| Dissolved Organic Carbon (DOC) is an important water quality parameter that affects the fate and dynamics of other carbon driven pollutants in the aquatic environment. The DOC concentrations in many surface waters across the northern hemisphere particularly in Sweden have been increasing during last decades. Increasing DOC concentrations can have important consequences, such as detrimental effects on drinking water quality thereby posing threats to humans and ecosystems heath. Therefore, there is an urgent need for improving our understanding of spatial-temporal variabilities (dynamics) of DOC concentrations in surface waters and the processes that drive DOC dynamics; such understanding is essential for improving our ability to predict impacts of future climate and environmental change (e.g. land use change, forest and agricultural management practices) on DOC in order to support decision-making for sustainable catchment management and planning. To achieve this need, however, we are faced with many challenges including the lack of in-situ DOC measurements at required spatial and temporal scales, and the complex processes controlling DOC confounded by interactions among numerous environmental and climatic factors. The proposed project will address these challenges by integrating state-of-the-art process-based model, satellite remote sensing data, climate change scenarios, and novel machine learning. The aims of this project are to quantify spatial-temporal dynamics of DOC in Swedish river catchments under past and current conditions and future land use and climate scenarios, and to explore environmental variables and processes controlling DOC dynamics. This project has four specific objectives: (1) to apply and evaluate a recently developed semi-distributed model with improved representation of DOC processes for simulating daily DOC concentrations in surface water in multiple Swedish river catchments with different characteristics; (2) to map and quantify land use/land cover changes in Swedish river catchments with long-term and multi-source satellite imagery data and predict DOC concentrations under different land use change scenarios for impact assessment; (3) to predict DOC concentrations under different future climate scenarios for assessing impacts of climate change, and analyze the spatial and temporal dynamics of DOC concentrations during the period 1990s-2100; (4) to investigate the environmental variables and processes that controlling the DOC dynamics using a novel machine learning technique. The applicant has strong demonstrated expertise in all essential methods (modelling, remote sensing, impacts assessment, machine learning, and data-model integration) and intensive experience in leading research projects, which lays solid foundation to ensure the success of this project. The outcome of this project will advance our understanding of DOC dynamics and impacts of climate and environmental changes, thereby providing a scientific basis to develop better management and conservation strategies for ensuring water quality and healthy aquatic ecosystems in Sweden under land use and climate changes. We will actively disseminate our project findings to the research community, the public and stakeholders for a broader implication.
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