Abstract
Realistic prediction of ecosystem responses to climate change requires not only a perfect model structure to represent the real-world processes but also parameterization to constrain model specifications and external forcing variables to reflect the environment that an ecosystem experiences to perform its functioning. Data assimilation is a statistical approach to model parameterization. This chapter introduces data assimilation, mainly focusing on concepts, procedure, and applications. We relate data assimilation to regression analysis to show a seven-step procedure: defining a research objective, having data, using one model, measuring data-model mismatches, minimizing the mismatches via global optimization, estimating parameters, and predicting ecosystem changes.
| Original language | English (US) |
|---|---|
| Title of host publication | Land Carbon Cycle Modeling |
| Subtitle of host publication | Matrix Approach, Data Assimilation, Ecological Forecasting, and Machine Learning, Second Edition |
| Publisher | CRC Press |
| Pages | 133-139 |
| Number of pages | 7 |
| ISBN (Electronic) | 9781040026298 |
| ISBN (Print) | 9781032698496 |
| DOIs | |
| State | Published - Jan 1 2024 |
| Externally published | Yes |
ASJC Scopus subject areas
- General Business, Management and Accounting
- General Agricultural and Biological Sciences
- General Earth and Planetary Sciences
- General Environmental Science
- General Engineering