Extreme-scale modeling – Role of micro-topographic variability on nutrient concentration and mean age dynamics
Excess reactive nitrogen in soils of intensively managed landscapes causes adverse environmental impact and continues to remain a global concern. Many novel strategies have been developed to provide better management practices, yet the problem remains unresolved. This work aims to initialize a high-performance computing framework to characterize the "age" of inorganic soil-nitrogen (nitrate, and ammonia/ammonium). We use the general theory of age, which provides an assessment of the time elapsed since inorganic nitrogen has been introduced into the soil system. However, large-scale simulations of biogeochemical processes at the emerging lidar-data resolution are numerically expensive due to the density of the computational grid and the iterative nature of the algorithms for solving nonlinearity. The development of high-performance computing, efficient and robust predictive models to estimate the mean nitrogen age and it links with ecohydrologic dynamics will greatly aid in disentangling complex nitrogen dynamics by providing a nuanced characterization of the time scales of soil-nitrogen transformation and transport processes. In this proposal, we develop a parallel, scalable and hybrid CPU-GPU modeling framework that links a grid-based model of biogeochemical processes1 with a distributed physically-based integrated surface-subsurface nutrient transport model to investigate the impacts of micro-topographic variability on large-scale biogeochemical dynamics. The new biogeochemical-nutrient model is overlaid on top of a large-scale ecohydrologic model developed on Blue Waters.