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Ecohydrological Modeling of Vegetation Dynamics Under Climate Change
This special issue belongs to the section “Ecohydrology“.
Special Issue Information
Dear Colleagues,
Vegetation dynamics play a critical role in regulating ecohydrological processes by mediating the exchanges of water, energy, and carbon between the land surface and the atmosphere. Under ongoing climate change, shifts in temperature, precipitation regimes, and the increasing frequency of extreme events, such as droughts and heat waves, are profoundly altering vegetation phenology, productivity, and structure, thereby reshaping terrestrial hydrological processes. Changes in vegetation cover and physiological functioning directly influence evapotranspiration, soil moisture, runoff generation, and groundwater recharge, leading to complex feedback within the climate–vegetation–hydrology system.
Ecohydrological modeling provides a powerful framework to quantify these interactions and to disentangle the combined effects of climate variability, vegetation dynamics, and human activities. Recent advances in process-based models, data assimilation, remote sensing, and machine learning techniques have significantly enhanced our ability to simulate vegetation-hydrology coupling across multiple spatial and temporal scales. However, substantial uncertainties remain in representing vegetation responses to climate extremes, land-use change, and future climate scenarios.
This Special Issue aims to bring together recent advances in ecohydrological modeling that improve our understanding of vegetation dynamics under climate change. Contributions focusing on (but are not limited to) model development, model-data integration, scenario simulations, and assessments of ecosystem and hydrological responses to climate variability and extremes are particularly welcome.
Dr. Shouzhi Chen
Guest Editor
Manuscript Submission Information
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Keywords
- ecohydrology
- vegetation dynamics
- climate change
- ecohydrological modeling
- vegetation phenology
- evapotran-spiration
- extreme climate events
- machine learning
- model-data integration
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