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National Science Foundation (NSF) 2022

Improving understanding of controls over spatial heterogeneity in dryland soil carbon pools in the age of big data

Dryland Soil Carbon

NATIONAL SCIENCE FOUNDATION

Titre : Improving understanding of controls over spatial heterogeneity in dryland soil carbon pools in the age of big data

Organismes NSF : DEB Division Of Environmental Biology

Durée : September 1, 2022 // August 31, 2025 (Estimated)

Résumé
This project will explore patterns and mechanistic controls over spatial heterogeneity in dryland soil organic carbon pools. Exploring patterns in two contrasting dryland settings, a semi-arid grassland with well-documented long-term management and vegetation change and a poorly characterized hyper-arid system, will provide deeper understanding of the relationships between environmental variables and soil organic carbon across drylands. Coupling this exploration of soil organic carbon spatial patterns with process modeling will enhance understanding of the mechanistic drivers of soil organic carbon heterogeneity. Spatial downscaling of a deep learning enhanced earth system modeling approach will provide insight into the fine scale mechanisms that drive soil organic carbon heterogeneity and how they respond to environmental change. This mid-career advancement grant will enable the primary investigator to : develop skills for handling and analyzing large and complex data sets ; use machine learning approaches to describe spatial patterns of heterogeneity in soil organic carbon pools in two contrasting dryland field sites where the primary investigator has extensive prior experience and data, and ; apply a deep learning enhanced earth system model to a dryland site and use this model to explore mechanistic drivers of carbon cycling. This project will build mutually beneficial partnerships between the primary investigator and two research partners, and an engineer with expertise in machine learning and remote sensing and an expert in ecological process models and deep learning enhanced earth system modeling.

Bureau de recherche parrainé  : Arizona State University

Financement : 495 783,00 US D

National Science Foundation

Page publiée le 9 novembre 2022