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Terrestrial biogeochemical models are essential tools to quantify climate–carbon cycle feedback and plant–soil relations from local to global scale. In this study, a theoretical basis is provided for the latest version of the Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. In this paper the improved soil hydrology as well as the soil carbon and nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology, soil nitrogen cycle, and soil organic carbon content estimation. Soil-hydrology-related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC and from 0.084 to 0.46 for soil evaporation; bias changed from −0.047 to −0.007 m3 m−3 for SWC and from −0.68 to −0.2 mm d−1 for soil evaporation). Simulations related to nitrogen balance and soil CO2 efflux were evaluated based on observations made in a long-term field experiment under crop rotation. The results indicated that the model is able to provide realistic nitrate content estimation for the topsoil. Soil nitrous oxide (N2O) efflux and soil respiration simulations were also realistic, with overall correspondence with the observations (for the N2O efflux simulation bias was between −0.13 and −0.1 mgNm-2d-1, and normalized root mean squared error (NRMSE) was 32.4 %–37.6 %; for CO2 efflux simulations bias was 0.04–0.17 gCm-2d-1, while NRMSE was 34.1 %–40.1 %). Sensitivity analysis and optimization of the decomposition scheme are presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation as well as nitrification and denitrification process estimation, which represents a major milestone.

Approximately 30% of anthropogenic carbon dioxide emissions are removed from the atmosphere annually by land-based carbon sinks, mainly forests. Extreme weather events such as droughts and heatwaves are expected to be more frequent and severe in the future affecting the carbon-water balance of ecosystems. Although empirical studies elucidate many of these processes, some questions cannot be addressed directly and require constructing in silico representations of real ecosystems. Process-based ecosystem models have recently received increased recognition due to their structural improvements and the increasing success of reproducing complex feedback mechanisms of carbon and water cycles.We used the Biome-BGCMuSo biogeochemical model to simulate pools and fluxes of carbon, water, and nitrogen in vegetation, litter, and soil on a daily scale. The model is under continuous development and in the last few years, the hydrological cycle submodel went through substantial improvements. We examined the model performance regarding water and carbon cycle simulation using the data from an ecosystem station covered by a circa 100-year-old unmanaged beech forest. The site is included in the FLUXNET global network of micrometeorological tower sites and is operated by the Global Change Research Institute of the Czech Academy of Sciences. The extensively tested model can help to understand complex feedback mechanisms under drought events as well as future climatic conditions and estimate future carbon sink potentials in Central Europe. In particular, we (i) evaluated the model performance using biomass, leaf area index (LAI) measurements, five-year-long eddy-covariance measurements of net ecosystem exchange (NEE) and evapotranspiration (ET), (ii) developed an efficient benchmark framework that highlighted structural and/or functional errors in the model, (iii) analyzed the simulated carbon and water cycle under drought events (consecutive dry days) occurring different time of the year and (iv) projected the effects of climate change on the forest carbon and water cycle up to 2100 using climate change scenarios from the FORESEE climatological dataset.We found that the simulated biomass and LAI values are in the range of the observations, NEE and ET are overestimated by 5% and 11% during the vegetation period, respectively. Simulation runs assuming 30-days drought events at different months in five different years caused immediate NEE decrease compared to simulations without drought events. We found the largest NEE difference (up to 10% on the average of five years) in the cases when the drought occurred in July, August, or in September. Simulations driven by climate change scenarios showed that NEE is expected to increase by the of the century, while the ET does not show any significant change in the future.

Among other factors, water availability strongly influences the amount and quality of crop yield. International interest in sustainable management of limited freshwater supplies has resulted in increased demand for measurements and modeling methods of cropland water balance components. In order to ensure adequate and sustainable crop production, it is necessary to understand the full water cycle of crop production including evapotranspiration. The purpose of the presented research is to quantify the soil water balance components of arable lands based on an experimental platform that can provide reference data for understanding processes and for model validation. Measurements by large weighing lysimeters are commonly used to test different evapotranspiration estimation methods. The data used for the research is provided by a weighing lysimeter station that was installed in 2018 at Martonvásár in Hungary. The station consists of twelve scientific lysimeters with soil temperature, soil water content, soil water potential sensors installed at several depths in the 2 m deep undisturbed soil profiles, and an ancillary meteorological tower. Every year since 2018 different crop varieties have been grown in six lysimeters. The other six lysimeters are not cultivated and are maintained vegetation-free (bare soil). The measurements are made with high accuracy and fine time resolution (1 reading per minute). Despite our best efforts, several types of errors occurred due to various reasons. The quality assurance and quality control (QA/QC) procedures used in the research help to minimize these errors in processing lysimeter datasets. A web application also contributes to a better interpretation of the data. The poster presents the first results with case studies focusing on wheat evapotranspiration.

