The world around us is constantly changing, and humans contribute to many of these changes. Land cover and land use (LCLU) changes over time have a significant impact on the functioning of the Earth, particularly climate change and global warming. Spatial data of LCLU changes find important applications in land management, monitoring the sustainable development of agriculture, forestry, rural areas, assessing the state of biodiversity and urban planning.

In the frame of the InCoNaDa project "Enhancing the user uptake of Land Cover / Land Use information derived from the integration of Copernicus services and national databases”, the maps of land cover (LC) changes were developed for two study areas - the Łódź Voivodeship in Poland and the Viken County in Norway. The detection of LC changes was performed on the annual bases for the period 2018-2021 based on the analysis of multitemporal optical data from the Sentinel-2 mission. The Google Earth Engine (GEE) platform was used, which allows to analyze satellite data and to perform spatial analyses anywhere in the World while providing computing power. The LC change detection method was divided into two phases. The first phase is based on the analysis of spectral signatures, and the second phase applies the machine learning Random Forest algorithm. The classification was performed separately for each time interval: 2018-2019, 2019-2020, 2020-2021. In this way, three independent classification models were developed for each study area. The following three LC change classes were distinguished:  a) no-change, b) forest loss, and c) construction sites and newly built-up areas. The minimum mapping unit (MMU) was 0.2 ha. The LC change detection models reached high accuracy - in both study areas for all time intervals, the overall accuracy was equal to or greater than 0.97 and the Kappa coefficient than 0.95. The independent verification carried out based on the aerial orthophotos proved that the overall accuracy of the LC changes is pretty good for both study areas (around 0.9). The changes occurring in the construction sites and newly built-up area class reached slightly lower accuracy and has the lowest precision. The presented method showed its universality and adaptability, giving the possibility for further development. We will present the method, algorithm, results and their verification for Poland and Norway.

How to cite: Rynkiewicz, A., Hoscilo, A., Chmielewska, M., Lewandowska, A., Aune-Lundberg, L., and Nilsen, A.: Detection of land cover changes based on the Sentinel-2 multitemporal data on the GEE platform, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-17586, https://doi.org/10.5194/egusphere-egu23-17586, 2023.

Vegetation change is one of the essential indices of global change. In the past 30 years, based on the remote sensing records, the whole world has shown an overall greening phenomenon, accompanied by regional browning. How to identify the drivers of regional-scale vegetation change, especially to distinguish between climate change and human activities remains a great challenge. Modeling studies show that the CO2 fertilization effect plays a dominant role, but the significant greening contribution of farmland areas at the global scale seems to indicate that human land management (LMC) activities have a huge impact. Methods can be divided into two categories: model and observation statistics. Models are easy to quantify contributions but lack descriptions of LMC processes, and regional-scale statistical methods are difficult to identify driving factors. This study proposes the theory of Paired Land Use Experiment (PLUE), which selects areas with large differences in land management and consistent climate change to achieve "control" of climate change and attribute the difference in vegetation change to on the LMC. The PLUE theory was applied in two selected regions around the world. First, the Khabur River plain on the border between Syria and Turkey was selected. The two countries occupy roughly the same area of the plain. The climate conditions on both sides are consistent with the changes, and the interannual time series correlation coefficient of the Enhanced Vegetation Index (EVI) after detrending exceeds 0.8 (p <0.01). For multi-year trends, this difference can be attributed to LMC. Combined with relevant reports on Syrian social development, social unrest has caused serious degradation of land management capabilities. Therefore, social unrest and occasional severe natural disasters have led to a continuous decline in land management capabilities in the region, further contributing to the "browning" of Syrian vegetation. Secondly, the Sanjiang Plain was selected. China and Russia roughly divided the plain into two, with farmland and temperate savannah as the main vegetation types on both sides. The temperature and precipitation changes in the two places were basically the same, and the leaf area index (LAI) showed a significant growth trend with the same magnitude. However, the seasonal characteristics of the LAI trend in the two regions are significantly different. Using the PLUE method, it can be seen that this difference is caused by land management, including the expansion of paddy fields and the increase in farmland management intensity (mechanization, pesticide and fertilizer application). At the same time, it is found that the climate residual method will give false conclusions in the attribution of interannual changes. In summary, the PLUE method can directly identify land management activities other than climate elements from observations at the regional scale, which is helpful for further research on the driving forces of long-term vegetation change trends.

