Using Lightning Data to Better Understand and Predict Flash Floods in the Mediterranean

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.

Operational prediction of flash floods caused by convective rainfall in mountainous areas requires accurate estimates or predictions of the rainfall distribution in space and time. The details of the spatial distribution are especially critical in complex terrain because the watersheds generally are small in size, and position errors in the placement of the rainfall can distribute the rain over the wrong watershed. In addition to the need for good rainfall estimates, accurate flood prediction requires a surface-hydrologic model that is capable of predicting stream or river discharge based on the rainfall-rate input data. In part 1 of this study, different techniques for the estimation and prediction of convective rainfall are applied to the Buffalo Creek, Colorado, flash flood of July 1996, during which over 75 mm of rain from a thunderstorm fell on the watershed in less than 1 h. The hydrologic impact of the rainfall was exacerbated by the fact that a considerable fraction of the watershed experienced a wildfire approximately two months prior to the rain event. Precipitation estimates from the National Weather Service Weather Surveillance Radar-1988 Doppler and the National Center for Atmospheric Research S-band, dual-polarization radar, collocated east of Denver, Colorado, were compared. Very short range simulations from a convection-resolving dynamic model that was initialized variationally using the radar reflectivity and Doppler winds were compared with simulations from an automated algorithmic forecast system that also employs the radar data. The radar estimates of rain rate and the two forecasting systems that employ the radar data have degraded accuracy by virtue of the fact that they are applied in complex terrain. Nevertheless, the dynamic model and automated algorithms both produce simulations that could be useful operationally for input to surface-hydrologic models employed for flood warning. Part 2 of this study, reported in a companion paper, describes experiments in which these radar-based precipitation estimates and dynamic model‐ and automated algorithm‐based precipitation simulations are used as input to a surface-hydrologic model for simulation of the stream discharge associated with the flood.

Flash flood prediction using an uncalibrated hydrological model and radar rainfall data in a Mediterranean watershed under changing hydrological conditions

Impact of land use changes on flash flood prediction using a sub-daily SWAT model in five Mediterranean ungauged watersheds (SE Spain)

Flash floods are an important component of the semiarid hydrological cycle, and provide the potential for groundwater recharge as well as posing a dangerous natural hazard. A number of catchment models have been applied to flash flood prediction; however, in general they perform poorly. This study has investigated whether the incorporation of light detection and ranging (lidar) derived data into the structure of a 1‐D flow routing model can improve the prediction of flash floods in ephemeral channels. Two versions of this model, one based on an existing trapezoidal representation of cross‐section morphology (K‐Tr), and one that uses lidar data (K‐Li) were applied to 5 discrete runoff events measured at two locations on the main channel of The Walnut Gulch Experimental Watershed, United States. In general, K‐Li showed improved performance in comparison to K‐Tr, both when each model was calibrated to individual events and during an evaluation phase when the models (and parameter sets) were applied across events. Sensitivity analysis identified that the K‐Li model also had more consistency in behavioral parameter sets across runoff events. In contrast, parameter interaction within K‐Tr resulted in poorly constrained behavioral parameter sets across the multidimensional parameter space. These results, revealed with a modeling focus on the structure of a particular element of a distributed catchment model, suggest that lidar derived cross‐section morphology can lead to improved, and more robust flash flood prediction.

Flash floods can be characterized by several variables. Of these, soil moisture (SM) is an important environmental factor that plays a key role in hydrological and ecological processes and affects the mechanisms that cause flash floods. To more accurately determine the occurrence probability of flash floods, the combined effects of soil moisture and rainfall indexes were considered in this paper, and the copula function approach was explored for use in joint probability analyses of flash flood risks. The results showed that (1) the Clayton copula function offered the best fit for the bivariate joint distribution and captured the occurrence probability of the combination of both peak flow (PF) and SM, while the t-copula function achieved the best fit for the multivariate joint distribution, which presented different combinations of characteristic flash flood parameters. (2) The joint distribution probability of flash floods increased with increasing risk parameter thresholds. Return period analysis indicated that the return periods of the bivariate joint distribution were smaller than those of the multivariate joint distribution. (3) If PF and SM are fixed, the occurrence probability of flash floods is higher in regions where the maximum 1-h rainfall is higher. This study provides an effective and quantitative approach to improving flash flood prediction and advances the application of this approach for the management of future flash flood risks.