Although small rural settlements are only minor individual sources of greenhouse gases and air pollution, their high overall quantity can significantly contribute to the total emissions of a region or country. The emissions of the rural lifestyle may be remarkably different from that of the urban and industrialized regions, but nevertheless they have been hardly studied so far. In this study, flux measurements at a tall-tower eddy covariance monitoring site and the footprint model FFP are used to determine the real-world wintertime CO, N2O, and CO2 emissions of a small village in western Hungary. The recorded emission densities, dominantly derived from residential heating, are 3.5 μg m-2 s-1, 0.043 μg m-2 s-1, and 72 μg m-2 s-1 for CO, N2O, and CO2, respectively. While the measured CO and CO2 emissions are comparable with those calculated using the assumed energy consumption and applying the according emission factors, the nitrous oxide emission exceeds the expected value by a magnitude. This may indicate that the nitrous oxide emissions are significantly underestimated in the emission inventories, and modifications in the methodology of emission calculations are necessary. Using a 3-dimensional forward transport model, we further show that, in contrast to the flux measurements, the concentration measurements at the regional background monitoring site are only insignificantly influenced by the emissions of the nearby village.

Background and aims This study was the first to test the efficiency of monitoring root electrical capacitance (CR*) non-destructively in the field to evaluate crop development under different environmental conditions. Methods A free-air CO2 enrichment (FACE) experiment was performed with two winter wheat cultivars, two levels (low and high) of nitrogen supply and two (ambient and elevated) of [CO2] in three replicate plots over two years. The validity of CR* as a proxy for root uptake activity was confirmed by tracking the ceptometer-based leaf area index (LAI). Results Repeated CR* measurements clearly demonstrated the seasonal dynamics in root development, with a peak at flowering, and the delayed growth in the second year caused by the unfavourable meteorological conditions. From the vegetative to flowering stages, CR* was strongly correlated with LAI (R2: 0.897–0.962). The positive effect of higher N supply and elevated [CO2] on crop growth was clearly indicated by the higher CR* values, associated with increased LAI, shoot dry mass (SDM) at flowering and grain yield (GY). The maximum CR* was closely related to GY (R2: 0.805 and 0.867) when the data were pooled across the N and CO2 treatments and the years. Unlike CR* and GY, SDM and LAI were significantly lower in the second year, presumably due to the enhanced root/shoot ratio induced by a severe spring drought. Conclusions The present results convincingly demonstrated the potential of the in situ root capacitance method to assess root responses dynamically, and to predict crop GY.

Terrestrial biogeochemical models are essential tools to quantify climate-carbon cycle feedback and plant-soil relations from local to global scale. In this study, theoretical basis is provided for the latest version of Biome-BGCMuSo biogeochemical model (version 6.2). Biome-BGCMuSo is a branch of the original Biome-BGC model with a large number of developments and structural changes. Earlier model versions performed poorly in terms of soil water content (SWC) dynamics in different environments. Moreover, lack of detailed nitrogen cycle representation was a major limitation of the model. Since problems associated with these internal drivers might influence the final results and parameter estimation, additional structural improvements were necessary. During the developments we took advantage of experiences from the crop modeller community where internal process representation has a long history. In this paper the improved soil hydrology and soil carbon/nitrogen cycle calculation methods are described in detail. Capabilities of the Biome-BGCMuSo v6.2 model are demonstrated via case studies focusing on soil hydrology and soil organic carbon content estimation. Soil hydrology related results are compared to observation data from an experimental lysimeter station. The results indicate improved performance for Biome-BGCMuSo v6.2 compared to v4.0 (explained variance increased from 0.121 to 0.8 for SWC, and from 0.084 to 0.46 for soil evaporation; bias changed from −0.047 to 0.007 m3 m−3 for SWC, and from −0.68 mm day−1 to −0.2 mm day−1 for soil evaporation). Sensitivity analysis and optimization of the decomposition scheme is presented to support practical application of the model. The improved version of Biome-BGCMuSo has the ability to provide more realistic soil hydrology representation and nitrification/denitrification process estimation which represents a major milestone.

