Dynamic Pluvial Flash Flooding Hazard Forecast Using Weather Radar Data

Abstract

Pluvial flash floods are among the most dangerous weather-triggered disasters, usually affecting watersheds smaller than 100 km2, 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.

A review of flash‐floods management: From hydrological modeling to crisis management

“…Various outputs were proposed: alerts and warning or their levels, warning reports, road inundation risk inundation and flood level. Their level or their emission can correspond to the exceedance of hydrological thresholds (rainfall and discharge) considering, for instance, return period, children as the most vulnerable population or previous flood impacts (Corral et al, 2019;Gonz alez-Cao et al, 2019;Naulin et al, 2013;Rapant & Kolejka, 2021;Versini, 2012). In two other cases, multiple criteria were utilized to calculate flood warning and realtime on-the-road flood risk index (Goodarzi et al, 2019;Kim et al, 2014).…”

“…On the other hand, Giordano et al (2017) investigated the interaction of actors during flash-floods, while Créton-Cazanave and Lutoff (2013) studied how crisis managers interpreted hydrological information. As for Rapant and Kolejka (2021), they evaluated their hydrological model outputs by presenting them to crisis managers. Another example is the work of Kumar et al (2023) and Yang et al (2015) who were interested in the use of deep learning to increase accuracy and efficiency of their forecasts.…”

The Use of Weather Radar Data: Possibilities, Challenges and Advanced Applications

“…This system allows one to make a quick evacuation decision to reduce the adverse effect. Rapant et al [20] presented a method with a different technique to obtain a dynamic pluvial flash flooding hazard forecast. This approach uses weather radar data to carry information on the current precipitation distribution, watershed and drainage network.…”

“…The developed method can provide a map of the flash flooding hazard distribution on the watercourse sections. This result allows an evaluation of the identified hazard and a risk estimation for inhabitants of the area, with a system of alerts for municipalities [20].…”

Modelo baseado em dados para predição de inundação em bacia urbana de Campinas visando a construção de um sistema de alerta antecipado

“…apud Rocha Filho et al 2013) relatam que o baixo tempo de resposta frequentemente observado em bacias urbanas requere uma elevada resolução temporal e espacial dos dados obtidos.SegundoLi et al (2021), as previsões de tempo meteorológico possuem auxílio de técnicas de radar há mais de 50 anos e nas últimas duas décadas passaram a ser empregadas amplamente nas aplicações hidrológicas. No que tange ao Brasil, com enfoque na região metropolitana da cidade de São Paulo, o monitoramento atmosférico começou a ser efetuado pelo Radar Meteorológico de São Paulo (RSP) em 1988 por meio da aquisição de um radar meteorológico banda S(Rocha Filho et al 2011).No contexto de inundações urbanas, comparando os sistemas de alerta de inundações que utilizam sensores instalados nos rios urbanos e os baseados em dados de radares, este último possui maior resolução espacial e temporal, gerando assim um maior tempo de previsão(Li et al 2021) e consequentemente melhores resultados na previsão dos fenômenos.São inúmeros os trabalhos que utilizam estimativas de precipitação oriundos de radares meteorológicos na previsão de inundações e enchentes(Rocha Filho et al 2011;Li et al 2021;Rapant & Kolejka, 2021) Li et al (2021 destacam o sistema de alerta de inundações desenvolvido pelo Texas Medical Center e a Rice University para uma sub-bacia intensamente urbanizada do Texas, a partir da aplicação de dados de precipitação obtidos pelo radar NEXRAD nos modelos hidrológicos disponíveis para a área. Conforme os autores, o sistema foi posto em prova em diversos eventos de inundação, dentre eles o furacão Harvey em 2017, e foi capaz de fornecer informações de alertas confiáveis, rápidos e precisos.Embora diversas propostas de sistemas de alerta antecipado para inundações tenham surgido nos últimos anos, vide os exemplos supracitados, suas aplicações são, muitas vezes, efetivas apenas para o local no qual o sistema foi desenvolvido, principalmente pelo fato que os processos de fluxo d'água apresentam uma relação não linear com a bacia hidrográfica e são afetados por diversos parâmetros locais, dentre eles: precipitação, uso e ocupação do solo, temperatura(Adnan et al 2019).…”