Abstract. 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.
Charting the long-term trends in European wheat and maize yields and harvested areas and the relation of yields to climatic and economic drivers, two profound spatial processes become apparent. One consequence of the relatively late modernization of Eastern Europe has been to shift the focus of grain production from West to East. The warming trend prevailing over the past decades in the summer and winter seasons has been accompanied by a South to North shift in the harvested areas. The combination of these two processes has meant that the north-eastern sector of the European grain chessboard has emerged as the main beneficiary. There, the relatively low sensitivity of cereals to climatic change plus high economic growth rates have been accompanied by the most dynamic increases in cereal yields on the continent. As a result, a modern version of the 3000 year-old grain distribution system of the Ancient World is being restored before our eyes. One noteworthy finding is that increasing January–March temperatures have had a significant positive impact on wheat yields from Northern to South-Eastern Europe, and this is, at least in part, compensating for the negative impact of summer warming.
Physical and hydraulic soil properties are essential input parameters for models from different sciences (e.g. hydrology, agriculture, water management, nature preservation). Generally texture composition, porosity and other easily measurable physical properties of soils are known. However, saturated hydraulic conductivity and characteristic values of the water retention curve are usually missing information. Therefore, based on the physical similarity of soils (classes), they are substituted by data derived from soil databases. The aim of this study was to assess the currently unknown uncertainties of such classified databases. To do so, a large variety of tests were carried out: (i) static and dynamic, (ii) 1D and 3D (iii) hydraulic and hydrologic applied tests, (iv) real and synthetic soils, parameterized accordingly, and (v) HUNSODA and/or HYPRES databases. The results were sorted with respect to FAO and USDA classification systems. Soil class overlapping was evaluated through the statistics of basic hydraulic parameters (retention curve, hydraulic conductivity). Indicators related to hydrologic extremities (excess water and drought) were used to quantify the uncertainties of soil texture based on parameter substitution. It was concluded that the two evaluated soil classification systems did not sort soils reliably from the hydrologic and hydraulic viewpoint: the test results of classes showed major overlaps. Moreover, in most cases class synthetic parameter combinations poorly represented real soils. As a general consequence the results based on classified soil databases should be accepted only with reservation.
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