| Records |
Author  |
Bellocchi, G.; Sándor, R. |
| Title |
Model intercomparison |
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Report |
| Year |
2015 |
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FACCE MACSUR Reports |
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6 |
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D-L2.4 |
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This deliverable focuses on some illustrative results obtained with different grassland- specific, grassland adapted crop and dynamic vegetation models selected out of the first list of models compiled in D-L2.1.1 to simulate biomass and flux data from grassland sites in Europe and peri-Mediterranean regions (D-L2.1.1 and D-L2.1.2). Results from uncalibrated simulations were documented in the D-L2.3 report as a blind exercise. Some model improvements are emphasized in this report due to the higher information level of the model calibrations. The complete set of results will include simulations from uncalibrated and calibrated models. No Label |
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MA @ admin @ |
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2108 |
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| Author |
Ben Touhami, H.; Bellocchi, G. |
| Title |
Bayesian calibration of the Pasture Simulation model (PaSim) to simulate European grasslands under water stress |
Type |
Journal Article |
| Year |
2015 |
Publication |
Ecological Informatics |
Abbreviated Journal |
Ecological Informatics |
| Volume |
30 |
Issue |
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Pages |
356-364 |
| Keywords |
Bayesian calibration framework; Grasslands; Pasture Simulation model; (PaSim); integrated assessment models; chain monte-carlo; climate-change; computation; impacts; vulnerability; likelihoods; france |
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As modeling becomes a more widespread practice in the agro-environmental sciences, scientists need reliable tools to calibrate models against ever more complex and detailed data. We present a generic Bayesian computation framework for grassland simulation, which enables parameter estimation in the Bayesian formalism by using Monte Carlo approaches. We outline the underlying rationale, discuss the computational issues, and provide results from an application of the Pasture Simulation model (PaSim) to three European grasslands. The framework was suited to investigate the challenging problem of calibrating complex biophysical models to data from altered scenarios generated by precipitation reduction (water stress conditions). It was used to infer the parameters of manipulated grassland systems and to assess the gain in uncertainty reduction by updating parameter distributions using measurements of the output variables. |
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English |
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1574-9541 |
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CropM, LiveM, ft_macsur |
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no |
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MA @ admin @ |
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4697 |
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Biewald, A. |
| Title |
Ethical aspects in the economic modeling of water policy options |
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2015 |
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FACCE MACSUR Reports |
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5 |
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Sp5-6 |
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MACSUR Science Conference 2015 »Integrated Climate Risk Assessment in Agriculture & Food«, 8–9+10 April 2015, Reading, UK |
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no |
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MA @ admin @ |
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2121 |
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| Author |
Biewald, A. |
| Title |
Climate dependent equilibrium model |
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Report |
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2015 |
Publication |
FACCE MACSUR Reports |
Abbreviated Journal |
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6 |
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Pages |
D-T2.3 |
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In the framework of AgMIP (Agricultural Model Intercomparison Project; www.agmip.org), several articles have been published in which about 10 leading, agro-economic models analysed the impact of climate change on agricultural yields, area, consumption and food prices (Lotze-Campen et al. 2014, Nelson et. al 2014a,b Schmitz et al. 2014). A part of these articles are available freely through the publisher (e.g. http://www.pnas.org/content/111/9/3274). PIK has not only contributed through model simulations with the spatially explicit, agro-economic model MAgPIE, but also by coordinating this activity. Starting with AgMIP phase II in 2015, AgMIP has now for the first time conducted the model-analysis for different “Shared Socio-economic Pathways” (short SSPs). A first study has been published in the renowned journal “Environmental Research Letters” (Wiebe et al. 2015). These are important contributions to task 2.3 which aimed at simulating the impact of global climate changes on agricultural systems.Another study which is under revision in the journal PNAS, investigates the impact of climate change on agricultural welfare. The results of this paper are based on simulations with 20 different General Circulation Models (GCMs). This provides the opportunity to understand the uncertainty inherent in the different climate models better and improves the credibility of results.All mentioned articles and results are based on harmonized yield changes, which are a result of multi-model simulations, conducted in the framework of ISI-MIP (Inter-Sectoral Impact Model Intercomparison Project) and coordinated at PIK. These model results are publicly available (www.isi-mip.org) and part of an open source strategy of the institute. The modelling group around the agro-economic model MAgPIE (Model of Agriculture and its Impact on the Environment) currently discusses an open source strategy for publishing the model code. As a first step, a detailed description of the model will be available shortly (http://redmine.pik-potsdam.de/projects/magpie/wiki).PIK and the modelling group around MAgPIE have also contributed to the geoportal GLUES (Global Assessment of Land Use Dynamics, Greenhouse Gas Emissions and Ecosystem Services) where project partners can publish and share global and regional data sets as well as model results on scenarios of land use, climate change and economic development. MAgPIE results on landuse change, emissions and deforestation for different socio-economic scenarios have been made available there (http://catalog-glues.ufz.de/terraCatalog/Start.do;jsessionid=80F6A3D2C446674B898881D0589887E4). No Label |
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MA @ admin @ |
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2112 |
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Biewald, A.; Lotze-Campen, H.; Otto, I.; Brinckmann, N.; Bodirsky, B.; Weindl, I.; Popp, A.; Schellnhuber, H.J. |
| Title |
The Impact of Climate Change on Costs of Food and People Exposed to Hunger at Subnational Scale |
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Report |
| Year |
2015 |
Publication |
PIK Report |
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128 |
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73 |
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ftnotmacsur |
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Climate change and socioeconomic developments will have a decisive impact on people exposed to hunger. This study analyses climate change impacts on agriculture and potential implications for the occurrence of hunger under different socioeconomic scenarios for 2030, focusing on the world regions most affected by poverty today: the Middle East and North Africa, South Asia, and Sub-Saharan Africa. We use a spatially explicit, agroeconomic land-use model to assess agricultural vulnerability to climate change. The aims of our study are to provide spatially explicit projections of climate change impacts on Costs of Food, and to combine them with spatially explicit hunger projections for the year 2030, both under a poverty, as well as a prosperity scenario. Our model results indicate that while average yields decrease with climate change in all focus regions, the impact on the Costs of Food is very diverse. Costs of Food increase most in the Middle East and North Africa, where available agricultural land is already fully utilized and options to import food are limited. The increase is least in Sub-Saharan Africa, since production there can be shifted to areas which are only marginally affected by climate change and imports from other regions increase. South Asia and Sub-Saharan Africa can partly adapt to climate change, in our model, by modifying trade and expanding agricultural land. In the Middle East and North Africa, almost the entire population is affected by increasing Costs of Food, but the share of people vulnerable to hunger is relatively low, due to relatively strong economic development in these projections. In Sub-Saharan Africa, the Vulnerability to Hunger will persist, but increases in Costs of Food are moderate. While in South Asia a high share of the population suffers from increases in Costs of Food and is exposed to hunger, only a negligible number of people will be exposed at extreme levels. Independent of the region, the impacts of climate change are less severe in a richer and more globalized world. Adverse climate impacts on the Costs of Food could be moderated by promoting technological progress in agriculture. Improving market access would be advantageous for farmers, providing the opportunity to profitably increase production in the Middle East and North Africa as well as in South Asia, but may lead to increasing Costs of Food for consumers. In the long-term perspective until 2080, the consequences of climate change will become even more severe: while in 2030 56% of the global population may face increasing Costs of Food in a poor and fragmented world, in 2080 the proportion will rise to 73%. |
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Potsdam |
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Potsdam-Institut für Klimafolgenforschung |
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TradeM |
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MA @ admin @ |
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5000 |
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