The functioning of the soil-plant biome is controlled by interactions and feedbacks among its mineral-microbe-fluid-metazoan (such as worms and insects)-and plant components. These interactions are critically important to both natural and agricultural systems, as they support plant health and growth, neutralize toxins, help plants resist disease and environmental stresses (including drought), and are key to healthy soil carbon stabilization. However, quantifying how microbes “live and work” as a community to regulate the plant-soil biome is challenging for a number of reasons, including: the diversity of relationships, the range of spatial and temporal scales and environmental conditions over which important interactions occur, interactions within the context of watershed fluxes and gradients and the poor characterization of these mostly uncultivated microbes and the environment they inhabit. This topic focuses on exploring experimental and modeling approaches purposefully designed to quantify soil microbe plant dynamics across scales.
Developing an understanding of the role that weathered and unweathered bedrock plays in providing water for plant growth and in influencing baseflow contributions and biogeochemical reactions is of critical importance to predicting watershed function. This theme explores relevant process investigation, modeling approaches and data needs required to develop an understanding of when, where and how – and under what hydrological conditions -bedrock compartments exert significant control on watershed hydrobiogeochemical exports.
While climate change, extreme weather, land-use change, and other perturbations (such as early snowmelt, floods, fires hurricanes and droughts) are significantly reshaping interactions among the vegetation, soil, fluvial, and subsurface compartments of watersheds throughout the world, uncertainty associated with predicting watershed resilience and recovery from disturbance is high. This topic includes consideration of methodologies to investigate watershed responses to disturbance, including experimental watershed manipulation.
Watersheds are complex systems that typically encompass a variety of subsystems, each having distinct vegetation-biogeochemical-hydrological interactions and responses to perturbations (such as droughts or early snowmelt). This topic focuses on discussing different constructs and approaches to investigate how watershed subsystems contribute to aggregated water, carbon, nitrogen, phosphorus, and contaminant exports from watersheds, with an underlying concept that rivers are integrators of terrestrial processes. This theme examines a variety of approaches that are being tested as well as new modeling schemas and data needs.
Information from modeling and field sensing systems are currently being used to inform each other, often at a single scale (or the next smaller or larger scale). However, lacking is a rationale co-design strategy that specifically targets the development of modeling and sensing systems across the range of scales and compartments potentially important for predicting watershed and river basin response to perturbations. A current opportunity exists to advance co-design strategies, toward models that can rapidly assimilate diverse, multi-scale data and toward sensing systems designed to collect data where uncertainty is the highest or where small/short events contribute the most significantly to larger scale system behavior. This challenge focuses on the co-design of tools and strategies needed to predict hydrological and biogeochemical interactions across scales (from soil microbiomes to single plant stands to crops to watersheds and river basins) and terrestrial system compartments (from bedrock to vadose zone to vegetative layer to boundary layer), informed in near real time by increasingly available field sensor networks and remote sensing data. This topic could include discussions of networked sensing strategies; informative model-data integration across scales; co-design concepts involving workflows, assimilation, uncertainty quantification and inverse strategies; and judicious integration of mechanistic and machine learning approaches toward identifying important changes in watershed system behavior in near real time.