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Aims: The relative importance of species within an ecosystem shows spatio‐temporal variability related to both the terrain features and numerous rapidly changing factors. Accordingly, functional and species patterns may show some level of persistence, or, due to various disturbances, fluctuations. Communities with high species richness were found to maintain a higher degree of stability than species‐poor or perturbed vegetations; however, diversity and its stability may be spatio‐temporally variable. Based on these considerations, our aim was to assess the responses of a sandy pasture, both species‐wise and at the community level, to the relatively invariant habitat, and to the more rapidly changing environmental conditions. Location: Hungary. Methods: We surveyed the vegetation in an area of about 1 ha by means of high‐resolution spatio‐temporal sampling: we recorded the surface aerial cover (%) of the plant species during 15 campaigns covering spring, summer and autumn aspects for seven years (2013–2019) in 80 × 60 m grids. The biological activity of the ecosystem (above‐ground biomass, soil respiration) was also followed up together with the environmental limiting factors. Results: During the study period, which was characterized by significant warming, the grassland showed a balanced physiological performance with year‐to‐year variability. Fluctuating differentiation of the plant species with different environmental optima along with the terrain attributes was assumed to be responsible for this balanced physiological performance. Although community‐level diversity remained stable, some species favoring cooler, wetter positions disappeared. Conclusions: Terrain features of the study area within 1.5 m or less elevation differences created considerably heterogeneous conditions for a high number (114) of plant species with different ecological needs to co‐occur. The stability of the diversity was found to show terrain relatedness, that is spatial as well as temporal patterns compared to the null model of spatio‐temporal independence, and the ecosystem functions followed these patterns. [ABSTRACT FROM AUTHOR]

The process-based biogeochemical model Biome-BGC was enhanced to improve its ability to simulate carbon, nitrogen, and water cycles of various terrestrial ecosystems under contrasting management activities. Biome-BGC version 4.1.1 was used as a base model. Improvements included addition of new modules such as the multilayer soil module, implementation of processes related to soil moisture and nitrogen balance, soil-moisture-related plant senescence, and phenological development. Vegetation management modules with annually varying options were also implemented to simulate management practices of grasslands (mowing, grazing), croplands (ploughing, fertilizer application, planting, harvesting), and forests (thinning). New carbon and nitrogen pools have been defined to simulate yield and soft stem development of herbaceous ecosystems. The model version containing all developments is referred to as Biome-BGCMuSo (Biome-BGC with multilayer soil module; in this paper, Biome-BGCMuSo v4.0 is documented).Case studies on a managed forest, cropland, and grassland are presented to demonstrate the effect of model developments on the simulation of plant growth as well as on carbon and water balance. [ABSTRACT FROM AUTHOR]

Accurate estimation of latent heat flux (LE) based on remote sensing data is critical in characterizing terrestrial ecosystems and modeling land surface processes. Many LE products were released during the past few decades, but their quality might not meet the requirements in terms of data consistency and estimation accuracy. Merging multiple algorithms could be an effective way to improve the quality of existing LE products. In this paper, we present a data integration method based on modified empirical orthogonal function (EOF) analysis to integrate the Moderate Resolution Imaging Spectroradiometer (MODIS) LE product (MOD16) and the Priestley-Taylor LE algorithm of Jet Propulsion Laboratory (PT-JPL) estimate. Twenty-two eddy covariance (EC) sites with LE observation were chosen to evaluate our algorithm, showing that the proposed EOF fusion method was capable of integrating the two satellite data sets with improved consistency and reduced uncertainties. Further efforts were needed to evaluate and improve the proposed algorithm at larger spatial scales and time periods, and over different land cover types. [ABSTRACT FROM AUTHOR]

The process-based biogeochemical model Biome-BGC was enhanced to improve its ability to simulate carbon, nitrogen and water cycles of various terrestrial ecosystems under contrasting management activities. Biome-BGC version 4.1.1 was used as base model. Improvements included addition of new modules such as the multilayer soil module, implementation of processes related to soil moisture and nitrogen balance, soil moisture related plant senescence, and phenological development. Vegetation management modules with annually varying options were also implemented to simulate management practices of grasslands (mowing, grazing), croplands (ploughing, fertilizer application, planting, harvesting), and forests (thinning). New carbon and nitrogen pools have been defined to simulate yield and soft stem development of herbaceous ecosystems. The model version containing all developments is referred to as Biome-BGCMuSo (Biome-BGC with multi-layer soil module). Case studies on a managed forest, cropland and grassland are presented to demonstrate the effect of model developments on the simulation of plant growth as well as on carbon and water balance. [ABSTRACT FROM AUTHOR]