Assessing the effects of climate and land use land cover changes on recent carbon storage in terrestrial ecosystem using model-satellite approach over Wallonia, Belgium

The use of a dynamic vegetation model, CARAIB, to estimate carbon sequestration from land-use and land-cover change (LULCC) offers a new approach for spatial and temporal details of carbon sink and for terrestrial ecosystem productivity affected by LULCC. Using the remote sensing satellite imagery (Landsat) we explore the role of land use land cover change (LULCC) in modifying the terrestrial carbon sequestration. We have constructed our LULCC data over Wallonia, Belgium, and compared it with the ground-based statistical data. However, the results from the satellite base LULCC are overestimating the forest data due to the single isolated trees. We know forests play an important role in mitigating climate change by capturing and sequestering atmospheric carbon. Overall, the conversion of land and increase in urban land can impact the environment. Moreover, quantitative estimation of the temporal and spatial pattern of carbon storage with the change in land use land cover is critical to estimate. The objective of this study is to estimate the inter-annual variability in carbon sequestration with the change in land use land cover. Here, with the CARAIB dynamic vegetation model, we perform simulations using remote sensing satellitebased LULCC data to analyse the sensitivity of the carbon sequestration. We propose a new method of using satellite and machine learning-based observation to reconstruct historical LULCC. It will quantify the spatial and temporal variability of land-use change during the 1985-2020 periods over Wallonia, Belgium at high resolution. This study will give the space to analyse past information and hence calibrate the dynamic vegetation model to minimize uncertainty in the future projection (until 2070). Further, we will also analyse the change in other climate variables, such as CO2, temperature, etc. Overall, this study allows us to understand the effect of changing land-use patterns and to constrain the model with an improved input dataset which minimizes the uncertainty in model estimation

Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula

peer reviewedVegetation models for climate adaptation and mitigation strategies require spatially high-resolution climate input data in which the error with respect to observations has been previously corrected. To quantify the impact of bias correction, we examine the effects of quantile-mapping bias correction on the climate change signal (CCS) of climate, extremes, and biological variables from the convective regional climate model COSMO-CLM and two dynamic vegetation models (LPJ-GUESS and CARAIB). COSMO-CLM was driven and analyzed at 3 km horizontal resolution over Central Europe (CE) and the Iberian Peninsula (IP) for the transient period 1980–2070 under the RCP8.5 scenario. Bias-corrected and uncorrected climate simulations served as input to run the dynamic vegetation models over Wallonia. Main result of the impact of bias correction on the climate is a reduction of seasonal absolute precipitation by up to −55% with respect to uncorrected simulations. Yet, seasonal climate changes of precipitation and also temperature are marginally affected by bias correction. Main result of the impact of bias correction on changes in extremes is a robust spatial mean CCS of climate extreme indices over both domains. Yet, local biases can both over- and underestimate changes in these indices and be as large as the raw CCS. Changes in extremely wet days are locally enhanced by bias correction by more than 100%. Droughts in southern IP are exacerbated by bias correction, which increases changes in consecutive dry days by up to 14 days/year. Changes in growing season length in CE are affected by quantile mapping due to local biases ranging from 24 days/year in western CE to −24 days/year in eastern CE. The increase of tropical nights and summer days in both domains is largely affected by bias correction at the grid scale because of local biases ranging within ±14 days/year. Bias correction of this study strongly reduces the precipitation amount which has a strong impact on the results of the vegetation models with a reduced vegetation biomass and increases in net primary productivity. Nevertheless, there are large differences in the results of the two applied vegetation models.Multisectoral analysis of climate and land use change impacts on pollinators, plant diversity and crops yields (MAPPY)Projets multilatéraux de recherche (PINT-MULTI

Impact of bias correction on climate change signals over central Europe and the Iberian Peninsula

Gefördert durch den Publikationsfonds der Universität Kasse

Impact of climate change on the distribution and diversity of vegetation species important for pollination

editorial reviewedPollination is a key ecosystem service vital to the preservation of wild plant communities and to sustain the yield of some agricultural crops. However, pollinators are rapidly declining in Europe, primarily as a result of human activity and climate change. Therefore, there is growing concern that observed declines in insect pollinators may impact on production and revenues from pollinator-dependent crops. In the forest, the presence of pollinators depends strongly on the openness of the canopy and the presence of wild plants that attract pollinators. The distribution of such plants is, therefore, crucial for estimating the pollinator presence. In general, however, there is incomplete knowledge of where those wild plants occur and how well they grow. To overcome this issue, we developed a species distribution model to predict the potential presence of important plant species for pollinators under present and future climatic conditions. The result of the distribution model is then re ned using the dynamic vegetation model CARAIB. By combining the results of the distribution model and CARAIB, we can determine where the plants are located and calculate their net primary productivities. The model projection for the end of the century highlights the large uncertainties in the future evolution of the pollinated plant species in the forest

