IMAGE - User contributions [en]

Land degradation/Description

2014-05-19T13:23:08Z

JeroenDolmans:

{{ComponentDescriptionTemplate
|Reference=Oldeman et al., 1991; Batjes, 1997; Harris et al., 2013;
|Description=Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:
==A. Risk of soil erosion caused by water==
Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:

* ''terrain erodibility index'': terrain erodibility represents the water erosion characteristics of the terrain in an index that combines surface relief and soil properties, expressed as index numbers. The relief index is a landform characteristic derived from a digital elevation model, calculated from the difference between minimum and maximum altitude in a 10 minute grid cell. The index is 1 for a difference of 300 m or more and zero for no altitude differences, with a linear relationship assumed between the two extremes. The soil erodibility index is derived from indices on soil texture, bulk density and soil depth. Soil characteristics were deduced from the 0.5x0.5 degree resolution in the [[WISE database]] ([[Batjes, 1997]]).

* ''rainfall erosivity index'': this index represents exposure to heavy rainfall, derived from the month of the year with the highest precipitation and number of wet (rainy) days in each month. Rainfall erosivity is largely determined by the intensity of rainfall events, because soil loss only occurs during periods of intense rainfall. Monthly rainfall intensities of between 0 and 2 mm per day are assigned an index value of zero, and days exceeding 20 mm receive a value of one, with a linear relationship assumed between these two end points. Climate data are used for the historical period ([[Harris et al., 2013]]). For future years, predictions are based on changes in precipitation according to scenarios generated by the climate model, see Component [[Atmospheric composition and climate]]. The number of wet days per month is assumed to be constant over time.

* ''land-use/land-cover index'': this index presents the level of protection against water erosion offered by various types of natural vegetation and crops. The basis for this index is the geographic distribution of land-cover types generated by the land-cover model. Most types of natural vegetation provide a high degree of protection against water erosion, while agriculture, and arable agriculture in particular, increases the vulnerability of the soil surface. A composite value is used for grid cells that contain agriculture, based on the distribution of agricultural crops in that world region.

All intermediate and resulting factors are expressed as dimensionless indices from zero to one, and so too is the end indicator, Water Erosion Sensitivity Index.

The susceptibility and sensitivity indices are calculated according to:

:T = (Ia+ SE)/2
:Ep = (T+R)/2
:WES = Ep•V

with:
: Ia = relief index (-)
: SE = soil erodibility index (-)
: T = terrain erodibility index (-)
: R = rainfall erosivity index (-)
: Ep = water erosion susceptibility index (-)
: V = land-use/land-cover index (-)
: WES = Water Erosion Sensitivity Index (-)

Management systems are in use around the world to reduce the risk of erosion, such as building terraces, zero tillage, planting or conserving protective vegetation zones around fields, and high capacity drainage systems. The Water Erosion Sensitivity Index cannot capture all these and other interventions for the current situation, let alone into the future. The index only indicates areas potentially under threat. Impacts on crop production and soil quality cannot be derived directly from the indicator.

Comparison of the calculation above and the GLASOD degradation status maps by [[Oldeman et al., 1991|Oldeman et al. (1991)]] shows maximum correspondence with use of the classification in Table 7.5.2. This classification can be used as a guide in analysing the water erosion sensitivity indicator.

<div class="thumbcaption dark">Classification of the Water Erosion Sensitivity Index</div>
<table class="pbltable">
<tr>
<th>Water Erosion Sensitivity Index</th>
<th>GLASOD soil degradation caused by water erosion</th>
</tr>
<tr>
<td>< 0.15</td>
<td>no/low</td>
</tr>
<tr>
<td>0.15 - 0.30</td>
<td>moderate</td>
</tr>
<tr>
<td>0.30 - 0.45</td>
<td>high</td>
</tr>
<tr>
<td>> 0.45</td>
<td>very high</td>
</tr>
</table>

==B. Human-induced soil changes==
Soil degradation is mostly reflected in changes in soil properties, such as soil depth, soil organic matter ({{abbrTemplate|SOM}}) content, and texture. Land cover and land use drive changes in soil properties. Land cover protects the soil against wind and water erosion, and provides organic matter to the soil. Land use tends to remove part of the biomass with harvested crops and residues and may increase mineralisation of SOM through tillage.

An empirical model denominated S-World has been developed that relates change in soil properties to topography, climate (average annual temperature and total annual precipitation), land management and land use, and land cover (as vegetation cover) ([[Stoorvogel, 2014]]; [[Stoorvogel et al., in preparation]]). The following soil properties are considered:
* topsoil depth,
* soil depth,
* soil organic matter in the topsoil and subsoil , and
* soil texture (sand and clay content).

S-World is based on the global Harmonised World Soil Database ([[HWSD database|HWSD]]; ([[FAO et al., 2009]]) and the [[WISE database|WISE soil profile database]] ([[Batjes, 2009]]). The compound mapping units in HWSD were disaggregated using detailed terrain information, so that each grid cell could be linked to a unique soil type described in the WISE database. For each soil type, ranges for the main soil characteristics described above were assessed on the basis of the WISE soil profiles. The range of variable, i.e., soil property v for every soil type s is subsequently defined as [v<sub>ls</sub>..v<sub>hs</sub>] in which v<sub>ls</sub> corresponds to the 1<sup>st</sup> decile and v<sub>hs</sub> to the 9<sup>th</sup> decile. S-World downscales each soil property v based on 5 landscape properties or explanatory factors [''p<sub>1</sub>,p<sub>2</sub>… p<sub>5</sub>'']. These explanatory factors are: temperature, precipitation, slope, land management, and land cover. The land management is set to:
* 1.0 for cropland,
* 0.5 for mosaics of cropland and pasture or natural vegetation,
* 0.3 for pasture, and
* 0.0 for natural vegetation;
Land cover is characterised by a remotely sensed {{abbrTemplate|NDVI}} map.