How to cite: Chen, T.: the Paired Land Use Experiments (PLUE) theory in driver identification of regional vegetation change, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10578, https://doi.org/10.5194/egusphere-egu23-10578, 2023.

Afforestation can impact surface temperature through local and nonlocal biophysical effects. However, the local and nonlocal effects of afforestation in China have rarely been explicitly investigated. In this study, we separate the local and nonlocal effects of idealized afforestation in China based on a checkerboard method and the regional Weather Research and Forecasting (WRF) Model. Two checkerboard pattern–like afforestation simulations (AFF1/4 and AFF3/4) with regularly spaced afforested and unaltered grid cells are performed; afforestation is implemented in one out of every four grid cells in AFF1/4 and in three out of every four grid cells in AFF3/4. The mechanisms for the local and nonlocal effects are examined through the decomposition of the surface energy balance. The results show that the local effects dominate surface temperature responses to afforestation in China, with a cooling effect of approximately −1.00°C for AFF1/4 and AFF3/4. In contrast, the nonlocal effects warm the land surface by 0.14°C for AFF1/4 and 0.41°C for AFF3/4. The local cooling effects mainly result from 1) enhanced sensible and latent heat fluxes and 2) decreases in downward shortwave radiation due to increased low cloud cover fractions. The nonlocal warming effects mainly result from atmospheric feedbacks, including 1) increases in downward shortwave radiation due to decreased low cloud cover fractions and 2) increases in downward longwave radiation due to increased middle and high cloud cover fractions. This study highlights that, despite the unexpected nonlocal warming effect, afforestation in China still has great potential in mitigating climate warming through biophysical processes.

How to cite: Chen, C., Ge, J., Guo, W., Cao, Y., Liu, Y., Luo, X., and Yang, L.: The Biophysical Impacts of Idealized Afforestation on Surface Temperature in China: Local and Nonlocal Effects, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3754, https://doi.org/10.5194/egusphere-egu23-3754, 2023.

Afforestation is an important mitigation strategy to climate change due to its carbon sequestration potential. Besides this positive biogeochemical effect on global CO₂ concentrations, afforestation also affects the regional climate by changing the biogeophysical land surface characteristics. In this study, we investigate the effects of an idealized global CO₂ reduction to pre-industrial conditions by a Europe-wide afforestation experiment on the regional longwave radiation balance, starting in the year 1986 from a continent entirely covered with grassland. Results show that the impact of biogeophysical processes on the surface temperatures is much stronger than of biogechemical processes. Furthermore, biogeophysically induced changes of the surface temperatures, atmospheric temperatures and moisture concentrations are as important for the regional greenhouse effect as the global CO₂ reduction. While the greenhouse effect is strengthened in winter, it is weakened in summer. On annual total, a Europe-wide afforestation has a regional warming effect, despite reduced CO₂ concentrations. Thus, even for an idealized reduction of the global CO₂ concentrations to pre-industrial levels, the European climate response to afforestation would still be dominated by its biogeophysical effects.

How to cite: Breil, M., Krawczyk, F., and Pinto, J.: The response of the regional longwave radiation balance and climate system in Europe to an idealized afforestation experiment, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7539, https://doi.org/10.5194/egusphere-egu23-7539, 2023.

The climate mitigation potential of terrestrial carbon dioxide removal (CDR) methods remains highly uncertain depending on the timing and magnitude of climate mitigation but also due to model uncertainty in the underlying Earth System models. In addition to these uncertainties, there are different approaches to measuring the effectiveness of CDR methods for climate change mitigation. In our study, we introduce various measures of effectiveness and evaluate the climate mitigation potential of afforestation and bioenergy plants under the low-emission scenario ssp126. To do this, we use the land component JSBACH3 of the Earth System Model MPI-ESM; afforestation is represented as the spatial increase of four forest plant functional types. Bioenergy plants are represented as highly efficient C4 plants (miscanthus and panicum). There are various assumptions concerning fossil fuel substitution and carbon capture and storage. Measuring effectiveness over time, we show how bioenergy plants become more effective over the long term compared to afforestation. In addition to this temporal measure, the climate mitigation potential of bioenergy plants depends significantly on the rate of fossil fuel substitution. Lastly, the spatial extent that is needed to match a given CDR amount in time by afforestation and BECCS can be quantified. Our study thus asses and compares the potentials of the two CDR methods highlighting the various perspectives of assessment.

How to cite: Egerer, S., Frank, S., and Pongratz, J.: Measures of effectiveness to compare the climate mitigation potential of afforestation and BECCS, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7863, https://doi.org/10.5194/egusphere-egu23-7863, 2023.