Soil organic carbon (SOC) is a mandatory pool in national inventory reports on greenhouse gas (GHG) emissions and removals to the UNFCCC. Hence, its accurate assessment is important. Modelling SOC changes for national GHG reports is encouraged, but the uncertainty related to this pool still presents a significant challenge, thus, verifying modelling results with field observations is essential. We used the process-based model Biome-BGCMuSo and assessed its suitability for use in Croatia’s GHG reporting. We modelled SOC stocks in the top 30 cm of the mineral soil layer (SOC30) for four different land-use (LU) categories (Deciduous/Coniferous Forest, Grassland and Annual Cropland) distributed in three biogeographical regions (Alpine, Continental and Mediterranean) and compared them with results of a national soil survey. A total of 573 plot level simulations were undertaken and results were evaluated at three stratification levels (LU, LU × biogeographical region, and plot). The model reproduced the overall country mean of SOC30 with no overall bias, and showed good performance at the LU level with no significant (p &lt, 0.05) difference for all LUs except Deciduous Forest (11% overestimation). At finer stratifications, the model performance considerably worsened. Further model calibration, improvement and testing, as well as repeated soil survey are needed in order to assess the changes in SOC30 and to evaluate the potential of the Biome-BGCMuSo model for use in GHG reporting.

Agro-ecosystem services are the various benefits (e.g., crop yield) that people freely obtain from the properly functioning agricultural lands. The estimated changes in climatic conditions including increasing temperature, with particular attention to the summer means, together with the expected changes in the temporal precipitation distribution pose enormous challenge to the agriculture. Currently, dynamic system models are most frequently used tools that are capable of estimating the prospective effects of climate change on agro-ecosystems. A deterministic biogeochemical model is presented that is developed by Hungarian scientists within the framework of the AgroMo project. The main goal of the AgroMo project is to develop climate-smart strategies in order to mitigate the effect of potential future hazards in the context of climate change by 1) creating a complex, state-of-the-art experimental platform; 2) producing ten new, 0.1° spatial resolution climate scenarios based on the RCP (Representative Concentration Pathway) 4.5 and RCP8.5 scenarios; 3) developing an integrated assessment and modeling framework that is capable of simulating every major land use types; 4) analyzing/simulating a great number of adaptation strategies that can be used to support decision makers. Based on the preliminary simulation results, climate change will most likely expose significant negative impact on the spring sown crops in Hungary. Although, the yield losses could be avoided with irrigation or could be mitigated with earlier sowing, the role of winter crops is likely to become more significant in Hungary in the future.

Accurate estimation of the timing of intensive spring leaf growth initiation at mid and\ud high latitudes is crucial for improving the predictive capacity of biogeochemical and Earth system\ud models. In this study, we focus on the modeling of climatological onset of spring leaf growth in\ud Central Europe and use three spring phenology models driven by three meteorological datasets. The\ud MODIS-adjusted NDVI3g dataset was used as a reference for the period between 1982 and 2010,\ud enabling us to study the long-term mean leaf onset timing and its interannual variability (IAV). The\ud performance of all phenology model–meteorology database combinations was evaluated with one\ud another, and against the reference dataset. We found that none of the constructed model–database\ud combinations could reproduce the observed start of season (SOS) climatology within the study region.\ud The models typically overestimated IAV of the leaf onset, where spatial median SOS dates were best\ud simulated by the models based on heat accumulation. When aggregated for the whole study area,\ud the complex, bioclimatic index-based model driven by the CarpatClim database could capture the\ud observed overall SOS trend. Our results indicate that the simulated timing of leaf onset primarily\ud depends on the choice of model structure, with a secondary contribution from the choice of the\ud driving meteorological dataset.

Process-based ecosystem models are versatile tools providing profound insights into ecosystem processes and interactions between vegetation and environment. The ongoing development of the Biome-BGCMuSo model has delivered multiple improvements in model structure and parameters, and subsequently in simulated ecosystem dynamics. Since the number of parameters has increased during the model development, model parametrisation for biomes or tree species of interest is required to enable reliable model usage in the future.Here we explore the issue of site-specific versus multi-site calibration of model parameters for the European beech (Fagus sylvatica L.) along an extended environmental gradient across Central Europe, covering Croatia, Hungary, Slovakia, Poland and the Czech Republic. First, thorough literature search for the plausible ranges of individual model parameters was conducted. This was followed by the sensitivity analysis to identify the most influential model parameters. Finally, model calibration was performed based on the generalised likelihood uncertainty estimation method and the data from long-term research plots located in the five countries. The calibration was conducted at levels of individual sites and the region as a whole to evaluate different aspects of site-specific and multi-site calibration approaches and to develop a generalised parameter set for the European beech in Central Europe.