The soil property v at location x with soil s is estimated as: {{FormulaAndTableTemplate|Formula1 LD}}
with w<sub>x </sub>being a weight w∈ [0..1] that determines where v is in the range [v<sub>ls</sub>..v<sub>hs</sub> ]. Different explanatory factors represented by the landscape properties determine w. The weight at location x is calculated as: {{FormulaAndTableTemplate|Formula4 LD}}
The weight w<sub>px</sub> for landscape property p is calculated as: {{FormulaAndTableTemplate|Formula2 LD}}
In which c<sub>pv</sub> is a constant that indicates the relative importance of the landscape property p for a soil property v. The sign of c<sub>pv</sub> indicates whether there is a positive or negative relationship between the landscape property and the soil property.

When: {{FormulaAndTableTemplate|Formula3 LD}}
, the w∈ [0..1] and all values in the range [v<sub>ls</sub>..v<sub>hs</sub> ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.

The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.

With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions ([[Van Beek, 2012]]). These soil characteristics can be used in other models in the IMAGE framework, such as [[LPJmL model|LPJmL]] (Component [[Carbon cycle and natural vegetation]] ) and [[GLOFRIS model|GLOFRIS]] (Component [[Flood risks]]), as alternative input to assess the consequences of historical or future land degradation.
|=1〗, the w∈ [0..1] and all values in the range [v_ls..v_hs ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.
The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.
With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions (Van Beek, 2012). These soil characteristics can be used in other models in the IMAGE framework, such as LPJmL (Section 6.1) and GLOFRIS (Section 7.4), as alternative input to assess the consequences of historical or future land degradation.
}}

Land degradation/Description

2014-05-19T13:22:00Z

JeroenDolmans:

{{ComponentDescriptionTemplate
|Reference=Oldeman et al., 1991; Batjes, 1997; Harris et al., 2013;
|Description=Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:
==A. Risk of soil erosion caused by water==
Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:

* ''terrain erodibility index'': terrain erodibility represents the water erosion characteristics of the terrain in an index that combines surface relief and soil properties, expressed as index numbers. The relief index is a landform characteristic derived from a digital elevation model, calculated from the difference between minimum and maximum altitude in a 10 minute grid cell. The index is 1 for a difference of 300 m or more and zero for no altitude differences, with a linear relationship assumed between the two extremes. The soil erodibility index is derived from indices on soil texture, bulk density and soil depth. Soil characteristics were deduced from the 0.5x0.5 degree resolution in the [[WISE database]] ([[Batjes, 1997]]).

* ''rainfall erosivity index'': this index represents exposure to heavy rainfall, derived from the month of the year with the highest precipitation and number of wet (rainy) days in each month. Rainfall erosivity is largely determined by the intensity of rainfall events, because soil loss only occurs during periods of intense rainfall. Monthly rainfall intensities of between 0 and 2 mm per day are assigned an index value of zero, and days exceeding 20 mm receive a value of one, with a linear relationship assumed between these two end points. Climate data are used for the historical period ([[Harris et al., 2013]]). For future years, predictions are based on changes in precipitation according to scenarios generated by the climate model, see Component [[Atmospheric composition and climate]]. The number of wet days per month is assumed to be constant over time.

* ''land-use/land-cover index'': this index presents the level of protection against water erosion offered by various types of natural vegetation and crops. The basis for this index is the geographic distribution of land-cover types generated by the land-cover model. Most types of natural vegetation provide a high degree of protection against water erosion, while agriculture, and arable agriculture in particular, increases the vulnerability of the soil surface. A composite value is used for grid cells that contain agriculture, based on the distribution of agricultural crops in that world region.

All intermediate and resulting factors are expressed as dimensionless indices from zero to one, and so too is the end indicator, Water Erosion Sensitivity Index.

The susceptibility and sensitivity indices are calculated according to:

:T = (Ia+ SE)/2
:Ep = (T+R)/2
:WES = Ep•V

with:
: Ia = relief index (-)
: SE = soil erodibility index (-)
: T = terrain erodibility index (-)
: R = rainfall erosivity index (-)
: Ep = water erosion susceptibility index (-)
: V = land-use/land-cover index (-)
: WES = Water Erosion Sensitivity Index (-)

Management systems are in use around the world to reduce the risk of erosion, such as building terraces, zero tillage, planting or conserving protective vegetation zones around fields, and high capacity drainage systems. The Water Erosion Sensitivity Index cannot capture all these and other interventions for the current situation, let alone into the future. The index only indicates areas potentially under threat. Impacts on crop production and soil quality cannot be derived directly from the indicator.

Comparison of the calculation above and the GLASOD degradation status maps by [[Oldeman et al., 1991|Oldeman et al. (1991)]] shows maximum correspondence with use of the classification in Table 7.5.2. This classification can be used as a guide in analysing the water erosion sensitivity indicator.

