The Dynamic Drivers of Flood Risk (DRIFT) – UKRI Future Leaders Fellowship; PI
- Oxford PDRA: Dr Simon Moulds
Across the globe, future flood risk is often assessed by using climate model outputs to try to describe the ‘drift’ (non-stationarity) in flood magnitude and frequency. Yet most of these flood projections cannot be taken at face value because they are based solely on expected changes in climate, without accounting for the many other factors that affect flooding such as changes in weather characteristics, antecedent conditions, land use and water management decisions.
This project – the Dynamic Drivers of Flood Risk (DRIFT) – aims to develop a more comprehensive understanding of flood non-stationarity by drawing on a holistic set of flood drivers across timescales. DRIFT will develop the first “hybrid”, multi-model, past-present-future prediction system, using machine learning to combine Earth observations with large ensembles of weather predictions and climate model projections. Past trends will be seamlessly linked with future projections over monthly to multi-decadal horizons.
This past-present-future prediction system will be integrated within a decision support framework, providing simple and intuitive ways to visualise flood evolution under various scenarios. DRIFT’s aim is to support stakeholders in making the best planning decisions to manage flood risks while achieving other co-benefits.
The Evolution of Global Flood Hazard and Risk (EvoFlood) – NERC Large Grant; WP1 lead
- Oxford PDRA: Dr Michel Wortmann
- Project website under construction
Evoflood aims to establish, at the global scale, the relative importance of changes in hydro-climatology versus the evolution of channel and floodplain morphology and connectivity (driven by shifts in precipitation patterns, streamflow regime, and catchment sediment fluxes) in causing changing flood hazard and risk. Within the Evoflood project, Louise Slater leads Workpackage 1 (Universities of Oxford, Birmingham, Reading and Southampton) which seeks to quantify global patterns, trends, and drivers of flooding over the past 40 years; and develop streamflow and sediment climatologies and scenarios of change, to inform the global flood model. The team will use existing observational and reanalysis datasets (global river flow, stage, and satellite datasets), to:
- develop the first global-scale assessment of spatial variations in, and temporal changes of, flood frequency;
- quantify their dependence on river and floodplain morphodynamics versus other (hydro-climatological) drivers of environmental change;
- derive a set of scenarios that characterise current and future catchment flow regimes and sediment delivery.
These future states will encompass a set of plausible time-varying flow discharge and sediment load regimes driven by projected changes in climate, land use, and societal development (especially major channel infrastructure such as dams).
The impacts of urbanisation on river flooding – John Fell Fund; PI
- PDRA: Dr Shasha Han
Changes in precipitation and land cover are important drivers of changes in catchment streamflow, yet quantifying their influence remains a major challenge. This work aims to understand how streamflow may evolve under different scenarios of future precipitation and urbanization across the UK. A collection of catchments that have experienced significant changes in flows and urbanization are investigated. Both historical observations and future projections of precipitation and urban land cover are extracted within each study catchment, for different emissions and socio-economic scenarios including Representative Concentration Pathways and Shared Socio-Economic Pathways. Distributional regression models are developed using historical precipitation, land cover, and streamflow, and employed to project future streamflow using bias-corrected projections of precipitation and land cover. The results improve our understanding of streamflow response to climate and land cover changes and provide further insights for water resources management and land use development.
Identifying how a non-stationary environment affects species persistence – ARC discovery project (co-I)
- Details forthcoming
Past grants (selection)
Financial planning for natural disasters: flood risk in Central Java – NERC/ESRC, co-I (2018-2020)
On the predictability of hydrological extremes using satellite data – SSPGS Seedcorn, Loughborough University, PI (2017)
Forecasting changes in atmospheric rivers, Institute for Advanced Studies, LU with IIT Indore, India, PI (2017)
To what extent have changes in river channel capacity contributed to flood hazard trends in England and Wales? British Society for Geomorphology, PI (2015)
Where do floodplains begin in fluvial networks? – National Center for Airborne Laser Mapping (NCALM), Geosensing Systems Engineering, High resolution Airborne LiDAR data, National Science Foundation, PI