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Related Experiment Video

Updated: Sep 10, 2025

Integrating Remote Sensing with Species Distribution Models; Mapping Tamarisk Invasions Using the Software for Assisted Habitat Modeling SAHM
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Combining Observations and Models: A Review of the CARDAMOM Framework for Data-Constrained Terrestrial Ecosystem

Matthew A Worden1, T Eren Bilir2, A Anthony Bloom2

  • 1Department of Earth System Science, Stanford University, Stanford, California, USA.

Global Change Biology
|August 26, 2025
PubMed
Summary

The CARbon DAta MOdel fraMework (CARDAMOM) integrates diverse terrestrial biosphere observations with ecosystem models like DALEC. This data assimilation approach enhances ecological insights and ecosystem process understanding for improved environmental change analysis.

Keywords:
Bayesian inferenceCARDAMOMDALECdata assimilationdata‐constrained modelmodel‐data fusion

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Area of Science:

  • Ecology
  • Environmental Science
  • Computational Modeling

Background:

  • Terrestrial biosphere observations are rapidly increasing in volume and variety.
  • Existing ecological models often struggle to integrate these observations and their uncertainties for parameterization.
  • Data assimilation frameworks offer a solution for model-data fusion.

Purpose of the Study:

  • To review the development and applications of the CARbon DAta MOdel fraMework (CARDAMOM).
  • To emphasize CARDAMOM's role in advancing ecosystem process understanding through data assimilation.
  • To provide community recommendations for future development and application of CARDAMOM.

Main Methods:

  • CARDAMOM utilizes a Bayesian approach with a Markov Chain Monte Carlo algorithm.
  • It enables data-driven calibration of the Data Assimilation Linked Ecosystem Carbon Model (DALEC) parameters and initial states.
  • Observation operators are used to integrate diverse datasets, from in situ measurements to satellite observations.

Main Results:

  • CARDAMOM facilitates the retrieval of localized model process parameters from varied datasets.
  • It allows for the analysis of spatially variable ecosystem responses to environmental change.
  • Challenges include data quality issues and trade-offs between model complexity and predictive performance.

Conclusions:

  • CARDAMOM provides a flexible tool for mechanistic understanding of terrestrial ecosystem dynamics.
  • Addressing challenges like parameter equifinality through new observations is crucial.
  • Recommendations include integrating machine learning and strengthening collaborations across scientific communities.