This work focuses on indoor air quality measurements carried out in an apartment in the suburban region of Budapest. The measurements were made by an IQAir AirVisual node air quality monitor which is a so-called low-cost sensor capable to monitor PM2.5 and carbon dioxide concentration. In this study we analyze data measured during January 2017 that was characterized by an extreme air pollution episode in Budapest. The aim of the study was to calculate daily indoor PM2.5 concentrations that are comparable with the outdoor concentrations provided by the official Hungarian Air Quality Monitoring Network. Given the fact that AirVisual Pro provides data with irregular sampling frequency, data processing is expected to influence the calculated daily mean concentrations. The results indicated that the uneven sampling frequency characteristic of AirVisual node indeed causes problems during data processing and has an effect on the calculated means. We propose a ‘best method’ for data processing for sensors with irregular sampling frequency.

Grasslands cover half of New Zealand’s land area, with much of it consisting of pastoral agriculture systems of varying intensity. Carbon fluxes from grazed pasture are thus a crucial part of the national carbon budget. We have used Biome-BGCMuSo v6 to model national CO2 fluxes from grasslands, calibrated with eddy covariance measurements at grazed farms at various sites around the country. We discuss the challenges of scaling up site measurements to the national level and modelling the diversity of New Zealand's pastoral sector. Model outputs will subsequently be used as a prior estimate of CO2 fluxes in an atmospheric inversion to obtain a total carbon budget for New Zealand as part of the CarbonWatch-NZ project.

Farmers and practising veterinarians have long suspected the impact of weather fronts on production and animal health. A common impression is that sows will farrow earlier in connection with a cold front. There might be a correlation between daily mortality and the occurrence of a strong atmospheric front. Population-based quantitative studies on weather fronts’ effects on animal health and production are very sparse in the scientific literature. In this study, the associations between the weather fronts and daily farrowing incidence, the pregnancy length and the daily death incidence were analysed. The results show that cold front increased the odds of more than daily six farrowings on the day of the front (with at least 3°C cooling OR: 4.79, 95%CI: 1.08-21.21, p=0.039). On the day of the front, with at least 3°C temperature change both the cold and the warm front increased the odds of the farrowing on the day ≥ 118th day of the gestation (OR: 3.10, 95%CI: 1.04-9.30, p=0.43 and OR: 4.39, 95%CI: 1.73-11.15, p=0.002, respectively). On the day after the day of front, the odds of farrowing on the ≤ 113th day of gestation are increased, if the temperature decrease was at least 2°C the OR: 2.30 (95%CI: 1.04-5.06, p=0.039). On the day after the warm front with at least 1°C temperature increase the odds of more than daily three deaths is increased (OR: 5.44, 95%CI: 1.23-24.05, p=0.025).

Soil organic matter (SOM) is one of five mandatory pools used in reporting of national greenhouse gas inventories under UNFCCC and EU regulations. Reporting on net change in soil organic carbon (SOC) under different land uses over time is challenging. The 2006 IPCC Guidelines for National Greenhouse Gas Inventories suggest that all estimates, including carbon (C) in SOM, should be transparent and consistent throughout the time series. For some countries assessing net change of SOC is often not easy due to lack of data, infrastructure or funding. Consequently, for the mineral part of the soil, frequently used is the simplest approach of assessment (Tier 1) which assumes no change in mineral SOC stocks. However, this assumption should be substantiated.There is a growing need for the use of higher tiers in reporting of C changes in SOM pool, by providing estimates from field measurements and modelling. While soil C modelling is cost-effective, and in some countries already found applicable for the purpose of reporting, field measurements of soil C stocks are expensive and time-consuming, but necessary for model calibration and validation.In our research we used Biome-BGCMuSo model, a biogeochemical model that simulates the storage and flux of water, C, and nitrogen (N) in the soil-plant-atmosphere system. Biome-BGCMuSo is a new variant of the well‑known Biome-BGC model with an improved multilayer soil module. We performed spatial modelling of SOC down to 30 cm for four different land-use categories of: deciduous forests, evergreen forests, annual croplands and grasslands, for the period 1990-2014. Eco-physiological parameters for each biome (i.e. land-use) were obtained from the literature. Meteorological data was obtained from open-access meteorological database FORESEE. Management activities (i.e. thinning, planting, mowing, fertilizing, and ploughing) where estimated based on available data and consultations with the local experts. Modelling results of SOC stocks were compared to field measurements. Trends of soil C change in period 1990-2014 under different land-uses were discussed.

