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Data-Model Integration to Unravel Critical Zone Dynamics: Challenges, Successes, and Future Directions
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DOI：10.1002/wat2.70040.png)
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Abstract
En 中文
In the Anthropocene-a period marked by rapid environmental change-understanding the critical zone (CZ), the Earth's outer layer where rock, soil, water, air, and living organisms interact, is crucial. This review emphasizes data-model integration, the process of combining observational data (collected from field and laboratory settings) with computational models (representations of processes) to enhance understanding of coupled systems, validate model predictions, and improve simulation accuracy in response to natural and anthropogenic drivers. We propose a three-tiered framework for data-model integration in CZ science. Tier 1 incorporates observational data into model inputs to test hypotheses and explore processes where understanding is limited, providing insights into CZ functions over long timescales or in data-scarce areas. Tier 2 compares model outputs with observations and focuses on validation and calibration. Tier 3 involves iterative data-model integration, in which models are continuously refined through feedback from new data and evolving scientific questions. While rare in CZ science, this approach holds promise for guiding new data collection, improving predictive accuracy and enabling hindcasts and earthcasts. We illustrate each tier with curated examples and discuss how the tiers reflect varying levels of knowledge about CZ function and may guide knowledge transfer to understudied areas. Finally, we identify key challenges and future directions, including scale integration, non-stationarity, model limitations, and the need for transparent sharing of data-model integration processes.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Quality Science of Water > Methods
Keywords:
critical zone
data-model integration
process-based models
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