Climate change is shifting patterns of weather and hydrology. Scotland’s net zero commitments aim to avoid dangerous climate change, with forest expansion and peatland restoration to generate carbon sinks. But geopolitical disruption to energy and trade has focused political attention on food security and agricultural reform. There are increasing demands for land to support multiple goals – including net zero policies, productive agriculture, and forests, and providing resilient ecosystem services – while maintaining and growing the rural economy. Hydrology is salient for many of these issues –affecting peatland and other carbon sinks, agricultural and forest yield, and water supply and quality. A major knowledge gap is how Scotland’s hydrological status will adjust under climate and land use change. This information is crucial as hydrological feedbacks on terrestrial ecosystems may determine the success of Scotland’s land use policies.
Current land use policies lack a clear assessment of hydrological risk, particularly the potential impacts of changes in soil moisture on tree growth, food production and peatland restoration. Effective decision making for land to support multiple goals requires a quantitative fore-sighting exercise to provide this risk and impact assessment. Scenario analysis, explored using process modelling, will help decision making by scoping the interactive effects of climate change and land management on Scotland’s terrestrial ecosystems and their hydrology, from local to national scales. Novel data sets, particularly from remote sensing of vegetation and soil, and new tools for calibrating process models, mean that there are exciting opportunities for science in this research area.
Coupling between carbon and water cycles and their sensitivities to climate necessitates a systems approach that can model ecosystem feedbacks and process interactions over the coming century. The trade-offs in land area allocated among land uses, and their changes over time in scenarios, require a spatially resolved and constrained approach for modelling. Thus, robust hydrological scenario exploration requires a quantitative ecosystem modelling approach, linked to national data sets, and climate and land use scenarios. Process modelling can generate spatially explicit outputs that cover the diversity of Scotland’s landscapes and their spatial arrangement. There are significant challenges to overcome, including effective modelling of organic soils which dominate Scotland’s landscapes, balancing model complexity against computational needs while ensuring realistic representation of processes, accuracy, and clear quantification of output uncertainty.
This project will research how soil moisture, evapotranspiration and runoff will respond to climate change, and how these interact with land use change over mineral and organic soils. Simulation modelling will quantify the future dynamics of Scotland’s land carbon sinks and sources, soil moisture, and their interactions. The project will connect world-class strengths in ecosystem model-data assimilation at Edinburgh and ecohydrology at UHI. The novelty here is that this approach (i) uses earth observations and detailed soil maps to set initial conditions and derive locally relevant parameters linked to land use; (ii) explicitly includes probabilistic uncertainty estimates in parameter estimation and propagates these into forecasts. The needs of decision makers for forecast confidence estimates are therefore met. The student will link to relevant science advisers in Scottish Government to connect this research to policy and to inform scenario development on land use.
The project will use an existing data-model framework, CARDAMOM, and ecosystem model, DALEC. The model represents the dynamics of ecosystem carbon and water pools through simulation of biophysical and biogeochemical fluxes (e.g., photosynthesis, evapotranspiration) and their sensitivity to climate, disturbance, management and land use. DALEC can represent both managed (plantation, arable, pastoral) systems and semi-natural systems (moorland, woodland). CARDAMOM calibrates the DALEC model at pixel scale using locally available data, including land use maps, soil maps (texture, soil C), and remote sensing data on fire, forest loss, biomass C stocks, and leaf area index dynamics. The project will use an existing UK implementation of DALEC funded in the NERC DARE-UK project as a starting point. The CARDAMOM approach will be updated to constrain DALEC plant and soil hydrological parameters using remote sensing data of vegetation and surface soil moisture time series.