Carbon cycling in forest ecosystems is affected by a number of interacting environmental factors. Here we analyse carbon sequestration in temperate forests composed of three common Central European species: Norway spruce, European beech and oak along an extended environmental gradient across Central Europe using long-term monitoring data and process-based modelling of forest dynamics. For the analyses we used selected ICP forest monitoring plots, long-term forest research plots from thinning trials, and highly-equipped intensively monitored plots from five central European countries: Croatia, Hungary, Slovakia, Poland and the Czech Republic. Their temporal development was simulated using a process-based model Biome-BGCMuSo, which is sensitive to soil and climate conditions. Since such models of forest growth dynamics implicitly describe relationships between forest productivity and environmental conditions, their implementation can reveal the main factors affecting carbon cycling in forests along the gradients of latitude, altitude, or other environmental factors as long as they are included in the models. The study indicates that by linking long-term monitoring data and forest growth modelling we can not only test the model capacity to simulate forest dynamics, but above all we can increase our capacity to address main challenges faced by the central European forestry with respect to the global climate change.

During the past 15 years, our research group was developing the Biome-BGCMAg (formerly known as Biome-BGCMuSo) biogeochemical model to improve its ability to simulate carbon and water cycle in different ecosystems, with options for managed croplands, grasslands, and forests. We made various model improvements based on the results of model validation and benchmarking. Our goal is to have a model that is suitable for estimating and predicting greenhouse gas fluxes of different ecosystems at various scales under changing management and climate conditions.The current, most recent model is called Biome-BGCMAg which is a process-based, biogeochemical model that simulates the storage and flux of water, carbon, and nitrogen in the soil-plant-atmosphere system. Biome-BGCMAg was derived from the widely known Biome-BGC v4.1.1 model developed by the Numerical Terradynamic Simulation Group (NTSG), University of Montana, USA. One of the most important model developments is the implementation of a multilayer soil module with water, carbon, nitrogen, and soil organic matter profiles. We implemented drought and anoxic soil state-related plant mortality. Alternative calculation methods for various processes were implemented to support possible algorithm ensemble modelling approach. Optional dynamic allocation algorithm was introduced using predefined phenophases based on growing degree day method. We implemented optional temperature dependence of allocation and possible assimilation downregulation as a function of temperature. Nitrogen budget simulation was improved. Furthermore, human intervention modules were developed to simulate cropland management (e.g. planting, harvest, ploughing, and application of fertilizers) and forest thinning. Dynamic whole plant mortality was implemented in the model to enable more realistic simulation of forest stand development. Last (but not least) conditional management (irrigation and mowing) was introduced to analyze the effect of different management strategies in the future. We started to build a sophisticated R based software to increase the visibility of the model and enable its use by the wider scientific community.In our first attempt to simulate national scale greenhouse gas budget with Biome-BGCMAg 2.0, we executed the model at 10 x 10 km spatial resolution for Hungary, using eco-physiological parameterization and prescribed management for maize, winter wheat, forests and grassland. The first results revealed that the spatial pattern of net primary production and crop yield is not represented well by the model. Based on the first experiences we introduced new features within Biome-BGCMAg 2.1 that address soil water deficit related photosynthesis down-regulation. Missing stomatal conductance effect on C4 photosynthesis was also addressed by the new developments.