<div class="thumbcaption">Classification of the Water Erosion Sensitivity Index</div>
<table class="pbltable">
<tr>
<th>Water Erosion Sensitivity Index</th>
<th>GLASOD soil degradation caused by water erosion</th>
</tr>
<tr>
<td>< 0.15</td>
<td>no/low</td>
</tr>
<tr>
<td>0.15 - 0.30</td>
<td>moderate</td>
</tr>
<tr>
<td>0.30 - 0.45</td>
<td>high</td>
</tr>
<tr>
<td>> 0.45</td>
<td>very high</td>
</tr>
</table>

==B. Human-induced soil changes==
Soil degradation is mostly reflected in changes in soil properties, such as soil depth, soil organic matter ({{abbrTemplate|SOM}}) content, and texture. Land cover and land use drive changes in soil properties. Land cover protects the soil against wind and water erosion, and provides organic matter to the soil. Land use tends to remove part of the biomass with harvested crops and residues and may increase mineralisation of SOM through tillage.

An empirical model denominated S-World has been developed that relates change in soil properties to topography, climate (average annual temperature and total annual precipitation), land management and land use, and land cover (as vegetation cover) ([[Stoorvogel, 2014]]; [[Stoorvogel et al., in preparation]]). The following soil properties are considered:
* topsoil depth,
* soil depth,
* soil organic matter in the topsoil and subsoil , and
* soil texture (sand and clay content).

S-World is based on the global Harmonised World Soil Database ([[HWSD database|HWSD]]; ([[FAO et al., 2009]]) and the [[WISE database|WISE soil profile database]] ([[Batjes, 2009]]). The compound mapping units in HWSD were disaggregated using detailed terrain information, so that each grid cell could be linked to a unique soil type described in the WISE database. For each soil type, ranges for the main soil characteristics described above were assessed on the basis of the WISE soil profiles. The range of variable, i.e., soil property v for every soil type s is subsequently defined as [v<sub>ls</sub>..v<sub>hs</sub>] in which v<sub>ls</sub> corresponds to the 1<sup>st</sup> decile and v<sub>hs</sub> to the 9<sup>th</sup> decile. S-World downscales each soil property v based on 5 landscape properties or explanatory factors [''p<sub>1</sub>,p<sub>2</sub>… p<sub>5</sub>'']. These explanatory factors are: temperature, precipitation, slope, land management, and land cover. The land management is set to:
* 1.0 for cropland,
* 0.5 for mosaics of cropland and pasture or natural vegetation,
* 0.3 for pasture, and
* 0.0 for natural vegetation;
Land cover is characterised by a remotely sensed {{abbrTemplate|NDVI}} map.

The soil property v at location x with soil s is estimated as: {{FormulaAndTableTemplate|Formula1 LD}}
with w<sub>x </sub>being a weight w∈ [0..1] that determines where v is in the range [v<sub>ls</sub>..v<sub>hs</sub> ]. Different explanatory factors represented by the landscape properties determine w. The weight at location x is calculated as: {{FormulaAndTableTemplate|Formula4 LD}}
The weight w<sub>px</sub> for landscape property p is calculated as: {{FormulaAndTableTemplate|Formula2 LD}}
In which c<sub>pv</sub> is a constant that indicates the relative importance of the landscape property p for a soil property v. The sign of c<sub>pv</sub> indicates whether there is a positive or negative relationship between the landscape property and the soil property.

When: {{FormulaAndTableTemplate|Formula3 LD}}
, the w∈ [0..1] and all values in the range [v<sub>ls</sub>..v<sub>hs</sub> ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.

The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.

With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions ([[Van Beek, 2012]]). These soil characteristics can be used in other models in the IMAGE framework, such as [[LPJmL model|LPJmL]] (Component [[Carbon cycle and natural vegetation]] ) and [[GLOFRIS model|GLOFRIS]] (Component [[Flood risks]]), as alternative input to assess the consequences of historical or future land degradation.
|=1〗, the w∈ [0..1] and all values in the range [v_ls..v_hs ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.
The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.
With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions (Van Beek, 2012). These soil characteristics can be used in other models in the IMAGE framework, such as LPJmL (Section 6.1) and GLOFRIS (Section 7.4), as alternative input to assess the consequences of historical or future land degradation.
}}

Land degradation/Description

2014-05-19T13:13:15Z

JeroenDolmans:

{{ComponentDescriptionTemplate
|Reference=Oldeman et al., 1991; Batjes, 1997; Harris et al., 2013;
|Description=Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:
==A. Risk of soil erosion caused by water==
Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:

* ''terrain erodibility index'': terrain erodibility represents the water erosion characteristics of the terrain in an index that combines surface relief and soil properties, expressed as index numbers. The relief index is a landform characteristic derived from a digital elevation model, calculated from the difference between minimum and maximum altitude in a 10 minute grid cell. The index is 1 for a difference of 300 m or more and zero for no altitude differences, with a linear relationship assumed between the two extremes. The soil erodibility index is derived from indices on soil texture, bulk density and soil depth. Soil characteristics were deduced from the 0.5x0.5 degree resolution in the [[WISE database]] ([[Batjes, 1997]]).

* ''rainfall erosivity index'': this index represents exposure to heavy rainfall, derived from the month of the year with the highest precipitation and number of wet (rainy) days in each month. Rainfall erosivity is largely determined by the intensity of rainfall events, because soil loss only occurs during periods of intense rainfall. Monthly rainfall intensities of between 0 and 2 mm per day are assigned an index value of zero, and days exceeding 20 mm receive a value of one, with a linear relationship assumed between these two end points. Climate data are used for the historical period ([[Harris et al., 2013]]). For future years, predictions are based on changes in precipitation according to scenarios generated by the climate model, see Component [[Atmospheric composition and climate]]. The number of wet days per month is assumed to be constant over time.

