Please improve: A nicely comprehensive review of Land-Surface-Models LSM is given in Blyth et al., 2020 being possibly completed by a reading to Fisher, 2020 for a different perspective on processes. The pioneering models were more what we nowadays call Process Based/Mechanistic models which, however contains a lot of parameters and parameterizations that have to ne calibrated, assimilated or characterized. This calibration is essentially a statistical step that in practice transform LSMs in a mix of PDEs, ODEs solver endowed with various techniques derived from statistics or, more recently, from machine learning (ML) (Pal et al., 2021). In some cases, statistical learning pretends to entirely substitute PB model which sometimes happens for some processes but is more rare in LSM as a whole.
It is apparent from the reading of the reviews above that the scope of LSM has been greatly expanded over the years above five main modelling domains: surface and canopy exchanges, soil and snow physics, water bodies, biogeochemistry and plant physiology and vegetation dynamics.
Some models have emerged as reference in literature. They include:
References
Blyth, Eleanor M., Vivek K. Arora, Douglas B. Clark, Simon J. Dadson, Martin G. De Kauwe, David M. Lawrence, Joe R. Melton, et al. 2021. “Advances in Land Surface Modelling.” Current Climate Change Reports 7 (2): 45–71. https://doi.org/10.1007/s40641-021-00171-5.
Fisher, Rosie A., and Charles D. Koven. 2020. “Perspectives on the Future of Land Surface Models and the Challenges of Representing Complex Terrestrial Systems.” Journal of Advances in Modeling Earth Systems 12 (4). https://doi.org/10.1029/2018ms001453.
Pal, Sujan, and Prateek Sharma. 2021. “A Review of Machine Learning Applications in Land Surface Modeling.” Earth 2 (1): 174–90. https://doi.org/10.3390/earth2010011.
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