In the arid and semiarid southwestern United States, both cool- and warm-season storms result in flash flooding, although the former storms have been much less studied. Here, we investigate a catalog of 52 flash-flood-producing storms over the 1996–2021 period for the arid Las Vegas Wash watershed using rain gauge observations, reanalysis fields, radar reflectivities, cloud-to-ground lightning flashes, and streamflow records. Our analyses focus on the hydroclimatology, convective intensity, and evolution of these storms. At the synoptic scale, cool-season storms are associated with open wave and cutoff low weather patterns, whereas warm-season storms are linked to classic and troughing North American monsoon (NAM) patterns. At the storm scale, cool-season events are southwesterly and southeasterly under open wave and cutoff low conditions, respectively, with long duration and low to moderate rainfall intensity. Warm-season storms, however, are characterized by short-duration, high-intensity rainfall, with either no apparent direction or southwesterly under classic and troughing NAM patterns, respectively. Atmospheric rivers and deep convection are the principal agents for the extreme rainfall and upper-tail flash floods in cool and warm seasons, respectively. Additionally, intense rainfall over the developed low valley is imperative for urban flash flooding. The evolution properties of seasonal storms and the resulting streamflows show that peak flows of comparable magnitude are “intensity driven” in the warm season but “volume driven” in the cool season. Furthermore, the distinctive impacts of complex terrain and climate change on rainfall properties are discussed with respect to storm seasonality.

Forecasting weather has significantly improved but continues to present challenges, such as prediction of flash floods, tornado outbreaks, and rapid intensification of tropical cyclones. We consider a possible influence on severe weather occurrence through solar wind coupling to the magnetosphere-ionosphere-atmosphere system, mediated by aurorally excited atmospheric gravity waves. Solar wind disturbances, including high-speed streams, high-density plasma adjacent to the heliospheric current sheet, and interplanetary coronal mass ejections, cause intensifications of ionospheric currents at high latitudes launching gravity waves globally propagating in the atmosphere [1]. While these gravity waves reach the troposphere with much attenuated amplitudes, they are subject to amplification when encountering opposing winds and vertical wind shears. They may contribute to release of conditional symmetric instabilities [2] leading to slantwise convection, latent heat release and intensification of storms. The ERA5 re-analysis is used to evaluate slantwise convective available potential energy (SCAPE) that is of importance in the development of storms. It has been shown that significant weather events, including explosive extratropical cyclones [3,4], rapid intensification of tropical cyclones [5], and heavy rainfall causing floods and flash floods [6,7] tend to occur following arrivals of solar wind high-speed streams from coronal holes. Further evidence is provided by superposed-epoch analysis of high-rate precipitation occurrence obtained from satellite-based precipitation data sets. To support the published results, the occurrence of heavy-rainfall-induced floods and cool season precipitation events in Canada, as well as large tornado outbreaks in the United States are studied in the context of solar wind.  [1] Mayr H.G., et al., Space Sci. Rev. 54, 297–375, 1990. doi:10.1007/BF00177800 [2] Chen T.-C., et al., J. Atmos. Sci. 75, 2425–2443. doi:10.1175/JAS-D-17-0221.1[3] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 149, 219–231. doi:10.1016/j.jastp.2016.04.002[4] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 171, 94–10, 2018. doi:10.1016/j.jastp.2017.07.023[5] Prikryl P., et al., J. Atmos. Sol.-Terr. Phys. 183, 36-60, 2019. doi:10.1016/j.jastp.2018.12.009[6] Prikryl P., et al., Ann. Geophys. 39 (4), 769–93, 2021. doi:10.5194/angeo-39-769-2021[7] Prikryl P., et al., Atmosphere 12 (9), 2021. doi:10.3390/atmos12091186. 