* ''land-use/land-cover index'': this index presents the level of protection against water erosion offered by various types of natural vegetation and crops. The basis for this index is the geographic distribution of land-cover types generated by the land-cover model. Most types of natural vegetation provide a high degree of protection against water erosion, while agriculture, and arable agriculture in particular, increases the vulnerability of the soil surface. A composite value is used for grid cells that contain agriculture, based on the distribution of agricultural crops in that world region.

All intermediate and resulting factors are expressed as dimensionless indices from zero to one, and so too is the end indicator, Water Erosion Sensitivity Index.

The susceptibility and sensitivity indices are calculated according to:

:T = (Ia+ SE)/2
:Ep = (T+R)/2
:WES = Ep•V

with:
: Ia = relief index (-)
: SE = soil erodibility index (-)
: T = terrain erodibility index (-)
: R = rainfall erosivity index (-)
: Ep = water erosion susceptibility index (-)
: V = land-use/land-cover index (-)
: WES = Water Erosion Sensitivity Index (-)

Management systems are in use around the world to reduce the risk of erosion, such as building terraces, zero tillage, planting or conserving protective vegetation zones around fields, and high capacity drainage systems. The Water Erosion Sensitivity Index cannot capture all these and other interventions for the current situation, let alone into the future. The index only indicates areas potentially under threat. Impacts on crop production and soil quality cannot be derived directly from the indicator.

Comparison of the calculation above and the GLASOD degradation status maps by [[Oldeman et al., 1991|Oldeman et al. (1991)]] shows maximum correspondence with use of the classification in Table 7.5.2. This classification can be used as a guide in analysing the water erosion sensitivity indicator.

'''Classification of the Water Erosion Sensitivity Index'''
<table class="pbltable">
<tr>
<th>Water Erosion Sensitivity Index</th>
<th>GLASOD soil degradation caused by water erosion</th>
</tr>
<tr>
<td>< 0.15</td>
<td>no/low</td>
</tr>
<tr>
<td>0.15 - 0.30</td>
<td>moderate</td>
</tr>
<tr>
<td>0.30 - 0.45</td>
<td>high</td>
</tr>
<tr>
<td>> 0.45</td>
<td>very high</td>
</tr>
</table>

==B. Human-induced soil changes==
Soil degradation is mostly reflected in changes in soil properties, such as soil depth, soil organic matter ({{abbrTemplate|SOM}}) content, and texture. Land cover and land use drive changes in soil properties. Land cover protects the soil against wind and water erosion, and provides organic matter to the soil. Land use tends to remove part of the biomass with harvested crops and residues and may increase mineralisation of SOM through tillage.

An empirical model denominated S-World has been developed that relates change in soil properties to topography, climate (average annual temperature and total annual precipitation), land management and land use, and land cover (as vegetation cover) ([[Stoorvogel, 2014]]; [[Stoorvogel et al., in preparation]]). The following soil properties are considered:
* topsoil depth,
* soil depth,
* soil organic matter in the topsoil and subsoil , and
* soil texture (sand and clay content).

S-World is based on the global Harmonised World Soil Database ([[HWSD database|HWSD]]; ([[FAO et al., 2009]]) and the [[WISE database|WISE soil profile database]] ([[Batjes, 2009]]). The compound mapping units in HWSD were disaggregated using detailed terrain information, so that each grid cell could be linked to a unique soil type described in the WISE database. For each soil type, ranges for the main soil characteristics described above were assessed on the basis of the WISE soil profiles. The range of variable, i.e., soil property v for every soil type s is subsequently defined as [v<sub>ls</sub>..v<sub>hs</sub>] in which v<sub>ls</sub> corresponds to the 1<sup>st</sup> decile and v<sub>hs</sub> to the 9<sup>th</sup> decile. S-World downscales each soil property v based on 5 landscape properties or explanatory factors [''p<sub>1</sub>,p<sub>2</sub>… p<sub>5</sub>'']. These explanatory factors are: temperature, precipitation, slope, land management, and land cover. The land management is set to:
* 1.0 for cropland,
* 0.5 for mosaics of cropland and pasture or natural vegetation,
* 0.3 for pasture, and
* 0.0 for natural vegetation;
Land cover is characterised by a remotely sensed {{abbrTemplate|NDVI}} map.

The soil property v at location x with soil s is estimated as: {{FormulaAndTableTemplate|Formula1 LD}}
with w<sub>x </sub>being a weight w∈ [0..1] that determines where v is in the range [v<sub>ls</sub>..v<sub>hs</sub> ]. Different explanatory factors represented by the landscape properties determine w. The weight at location x is calculated as: {{FormulaAndTableTemplate|Formula4 LD}}
The weight w<sub>px</sub> for landscape property p is calculated as: {{FormulaAndTableTemplate|Formula2 LD}}
In which c<sub>pv</sub> is a constant that indicates the relative importance of the landscape property p for a soil property v. The sign of c<sub>pv</sub> indicates whether there is a positive or negative relationship between the landscape property and the soil property.

When: {{FormulaAndTableTemplate|Formula3 LD}}
, the w∈ [0..1] and all values in the range [v<sub>ls</sub>..v<sub>hs</sub> ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.

The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.

