Impact‐Based Forecasting for Pluvial Floods

Abstract

Pluvial floods in urban areas are caused by local, fast storm events with very high rainfall rates, which lead to inundation of streets and buildings before the storm water reaches a watercourse. An increase in frequency and intensity of heavy rainfall events and an ongoing urbanization may further increase the risk of pluvial flooding in many urban areas. Currently, warnings for pluvial floods are mostly limited to information on rainfall intensities and durations over larger areas, which is often not detailed enough to effectively protect people and goods. We present a proof‐of‐concept for an impact‐based forecasting system for pluvial floods. Using a model chain consisting of a rainfall forecast, an inundation, a contaminant transport and a damage model, we are able to provide predictions for the expected rainfall, the inundated areas, spreading of potential contamination and the expected damage to residential buildings. We use a neural network‐based inundation model, which significantly reduces the computation time of the model chain. To demonstrate the feasibility, we perform a hindcast of a recent pluvial flood event in an urban area in Germany. The required spatio‐temporal accuracy of rainfall forecasts is still a major challenge, but our results show that reliable impact‐based warnings can be forecasts are available up to 5 min before the peak of an extreme rainfall event. Based on our results, we discuss how the outputs of the impact‐based forecast could be used to disseminate impact‐based early warnings.

Hybrid forecasting: using statistics and machine learning to integrate predictions from dynamical models

“…Hybrid methods may shorten the traditional forecasting approach by going 'end-to-end', potentially skipping out some of the intermediary steps in a conventional modelling chain, such as downscaling, bias correction and hydrological modelling. This offers significant potential for applications where the run time of physically based models limits the ability to provide forecasts with a useful lead time for action -such as surface water (Rözer et al, 2021) or flash flood forecasts.…”

“…Ravuri et al, 2021;Neri et al, 2019), and geographical domains (from point to street-level, single river catchment through to global approaches). Hybrid models have been applied to predict a variety of hydrometeorological variables, including extreme heat and precipitation (Miller et al, 2021;Najafi et al, 2021;Miao et al, 2019;Ma et al, 2022), seasonal climate variables (Golian et al, 2022;Baker et al, 2020), tropical cyclones/hurricanes (Vecchi et al, 2011;Murakami et al, 2016;Kang and Elsner, 2020;Klotzbach et al, 2020), streamflow (Wood and Schaake, 2008;Mendoza et al, 2017;Rasouli et al, 2012;Duan et al, 2020), flooding (Slater and Villarini, 2018), drought (Madadgar et al, 2016;Wu et al, 2021), sea level (Khouakhi et al, 2019), and reservoir levels (Tian et al, 2021), over a range of and predictions have numerous operational and strategic applications, including water resources planning, reservoir inflow management (Tian et al, 2021;Essenfelder et al, 2020), surface water flooding (Rözer et al, 2021), flood risk mitigation, navigation (Meißner et al, 2017), and agricultural crop forecasting (Cao et al, 2022;Slater et al, 2021b). The envisaged dynamical predictors may include various model outputs such as meteorological forecasts with lead times up to 14 days; initialized climate predictions with sub-seasonal to decadal lead times; sub-seasonal runoff predictions, and/or land surface 110 2 Hybrid forecasting Hybrid forecasting encompasses approaches for pre-/post-processing hydroclimate predictions (Section 2.1), and for developing predictive models themselves, including short-term hybrid forecasts (Section 2.2), or sub-seasonal to decadal predictions (Section 2.3), and the integration of ML within parallel and coupled hybrid models (Section 2.4 and Table 3).…”

Österreichweite Regionalisierung bodenhydraulischer Eigenschaften

“…Konvektive, raumzeitlich gesehen sehr kleinskalige Niederschlagsereignisse überschreiten aufgrund ihrer hohen Intensität rasch die Infiltrationsraten des Bodens. Die damit einhergehende Bildung von großen Mengen an Oberflächenabfluss führen häufig zu pluvialem Hochwasser (Rözer et al 2021;ÖWAV-EP 2020). Solche Wetterextreme sind nur schwer zu prognostizieren und treten oft in Gebieten auf, die aus historischer Sicht nicht als hoch-Österreichweite Regionalisierung bodenhydraulischer Eigenschaften wassergefährdet gelten (Rözer et al 2016(Rözer et al , 2021.…”

“…Die damit einhergehende Bildung von großen Mengen an Oberflächenabfluss führen häufig zu pluvialem Hochwasser (Rözer et al 2021;ÖWAV-EP 2020). Solche Wetterextreme sind nur schwer zu prognostizieren und treten oft in Gebieten auf, die aus historischer Sicht nicht als hoch-Österreichweite Regionalisierung bodenhydraulischer Eigenschaften wassergefährdet gelten (Rözer et al 2016(Rözer et al , 2021. Um eine fundierte Risikoabschätzung vornehmen zu können, ist es notwendig, großflächig Infiltrationskapazitäten darzustellen bzw.…”

Data-Driven Community Flood Resilience Prediction

“…Over the years, numerous researchers have embarked on flood categorization and prediction studies [20][21][22][23]. However, most such studies focused on the hazard's features and, to a lesser extent, on the direct impact and losses due to the flood hazard or long-term recovery cost and time [24][25][26][27][28][29]. In this respect, this study aims at developing a prediction framework that classifies the long-term potential impacts, recovery, and resilience of the exposed community, a categorization that captures the resilience of the exposed communities rather than simply the hazard's characteristics.…”