IMAGE framework summary/Description: Difference between revisions
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For energy use and supply, the IMAGE framework uses a detailed energy system model (The IMage Energy Regional model, [[TIMER model|TIMER]]) to describe the long-term dynamics of the energy system; see [[Energy supply and demand]]. This includes demand for energy services and end-use energy carriers, and the role of fossil fuels versus alternative supply options such as renewables and nuclear power to meet the demands . The model determines the demand for energy services on the basis of primary drivers and assumptions on lifestyle. This demand is fulfilled by final energy carriers, which are, in turn, produced from primary energy sources. Both the mix of final energy carriers and the technologies to produce them are chosen on the basis of their relative costs. Key processes that determine these costs include technology development and resource depletion, but also preferences, fuel trade assumptions and policies play a role. The output of the model demonstrates how energy intensity, fuel costs and competing non-fossil supply technologies develop over time. Implementation of emissions mitigation is generally modeled on the basis of price signals. A carbon tax (used as a generic measure of climate policy) induces additional investments in energy efficiency, in fossil-fuel substitution, bioenergy, nuclear power, solar power, wind power and carbon capture and storage. The energy model is linked to other parts of the IMAGE model via calculated emissions (see further) and demand for bio-energy production (generating input into the land use model). | For energy use and supply, the IMAGE framework uses a detailed energy system model (The IMage Energy Regional model, [[TIMER model|TIMER]]) to describe the long-term dynamics of the energy system; see [[Energy supply and demand]]. This includes demand for energy services and end-use energy carriers, and the role of fossil fuels versus alternative supply options such as renewables and nuclear power to meet the demands . The model determines the demand for energy services on the basis of primary drivers and assumptions on lifestyle. This demand is fulfilled by final energy carriers, which are, in turn, produced from primary energy sources. Both the mix of final energy carriers and the technologies to produce them are chosen on the basis of their relative costs. Key processes that determine these costs include technology development and resource depletion, but also preferences, fuel trade assumptions and policies play a role. The output of the model demonstrates how energy intensity, fuel costs and competing non-fossil supply technologies develop over time. Implementation of emissions mitigation is generally modeled on the basis of price signals. A carbon tax (used as a generic measure of climate policy) induces additional investments in energy efficiency, in fossil-fuel substitution, bioenergy, nuclear power, solar power, wind power and carbon capture and storage. The energy model is linked to other parts of the IMAGE model via calculated emissions (see further) and demand for bio-energy production (generating input into the land use model). | ||
The model can be used to make detailed projections of energy developments, for instance with and without climate policy. In the Rio+20 baseline scenario, the baseline scenario (without climate policy) projects a 65% increase in energy consumption in the 2010-2050 period, driven by continued population and economic growth. With no fundamental change in current policies, fossil fuels are expected to retain a large market share as their market price is expected, to stay below the alternative fuels for the vast majority of applications. In climate policy scenarios, the inclusion of a carbon price leads to a an increased share for of different technologies and resources, such as carbon-capture and storage, nuclear power and renewables; see Figure 2.4. | ''The model can be used to make detailed projections of energy developments, for instance with and without climate policy. In the Rio+20 baseline scenario, the baseline scenario (without climate policy) projects a 65% increase in energy consumption in the 2010-2050 period, driven by continued population and economic growth. With no fundamental change in current policies, fossil fuels are expected to retain a large market share as their market price is expected, to stay below the alternative fuels for the vast majority of applications. In climate policy scenarios, the inclusion of a carbon price leads to a an increased share for of different technologies and resources, such as carbon-capture and storage, nuclear power and renewables; see Figure 2.4.'' | ||
====Food consumption and agriculture==== | ====Food consumption and agriculture==== | ||
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There are several ways by which the human system directly influences the earth system. Land allocation and atmospheric emissions form two of the most important factors, others include water extraction, and water and soil pollution. The two main factors are described in IMAGE in separate sub-models. | There are several ways by which the human system directly influences the earth system. Land allocation and atmospheric emissions form two of the most important factors, others include water extraction, and water and soil pollution. The two main factors are described in IMAGE in separate sub-models. | ||
See the background information on [[Downscaling|Downscaling as a tool to link different geographical scales]]. | |||
====Land allocation==== | ====Land allocation==== | ||
