Evaluating gross primary productivity, soil moisture and evapotranspiration derived from multiple Noah-MP dynamic vegetation schemes and satellite observations across land cover types in the Mediterranean region

Land surface-atmosphere interactions are strongly influenced by vegetation, since the latter drives the exchange of energy, water and carbon at canopy level via transpiration and photosynthesis. These biochemical processes are related to both the stomatal response to meteorological variations (linking the canopy to the deepest soil layers), and the allocation of carbon in different parts of the plant such as roots and leaves. 

In recent years, the characterization of these processes has gained increasing attention in land surface models (LSMs), which are powerful tools that reproduce the soil-plant-atmosphere continuum and the mutual feedback of its components. Vegetation in LSMs is described either statically -- based on a prescribed vegetation climatology or cover -- or dynamically, that is, evolving in time its characteristics such as leaf area index and vegetation cover fraction, among the others. However, the dynamic simulation of vegetation is often simplified in LSMs with respect to state-of-the-art bio-geophysical and forest models. 

In the Noah Multi-Parametrization (Noah-MP, v. 4.0.1) LSM, multiple parametrizations are available for each individual sub-process scheme such as dynamic vegetation, runoff partitioning, groundwater recharge and radiative transfer through the canopy, among others. It is thus important to identify the land cover type, soil and climate characteristics of the specific study site and tailor the parametrization to find the “optimal” combination of sub-process schemes, i.e. the one which best reproduces in-situ observations. 

In this study, we evaluate point-scale simulations generated using different parametrizations of dynamic vegetation schemes within Noah-MP, run in offline mode within the NASA’s Land Information System (LIS). We compare the LSM results of gross primary productivity, soil moisture and evapotranspiration over several years between 2000 and 2023 to both ground-based estimates and remote sensing datasets derived from multiple observations and platforms such as MODIS, OCO-2, MSG and FLUXCOM. The study focuses on sites along the Italian peninsula, mostly forests, with croplands and grasslands as well, some of which are equipped with Eddy-covariance stations for carbon and water fluxes measurements and are included in the FLUXNET network. 

The sites are all natural, rain-fed ecosystems mostly located in drought-prone, Mediterranean regions. This study is meant to reveal previously neglected uncertainties in dynamic vegetation simulations, especially in dry regions, and to fine-tune the combination of sub-processes schemes in Noah-MP for future data assimilation experiments.