The Dynamic Drivers of Flood Risk (DRIFT)

Floods impact approximately 250 million people annually and are feared to keep rising as the climate changes. However, there are critical gaps in our understanding of what drives changes in the size, occurrence, and characteristics of river floods. Further, our projections of future floods remain hindered by a severe ‘cascade of uncertainties’ arising from the biases in climate model outputs and hydrological model structures, uncertain emissions scenarios, oversimplified river networks, and limited global information on human impacts. These gaps affect stakeholders' ability to make planning decisions about appropriate levels of flood risk management, land use, or infrastructure required to protect populations.
The Dynamic Drivers of Flood Risk (DRIFT) project was designed to address challenges in understanding past and future changes in flood characteristics to improve decision-making support. DRIFT’s overarching aim is to develop a unified understanding of flood nonstationarity from the past into the future (1950-2100), transitioning seamlessly from short to long timescales.
Recognizing the urgency of supporting societies exposed to flooding, DRIFT-2 (2025-2028) builds on DRIFT-1 (2021-2025) in seeking to build the first global models targeted at unravelling flood-generating mechanisms and generating more reliable future projections. Large-sample (multi-catchment) causal and explainable machine learning models will be developed describing the influence of changing climate, engineering structures, and land cover on flood characteristics, such as flood peaks, return periods, probabilities, durations and extent. Past trends in flood characteristics will be seamlessly linked with future climate predictions and projections.
These efforts will lean on the Hydro-Climate Extremes Research Group’s recent advances, including the largest existing dataset of hydroclimatic observations in over 60,000 river catchments (OHDB), the first physically-realistic global river network...