Project overview
Understanding the variability of the terrestrial hydrological cycle under climate change and other human impacts is crucial to predicting the future evolution of the water, energy and carbon cycles, and how to develop mitigation and adaptation strategies to reduce negative impacts, particularly of extremes such as floods and droughts. Soil moisture (SM) is the key state variable of the terrestrial hydrological cycle and controls the evolution of these other cycles. SM varies over a wide range of spatial and temporal scales (from metres to landscape scales, and from hours to interannually), driving complex interactions between cycles. However, our knowledge of this variability and its drivers is limited to a small set of field studies or coarse resolution (> 1km) and uncertain satellite and model estimates. This hampers our understanding of how the terrestrial hydrological cycle varies and how it will evolve in the future, and for a range of SM dependent applications. These applications include risk assessment and monitoring of hydrometeorological hazards (flood, landslide, drought, wildfire); carbon storage and natural resource management; mitigation of water-borne disease; and habitat conservation. The proposed research is therefore aimed at transforming our understanding of the variability of SM and its feedbacks with land- atmosphere processes, and the implications for applications. The project will innovate by bringing together cutting-edge modelling and data assimilation approaches, and multiple data sources, to answer 2 key research questions about our understanding of water- energy interactions at the scales of biophysical processes: What are the key scales of variability of SM across different landscapes and the drivers, and how do these drivers intersect to generate wet and dry extremes? This question seeks to know the scale dependency of SM variability, its drivers (e.g. soils, land cover/use, weather and climate) and their complex interactions. A range of processes (e.g. land-atmosphere energy and moisture transfer; biogeochemical cycling; surface-groundwater interactions) are known to be dependent on and feedback with SM variations, but our understanding is limited to small-scale, site-specific studies. How can improved representation of SM variability at process scales improve monitoring and prediction, and benefit SM dependent applications? A wide range of applications are dependent on SM, yet current approaches are generally based on coarse resolution and simple approaches, which limits their accuracy and utility. The research will develop novel, global datasets of high-resolution hydrological variables, based on innovative use of a ?hyper-resolution? land-surface model constrained by satellite data. This will be the first dataset at such resolution globally that is physically consistent across variables. Statistical analysis methods and model experiments will be used to understand the spatio-temporal variability of SM and identify the drivers and feedbacks of variability and how these generate dry and wet extremes. We will apply this understanding to transform how data are used for SM-dependent sectors and applications, focussed on a co-developed case study on drought monitoring. The team is led by Prof. Justin Sheffield, who has a long track record of research in this area, publishing extensively on hydrological processes and extremes under climate change, and the application to hazard risk reduction, water and food security, and climate mitigation and adaptation. Working with international stakeholders, this research has been translated into benefits for a range of end-users, particularly in low/middle income countries, and has been recognised by several major international awards.
