Ecosystems & Biogeochemical Dynamics Laboratory - NSF-CDI-Type II - Department of Earth, Atmospheric, and Planetary Sciences - Purdue University Skip to main content
Ecosystems & Biogeochemical Dynamics Laboratory Department of Earth, Atmospheric, and Planetary Sciences

NSF-CD1-Type II

Collaborative Research: A Paradigm Shift in Ecosystem and Environmental Modeling: An Integrated Stochastic, Deterministic, and Machine Learning Approach

Duration:09/2010-08/2014
Award Amount:$1,591,428 out of $1,941,424

Participants
Prof. Zhuang is Lead PI in collaboration with M. Crawford, H. Zhang, D. Xiu, J. Zhang at Purdue, J. Melillo at MBL, Woods Hole MA, J. Reilly at MIT

Project Objectives

This project will advance systems modeling approaches by developing a suite of stochastic modeling approaches, coupled with geostatistical and machine learning techniques. The new system modeling approach will utilize both in situ and satellite remotely sensed data to improve system model parameters and model structure. These novel developments, together with observed data, will advance ecosystem and environmental sciences through computational thinking. The proposed approach will be used to develop a cyber-enabled stochastic carbon-weather system to provide more adequate quantification of regional carbon exchanges, which is critical to better understanding carbon-climate-atmosphere feedbacks and facilitating climate-policy making.

The proposed approach will transform the current system modeling approach by

  1. developing a stochastic version of the deterministic differential equation models of ecosystems and environmental systems;
  2. developing geospatial statistical techniques to fully exploit multifaceted observational data to improve model parameterization;
  3. developing advanced statistical and machine learning techniques to further utilize observational data to improve model structure; and
  4. applying the improved model to examine the societal and biogeochemical impacts of land use change.

Advantages of the proposed cyber-enabled terrestrial ecosystem model will include:

  1. Efficiently quantifying regional net carbon exchanges and associated uncertainty and
  2. Improving system model parameters and structure using advanced statistical and machine learning techniques and spatiotemporal data acquired over the U.S.

Project deliverables include:

  1. An innovative, cyber-enabled carbon-weather system that can quantify net carbon exchanges and associated probabilistic information at high spatial and temporal resolution for the continental U.S. and
  2. a suite of transformative advanced mathematical, statistical and system modeling techniques that could be applied to other complex modeling fields (e.g., hydrological modeling).

This project will significantly advance ecosystem sciences with computational thinking and will provide a unique opportunity to train a new generation of scientists in a highly interdisciplinary research environment.