Using the demand for land to produce agricultural food products and bio-energy, the Land Allocation model determines at a 5 min x 5 min grid where this production occurs using a set of allocation rules. For instance, grid cells with a high potential to produce agricultural products (in terms of climate, soil types), that are near water (for transport and irrigation) and near to existing urban or agricultural areas are considered to be most suitable for agricultural production. In the model these rules in combination with regional preferences for different types of production systems, determined from historical calibration, are used to allocated land use to the grid. | Using the demand for land to produce agricultural food products and bio-energy, the Land Allocation model determines at a 5 min x 5 min grid where this production occurs using a set of allocation rules. For instance, grid cells with a high potential to produce agricultural products (in terms of climate, soil types), that are near water (for transport and irrigation) and near to existing urban or agricultural areas are considered to be most suitable for agricultural production. In the model these rules in combination with regional preferences for different types of production systems, determined from historical calibration, are used to allocated land use to the grid. | ||
As observed in other baseline scenarios, in the Rio+20 baseline the expansion of agricultural production in tropical regions leads to a loss of natural ecosystems, and an associated loss of biodiversity. In fact, most of the expansion is projected to occur in highly productive ecosystems near existing agricultural areas, thus including tropical forests and woodland, other high nature value savanna and grassland area. At the same time, in the temperate zones there is actually a contraction of the agricultural area. Here, the grid cells least suitable for production potential are abandoned. The resulting changes in agricultural area are depicted in Figure 2.5. | ''As observed in other baseline scenarios, in the Rio+20 baseline the expansion of agricultural production in tropical regions leads to a loss of natural ecosystems, and an associated loss of biodiversity. In fact, most of the expansion is projected to occur in highly productive ecosystems near existing agricultural areas, thus including tropical forests and woodland, other high nature value savanna and grassland area. At the same time, in the temperate zones there is actually a contraction of the agricultural area. Here, the grid cells least suitable for production potential are abandoned. The resulting changes in agricultural area are depicted in Figure 2.5.'' | ||
====Emissions==== | ====Emissions==== | ||
Emissions are described in IMAGE as a function of activity levels in the energy system, in industry, in agriculture, and of the assumed abatement actions ([[Emissions]]). The model describes the emissions of all major greenhouse gases, and many air pollutants (calibrated to current international emission inventories). In some cases, the calculation of emissions is done using detailed process representation (e.g. emissions from cultivated land and land-cover change) but, in most cases, exogenous emission factors are used. The development of emission factors over time has been estimated based on relevant hypothesis, sometimes assuming constant emission factors, but often assuming that emission factors decrease over time along with economic development (consistent with the so-called environmental Kuznets curve). Emissions factors of greenhouse gases reflect estimates per region, sector and gas from the [[FAIR model]] ([[Climate policy]]). | Emissions are described in IMAGE as a function of activity levels in the energy system, in industry, in agriculture, and of the assumed abatement actions ([[Emissions]]). The model describes the emissions of all major greenhouse gases, and many air pollutants (calibrated to current international emission inventories). In some cases, the calculation of emissions is done using detailed process representation (e.g. emissions from cultivated land and land-cover change) but, in most cases, exogenous emission factors are used. The development of emission factors over time has been estimated based on relevant hypothesis, sometimes assuming constant emission factors, but often assuming that emission factors decrease over time along with economic development (consistent with the so-called environmental Kuznets curve). Emissions factors of greenhouse gases reflect estimates per region, sector and gas from the [[FAIR model]] ([[Climate policy]]). | ||
In the Rio+20 baseline, increasing energy and agricultural production levels lead to an increase of associated greenhouse gas emissions. For air pollutants, the emission trends are more diverse: a decrease is projected in high-income countries, as emission factors drop faster than activity levels increase). In most developing country regions, however, increasing energy production is projected to be associated with more air pollution. The model can also be used to design scenarios that are consistent with different climate targets. For reaching the 2oC target, global [[GHG]] emissions would need to be reduced by around 50% in 2050. | ''In the Rio+20 baseline, increasing energy and agricultural production levels lead to an increase of associated greenhouse gas emissions. For air pollutants, the emission trends are more diverse: a decrease is projected in high-income countries, as emission factors drop faster than activity levels increase). In most developing country regions, however, increasing energy production is projected to be associated with more air pollution. The model can also be used to design scenarios that are consistent with different climate targets. For reaching the 2oC target, global [[GHG]] emissions would need to be reduced by around 50% in 2050.'' | ||