Forests can significantly influence local climate both by altering the carbon cycle (biogeochemical effects) and changing the surface energy budget (biophysical effects). Recent debates on the impacts of forestry and deforestation on the climate have focussed on trees' capacity to store carbon, but usually, the biogeophysical implications are not taken into account. In this study, we explore how regional-scale forestation and deforestation affect the Earth's energy balance, which in turn affect temperature and precipitation. We perform simulations where the vegetation cover is either increased or decreased while the carbon dioxide mixing ratio is kept constant, at a convection-permitting grid spacing of 5 km over the larger European domain using the regional climate model (RegCM4) coupled with CLM4.5. Over a time window of 11 years from 2004 to 2014, we study how the change in the forest cover affects the main atmospheric variables both at local level (local effects) and in the neighborhood (non-local effects). The need for a comprehensive understanding of how forests and climate impact each other is nowadays particularly relevant for Europe, and our analysis is one step forward in the direction of supporting the design of new policies and adaptation plans by pointing out the areas where afforestation efforts could mitigate the effects of climate change. This would improve the design of ambitious environmental policies like the European Green Deal project.

How to cite: Caporaso, L., Piccardo, M., Massaro, E., Duveiller, G., and Cescatti, A.: Adaptation strategies in Europe: the biophysical impact of forest cover change on climate simulated at high spatial resolution, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6863, https://doi.org/10.5194/egusphere-egu23-6863, 2023.

The H2020 research project LANDMARC¹ (Land Use Based Mitigation for Resilient Climate Pathways) will enhance understanding of the realistic potential of land-based negative emission solutions in agriculture, forestry, and other land use sectors. An important component of LANDMARC is modeling the effects of land-based mitigation technologies (LMT) on carbon fluxes and climate on the global scale.

We will present the development of a coupled modeling system consisting of the EC-Earth3-CC Earth System Model (ESM) and the LandSHIFT-G land use model. In this model system, LandSHIFT-G models sequences of land-use maps on a spatial resolution of 5 arc-minutes by integrating assumptions on the future development of the agricultural sector (e.g. crop/livestock production, changes in average crop yields) and assumptions on the implementation of a selection of LMTs as specified in global or regional scaling scenarios. Based on these land-use/land-cover changes (LULCC), EC-Earth3-CC simulates potential effects on vegetation (both natural and managed) and atmospheric CO2 concentrations and climate variables on a spatial resolution of approximately 70 km. Changes in potential crop yields due to climate change are fed back to the land-use model, potentially affecting subsequent land-use patterns. Carbon fluxes between the atmosphere, vegetation, and soils as well as crop yields are modeled by the dynamic vegetation model LPJ-GUESS, which is a component of EC-Earth3-CC. Hence, the system addresses three feedback loops between land-use, vegetation and climate. Land-use change is impacted by crop yields and pasture productivity. Carbon fluxes as well as crop yields and pasture productivity are impacted by land-use change and climate while climate is influenced by changes in atmospheric CO2 and land surface properties.

In the first version of the model system, it is foreseen to cover six different LMTs building on the capabilities of the models that are coupled. The set consists of (i) enhancement of carbon in vegetation and soils by afforestation, increasing soil carbon by (ii) no/reduced tillage agriculture and (iii) organic farming, combination of fossil fuel substitution and medium to long-term storage of carbon by (iv) BECCS (bio-energy and carbon capture and storage) and (v) application of biochar on cropland, as well as (vi) avoiding deforestation by  enhanced cropland irrigation.

In the current implementation, a simulation is done with a simplified modeling system, in which LandSHIFT-G is only coupled to LPJ-GUESS which is driven by atmospheric forcings from a previous EC-Earth3-CC model run, thus neglecting the dynamic effects under which the carbon cycle and land cover change impact the climate. As a proof of concept, we will present results of preliminary experiments simulating the effect of selected LMTs, e.g. afforestation, on the total carbon storage on land.

¹ The LANDMARC project has received funding from the European Union’s Horizon 2020 Research and Innovation Programme under grant agreement No 869367.

How to cite: Wimmer, F., Tourigny, E., Martinez Cano, I., Stuch, B., and Schaldach, R.: Modeling the effect of land-based mitigation technologies on the carbon cycle and climate, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-15474, https://doi.org/10.5194/egusphere-egu23-15474, 2023.

