generation and impart specific features to it. The first is the physical process, which generates the change of position between the lithosphere, atmosphere, and hydrosphere. Second, the flooded area, depth of inundation, and its duration depend on the geographic situation of the region where flood takes place. Due to the great variety in operation of the natural processes and the endless variation in the condition of the geographic arena where they act, many different kinds and scales of floods can be distinguished. There are at least 18 major types of floods of natural origin. Growing human influence has become evident from the increasing frequency of floods of anthropogenic origin. The main purpose of this chapter is to identify the general system of floods in terms of types, causes, problems caused by flood, and how to determine the amount of floodwater and prevention with application to the areal case study. Wadi Sudr, Sinai, Egypt is used as a real case study. The description of this wadi is defined. Recorded rainfall depths are studied and analyzed, and the IDF curve is generated. Runoff hydrograph of the wadi is estimated, and locations of protection measures using a number of dams and open channels have been selected and designed to protect the area from flood hazards.

Flash floods are one of the most serious mountain disasters in Linzhi, China, pose a grave threat to the lives and property of the residents. However, the current regional vulnerability research cannot meet the needs of refined disaster prevention and mitigation in mountainous areas. Therefore, to address this knowledge gap, this study presented a vulnerability assessment framework base on spatialization technique along with the triangular fuzzy number-based analytic hierarchy process (TFN-AHP) and random forest (RF) model. To achieve this goal, an assessment system containing 12 indicators was constructed from the three perspectives of exposure, susceptibility and coping capacity. Firstly, factor spatialization is introduced in data preprocessing. Then TFN-AHP and the RF model were used to calculate the exposure, susceptibility, and coping capacity. Finally, the spatial distribution of the vulnerability throughout Linzhi was obtained. According to the results, the exposure, susceptibility and coping capacity in most areas are extremely low and low, accounting for 74.1%, 99% and 99%, respectively. The vulnerability is similar to the exposure that most of the regions have extremely low vulnerability and low vulnerability, accounting for 83.5%. The moderate vulnerability regions account for 8.5%, and the high vulnerability regions account for only 1%. The assessment results show that high-exposure areas mainly exhibit a linear distribution, and mainly include river valleys, cultivated land, and residential areas. Obviously, these areas are mainly distributed in the population and economic agglomeration areas and on low-altitude terrain. The distribution of high and comparatively high vulnerability zones, where economic and human activities are considerably high, are closely to the topography of the catchment. Thus, the results of this study provide scientific and technological evidence for resettlement, population distribution adjustment, and prevention and mitigation of flash floods in Linzhi.

Assessment of flash flood risk based on improved analytic hierarchy process method and integrated maximum likelihood clustering algorithm

Risk assessment and sensitivity analysis of flash floods in ungauged basins using coupled hydrologic and hydrodynamic models