Molecular Observation Networks for Enhancing Ecosystem Modeling
Abstract
Molecular processes that enable carbon and nutrient cycling in soils are heterogeneous across landscapes and through time. This spatiotemporal variability is an essential characteristic that supports ecosystem function, soil health, and organic matter processing. Yet, biogeochemical process variability is one of the most challenging characteristics to observationally capture at landscape scale. Traditional point-sampling methods that provide "high fidelity" biogeochemical observations represent infinitesimally small fractions of the landscape and vanishingly short periods of time. Moreover, detailed compositional information needed to support substrate-specific mechanistic modeling of organic carbon reactions generally is not available. The dearth of such information represents a major weakness in our ability to anticipate and model evolving soil biogeochemical status. Spatially and temporally resolved observational data are needed across landscapes. At the present, however, fixed field sensing instrumentation, sampling methodologies, geophysical, and remote sensing platforms that would be needed generally do not exist as integrated capabilities.
In this presentation, we will highlight recent advances in soil and watershed observational methods, including soil sampling and carbon molecular analysis, sensor and geophysical monitoring, and remote sensing, through the lens of biogeochemical reactive transport modeling. Based on this synthesis, we suggest best practices for community-based research to facilitate transformational changes in our understanding of soil systems and connected natural waters. Major insights include the conclusion that a single reactive transport modeling platform is needed to support real-time modeling and gapfilling of different types of observational data. New scalable biogeochemical sensing capabilities are needed to provide in-situ measurements. Moreover, greater integration of all major measurement modalities, such as high-resolution soil carbon compositional analysis, geophysical, automated sensors, and remote sensing methods will be critical.- Publication:
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AGU Fall Meeting Abstracts
- Pub Date:
- December 2022
- Bibcode:
- 2022AGUFM.B22I1554B