With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions ([[Van Beek, 2012]]). These soil characteristics can be used in other models in the IMAGE framework, such as [[LPJmL model|LPJmL]] (Component [[Carbon cycle and natural vegetation]] ) and [[GLOFRIS model|GLOFRIS]] (Component [[Flood risks]]), as alternative input to assess the consequences of historical or future land degradation.
|=1〗, the w∈ [0..1] and all values in the range [v_ls..v_hs ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.
The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.
With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions (Van Beek, 2012). These soil characteristics can be used in other models in the IMAGE framework, such as LPJmL (Section 6.1) and GLOFRIS (Section 7.4), as alternative input to assess the consequences of historical or future land degradation.
}}

Land degradation/Description

2014-05-19T13:08:30Z

JeroenDolmans:

{{ComponentDescriptionTemplate
|Reference=Oldeman et al., 1991; Batjes, 1997; Harris et al., 2013;
|Description=Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:
==A. Risk of soil erosion caused by water==
Water erosion is the main cause of land degradation (1049 million hectares (Mha), followed by wind erosion (548 Mha), chemical degradation (239 Mha) and physical degradation (83 Mha) (GLASOD; [[Oldeman et al., 1991|Oldeman et al. (1991)]]). IMAGE assesses soil erosion by water ([[Hootsmans et al., 2001]]) by calculating a water erosion sensitivity index, ranging from zero (no erosion risk) to one (extremely high erosion risk). This risk is calculated for each grid cell as the compounded result from the following indices:

* ''terrain erodibility index'': terrain erodibility represents the water erosion characteristics of the terrain in an index that combines surface relief and soil properties, expressed as index numbers. The relief index is a landform characteristic derived from a digital elevation model, calculated from the difference between minimum and maximum altitude in a 10 minute grid cell. The index is 1 for a difference of 300 m or more and zero for no altitude differences, with a linear relationship assumed between the two extremes. The soil erodibility index is derived from indices on soil texture, bulk density and soil depth. Soil characteristics were deduced from the 0.5x0.5 degree resolution in the [[WISE database]] ([[Batjes, 1997]]).

* ''rainfall erosivity index'': this index represents exposure to heavy rainfall, derived from the month of the year with the highest precipitation and number of wet (rainy) days in each month. Rainfall erosivity is largely determined by the intensity of rainfall events, because soil loss only occurs during periods of intense rainfall. Monthly rainfall intensities of between 0 and 2 mm per day are assigned an index value of zero, and days exceeding 20 mm receive a value of one, with a linear relationship assumed between these two end points. Climate data are used for the historical period ([[Harris et al., 2013]]). For future years, predictions are based on changes in precipitation according to scenarios generated by the climate model, see Component [[Atmospheric composition and climate]]. The number of wet days per month is assumed to be constant over time.

* ''land-use/land-cover index'': this index presents the level of protection against water erosion offered by various types of natural vegetation and crops. The basis for this index is the geographic distribution of land-cover types generated by the land-cover model. Most types of natural vegetation provide a high degree of protection against water erosion, while agriculture, and arable agriculture in particular, increases the vulnerability of the soil surface. A composite value is used for grid cells that contain agriculture, based on the distribution of agricultural crops in that world region.

All intermediate and resulting factors are expressed as dimensionless indices from zero to one, and so too is the end indicator, Water Erosion Sensitivity Index.

The susceptibility and sensitivity indices are calculated according to:

:T = (Ia+ SE)/2
:Ep = (T+R)/2
:WES = Ep•V

with:
: Ia = relief index (-)
: SE = soil erodibility index (-)
: T = terrain erodibility index (-)
: R = rainfall erosivity index (-)
: Ep = water erosion susceptibility index (-)
: V = land-use/land-cover index (-)
: WES = Water Erosion Sensitivity Index (-)

Management systems are in use around the world to reduce the risk of erosion, such as building terraces, zero tillage, planting or conserving protective vegetation zones around fields, and high capacity drainage systems. The Water Erosion Sensitivity Index cannot capture all these and other interventions for the current situation, let alone into the future. The index only indicates areas potentially under threat. Impacts on crop production and soil quality cannot be derived directly from the indicator.

Comparison of the calculation above and the GLASOD degradation status maps by [[Oldeman et al., 1991|Oldeman et al. (1991)]] shows maximum correspondence with use of the classification in Table 7.5.2. This classification can be used as a guide in analysing the water erosion sensitivity indicator.

'''Classification of the Water Erosion Sensitivity Index'''
<table class="StandardTable">
<tr>
<th>Water Erosion Sensitivity Index</th>
<th>GLASOD soil degradation caused by water erosion</th>
</tr>
<tr>
<td>< 0.15</td>
<td>no/low</td>
</tr>
<tr>
<td>0.15 - 0.30</td>
<td>moderate</td>
</tr>
<tr>
<td>0.30 - 0.45</td>
<td>high</td>
</tr>
<tr>
<td>> 0.45</td>
<td>very high</td>
</tr>
</table>

==B. Human-induced soil changes==
Soil degradation is mostly reflected in changes in soil properties, such as soil depth, soil organic matter ({{abbrTemplate|SOM}}) content, and texture. Land cover and land use drive changes in soil properties. Land cover protects the soil against wind and water erosion, and provides organic matter to the soil. Land use tends to remove part of the biomass with harvested crops and residues and may increase mineralisation of SOM through tillage.

An empirical model denominated S-World has been developed that relates change in soil properties to topography, climate (average annual temperature and total annual precipitation), land management and land use, and land cover (as vegetation cover) ([[Stoorvogel, 2014]]; [[Stoorvogel et al., in preparation]]). The following soil properties are considered:
* topsoil depth,
* soil depth,
* soil organic matter in the topsoil and subsoil , and
* soil texture (sand and clay content).

