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Hoff, H.; Gerten, D.; Waha, K.; Warner, J.; Keulertz, M.; Sojamo, S. |
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Title |
Green and Blue Water in Africa: How Foreign Direct Investment can Support Sustainable Intensification |
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Book Chapter |
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Year |
2013 |
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359-375 |
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Xc |
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Routledge |
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Allan, T.; Keulertz, M.; Sojamo, S.A. |
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Handbook of Land and Water Grabs in Africa |
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no |
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MA @ admin @ |
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2480 |
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Author |
Köchy, M. |
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Title |
FACCE MACSUR Joint Workshops 2015 |
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Report |
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Year |
2015 |
Publication |
FACCE MACSUR Reports |
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7 |
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Pages |
H0.3-M1 |
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Keywords |
XC activities, Hub |
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Abstract |
FACCE MACSUR comprises many different groups whose work contribute to improving the European capacity of modelling agriculture with climate change and providing an assessment of these impacts for stakeholders. Some groups work on methodological issues in a single discipline, others work on cross-disciplinary concepts. The meeting provided an opportunity for the members of the groups to meet for intensive discussions and exchange of ideas, which is not as easily done in phone or video conferences. Various groups also met with each other to agree on work plans and common settings for research. Overall, 105 researchers attended the workshops. For coordinating work with the global program AgMIP, AgMIP’s principle investigator John Antle attended the meeting and, meeting in a video call, coordination teams of MACSUR and AgMIP agreed to continue the successful collaboration in the future. Major overarching outcomes of the meetings were agreements on policy and climate scenarios recommended to be used within MACSUR, development of an approach to quantify effects of extreme climatic events on socio-economic indicators, and closer collaboration among several groups at the level of regional case studies. |
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MA @ admin @ |
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2079 |
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Author |
Hidy, D.; Barcza, Z.; Haszpra, L.; Churkina, G.; Pintér, K.; Nagy, Z. |
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Title |
Development of the Biome-BGC model for simulation of managed herbaceous ecosystems |
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Journal Article |
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Year |
2012 |
Publication |
Ecological Modelling |
Abbreviated Journal |
Ecol. Model. |
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Volume |
226 |
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Pages |
99-119 |
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Keywords |
biogeochemical model; biome-bgc; grassland; management; soil moisture; bayesian calibration; carbon flux model; regional applications; bayesian calibration; use efficiency; general-model; exchange; balance; climate; grassland; variability |
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Abstract |
Apart from measurements, numerical models are the most convenient instruments to analyze the carbon and water balance of terrestrial ecosystems and their interactions with changing environmental conditions. The process-based Biome-BGC model is widely used to simulate the storage and flux of water, carbon, and nitrogen within the vegetation, litter, and soil of unmanaged terrestrial ecosystems. Considering herbaceous vegetation related simulations with Biome-BGC, soil moisture and growing season control on ecosystem functioning is inaccurate due to the simple soil hydrology and plant phenology representation within the model. Consequently, Biome-BGC has limited applicability in herbaceous ecosystems because (1) they are usually managed; (2) they are sensitive to soil processes, most of all hydrology; and (3) their carbon balance is closely connected with the growing season length. Our aim was to improve the applicability of Biome-BGC for managed herbaceous ecosystems by implementing several new modules, including management. A new index (heatsum growing season index) was defined to accurately estimate the first and the final days of the growing season. Instead of a simple bucket soil sub-model, a multilayer soil sub-model was implemented, which can handle the processes of runoff, diffusion and percolation. A new module was implemented to simulate the ecophysiological effect of drought stress on plant mortality. Mowing and grazing modules were integrated in order to quantify the functioning of managed ecosystems. After modifications, the Biome-BGC model was calibrated and validated using eddy covariance-based measurement data collected in Hungarian managed grassland ecosystems. Model calibration was performed based on the Bayes theorem. As a result of these developments and calibration, the performance of the model was substantially improved. Comparison with measurement-based estimate showed that the start and the end of the growing season are now predicted with an average accuracy of 5 and 4 days instead of 46 and 85 days as in the original model. Regarding the different sites and modeled fluxes (gross primary production, total ecosystem respiration, evapotranspiration), relative errors were between 18-60% using the original model and 10-18% using the developed model; squares of the correlation coefficients were between 0.02-0.49 using the original model and 0.50-0.81 using the developed model. (c) 2011 Elsevier B.V. All rights reserved. |
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English |
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0304-3800 |
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LiveM |
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MA @ admin @ |
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4472 |
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