===The earth system=== | ===The earth system=== | ||
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A key component of the IMAGE model is formed by the detailed characterization of land cover and the associated impacts on the carbon cycle. This sub-model determines the productivity at the level of grids cells for both natural ecosystems and crops on the basis of so-called plant functional types. Key inputs to determine the productivity include climate conditions, soil types and assumed technology/management levels. The model iterates with the agricultural production components described earlier as it provides input on potential productivity, while the land used for agriculture and forestry forms a key input here. The changes in land cover, driven by changes in land use or climate, obviously have major consequences for the carbon cycle at grid cell level. In addition to these terrestrial carbon fluxes, the model also describes the uptake of carbon by oceans. | A key component of the IMAGE model is formed by the detailed characterization of land cover and the associated impacts on the carbon cycle. This sub-model determines the productivity at the level of grids cells for both natural ecosystems and crops on the basis of so-called plant functional types. Key inputs to determine the productivity include climate conditions, soil types and assumed technology/management levels. The model iterates with the agricultural production components described earlier as it provides input on potential productivity, while the land used for agriculture and forestry forms a key input here. The changes in land cover, driven by changes in land use or climate, obviously have major consequences for the carbon cycle at grid cell level. In addition to these terrestrial carbon fluxes, the model also describes the uptake of carbon by oceans. | ||
The land-use trends in the Rio+20 baseline described earlier lead to a net expansion of agricultural land, and thus a net loss of forest,(in particular tropical). These deforestation trends imply that the terrestrial system is a net source of carbon over the next decades. The net emissions from land, however, decline over time as a result of regrowth in the temperate zone. As a result, in the second half of the 21st century there is even a small net uptake. The emission from the energy system imply that all-in-all there is a rapid increase in atmospheric CO2 concentration. | ''The land-use trends in the Rio+20 baseline described earlier lead to a net expansion of agricultural land, and thus a net loss of forest,(in particular tropical). These deforestation trends imply that the terrestrial system is a net source of carbon over the next decades. The net emissions from land, however, decline over time as a result of regrowth in the temperate zone. As a result, in the second half of the 21st century there is even a small net uptake. The emission from the energy system imply that all-in-all there is a rapid increase in atmospheric CO2 concentration.'' | ||
====Nutrient cycle==== | ====Nutrient cycle==== | ||
The Global Nutrient Model describes the fate of nitrogen (N) and phosphorus (P) emerging from concentrated point sources such as human settlements, and from dispersed or non-point sources such as agricultural and natural land. Through rivers and lakes, the nutrient surplus eventually enters coastal water bodies. Key drivers that demine the nutrient emissions of concentrated and point sources include the agricultural production with its fertilizer application, urban and rural population by sanitation system and level of wastewater treatment. For example, the model calculates the annual surface nitrogen balance from inputs such as biological nitrogen fixation, atmospheric nitrogen deposition and the application of synthetic nitrogen fertilizer and animal manure, and outputs such as nitrogen removal from the field by crop harvesting, hay and grass-cutting and grazing. The outflow of nutrients from the soil in combination with emissions from point sources and direct atmospheric deposition also determine the loading of nutrients to the surface water. | The Global Nutrient Model describes the fate of nitrogen (N) and phosphorus (P) emerging from concentrated point sources such as human settlements, and from dispersed or non-point sources such as agricultural and natural land. Through rivers and lakes, the nutrient surplus eventually enters coastal water bodies. Key drivers that demine the nutrient emissions of concentrated and point sources include the agricultural production with its fertilizer application, urban and rural population by sanitation system and level of wastewater treatment. For example, the model calculates the annual surface nitrogen balance from inputs such as biological nitrogen fixation, atmospheric nitrogen deposition and the application of synthetic nitrogen fertilizer and animal manure, and outputs such as nitrogen removal from the field by crop harvesting, hay and grass-cutting and grazing. The outflow of nutrients from the soil in combination with emissions from point sources and direct atmospheric deposition also determine the loading of nutrients to the surface water. | ||