Increasing crop productivity while keeping detrimental side-effects on the environment low is a major challenge for global agriculture. Cover crops (CCs), mostly grown during the fallow period and incorporated in soils, are expected to improve soil fertility and crop yields while reducing chemical fertilizer use, with climate change mitigation co-benefits. However, quantifying these ecosystem services across global agricultural lands remains uncertain. In this study we investigate how the use of herbaceous CCs with and without biological nitrogen (N) fixation affects yields and cropland carbon and nitrogen balances using the dynamic global vegetation model LPJ-GUESS. Model performance is evaluated against observations from field trials worldwide as well as other published model-based estimates. LPJ-GUESS generally captures the observed enhanced soil carbon, reduced N leaching, and yield changes caused by CCs. We found that the combination of N-fixing CCs with no-tillage management could potentially increase soil carbon storage by 7% (+0.32 Pg C yr^-1 in global croplands) while reducing N leaching by 41% (-7.3 Tg N yr^-1) compared with bare fallows after 36 years of simulation. This integrated practice is accompanied by a 2% increase in total crop production (+37 million tonnes yr^-1 including wheat, maize, rice, and soybean) in the last decade of the simulation. Legume cover cropping is found to contribute more to increasing the subsequent crop yields than non-legumes. The effects of CCs on crop productivity are highly dependent on the main food crop types, chemical fertilizer use, and management duration, with smallest yield changes found in soybean systems and highly fertilized agricultural soils. Our results demonstrate the possibility of conservation agriculture when targeting long-term environmental sustainability without compromising crop production in global croplands.

How to cite: Ma, J., Anthoni, P., Olin, S., Rabin, S., Bayer, A., and Arneth, A.: Integrating cover crops with no-tillage benefits crop yields, increases soil carbon storage while reducing nitrogen leaching in global croplands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3159, https://doi.org/10.5194/egusphere-egu23-3159, 2023.

As the most dominant freshwater-use practice, irrigation plays an important role in global and regional environmental changes. Its extent experienced a substantial increase during the 20^th century, from less than 100 mha before 1950 to about 300 mha around the year 2000. To advance the scientific understanding of the irrigation-expansion-induced impacts during the last century, we launched a model-intercomparison project (MIP), through which we intend to discover its effects on different sectors, i.e. water, climate, and agriculture, with Earth system models. In the protocol, two experiments are designed, i) simulation with transient irrigation extent and ii) simulation with the irrigation extent fixed at the level of the year 1901. For every experiment, three ensemble members are required to reduce the uncertainty. Currently, the analysis of outputs will be focused on climate extremes, the water cycle, vegetation-carbon interactions and some social implications. In the next phase of IRRMIP, we plan to combine the Earth system model community with both the hydrological model and crop model communities.

How to cite: Yao, Y., Aas, K. S., Arboleda Obando, P., Bentsen, M., Chen, L., Cook, B., Devaraju, N., Ducharne, A., Gosling, S., Hartley, A., Jägermeyr, J., Jones, C., Kim, H., Lawrence, D., Lawrence, P., Leung, R., Lo, M.-H., and McDermid, S. and the IRRMIP members: Irrigation-expansion-induced impacts model-intercomparison project (IRRMIP), EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14584, https://doi.org/10.5194/egusphere-egu23-14584, 2023.

In comparison to naturally developed cities, a new town is strategically built in a short period of time according to development plans. It is considered as an appropriate study area for analyzing the urban climate issues such as Local Climate Zone (LCZ) and Urban Heat Islands (UHIs) phenomenon that are differently generated according to urban planning and development. However, there are few research on comparative investigation of new towns based on urban planning due to several external variables such as environmental considerations and economic situations. In this study, we suggest comprehensive method for determining and comparing changes in LCZ distribution and UHI phenomenon in two new towns in South Korea  with different urban planning. The LCZ distribution for each new town was analyzed using Sentinel 1&2 imagery as the main material, and Convolutional Neural Networks (CNN) method, a one of the deep learning algorithms. In addition, the UHI phenomenon was analyzed using Landsat imagery and the constructed LCZ map. These results have the potential to improve knowledge of the thermal environmental implications of urbanization and give guidance for sustainable urban development and maintenance when combined with architectural evaluation models.

How to cite: Lee, K. and Park, S.: Analysis of changes in Local Climate Zone and Urban Heat Island phenomenon of new towns in South Korea according to urban planning, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4131, https://doi.org/10.5194/egusphere-egu23-4131, 2023.