Spring leaf unfolding is a spectacular recurring event at the mid- and high latitudes that is associated with deciduous vegetation. Several lines of evidence indicate that the timing of spring green-up (i.e. the start of the season, SOS) changed in the past decades resulting in an earlier leaf unfolding - a phenomenon which is considered to be a major indicator of the effects of global warming. Contrary to the timing of the SOS, considerably less attention was paid to studying the dynamics of vegetation green-up, characterized by the leaf unfolding speed or the duration of spring green-up. The importance of studying the spring green-up dynamics lies in the fact that the duration of leaf development and timing of the onset of growth jointly determine the annual cycle of vegetation activity including carbon and energy balance, canopy conductance and evapotranspiration.The aim of our research was to characterize the dynamics of leaf unfolding of deciduous broadleaf forests in the wider Carpathian Basin, located in Central Europe, using satellite remote sensing. The study was based on the Normalized Difference Vegetation Index (NDVI) time-series derived from the MOD09A1 official MODIS products during 2000–2019, the IGBP land cover classification dataset of the MCD12Q1 products, the CORINE 2012 (CLC2012) land cover dataset, the SRTM elevation dataset, and the FORESEE meteorological database. Our results clearly show that there is considerable interannual variability in the green-up duration of the deciduous broadleaf forest during 2000–2019. The last three years had, on average, the shortest (2018) and the two longest (2017 and 2019) recorded green-up durations in the region. Observed variability was partially attributed to the meteorological conditions, namely the extreme weather events occurring during the spring. We demonstrate that the meteorological conditions during the green-up period have a strong effect on the duration. The relationship between the SOS and the green-up duration reveals that the SOS also played an important role as a driver. Our results also reveal considerable elevation dependency both in the green-up duration and also in its correlation with SOS. Multiple linear regression models based on the SOS and the meteorological variables were also created to explain and predict the green-up duration.

Nitrous oxide is one of the atmospheric greenhouse gases whose amount is significantly influenced by human activity. Its major anthropogenic sources are the agricultural soils but the emission is known only with large uncertainty yet. The paper presents a tall tower based measuring system installed in Hungary, which is designed for the long-term monitoring of nitrous oxide emission of a regionally typical composition of agricultural fields by means of eddy covariance technique. Due to the careful calibration of the gas analyzer applied the measuring system is also suitable for the recording of the atmospheric concentration of nitrous oxide on the globally compatible scale (WMO X2006A). The paper reports the results of the first two years of the monitoring program, which is the first of its kind in Central Europe. For the period of July 2015–June 2017 the concentration measurements indicate an increasing trend of 0.91 nmol mol−1 year−1 with an average concentration of 330.64 nmol mol−1. During the two years of the project, the monitoring system recorded a total of 441 ± 195 mg N2O-N m−2 nitrous oxide emission with late spring/early summer maximum. The measurements also revealed the episodic nature of the emission typically triggered by major precipitation events.

The objective of this study was to investigate the effect of soil tillage and crop type on soil respiration (Rs) in a typical Central European agricultural site characterized by crop rotation. The weekly Rs and supporting environmental variables were measured under different crop types (including winter and summer crops) for 5 consecutive years under mouldboard ploughing (MP) and no-tillage (NT) treatments. The long-term mean Rs was 0.093 mg CO2 m−2 s−1 in NT versus 0.086 mg CO2 m−2 s−1 in MP. Soil respiration was significantly higher in NT treatment regardless of crop type and weather conditions in most of the study period. The difference between the treatments was larger for summer crops than for winter crops. The observed differences were more pronounced during the growing season, which cannot be explained solely by the observed plant production related data. Soil temperature did not differ in the two contrasting treatments, but soil water content was significantly higher in the NT treatment which might contribute to the observed differences in Rs. Six models were tested to simulate Rs in MP and NT treatment based on the observed environmental variables (soil temperature and soil water content). Normalized Difference Vegetation Index (NDVI) was also included as a predictor in four models to serve as a proxy for root activity. Inclusion of NDVI clearly improved the performance of the Rs models when the entire dataset was simulated including vegetated and non-growing season data. Performance and structure of the proposed Rs models varied between crop types and also between treatments (MP and NT). The preferred models explained 42% and 44% of the observed Rs variance in MP and NT, respectively. We provided explicit Rs model equations for the entire time series and also for sunflower, maize and the non-growing season period. The results suggest that there is added value in the construction of crop-specific Rs models, and also treatment specific models. Methodology related uncertainty of the Rs observations calls for longer datasets and improved modeling approaches including Bayesian and probabilistic methods.

