Pluvial flooding is on the rise as more cities are challenged by a changing climate and local drivers: increased urbanisation and inadequate sewer systems. Sustainable flood risk management requires a hybrid of structural and non-structural measures to ensure water-hazard resilient cities. In this regard, flood forecasting and early warning systems have been proposed as a “low regret” measure to reduce flooding and increase preparedness through forecast-based actions. However, many cities, especially in data-scarce regions, lack the capacity to produce high-quality rainfall forecasts and well-calibrated flood predictions (including timing, water levels, extent and impact). This limitation creates a cascading effect, hindering the ability to make reliable decisions due to uncertainties in the forecast or inaccuracies in the input data. For example, decisions in anticipatory flood management become problematic due to their dependencies on knowledge derived from uncertain data and the consequences of incorrect predictions and/or actions.

Previous studies have focused on improving the accuracy of predictions or models, but there has been comparatively less focus on addressing the complexities of flood forecasts and the decision support chain, particularly under conditions of limited data and uncertainty. Probabilistic forecast knowledge is beneficial for quantifying uncertainty and has been acknowledged to support decision-making, but there is no consensus on the most suitable and effective way to incorporate it for decision-making.

This thesis aims to explore how pluvial flood forecasting and decision support at local scales in data-scarce cities can be improved, with a focus on using available data, method suitability and incorporating uncertainty into the decision-making process for anticipatory flood action. This research was motivated by practitioners’ need to take action ahead of time, despite limited and uncertain data, which is so often the case in cities in the Global South. The research was carried out in the coastal city of Alexandria, Egypt, which experiences annual flooding due to winter storms.

The research adopted an incremental and sequential approach, in which each research outcome is built upon in the next step and used as input for the next objective, thereby integrating the data, models and knowledge. First, the research investigated how available data can be combined for flood forecasting using a rainfall threshold method, crowd-sourced information and global ensemble forecasts. Next, the study evaluated the suitability and limitations of using a downscaled high-resolution Weather Research and Forecasting model for deterministic rainfall forecasts at multiple spatial scales for different urban flood forecasting approaches. A high-resolution limited-area ensemble rainfall forecasting model was then configured and simulated. The resulting probabilistic distributions were incorporated into a Bayesian decision framework to translate probabilistic rainfall forecasts into actionable information.

Findings show that, despite limited data, using rainfall forecasts alongside non-traditional data sources can effectively predict flooding and improve decision-making. Although this method introduces some complexity compared to widely used deterministic rainfall forecasts, it offers a balance between a simplified approach and forecast uncertainty when limited data exist.

Regarding the use of higher-resolution forecasts, the results showed that, despite the need for very high-resolution forecasts for urban flood forecasting, there are limits when using downscaled models, as outcomes vary depending on the model configuration, parameterisation and the flood forecast approach. These findings highlight the need to consider the interdependencies between meteorological forecasts and their application to flood forecasting.

Finally, the thesis demonstrated the value of using a Bayesian decision framework for incorporating knowledge of flood vulnerability and local flooding at the neighbourhood scale for triggering equitable flood early actions. The proposed probabilistic framework showed the potential benefits of a user-preference parametric loss function that assigns weights to different vulnerability classes. The Bayesian decision framework served as a valuable decision support tool which incorporates uncertainty, decision makers’ risk-averse attitudes and prioritises equitable anticipatory flood actions to reduce flood damage.

The primary aim of this research was not to make forecasts more accurate, but rather to develop a greater understanding of the usefulness of imperfect data, forecasts and models in decision-making for pluvial flood forecasting and local anticipatory flood management. The research raises themes of fit-for-purpose data and creating value from imperfect information by examining the use and limitations of data and the interdependencies of the flood forecasting and decision-making chain. This approach supports uncertainty-aware flood forecasting and decision-making instead of creating an illusion of certainty.

Increasing global population and domestic migration have a significant impact on global land-use and land-cover (LULC) changes. Global LULC changes in recent times led to a reduction in vegetation cover within all types of ecosystems, which are potentially linked to human activities. LULC changes are not only affecting the quantity of green spaces but also the pattern of green spaces, which are becoming more fragmented and scattered into smaller areas. Those changes would influence the hydrological cycle resulting in a decrease in urban ecosystem services leading to an increase in various natural hazards, for instance, high urban temperature and urban flooding. Ecosystem services are the varied benefits to humans from the natural environment and healthy ecosystems. In an urban area, ecosystem services include temperature regulation, carbon storage, water regulation, and recreation. An increase in urban temperature is mainly related to a decrease in evapotranspiration, while urban flooding is related to a decrease in infiltration, a decrease in flow resistance, and a reduction in rainfall interception. Furthermore, high urban temperatures and urban flooding might occur in two different time periods, but they can occur in the same area.....

Flooding is one of the most frequently occurring and damaging natural disasters worldwide. Quantitative flood risk management (FRM) in the modern context demands statistically robust approaches (e.g. probabilistic) due to the need to deal with complex uncertainties. However, probabilistic estimates often involve ensemble 2D hydraulic model runs resulting in large computational costs.
Additionally, modern FRM necessitates the involvement of a broad range of stakeholders via co-design sessions. This makes it necessary for the flood models, at least at a simplified level, to be understood by and accessible to non-specialists.
This study was undertaken to develop a flood modelling system that can provide rapid and sufficiently accurate estimates of flood risk within a methodology that is accessible to a wider range of stakeholders for a coastal city – Can Tho city, Mekong Delta, Vietnam.
A web-based hydraulic tool, Inform, was developed based on a simplified 1D model for the entire Mekong Delta, flood hazard and damage maps, and estimated flood damages for the urban centre of Can Tho city (Ninh Kieu district), containing the must-have features of a co-design tool (e.g. inbuilt input library, flexible options, easy to use, quick results, user-friendly interface). Inform provides rapid flood risk assessments with quantitative information (e.g. flood levels, flood hazard and damage maps, estimated damages) required for co-designing efforts aimed at flood risk reduction for Ninh Kieu district in the future.

Sea levels are rising globally and the melting Antarctic ice sheet is attributed to be the primary contributor. Due to the deep uncertainty of the causal representative carbon pathways, the plausible range of sea levels rise varies between 0,26 - 2,43 m for 2100 and 0,50 - 15,52 m for 2300. To address deep uncertainty, decision making methods arise which struggle with including measures preparing the shift from incremental to transitional strategies required for the case of extreme sea level rise scenarios. This research addresses flood risk management in the Netherlands, a country which will face ecological, economical, technological and political challenges as sea levels climb. In anticipation to extreme sea level rise and its challenges, the Dutch process of coastline shortening and solidating can be carried through. Another viable option is a more gradual zone between land and water. To review both options, the distribution of systems sensitive to flooding is mapped. The overlay of systems facilitates the establishment of a framework which distinguishes elements based on their pace of transition and offers a multilayered image differentiating regions with an emphasis on either population or nature. This dichotomy structures the proposal for the transitional territorial outline by harmoniously applying a superimposed coastline and a dynamic coastline respectively. Tipping points of flood defence measures assess the physical feasibility of the transition which is mainly constrained by societal limitations. This results in incremental improvements, further increasing transfer costs, path dependency and the difficulty to shift to transitional strategies. Throughout history, only catastrophic floods were leading to transitional flood measures, but transition management is referred to as enabling development of a long-term sustainability perspective, linked to desired societal transitions, to guide accelerated social innovation in the short term. The theory’s transition arena with frontrunners and a transition image is capable of opposing the resistance-giving, established standard and is instrumental for communication, discussion and evolvement of the transition of flood risk management.