In the Rio+20 scenario, further increase of the global population and growth of agricultural production adds to pressures on the nutrient cycle. While increasing waste water treatment and improved agricultural practices mitigate some of the increased nutrient loading, these processes are insufficient to offset the increased fertilizer application to sustain intense agriculture. This leads to a significant further imbalance of both the nitrogen and phosphorus cycle, with consequences for water quality in rivers, lakes and coastal seas | ''In the Rio+20 scenario, further increase of the global population and growth of agricultural production adds to pressures on the nutrient cycle. While increasing waste water treatment and improved agricultural practices mitigate some of the increased nutrient loading, these processes are insufficient to offset the increased fertilizer application to sustain intense agriculture. This leads to a significant further imbalance of both the nitrogen and phosphorus cycle, with consequences for water quality in rivers, lakes and coastal seas.'' | ||
====Atmospheric composition and climate change==== | ====Atmospheric composition and climate change==== | ||
Data on emissions of greenhouse gases and air pollutants are used in IMAGE to calculate changes in concentrations of greenhouse gases, ozone precursors and species involved in aerosol formation at a global scale ([[Atmospheric composition and climate]]). Climatic change is calculated as global mean temperature changes using a slightly adapted version of the [[MAGICC]]6 climate model. Climatic change does not manifest itself uniformly over the globe, and patterns of temperature and precipitation are uncertain and differ between complex climate models. Therefore, changes in temperature and precipitation in each 0.5 x 0.5 degree grid cell are derived from the global mean temperature using a pattern-scaling approach. The model accounts for important feedback mechanisms related to changing climate, notably growth characteristics in the crop model, carbon dioxide concentrations (carbon fertilization) and land cover (biome types). | Data on emissions of greenhouse gases and air pollutants are used in IMAGE to calculate changes in concentrations of greenhouse gases, ozone precursors and species involved in aerosol formation at a global scale ([[Atmospheric composition and climate]]). Climatic change is calculated as global mean temperature changes using a slightly adapted version of the [[MAGICC]]6 climate model. Climatic change does not manifest itself uniformly over the globe, and patterns of temperature and precipitation are uncertain and differ between complex climate models. Therefore, changes in temperature and precipitation in each 0.5 x 0.5 degree grid cell are derived from the global mean temperature using a pattern-scaling approach. The model accounts for important feedback mechanisms related to changing climate, notably growth characteristics in the crop model, carbon dioxide concentrations (carbon fertilization) and land cover (biome types). | ||
In the Rio+20 baseline, increasing fossil fuel use leads to increasing [[GHG]] emissions and air pollution levels. For the 2010–2050 period, GHG emissions are projected to increase by about 60%. As a result global temperature is expected to increase to around 4°C above pre-industrial levels by 2100, most likely passing the 2°C level before 2050; see Figure 2.7. | ''In the Rio+20 baseline, increasing fossil fuel use leads to increasing [[GHG]] emissions and air pollution levels. For the 2010–2050 period, GHG emissions are projected to increase by about 60%. As a result global temperature is expected to increase to around 4°C above pre-industrial levels by 2100, most likely passing the 2°C level before 2050; see Figure 2.7.'' | ||
====Water==== | ====Water==== | ||
The [[LPJmL model]] used for a description of vegetation and carbon cycle also includes a global hydrological model ([[Hydrological cycle]]). With this coupled hydrological model, IMAGE scenarios now also capture future changes in water availability, agricultural water use and water shortage.. Water demand for irrigated agriculture is calculated inLPJmL, based on requirements for evapotranspiration for the crop types grown on irrigated land. Water demand for other sectors (households, manufacturing, electricity and livestock) is currently adopted from exogenous scenarios. The most frequently used are WaterGap projections of the University of Kassel, adjusted to remain consistent with projections for households, livestock numbers, industrial value added, and for thermal electricity production as projected with IMAGE-TIMER. | The [[LPJmL model]] used for a description of vegetation and carbon cycle also includes a global hydrological model ([[Hydrological cycle]]). With this coupled hydrological model, IMAGE scenarios now also capture future changes in water availability, agricultural water use and water shortage.. Water demand for irrigated agriculture is calculated inLPJmL, based on requirements for evapotranspiration for the crop types grown on irrigated land. Water demand for other sectors (households, manufacturing, electricity and livestock) is currently adopted from exogenous scenarios. The most frequently used are WaterGap projections of the University of Kassel, adjusted to remain consistent with projections for households, livestock numbers, industrial value added, and for thermal electricity production as projected with IMAGE-TIMER. | ||