The present study focuses on the leaf unfolding dynamics of deciduous broadleaf forests in Central Europe. MODerate resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) was used to quantify green-up duration (GUD) for the wider Carpathian Basin located in Central Europe, covering the time period 2000–2019. GUD was calculated for ~170 000 pixels with deciduous broadleaf forest cover at 500 m spatial resolution. The GUD exhibited large interannual and elevation-dependent variability where the latter likely indicates the distribution of the different species. The longest mean GUD occurred in 2017 (32.7 days), while the shortest (14.5 days) was associated with 2018. The relationship between the start of leaf unfolding (SOS) and the GUD (R = -0.62) reveals that the timing of the bud break plays an important role in the leaf unfolding process. Multiple linear regression models were constructed to explain and forecast the GUD based on the date of SOS, the elevation and the meteorological variables. The main explanatory variable was the SOS date, explaining 38.3% of the GUD variability (RMSE=8 days), while the addition of the elevation and its square to the model increased the explained variance to 47.8% (RMSE=7.34 days). Further addition of meteorological variables covering periods prior to and after the SOS increased the explained variance (the best R2 was 0.65). The results indicate the complexity of processes that drive the leaf unfolding. We propose that future studies should consider SOS, elevation and meteorological variables together to interpret GUD dynamics. Earlier SOS implies longer GUD, while delayed SOS is associated with short GUD, which means that the benefits of climate change possibly realized as a longer growing season could be smaller than anticipated. Accurate estimation of the SOS is a prerequisite for the successful modeling of the GUD.

Productivity of arable lands highly depends on the management techniques and their timing. Climate change urges the need for adaptive management tools, such as methods for optimization of planting date (PD). In existing crop models PD is usually specified by the user as a fixed date or through a set of rules which depend on diverse environmental conditions. However, validated rules of PD calculation are rare in the existing literature. In this study we strived to develop methods that could reliably estimate the PDs based on soil temperature and soil moisture, as well as to provide tool for PD projections under climate change. PD data from 294 agricultural enterprises in Hungary during the period from 2001 to 2010 were used to validate the PD methods. Effect of climate change on the timing of PD was evaluated using an ensemble of 10 climate change projections. Meteorological and soil data were obtained from the Open Database for Climate Change Related Impact Studies in Central Europe (FORESEE) and Soil and Terrain (SOTER) databases. The 4M crop model was used for crop yield simulations. Relative to the present day conditions, our analysis predicts a shift to earlier PDs for maize (approx. 12 days) and later PD for winter wheat (approx. 17 days) for the 2071–2100 period. The results indicated that maize PDs should be changed according to the earlier start of the growing season in spring. In contrast, currently used PDs should be preserved for winter wheat to avoid climate change related yield loss. Our analyses showed that the proposed PD estimation methods performed better than other eight tested methods. The advantage of our novel rules is that they could be applied for other crop models, as well.

Eddy-covariance based carbon flux datasets spanning decades are becoming available worldwide due to the effort of associated scientists. Tall tower based monitoring stations are relatively rare, but provide important information about the carbon balance of a larger region surrounding the tower. In this study we report and analyze the 21-year-long dataset provided by the Hungarian tall tower site, Hegyhatsal. The daily and annual cycles of net ecosystem exchange (NEE), gross primary production, and total ecosystem respiration are presented. Footprint analysis reveals that the fluxes mostly originate from the surrounding arable lands; the main source region is located within 1 km around the tower. Long-term, mean NEE was -0.467 gC m-2 day-1, or -170 gC m-2 year-1, revealing that the complex region was a net carbon sink from the atmospheric perspective. Trend analysis indicates that overall, NEE decreased (i.e. became more negative, which means stronger sink) by 12 gC m-2 year-1, a trend that is explained by improved agrotechnology and climate change. Net biome production (NBP) was estimated using crop census data and assumptions about the management practices that affect lateral carbon flux. Long term mean NBP was -30 gC m-2 year-1, which indicates that the soils may be losing carbon, though this loss is within the range of uncertainty of the measurements (~50 gC m-2 year-1). Analysis of county-scale yield statistics suggested that the results can be representative to at least county scale (1651 km2). Interannual variability was analyzed by using environmental variables such as maximum and minimum temperature, precipitation, vapor pressure deficit, soil water content, and satellite based vegetation index aggregated at monthly and longer time intervals. The results indicate that environmental conditions in spring have a major role in the annual carbon balance. Moreover, water availability represented by soil water content rather than by precipitation is a major driver of the interannual variability of NEE. The statistical analysis suggested that the large positive NEE anomaly during 2001–2003 was caused by interplay among the environmental drivers, in particular maximum temperature, vapor pressure deficit and radiation in April, followed by a soil water content deficit during the growing season. This novel statistical analysis provides insight into the drivers of the carbon balance of a mixed agricultural region.