Land use is the main direct driver of biodiversity loss and Southern-Asia is, globally, one of the regions under the highest land-use change. Here we estimate how mammals that play a key role in the ecosystem functioning will cope with landscape transformations. We used the a state-of-the-art spatially-explicit agent-based model (RangeShifter) combining local density-dependence on fecundity, stage-structured demographics and dispersal to predict the occupancy and abundance for large-body size carnivorous species (Panthera tigris, Panthera pardus) mid-sized and small carnivorous (Cuon alpinus, Felis chaus, Vulpes vulpes and Prionailurus bengalensis) and two Cetartiodactyla species (Sus scrofa and Gazella benetti) in Southern Asia. In addition, we estimated how species-richness changed through time. The model was projected to the period 1850 to 2100 under two socio-economic pathways, representing an intermediate scenario (SSP2-4.5) and a fossil-fueled development scenario (SSP5-8.5). We found mixed-response to land-use across species. We estimate the mean total proportion of remaining individuals to be 0.60 (SD = 0.24) under SSP2 and 0.64 (SD = 0.37) under SSP5 compared to baseline land use in 1850. The drop in the total number of occupied cells is of lower magnitude (SSP2: mean = 0.82, SD = 0.27; SSP5: mean = 0.84, SD = 0.32). Mean species richness per cell followed a decline throughout the 20th century (mean = 0.90, SD = 0.15) followed by increase from current time up to 2100 under both scenarios (SSP2: mean = 0.95, SD = 0.18; SSP5: mean = 0.97, SD = 0.22). Our results support biotic homogenization with spread of widespread species and restriction of forest-specialists. We confirm a disproportionate and negative influence of loss of non-disturbed patches, and lower landscape permeability in large mammals, potentially leading to considerable change in mammalian biomass in the ecosystem. These findings suggest that a middle-road socio-economic pathway (SSP2) is not enough to maintain or recover populations compared to pre-disturbance levels.

How to cite: Pinto da Silva, A., Thörn, F., Malchow, A.-K., Zurell, D., and Cabral, J.: Land-use following a middle-road socio-economic pathway (SSP2) is not enough to recover mammal populations in Southern-Asia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3539, https://doi.org/10.5194/egusphere-egu23-3539, 2023.

Land use land cover (LULC) data are crucial for modeling a wide range of environmental conditions. So far, access to high-resolution LULC products at a global and regional scale for public use has been difficult, especially in developing countries/regions (Doelman et al., 2018). Land Use Land Cover (LULC) change simulation models are a powerful tool for analyzing the causes and effects of LULC dynamics under different scenarios. Scenario-based simulations of future land-use change can provide important information for evaluating the impacts of land strategies under different conditions. In this study, we project the future land use data at a 1-km resolution that comprises six land use types, adopting the newest integrated scenarios of the shared socioeconomic pathways and the representative concentration pathways (SSPs-RCPs) over Ethiopia. To generate this high-resolution land-use product, we use the FLUS model to simulate future land-use dynamics. The process of developing a future land dataset for Ethiopia can be divided into two parts. The first part is the estimation of the future area demands of different land use types under different SSP-RCP scenarios extracted from the LUH2 (Land-Use Harmonization 2) datasets which is available for free at http://luh.umd.edu/index.shtml. This dataset comprises a global projection of multiple land types for successive years from 2015 to 2100 under different SSP-RCP scenarios with a 0.25° resolution (approximately 25 km at the equator). The second part is conducting a 1-km spatial land simulation using the future land use simulation (FLUS) model under the macro constraints of the demands. In this sense, we select a series of relevant spatial driving factors, such as socioeconomic (GDP, population), distance factors (urban center, roads, and rivers), and natural factors (climate, topography, and soil quality). On this basis, a new set of land use projections, with a temporal resolution of 10 years and a spatial resolution of 1km, in eight SSP-RCP scenarios, comprising six land use types in Ethiopia is produced. This dataset shows good performance compared to remotely sensed ESA CCI-LC data. The results show that our land use simulation yields a satisfactory accuracy (Kappa = 0.8, OA = 0.9, and FoM = 0.1). Because of the advantages of the fine resolution, current scenarios, and multiple land types, our dataset provides powerful data support for environmental impact assessment and climate research, including but not limited to climate models.

How to cite: Brhane, E. S. and Dairaku, K.: Future Land Use Change projection under SSP-RCP scenarios over Ethiopia, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10587, https://doi.org/10.5194/egusphere-egu23-10587, 2023.