A scenario-incorporating analysis of the propagation of uncertainty to flash flood simulations

More frequent high-intensity, short-duration rainfall events increase the risk of flash floods on steeply sloped watersheds. Where measured data are unavailable, numerical models emerge as valuable tools for predicting flash floods. Recent applications of various hydrological and hydrodynamic models to predict overland flow have highlighted the need for improved representations of the complex flow processes that are inherent in flash floods. This study aimed to identify an optimal modeling approach for characterizing leaf litter losses during flash floods. At a gauged watershed in the Hidegvíz Valley in Hungary, a physical-based model was calibrated using two distinct rainfall–runoff events. Two modeling methodologies were implemented, integrating canopy interception and leaf litter storage, to understand their contributions during flash flood events. The results from the model’s calibration demonstrated this approach’s effectiveness in determining the impact of leaf litter on steep-sloped watersheds. Soil parameters can estimate the behavior of leaf litter during flash flood events. In this study, hydraulic conductivity and initial water content emerged as critical factors for effective parametrization. The findings underscore the potential of a hydrodynamic model to explore the relationship between leaf litter and flash flood events, providing a framework for future studies in watershed management and risk-mitigation strategies.

Flash flood is one of Malaysia's most severe natural disasters, causing major casualties and economic losses due to two local seasonal monsoons. Research shows the possible correlation between lightning events and heavy precipitation associated with flash floods. This study aims to discover the relationship between the number of lightning occurrences with the amount of rain in 24 hours. This study analyzed 5 flash flood events within a 100 km radius from Universiti Teknologi Malaysia, Johor, recorded from 2013 until 2017. The lightning data is obtained from Tenaga Nasional Berhad Research (TNBR), while rainfall data is received from the Department of Irrigation and Drainage (DID). The results show that the r-values between lightning and rainfall range from 0.6 to 1.0. The different values due to the number of lightning and rainfall occurrences are inconsistent for each case. It is also shown that 2 hours before the events, the number of lightning increases during a rainfall event is related to rainfall rate.

Flash floods represent a significant threat, triggering severe natural disasters and leading to extensive damage to properties and infrastructure, which in turn results in the loss of lives and significant economic damages. In this study, a comprehensive statistical approach was applied to future flood predictions in the coastal basin of North Al-Abatinah, Oman. In this context, the initial step involves analyzing eighteen General Circulation Models (GCMs) to identify the most suitable one. Subsequently, we assessed four CMIP6 scenarios for future rainfall analysis. Next, different Machine Learning (ML) algorithms were employed through H2O-AutoML to identify the best model for downscaling future rainfall predictions. Forty distribution functions were then fitted to the future daily rainfall, and the best-fit model was selected to project future Intensity-Duration-Frequency (IDF) curves. Finally, the Soil and Water Assessment Tool (SWAT) model was utilized with sub-daily time steps to make accurate flash flood predictions in the study area. The findings reveal that IITM-ESM is the most effective among GCM models. Additionally, the application of stacked ensemble ML model proved to be the most reliable in downscaling future rainfall. Furthermore, this study highlighted that floods entering urbanized areas could reach 20.33 and 20.70 m³/s under pessimistic scenarios during rainfall events with 100 and 200-year return periods, respectively. This hierarchical comprehensive approach provides reliable results by utilizing the most effective model at each step, offering in-depth insight into future flash flood prediction.

<p>Predicting flash floods at short time scales as well as their impacts is of vital interest to forecasters, emergency managers and community members alike. Particularly, characteristics such as location, timing, and duration are crucial for decision-making processes for the protection of lives, property and infrastructure. Even though these characteristics are primarily driven by the causative rainfall and basin geomorphology,  untangling the complex interactions between precipitation and hydrological processes becomes challenging due to the lack of observational datasets which capture diverse conditions.</p><p>This work follows upon previous efforts on incorporating spatial rainfall moments as viable predictors for flash flood event characteristics such as lag time and the exceedance of flood stage thresholds at gauged locations over the Conterminous United States (CONUS). These variables were modeled by applying various supervised machine learning techniques over a database of flood events. The data included morphological, climatological, streamflow and precipitation data from over 21,000 flood-producing rainfall events – that occurred over 900+ different basins throughout the CONUS between 2002-2011. This dataset included basin parameters and indices derived from radar-based precipitation, which represented sub-basin scale rainfall spatial variability for each storm event. Both classification and regression models were constructed, and variable importance analysis was performed in order to determine the relevant factors reflecting hydrometeorological processes. In this iteration, a closer look at model performance consistency and variable selection aims to further explore rainfall moments’ explanatory power of flood characteristics. </p>