Background Covariances among major anthropogenic greenhouse gases were studied during three cold-air pool episodes in the Pannonian Basin to better constrain their emission factors for Europe. Results On the base of observed covariance between carbon dioxide, methane, carbon monoxide and nitrous oxide atmospheric dry air mole fraction in a region of the Pannonian (Carpathian) Basin during three cold-air pool episodes in January–February 2017, emission factors relative to carbon dioxide were determined. For the determination of the emission of carbon dioxide, a simple boundary-layer budget model was compiled. The model gave 6.3 g m−2 day−1 carbon dioxide emission for the footprint area of the measurements on average for the period of the episodes. The 6.7–13.8 nmol μmol−1, 0.15–0.31 nmol μmol−1 and 15.0–25.8 nmol μmol−1 ratios for CH4:CO2, N2O:CO2 and CO:CO2, respectively, correspond to 15.3–31.7 mg m−2 day−1 methane, 0.9–2.0 mg m−2 day−1 nitrous oxide and 60.0–103.4 mg m−2 day−1 carbon monoxide emissions for the region. These values are somewhat higher than the officially reported bottom-up annual national averages for Hungary, which are explained by the winter conditions and intensive domestic heating. Conclusions The study indicated the high share of biomass burning in residential heating in rural environment that results in high carbon monoxide emission relative to that of carbon dioxide. It also indicated that the actual emission factor for nitrous oxide may exceed the range given in the guidelines for inventory compilation, which should be taken into account in reporting. It is shown that even a simple boundary-layer budget model might give realistic emission estimation under cool-air pool episodes.

The Carpathians are the largest European mountain range and harbour exceptional biodiversity. However, recent and anticipated changes in climate along with rapid social and economic development suggest that the region’s values may not be sustained. We strived to identify the main regional climate change hot-spots and evaluate the distribution of climatically exposed land-use types and ecosystems. The analysis was based on 10 climate models driven by the emission scenario A1B. To identify the hot-spots, we adopted a methodology based on change trajectories in a multidimensional climate space. Three hot-spots were in the Western Carpathians (Czech Republic, Slovakia, and Hungary), two were in Ukraine, and three were in the Romanian and Serbian Carpathians. Regions with the highest aggregate climate exposure (i.e. above 70 % of the regional range) were mostly covered by broadleaved forests (39 %), agricultural land (30 %), and pastures and woodlands (15 %). These regions also contained 15 % of protected areas and 36 % of the total human population in the Carpathians. While growing season length was the main factor affecting hot-spot magnitude in the north-west, precipitation-related variables were the main factors in the east and south. Analysis of inter-climate model variability indicated that the level of confidence in hot-spot position and magnitude differed among hot-spots. In addition to identifying a large-scale regional pattern of climate change, we showed that there are sub-regions with remarkably high climate exposure. The hot-spot distribution in lower elevations suggests that Carpathian ecosystems in water-limited environment may be particularly exposed to climate change.

In the present study, multiple linear regression models were constructed to simulate the yield of winter wheat, rapeseed, maize and sunflower in Hungary for the 2000–2016 time period. We used meteorological data and soil water content from meteorological reanalysis as predictors of the models in monthly resolution. We included annual fertilizer amount in the analysis to remove trend from the census data. We also used remote sensing based vegetation index to extend the approach for early crop yield forecast purposes and to study the added value of proxy data on the predictive power of the statistical models. Using a stepwise linear regression-like method the most appropriate models were selected based on the statistical evaluation of the model fitting. We provided simple equations with well interpretable coefficients that can estimate crop yield with high accuracy. Cross-validated explained variance were 67% for winter wheat, 76% for rapeseed, 81% for maize and 68.5% for sunflower. The modelling exercise showed that positive anomaly of minimum temperature in May has a substantial negative effect on the final crop yield for all four crops. For winter wheat increasing maximum temperature in May has a beneficial effect, while higher-than-usual vapour pressure deficit in May decreases yield. For maize soil water content in July and August is crucial in terms of the final yield. Incorporation of the vegetation index improved the predictive power of the models at country scale, with 10%, 2% and 4% for winter wheat, rapeseed and maize, respectively. At the county level, remote sensing data improved the overall predictive power of the models only for winter wheat. The results provide simple yet robust models for spatially explicit yield forecast as well as yield projection for the near future.