Search

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.

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.