Augsburg University

Mca Pilot Pui: Data Intensive Research Training (DIRT) in Forecasting Soil Respiration at Core Terrestrial Neon Sites -in This NSF Mid-career Advancement (MCA) Award, the Pi Will Expand His Knowledge and Skills in Advanced Ecosystem Informatics to a ?big Data? Environmental Data Science Problem. the Science of Ecological Forecasting Makes Predictions About Ecosystems in Response to Environmental Change. Ecological Forecasts in Turn, Aid in Development of Nature-based Solutions for Earth's Changing Climate. Some Forecasts Have a Large Degree of Uncertainty, Such as Those for Terrestrial Carbon Cycling. Gains and Losses of CO2 From the Soil, Known as Soil Efflux, Are an Important Source of This Uncertainty Because They Depend on Climate Inputs With a High Degree of Variability, Such as Temperature and Precipitation. This Research Will Develop Open-access Real-time Forecasts of Soil CO2 Effluxes Using Mathematical and Computational Approaches. Forecasts Will Be Developed Across 47 Long-term Research Sites in the Continental United States That Are Part of the Nsf-supported National Ecological Observatory Network (neon). the Principal Investigator (pi), in Partnership With a Mentor From the Nsf-supported Ecological Forecasting Initiative, Will Further Develop a Tool to Address a Community Forecasting Challenge for Soil Effluxes, Leading to Its Broader Adoption in the Ecological Community. the Skills Gained Through This Project Will Provide a Long-term and Sustainable Research Trajectory for the Pi, and Will Help Him Develop New Undergraduate Research and Training Experiences. Real-time Forecasts of Components of the Terrestrial Carbon Cycle (i.e. Gross Primary Productivity and Net Ecosystem Carbon Exchange) Are Important for Understanding and Developing Nature-based Solutions in a Changing Climate. Despite the Importance of Soil Carbon Storage to Climate Mitigation, Standardized Prediction of Terrestrial Soil Carbon Efflux Across Seasonal and Interannual Timescales Lags Behind Other Terrestrial Carbon Forecasts With Increased Model Forecast Uncertainty From a (current) Lack of Structural Representation of Soil Processes. This Project Will Develop Forecasts for Soil Carbon Efflux Across Seasonal and Interannual Timescales at All Neon Terrestrial Sites. Project Deliverables Include Incorporation of a Suite of Soil Carbon Models (spanning a Range of Model Complexity) Into an Integrated Informatics Toolbox for Ecosystem Modeling (the Predictive Ecosystem Analyzer or Pecan) to Generate Soil Efflux Forecasts. Modeled Effluxes Will Be Validated Against Existing Measurements or Databases (e.g. Soil Respiration Database or the Continuous Soil Respiration Database) Where Appropriate. This Project Will Directly Connect Neon Data and Ecological Forecasting Techniques With the Broader Ecological Community. Developed Methods and Tools Will Be Open-access for the Scientific Community, Notably Through an Open-source R Package and by Incorporation of Models Into the Pecan Ecological Informatics Workflow. This Award Reflects NSF'S Statutory Mission and Has Been Deemed Worthy of Support Through Evaluation Using the Foundation's Intellectual Merit and Broader Impacts Review Criteria.