Vegetation Growth Models Improve Surface Layer Flux Simulations of a Temperate Grassland

Abstract

Grassland models represent interactions of plant growth with soil and agricultural management based on underlying processes in different degrees of detail. To better understand the impact of these differences on the simulation of energy and matter exchange at the land-surface layer, we compared the ability of five land-surface models with different degrees of complexity to simulate energy fluxes in an intensively managed grassland in Switzerland. The aim was to evaluate the impacts of biomass growth, biomass harvest, soil profile characterization, and rooting depth on the dynamics of simulated near-surface soil moisture contents and energy fluxes. The case study included a comparison of model results with continuous observations of latent heat, sensible heat, and net radiation for a site-year. Energy fluxes were simulated more accurately by including a biomass growth model, encompassing the abrupt decline in leaf area caused by harvest. Sitespecific soil parametrization in combination with the absence of restrictions on rooting depth also improved the simulation results. The simulated energy fluxes of the five models differed significantly in the hot, dry month of July 2010 but were negligible under moist conditions in May. We conclude that the application of dynamic vegetation growth models improves energy flux simulations at the field scale in intensively managed grasslands during summer if biomass harvest dates and site-specific soil profile descriptions are considered. Our results imply that regional-scale simulations of grasslands will benefit significantly from high-resolution input information on soil properties, land use, and management.Abbreviations: LAI, leaf area index; LSM, land surface model; HPM, Hurley Pasture Model; WFPS, water-filled pore space.Grasslands cover >25% of the Earth's surface area and about 70% of the agricultural land area (FAO, 2015, p. 50). Therefore, a good representation of grassland systems in models plays a crucial role in reliably estimating energy and matter exchange between the land surface and the atmosphere at regional and global scales. During the last decades, increasingly improved terrestrial ecosystem models have been developed and applied to simulate energy, water and C fluxes between biosphere and atmosphere (Ma et al., 2015;Ershadi et al., 2014) and to estimate crop production under expected climate change (Asseng et al., 2013).However, compared with the numerous accessible forest and crop system models and in spite of the widespread surface area occupied by grassland, only a few grassland-specific models are available (Chang et al., 2013;Ma et al., 2015;Senapati et al., 2016), which for their part additionally address specific research questions. Among others, these are the CENTURY model built to quantify C, N, P, and S turnover on a monthly basis (Parton et al., 1998) and rebuilt for daily time steps under the name DAYCENT, the grassland model GEM (Chen et al., 1996a) focusing on natural grasslands, the LINGRA model (Schapendonk et al., 1998) to particularly pre...

A Bayesian sequential updating approach to predict phenology of silage maize

“…To simulate the soil-crop system, we used the SPASS crop growth model (Wang, 1997). SPASS is implemented in the Expert-N 5.0 (XN5) software package (Heinlein et al, 2017;Klein et al, 2017;Priesack, 2006). In XN5, the SPASS crop model is coupled to the Richards equation for soil-water movement as implemented in the model Hydrus-1D (Šimůnek et al, 1998).…”

Comment on bg-2021-238

“…To simulate the soil-crop system, we used the SPASS crop growth model (Wang, 1997). SPASS is implemented in the Expert-N 5.0 (XN5) software package (Heinlein et al, 2017;Klein et al, 2017;Priesack, 2006). In XN5, the SPASS crop model is coupled to the Richards equation for soil-water movement as implemented in the model Hydrus-1D (Šimůnek et al, 1998).…”

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“…Current Dynamic Global Vegetation Models describe plant biophysical and biogeochemical relationships in terms of leaf area (Oleson et al, 2013;Mahowald et al, 2016). However, model assumptions about vegetation structure development in response to growth and atmospheric exchange signify a sensitivity that must be thoroughly validated, particularly for highly dynamic vegetation such as managed humid temperate grasslands (Novick et al, 2004;Fatichi et al, 2014aFatichi et al, , 2014bKlein et al, 2018;Sándor et al, 2018). Furthermore, the classical methods for field observation of vegetation structure, through destructive sampling and canopy light transmissivity surveys, showed substantial potential for systematic error, and serves as a cautionary lesson.…”