Modeling Process‐Based Biogeochemical Dynamics in Surface Fresh Waters of Large Watersheds With the IMAGE‐DGNM Framework. (9th November 2020) - Vilmin, L.; Mogollón, J. M.; Beusen, A. H. W.; van Hoek, W. J.; Liu, X.; Middelburg, J. J.; Bouwman, A. F.
Abstract: Over the last centuries, human activities have exerted increasing pressures on the environment, leading to drastic alterations in the functioning of freshwater bodies (e.g., eutrophication). Global biogeochemical models have proven crucial to investigate interactions between humans, hydrology, and water quality of surface fresh waters. However, most do not account for high‐resolution spatial and temporal variability within watersheds, and they typically lack any representation of benthic dynamics that can drive pollution legacy effects. We present here the Integrated Model to Assess the Global Environment‐Dynamic Global Nutrient Model (IMAGE‐DGNM), which couples global, spatially explicit hydrology and integrated assessment models with process‐based biogeochemistry in surface fresh waters. The new Dynamic In‐Stream Chemistry (DISC) module calculates advective transport from headwaters to estuaries, processes in the water column and in bed sediments, as well as the exchanges between these two compartments. As application examples of IMAGE‐DGNM, we simulate sediment dynamics and nitrogen cycling in two large river basins. We assess in‐stream concentration time series at specific locations, and identify governing processes in transfers along the aquatic continuum. Results highlight the importance of benthic dynamics in watersheds highly perturbed by damming. The implementation of such dynamics within IMAGE‐DGNM allows for including the temporal effect of pollution legacies in large scale water quality studies and shifts in pollutant speciation along river continua. This new framework therefore incorporates new features for large basin to global scale studies that are crucial to better predict the effects on receiving ecosystems and evaluate future environmental management pathways. Plain Language Summary: Humans have strongly modified the functioning of the Earth's surface fresh waters, through pollution emissions and infrastructure, which has led to widespread ecological deterioration. Over the past decades, our understanding of the interactions between humans and the environment has been translated into models to investigate future sustainable pathways. However, large‐scale water quality models are usually too coarse to identify spatiotemporal pollution hotspots within river networks. Most of all, they lack any representation of pollution remobilization from bed sediments, which can delay the response to mitigation measures. To bridge these gaps, we developed a new tool simulating in‐stream pollutant transfer and transformation processes, allowing for the assessment of changes in the water quality over time within whole river networks. This tool is applicable globally and is applied here to two large watersheds to simulate sediment and nitrogen dynamics. Our results show that including processes in bed sediments is crucial to correctly assess water quality in heavily dammed river networks, such as the Mississippi. The new tool is of great importance for future projections and policy development. It will allow for identifying efficient mitigation options, pinpointing vulnerable areas within global river networks, and assessing the time needed for freshwaters ecosystems' recovery. Key Points: IMAGE‐DGNM is a new spatially explicit, globally applicable framework that dynamically simulates in‐stream biogeochemical processes Simulated compounds, transformation and exchange processes, and their parameterization are user defined IMAGE‐DGNM can be used for hindcasting and projections, including on long‐term pollution legacy effects in fresh waters