Impact‐based forecasting for pluvial floods

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

Development and Testing of Numerical Hydrodynamic Tools for Large-scale Flood Hazard and Risk Assessment

The increasing availability of high-resolution topographic data and the ever-growing computational potential of workstations enable us to simulate inundation events with higher accuracy across geographically larger areas. Recent studies suggest using fully two-dimensional (2D) models with high resolution in order to avoid uncertainties and limitations coming from the incorrect interpretation of flood dynamics and unrealistic reproductions of the terrain topography. Additionally, low-frequency high-magnitude events bring additional challenges as conventional structural flood protection systems (e.g. levees), which are omnipresent in floodplain landscapes, might collapse due to hydraulic conditions such as high water loads, durations and velocities, or geotechnical factors that weaken structures (e.g. burrowing animal activities). Therefore, it is important to jointly consider the distribution of the inundated zones, potential levee breaching and holistic river-system behaviour when assessing flood hazard. In order to address the abovementioned challenges the present research focuses on the high-resolution flood simulations performed on geographically large areas using 2D inundation models with a specific focus on complex topography (e.g. main and minor levee systems, embankments, artificial canals, etc.). Our study evaluates and compares numerical models of different complexity by testing them on a floodplain inundation event that occurred in the basin of the Secchia River, Northern Italy, on 19th January, 2014. Then, we test fully 2D raster-based model to simulate the event on the 350 km long stretch on the mid-lower portion of the Po River and provide insight on the input terrain resolutions, accuracy and computation time. Moreover, this Thesis aims at developing and testing a new tool, which allows for an efficient levee breach modelling and river dynamic tracking in fully 2D mode