Flash floods, characterized by rapid streamflow response to rainfall, pose a significant natural hazard, particularly in small tropical watersheds (< 40 km2). Understanding the role of rainfall event characteristics, including amount, intensity, and spatial structure, is crucial for addressing and predicting flash floods. This study employs the WRF-Hydro® model with high-resolution (250-m, hourly) rainfall data and the Random Balance Designs – Fourier Amplitude Sensitivity Testing (RBD-FAST) method to investigate how rainfall impacts streamflow, specifically peak flow events, in seven watersheds on Oʻahu, USA.Analyzing storm events from 2015 to 2020, we examined peak flow responses to corresponding rainfall event characteristics and estimated their contributions to model efficiency. In addition, (1) random redistribution of rainfall and (2) spatial shifting of rainfall were experimented with to assess the sensitivity of peak flow to rainfall event characteristics. Not only the rainfall amount and intensity but heavy rainfall areas (>= 25 mm) within an event also exerted a significant impact on peak flow, while other spatial features contributed varying degrees of influence. Notably, spatially shifting rainfall for at least 250-m in any direction highly affected event peak streamflow, emphasizing the importance of rainfall amount, intensity, heavy rainfall areas, total rainfall areas, and connectivity among rainfall areas.Given the significance of rainfall's spatial heterogeneity, these findings underscore the benefits of incorporating rainfall spatial characteristics in probabilistic flood forecasting and the mitigation of flood risks. This research contributes valuable insights for enhancing flood prediction strategies in small tropical watersheds, providing a basis for informed decision-making and risk management.

Mountain regions exhibit complex topography and climate patterns, leading to highly variable meteorological conditions that complicate the prediction of heavy rainfall, flash floods, and landslides. Gridded radar rainfall data are therefore essential for monitoring and forecasting intense storms in mountainous catchments where rain gauge observations are limited. The reliability of near-real-time radar rainfall products depends on individual radar data quality, radar compositing techniques, and bias correction procedures. Hydrological modelling is a key component of operational Early Warning Systems (EWS) for monitoring and forecasting runoff conditions; however, model accuracy remains sensitive to uncertainties in measurement instruments, rating curves, and rainfall inputs. This study aims to investigate the impact of rainfall input quality on the accuracy of flood simulations in a mountainous catchment in northern Thailand, namely the Klong Suan Mak basin. Radar compositing was performed using data from three weather radar stations: Omkoi (approximately 180 km northwest), Takhli (approximately 170 km southeast), and Chainat (approximately 167 km southeast) relative to the Klong Suan Mak basin, with a quality-index-based approach applied to rainfall estimation. A spatially distributed, physically based hydrological model for flash flood simulation was driven by three rainfall inputs: (i) rain gauge observations, (ii) an event-based bias-corrected radar composite, and (iii) an hourly Kalman filter–based bias-corrected radar composite. Model calibration was performed using the Flow Duration Curve (FDC) approach in logarithmic scale, based on discharge observations and spatial rainfall inputs during the 2022 flood events.Results clearly indicate that the sparse rain gauge network in the mountainous region yields the poorest flood simulation performance. In contrast, radar-based rainfall products exhibit more complex behavior: although the hourly Kalman filter–based product provides the highest rainfall data quality, variability in its bias factor increases uncertainty in model parameter estimation. By comparison, the event-based bias-corrected radar composite with a single bias factor yields more stable model parameters, making it more suitable for both calibration and validation across multiple events. Consequently, the event-based radar rainfall product was adopted as the baseline input to stabilize model parameters and subsequently integrated with the dynamic Kalman filter–based product, leading to a significant enhancement in model performance and improving prediction accuracy by up to 32%, particularly during high-discharge periods where the dynamic Kalman filter–based radar rainfall data exhibited a significant improvement in efficiency. This integrated approach has strong potential to support multi-hazard mitigation, including landslides and soil erosion. When combined with short-term radar rainfall nowcasting, it could provide critical lead time for disaster preparedness and national early warning systems.