Wintertime grassland dynamics may influence belowground biomass under climate change: a model analysis

Katata, Genki; Grote, Rüdiger; Mauder, Matthias; Zeeman, Matthias; Ota, Masakazu

doi:10.5194/bg-17-1071-2020

Cited by 10 publications

(7 citation statements)

References 60 publications

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“…In addition to the uncertainty associated to climate projections ( IPCC, 2014 ; Knutti and Sedláček, 2013 ), there are uncertainties associated with exceeding boundary conditions of current process descriptions of biogeochemical models. For instance, climate change could modify overwintering mechanisms ( Ergon et al, 2018 ; Katata et al, 2020 ), leading to altered plant storage dynamics and thus altered spring growth ( Rapacz et al, 2014 ). Considering these uncertainties, the first cutting dates in Fendt at the end of the century (DOY 72 = 13 th of March) in the high-emissions scenario RCP 8.5 appear debatable, particularly because they entail radiation intensities that are low for supporting plant growth ( Höglind et al, 2013 ).…”

Section: Discussionmentioning

confidence: 99%

Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity

Petersen

Kraus

Calanca

et al. 2021

European Journal of Agronomy

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Highlights Earlier onset of pre-alpine cut grassland growing season entails shifts to earlier cutting dates and more cuts per year. Dynamic simulation of cutting dates can tackle these shifts if temperature and soil moisture are considered. Climate change increase pre-alpine grassland yields only if growth is not limited by soil moisture and nitrogen.

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Section: Discussionmentioning

confidence: 99%

Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity

Petersen

Kraus

Calanca

et al. 2021

European Journal of Agronomy

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“…The ordinary least squares method and Pearson correlation analysis were employed to explore the temporal dynamics of growing season NDVI (GSN) and climate factors and detect the relationships between them [30,[42][43][44][45]. Then, we further evaluate the direct and indirect effects of climate variables on vegetation GSN with the method of path analysis.…”

Section: Study Methodsmentioning

confidence: 99%

Quantitative Effects of Climate Change on Vegetation Dynamics in Alpine Grassland of Qinghai-Tibet Plateau in a County

et al. 2022

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Alpine grassland in the Qinghai-Tibet Plateau is known to be sensitive to climate change. To quantify the impacts of climate change on alpine ecosystems at small scale, Wulan County in Qinghai Province was taken as the research object, and the relationships between vegetation dynamics and climate changes and the direct and indirect effects of climate factors on vegetation dynamics were analyzed using the methods of ordinary least squares, Pearson correlation analysis and path analysis, on the basis of MOD13A3 data and meteorological data from 2001 to 2020. The results showed that the Normalized Difference Vegetation Index (NDVI) in the growing season of the county and 5 vegetation types showed similar fluctuation processes and relationships with climate factors during the study period. The growing season NDVI (GSN) of shrubland and desert steppe respectively were the highest and lowest. The yearly mean values of GSN over the county ranged from 0.151 to 0.264, and increased extremely significantly with years at a rate of 0.0035 yr−1. Spatially, GSN gradually decreased from northeast to southwest, and 97.2% of the county area showed an increasing trend in GSN. With years, growing season evaporation (GSE) decreased extremely significantly at a rate of 29.6 mm yr−1, while growing season average relative humidity (GSARH) showed a significant increasing trend at a rate of 0.16% yr−1. The correlations and effects of GSE, GSARH, and growing season precipitation (GSP) on vegetation dynamics were weakened in turn. GSE was the main direct effect factor, and the latter two were the indirect effect factors through GSE. The total contribution rates of GSE, GSARH and GSP to vegetation dynamics was about 78.0%. However, growing season average temperature (GSAT) had little effect on vegetation dynamics. This study provides information for understanding the characteristics of vegetation dynamics of alpine grassland in the Qinghai-Tibet Plateau at a small scale.

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“…Jones et al, 1980a;Kahmen et al, 2005;Hui and Jackson, 2006;Wedderburn et al, 2010;Skinner and Comas, 2010;Padilla et al, 2013;Nosalewicz et al, 2018;Meurer et al, 2019). Excess carbohydrates produced by grasses during periods of "sink-limited" growth are stored as nonstructural reserves, mostly in the tiller bases and roots (Thomas, 1991;Johansson, 1993;Volaire et al, 1998;Thomas and James, 1999;Østrem et al, 2011;Martínez-Vilalta et al, 2016;Hofer et al, 2017;Katata et al, 2020). These non-structural carbohydrates contribute to rapid recovery of growth after drought or defoliation by grazing or harvesting (Morvan-Bertrand et al, 1999;Jing et al, 2012;Schmitt et al, 2013;Benot et al, 2019).…”

Section: Growth Model For Perennial Grasslandmentioning

confidence: 99%

Coupled modelling of hydrological processes and grassland production in two contrasting climates

Jarvis

Groh

Lewan

et al. 2021

Preprint

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Abstract. Projections of global climate models suggest that ongoing human-induced climate change will lead to an increase in the frequency of severe droughts in many important agricultural regions of the world. Eco-hydrological models that integrate current understanding of the interacting processes governing soil water balance and plant growth may be useful tools to predict the impacts of climate change on crop production. However, the validation status of these models for making predictions under climate change is still unclear, since few suitable datasets are available for model testing. One promising approach is to test models using data obtained in “space-for-time” substitution experiments, in which samples are transferred among locations with contrasting current climates in order to mimic future climatic conditions. An important advantage of this approach is that the soil type is the same, so that differences in soil properties are not confounded with the influence of climate on water balance and crop growth. In this study, we evaluate the capability of a relatively simple eco-hydrological model to reproduce 6 years (2013–2018) of measurements of soil water contents, water balance components and grass production made in weighing lysimeters located at two sites within the TERENO-SoilCan network in Germany. Three lysimeters are located at an upland site at Rollesbroich with a cool, wet climate, while three others had been moved from Rollesbroich to a warmer and drier climate on the lower Rhine valley floodplain at Selhausen. Four of the most sensitive parameters in the model were treated as uncertain within the framework of the GLUE (Generalized Likelihood Uncertainty Estimation) methodology, while the remaining parameters in the model were set according to site measurements or data in the literature. The model accurately reproduced the measurements at both sites, and some significant differences in the posterior ranges of the four uncertain parameters were found. In particular, the results indicated greater stomatal conductance as well an increase in dry matter allocation below-ground and a significantly larger maximum root depth for the three lysimeters that had been moved to Selhausen. As a consequence, the apparent water use efficiency (above-ground harvest divided by evapotranspiration) was significantly smaller at Selhausen than Rollesbroich. Data on species abundance on the lysimeters provide one possible explanation for the differences in the plant traits at the two sites derived from model calibration. These observations showed that the plant community at Selhausen had changed significantly in response to the drier climate, with a significant decrease in the abundance of herbs and an increase in the proportion of grass species. The differences in root depth and leaf conductance may also be a consequence of plasticity or acclimation at the species level. Regardless of the reason, we may conclude that such adaptations introduce significant additional uncertainties into model predictions of water balance and plant growth in response to climate change.

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Wintertime grassland dynamics may influence belowground biomass under climate change: a model analysis

Cited by 10 publications

References 60 publications

Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity

Dynamic simulation of management events for assessing impacts of climate change on pre-alpine grassland productivity

Quantitative Effects of Climate Change on Vegetation Dynamics in Alpine Grassland of Qinghai-Tibet Plateau in a County

Coupled modelling of hydrological processes and grassland production in two contrasting climates

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