Recent Advances in Earth Surface Processes: From Weathering to Climate Change
A special issue of Atmosphere (ISSN 2073-4433). This special issue belongs to the section "Biosphere/Hydrosphere/Land–Atmosphere Interactions".
Deadline for manuscript submissions: 31 October 2025 | Viewed by 1585
Special Issue Editors
Interests: land surface model; high-resolution land surface modeling; detection and attribution; hydrological extremes
Interests: land data development; machine learning; land surface modeling; soil
Special Issues, Collections and Topics in MDPI journals
Interests: climate change and future projection; soil memory and seasonal prediction; land-ocean-atmosphere interaction
Special Issue Information
Dear Colleagues,
Land surface processes (LSPs) play a crucial role in the Earth system, encompassing various processes such as soil moisture and temperature, vegetation dynamics, snow accumulation and melt, and streamflow. In recent decades, these processes have been undergoing notable transformations at different scales, leading to substantial alterations in ecohydrological and thermal variables, as well as extreme events like droughts and floods.
In recent years, numerous efforts, such as advanced remote sensing technologies, high-resolution land surface models, data assimilation methods, and machine learning approaches, have been made to better understand LSPs and their changes. However, changes in LSPs are highly complex due to multiscale factors such as climate change, land cover changes, and human water interventions, as well as the heterogeneous nature of land surface characteristics. Continued efforts are still needed to comprehensively harness recent advancements in this field.
This Special Issue aims to publish papers including, but not limited to, the following: advances in remote sensing technologies and data assimilation methods for monitoring land surface hydrothermal states, developing new parameter datasets and physical parameterization schemes for land surface models to better simulate LSPs, establishing innovative approaches (e.g., machine learning and data-driven methods) for modeling and predicting LSPs, the detection and attribution of the changes in LSPs, and land–atmosphere interactions.
Dr. Peng Ji
Prof. Dr. Wei Shangguan
Dr. Kai Yang
Dr. Yang Jiao
Guest Editors
Manuscript Submission Information
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Keywords
- land surface processes
- land surface model
- high-resolution land surface monitoring, modeling, and forecasting
- detection and attribution
- data assimilation and machine learning
- land–atmosphere interaction
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