What future for pollinators in the understorey vegetation under the impact of climate change ?

editorial reviewed<p>Although understorey biomass is negligible in comparison to overstorey biomass, understorey vegetation supports the majority of biodiversity within forests. The diversity of  plant species in the understorey is important for pollinators, such as bees and butterflies, which use the available resources for food and shelter. However, the future of understorey vegetation is uncertain due to the impact of climate change and human activities.  Climate change and forest management are known to be among the most important factors affecting the diversity and abundance of understorey plant species. Most studies on understorey vegetation has often been limited in scope, either focusing on a small number of specific plant species or large-scale studies of plant functional types. In this study, we take a more comprehensive approach by combining the results of a species distribution model with a dynamic vegetation model to simulate the evolution of understorey vegetation at the species level. We select a set of 30 species important for pollinators. In order to cover a large climatic gradient, simulations are performed over the Walloon region in Belgium and the Eisenwurzen region in Austria. The climate dataset is provided by the regional climate model COSMO-CLM, which has a 3 km spatial resolution and covers the period from 1980 to 2070 under different greenhouse gas concentration scenarios (RCP 2.6 and RCP 8.5). Additionally, we investigate the effect of different forest management practices (thinning and clear-cutting) on overstorey and how they impact understorey vegetation. Overall, the study aims to provide new insights into the current and future state of understorey vegetation with a focus on the impact of climate change and forest management on key pollinator resources.</p&gt

Assessing the effects of climate and land use changes on the distribution and growth of important plants species for pollinators

Pollination is a key ecosystem service vital to the preservation of wild plant communities and good agricultural behaviour. However, pollinators are rapidly declining in Europe, primarily as a result of human activity and climate change. Therefore, there is growing concern that observed declines in insect pollinators may impact on production and revenues from pollinator-dependent crops. In the forest, the presence of pollinators depends strongly on the openness of the canopy and the presence of wild plants that attract pollinators. The distribution of such plants is, therefore, crucial for estimating the pollinators presence. In general, however, there is incomplete knowledge of where those wild plants occur and how well they grow. To overcome this issue, we developed a species distribution model to predict the potential presence of important plant species for pollinators under present and future climatic conditions. The result of the distribution model is then refined using the dynamic vegetation model CARAIB. By combining the results of the distribution model and CARAIB, we can determine where the plants are located and calculate their net primary productivities.Multisectoral analysis of climate and land use change impacts on pollinators, plant diversity and crops yield

Workshop Summary:Exoplanet Orbits and Dynamics

Exoplanetary systems show a wide variety of architectures, which can be explained by different formation and dynamical evolution processes. Precise orbital monitoring is mandatory to accurately constrain their orbital and dynamical parameters. Although major observational and theoretical advances have been made in understanding the architecture and dynamical properties of exoplanetary systems, many outstanding questions remain. This paper aims to give a brief review of a few current challenges in orbital and dynamical studies of exoplanetary systems and a few future prospects for improving our knowledge. Joint data analyses from several techniques are providing precise measurements of orbits and masses for a growing sample of exoplanetary systems, both with close-in orbits and with wide orbits, as well as different evolutionary stages. The sample of young planets detected around stars with circumstellar disks is also growing, allowing for simultaneous studies of planets and their birthplace environments. These analyses will expand with ongoing and future facilities from both ground and space, allowing for detailed tests of formation, evolution, and atmospheric models of exoplanets. Moreover, these detailed analyses may offer the possibility of finding missing components of exoplanetary systems, such as exomoons, or even finding new exotic configurations such as co-orbital planets. In addition to unveiling the architecture of planetary systems, precise measurements of orbital parameters and stellar properties—in combination with more realistic models for tidal interactions and the integration of such models in N-body codes—will improve the inference of the past history of mature exoplanetary systems in close-in orbits. These improvements will allow a better understanding of planetary formation and evolution, placing the solar system in context.</p