The increases projected in agricultural , energy and industry production households lead to an increasing demand for water. At the same time, climate change also impacts the water cycle. While overall climate change is projected to lead to more precipitation, geographical patterns show changes to both dryer and wetter gridcells. As a result, the water balance improves in some regions, while it deteriorates in other regions. The pattern of these changes is very uncertain. In combination with increased demand, however, the number of people confronted with serious water stress is projected to increase significantly. | ''The increases projected in agricultural , energy and industry production households lead to an increasing demand for water. At the same time, climate change also impacts the water cycle. While overall climate change is projected to lead to more precipitation, geographical patterns show changes to both dryer and wetter gridcells. As a result, the water balance improves in some regions, while it deteriorates in other regions. The pattern of these changes is very uncertain. In combination with increased demand, however, the number of people confronted with serious water stress is projected to increase significantly.'' | ||
=== Impacts of environmental change=== | === Impacts of environmental change=== | ||
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Biodiversity loss is described in the impact model [[GLOBIO model|GLOBIO]] in the form of calculated changes in MSA (mean species abundance). The MSA indicator maps the effect of a suite of direct and indirect drivers of biodiversity loss provided by IMAGE. GLOBIO 3 takes into account the impacts of climate, land-use change, ecosystem fragmentation, expansion of infrastructure, disturbance of habitats, and acid and reactive nitrogen deposition ([[Terrestrial biodiversity]]). Their compound effect on biodiversity is computed using the GLOBIO3 model for terrestrial ecosystems. As the IMAGE and the GLOBIO3 models are spatially explicit, the impacts on MSA can be analysed by region, main biome and pressure factor. Recently, a similar model was developed to map biodiversity in fresh water; see [[Aquatic biodiversity]]. | Biodiversity loss is described in the impact model [[GLOBIO model|GLOBIO]] in the form of calculated changes in MSA (mean species abundance). The MSA indicator maps the effect of a suite of direct and indirect drivers of biodiversity loss provided by IMAGE. GLOBIO 3 takes into account the impacts of climate, land-use change, ecosystem fragmentation, expansion of infrastructure, disturbance of habitats, and acid and reactive nitrogen deposition ([[Terrestrial biodiversity]]). Their compound effect on biodiversity is computed using the GLOBIO3 model for terrestrial ecosystems. As the IMAGE and the GLOBIO3 models are spatially explicit, the impacts on MSA can be analysed by region, main biome and pressure factor. Recently, a similar model was developed to map biodiversity in fresh water; see [[Aquatic biodiversity]]. | ||
For biodiversity, a further decline is projected in the Rio+20 baseline at an almost unchanged rate as in the past century. While historically, habitat loss has been the most important driver of biodiversity loss, in the future decades climate change, forestry and infrastructure expansion are projected to become the more important pressure factors. | ''For biodiversity, a further decline is projected in the Rio+20 baseline at an almost unchanged rate as in the past century. While historically, habitat loss has been the most important driver of biodiversity loss, in the future decades climate change, forestry and infrastructure expansion are projected to become the more important pressure factors.'' | ||
====Human development==== | ====Human development==== | ||
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Hunger, defined as the proportion of the population with food consumption below the minimum dietary energy requirement, is determined using an assumed distribution of food intake over individuals on the basis of the mean food availability per capita (from other parts of IMAGE) and a coefficient of variation. Water supply levels and sanitation were modelled separately for urban and rural populations by applying regressions based on available data for 1990 and 2000. The explanatory variables include GDP per capita, urbanisation rate and population density. | Hunger, defined as the proportion of the population with food consumption below the minimum dietary energy requirement, is determined using an assumed distribution of food intake over individuals on the basis of the mean food availability per capita (from other parts of IMAGE) and a coefficient of variation. Water supply levels and sanitation were modelled separately for urban and rural populations by applying regressions based on available data for 1990 and 2000. The explanatory variables include GDP per capita, urbanisation rate and population density. | ||
As far as global hunger is concerned, the Rio+20 scenario shows some clear improvement, in contrast to the last few decades. This improvement is a consequence of a rapid income growth in low-income regions and leveling-off of population growth. | ''As far as global hunger is concerned, the Rio+20 scenario shows some clear improvement, in contrast to the last few decades. This improvement is a consequence of a rapid income growth in low-income regions and leveling-off of population growth.'' | ||
====Other impacts==== | ====Other impacts==== | ||
Other impacts calculated by the IMAGE framework are not described here, including flood risks ([[Flood risks]]), land degradation ([[Soil degradation]]) and ecological goods and services ([[Ecosystem goods and services]]). | Other impacts calculated by the IMAGE framework are not described here, including flood risks ([[Flood risks]]), land degradation ([[Soil degradation]]) and ecological goods and services ([[Ecosystem goods and services]]). | ||
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Revision as of 13:18, 11 December 2013
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