S-World is based on the global Harmonised World Soil Database ([[HWSD database|HWSD]]; ([[FAO et al., 2009]]) and the [[WISE database|WISE soil profile database]] ([[Batjes, 2009]]). The compound mapping units in HWSD were disaggregated using detailed terrain information, so that each grid cell could be linked to a unique soil type described in the WISE database. For each soil type, ranges for the main soil characteristics described above were assessed on the basis of the WISE soil profiles. The range of variable, i.e., soil property v for every soil type s is subsequently defined as [v<sub>ls</sub>..v<sub>hs</sub>] in which v<sub>ls</sub> corresponds to the 1<sup>st</sup> decile and v<sub>hs</sub> to the 9<sup>th</sup> decile. S-World downscales each soil property v based on 5 landscape properties or explanatory factors [''p<sub>1</sub>,p<sub>2</sub>… p<sub>5</sub>'']. These explanatory factors are: temperature, precipitation, slope, land management, and land cover. The land management is set to:
* 1.0 for cropland,
* 0.5 for mosaics of cropland and pasture or natural vegetation,
* 0.3 for pasture, and
* 0.0 for natural vegetation;
Land cover is characterised by a remotely sensed {{abbrTemplate|NDVI}} map.

The soil property v at location x with soil s is estimated as: {{FormulaAndTableTemplate|Formula1 LD}}
with w<sub>x </sub>being a weight w∈ [0..1] that determines where v is in the range [v<sub>ls</sub>..v<sub>hs</sub> ]. Different explanatory factors represented by the landscape properties determine w. The weight at location x is calculated as: {{FormulaAndTableTemplate|Formula4 LD}}
The weight w<sub>px</sub> for landscape property p is calculated as: {{FormulaAndTableTemplate|Formula2 LD}}
In which c<sub>pv</sub> is a constant that indicates the relative importance of the landscape property p for a soil property v. The sign of c<sub>pv</sub> indicates whether there is a positive or negative relationship between the landscape property and the soil property.

When: {{FormulaAndTableTemplate|Formula3 LD}}
, the w∈ [0..1] and all values in the range [v<sub>ls</sub>..v<sub>hs</sub> ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.

The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.

With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions ([[Van Beek, 2012]]). These soil characteristics can be used in other models in the IMAGE framework, such as [[LPJmL model|LPJmL]] (Component [[Carbon cycle and natural vegetation]] ) and [[GLOFRIS model|GLOFRIS]] (Component [[Flood risks]]), as alternative input to assess the consequences of historical or future land degradation.
|=1〗, the w∈ [0..1] and all values in the range [v_ls..v_hs ] are possible based on the landscape properties. Although in practice c is specific for each landscape property, soil type, and soil property, data are lacking to estimate c at that level of specificity. Therefore the model assumes that c is constant per soil and landscape property, or, in other words, the relative impact of landscape properties on a specific soil property is assumed to be constant over the different soil types.
The soil properties are estimated based on land management and land use. This allows for the estimation of soil properties under pristine conditions. For future years, the NDVI map is changed as a function of land use, forest management and assumptions on degradation. To assess pristine conditions, soil properties are calculated with land use set at natural, and land cover represented by the NDVI under pristine conditions.
With this procedure, a change in soil properties (topsoil depth, soil depth, SOM in topsoil and subsoil, and soil texture) can be calculated as a result of land use and land cover. Subsequently, additional soil characteristics, such as water holding capacity and water infiltration rate, can be derived from these soil property values by using pedo-transfer functions (Van Beek, 2012). These soil characteristics can be used in other models in the IMAGE framework, such as LPJmL (Section 6.1) and GLOFRIS (Section 7.4), as alternative input to assess the consequences of historical or future land degradation.
}}

Template:FrameworkSummaryPartTemplate

2014-05-19T12:53:19Z

JeroenDolmans:

<noinclude>
This is the "FrameworkSummaryPartTemplate" template.
It should be called in the following format:
<pre>
{{FrameworkSummaryPartTemplate

|Pagelabel=
|Sequence=
|Reference=
|Description=
}}
</pre>
Edit the page to see the template text.
</noinclude><includeonly>{{IconTemplate|{{#titleparts: {{PAGENAME}}|1}} }}
{{ContentPartsTemplate|PagePartHeader=Parts of {{#titleparts: {{PAGENAME}}|1}}|subpage=1 }}__TOC__
<div class="container">{{InfoBoxSubPageTemplate|PagePartHeader=Driver parts|Reference={{{Reference|}}} }}<div class="page_standard">{{{Description|}}}
</div></div>
{{ContentPartsTemplate|PagePartHeader=Parts of {{#titleparts: {{PAGENAME}}|1}}|subpage=1 }}
[[Category:FrameworkSummaryPart]]
{{#arraymap:{{{Reference|}}}|;|x|[[HasReference::x| ]]|}}

[[HasPageLabel::{{{PageLabel}}}| ]]
[[BelongsToPage::{{#titleparts: {{PAGENAME}}|1}}| ]]
[[HasSequence::{{{Sequence|}}}| ]]

</includeonly>

Template:ComponentTemplate2

2014-05-19T12:40:01Z

JeroenDolmans:

<noinclude>
This is the "ComponentTemplate2" template.
It should be called in the following format:
<pre>
{{ComponentTemplate2
|Application=
|IMAGEComponent=
|ExternalModel=
|KeyReference=
|Reference=
|Description=
}}
</pre>
Steps in this template:
# Icon en De subpages van deze pagina ophalen en weergeven in toc style plus de toc
# De InfoboxTemplate aanroep
# De flowchart
# We beginnen de Component-beschrijving met de KeyPolicyQuestions
# description
# De input and outputs
# De parts of the component again
</noinclude><includeonly>{{IconTemplate| {{PAGENAME}}}}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}} }}
__TOC____NOEDITSECTION__
<div class="container">{{InfoBoxTemplate|IMAGEComponent={{{IMAGEComponent|}}}|Application={{{Application|}}}|ExternalModel={{{ExternalModel|}}}|KeyReference={{{KeyReference|}}}|Reference={{{Reference|}}} }}{{DisplayFlowchartTemplate|{{PAGENAME}} }} <div class="page_standard">{{DisplayKeyPolicyQuestionsTemplate|{{PAGENAME}} }}
{{#ifeq:{{{Description|}}}|||<h2>Introduction</h2>
{{{Description|}}}
</div></div>
}}
{{InputOutputParameterTemplate}}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}} }}


[[Category:Component]]
[[HasTitle::{{PAGENAME}}| ]]
{{#ifeq:{{{FrameworkElementType|}}}|||[[FrameworkElementType::{{{FrameworkElementType|}}}| ]] }}

{{#ifeq:{{{AggregatedComponent|}}}|||[[BelongsToAggregatedComponent::{{{AggregatedComponent}}}|]]}}
[[HasComponentCode::{{{ComponentCode|}}}|]]
{{#ifeq:{{{InputVar|}}}|||{{#arraymap:{{{InputVar|}}}|;|x|[[HasInputVar::x| ]]| }} }}
{{#ifeq:{{{OutputVar|}}}|||{{#arraymap:{{{OutputVar|}}}|;|x|[[HasOutputVar::x| ]]| }} }}
{{#ifeq:{{{Parameter|}}}|||{{#arraymap:{{{Parameter|}}}|;|x|[[HasParameter::x| ]]| }} }}
[[HasPageLabel::Introduction page| ]]
[[HasDetailedDescription::{{PAGENAME}}/Description| ]]
[[HasPolicyIntervention::{{PAGENAME}}/Policy issues| ]]
[[HasDataLimitations::{{PAGENAME}}/Data_uncertainties_limitations| ]]
[[HasAllReferences::{{PAGENAME}}/References| ]]

</includeonly>

Template:ComponentTemplate2

2014-05-19T12:39:23Z

JeroenDolmans:

<noinclude>
This is the "ComponentTemplate2" template.
It should be called in the following format:
<pre>
{{ComponentTemplate2
|Application=
|IMAGEComponent=
|ExternalModel=
|KeyReference=
|Reference=
|Description=
}}
</pre>
Steps in this template:
# Icon en De subpages van deze pagina ophalen en weergeven in toc style plus de toc
# De InfoboxTemplate aanroep
# De flowchart
# We beginnen de Component-beschrijving met de KeyPolicyQuestions
# description
# De input and outputs
# De parts of the component again
</noinclude><includeonly>{{IconTemplate| {{PAGENAME}}}}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}} }}
__TOC____NOEDITSECTION__
<div class="container">{{InfoBoxTemplate|IMAGEComponent={{{IMAGEComponent|}}}|Application={{{Application|}}}|ExternalModel={{{ExternalModel|}}}|KeyReference={{{KeyReference|}}}|Reference={{{Reference|}}} }}{{DisplayFlowchartTemplate|{{PAGENAME}} }} <div class="page_standard">{{DisplayKeyPolicyQuestionsTemplate|{{PAGENAME}} }}
{{#ifeq:{{{Description|}}}|||<h2>Introduction</h2>
{{{Description|}}}</div></div>
}}
{{InputOutputParameterTemplate}}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}} }}


[[Category:Component]]
[[HasTitle::{{PAGENAME}}| ]]
{{#ifeq:{{{FrameworkElementType|}}}|||[[FrameworkElementType::{{{FrameworkElementType|}}}| ]] }}

{{#ifeq:{{{AggregatedComponent|}}}|||[[BelongsToAggregatedComponent::{{{AggregatedComponent}}}|]]}}
[[HasComponentCode::{{{ComponentCode|}}}|]]
{{#ifeq:{{{InputVar|}}}|||{{#arraymap:{{{InputVar|}}}|;|x|[[HasInputVar::x| ]]| }} }}
{{#ifeq:{{{OutputVar|}}}|||{{#arraymap:{{{OutputVar|}}}|;|x|[[HasOutputVar::x| ]]| }} }}
{{#ifeq:{{{Parameter|}}}|||{{#arraymap:{{{Parameter|}}}|;|x|[[HasParameter::x| ]]| }} }}
[[HasPageLabel::Introduction page| ]]
[[HasDetailedDescription::{{PAGENAME}}/Description| ]]
[[HasPolicyIntervention::{{PAGENAME}}/Policy issues| ]]
[[HasDataLimitations::{{PAGENAME}}/Data_uncertainties_limitations| ]]
[[HasAllReferences::{{PAGENAME}}/References| ]]

</includeonly>

Template:ComponentTemplate2

2014-05-19T12:37:43Z

JeroenDolmans:

<noinclude>
This is the "ComponentTemplate2" template.
It should be called in the following format:
<pre>
{{ComponentTemplate2
|Application=
|IMAGEComponent=
|ExternalModel=
|KeyReference=
|Reference=
|Description=
}}
</pre>
Steps in this template:
# Icon en De subpages van deze pagina ophalen en weergeven in toc style plus de toc
# De InfoboxTemplate aanroep
# De flowchart
# We beginnen de Component-beschrijving met de KeyPolicyQuestions
# description
# De input and outputs
# De parts of the component again
</noinclude><includeonly>{{IconTemplate| {{PAGENAME}}}}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}} }}
__TOC____NOEDITSECTION__
<div class="container">{{InfoBoxTemplate|IMAGEComponent={{{IMAGEComponent|}}}|Application={{{Application|}}}|ExternalModel={{{ExternalModel|}}}|KeyReference={{{KeyReference|}}}|Reference={{{Reference|}}} }}{{DisplayFlowchartTemplate|{{PAGENAME}} }} <div class="page_standard">{{DisplayKeyPolicyQuestionsTemplate|{{PAGENAME}} }}
{{#ifeq:{{{Description|}}}|||<h2>Introduction</h2>
{{{Description|}}}
}}
{{InputOutputParameterTemplate}}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}} }}</div></div>


[[Category:Component]]
[[HasTitle::{{PAGENAME}}| ]]
{{#ifeq:{{{FrameworkElementType|}}}|||[[FrameworkElementType::{{{FrameworkElementType|}}}| ]] }}

{{#ifeq:{{{AggregatedComponent|}}}|||[[BelongsToAggregatedComponent::{{{AggregatedComponent}}}|]]}}
[[HasComponentCode::{{{ComponentCode|}}}|]]
{{#ifeq:{{{InputVar|}}}|||{{#arraymap:{{{InputVar|}}}|;|x|[[HasInputVar::x| ]]| }} }}
{{#ifeq:{{{OutputVar|}}}|||{{#arraymap:{{{OutputVar|}}}|;|x|[[HasOutputVar::x| ]]| }} }}
{{#ifeq:{{{Parameter|}}}|||{{#arraymap:{{{Parameter|}}}|;|x|[[HasParameter::x| ]]| }} }}
[[HasPageLabel::Introduction page| ]]
[[HasDetailedDescription::{{PAGENAME}}/Description| ]]
[[HasPolicyIntervention::{{PAGENAME}}/Policy issues| ]]
[[HasDataLimitations::{{PAGENAME}}/Data_uncertainties_limitations| ]]
[[HasAllReferences::{{PAGENAME}}/References| ]]

</includeonly>

Template:FrameworkSummaryTemplate

2014-05-19T12:27:21Z

JeroenDolmans:

<noinclude>
This is the "FrameworkSummaryTemplate" template.
It should be called in the following format:
<pre>
{{FrameworkSummaryTemplate}}

|Overview=
|Reference=
|Description=
}}
</pre>
Steps in this template are described in ComponentTemplate
</noinclude><includeonly>{{IconTemplate|{{#titleparts: {{PAGENAME}}|1}} }}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}__TOC__ __NOEDITSECTION__
<div class="container">{{InfoBoxTemplate|Overview={{{Overview|}}}|Application={{{Application|}}}|KeyReference={{{KeyReference|}}}|Reference={{{Reference|}}} }}
<div class="page_standard">{{DisplayKeyPolicyQuestionsTemplate|{{PAGENAME}} }}
{{#ifeq:{{{Description|}}}|||<h2>Introduction</h2>
{{{Description|}}}
}}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}
</div></div>


[[Category:FrameworkSummary]]
[[HasTitle::{{PAGENAME}}| ]]

[[HasComponentCode::{{{ComponentCode|}}}|]]
[[HasPageLabel::Introduction page| ]]
[[HasAllReferences::{{PAGENAME}}/References| ]]
</includeonly>

Template:FrameworkSummaryTemplate

2014-05-19T12:26:44Z

JeroenDolmans:

<noinclude>
This is the "FrameworkSummaryTemplate" template.
It should be called in the following format:
<pre>
{{FrameworkSummaryTemplate}}

|Overview=
|Reference=
|Description=
}}
</pre>
Steps in this template are described in ComponentTemplate
</noinclude><includeonly>{{IconTemplate|{{#titleparts: {{PAGENAME}}|1}} }}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}__TOC__ __NOEDITSECTION__
<div class="container"><div class="page_standard">{{InfoBoxTemplate|Overview={{{Overview|}}}|Application={{{Application|}}}|KeyReference={{{KeyReference|}}}|Reference={{{Reference|}}} }}
{{DisplayKeyPolicyQuestionsTemplate|{{PAGENAME}} }}
{{#ifeq:{{{Description|}}}|||<h2>Introduction</h2>
{{{Description|}}}
}}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}
</div></div>


[[Category:FrameworkSummary]]
[[HasTitle::{{PAGENAME}}| ]]

[[HasComponentCode::{{{ComponentCode|}}}|]]
[[HasPageLabel::Introduction page| ]]
[[HasAllReferences::{{PAGENAME}}/References| ]]
</includeonly>

Template:FrameworkSummaryTemplate

2014-05-19T12:24:44Z

JeroenDolmans:

<noinclude>
This is the "FrameworkSummaryTemplate" template.
It should be called in the following format:
<pre>
{{FrameworkSummaryTemplate}}

|Overview=
|Reference=
|Description=
}}
</pre>
Steps in this template are described in ComponentTemplate
</noinclude><includeonly>{{IconTemplate|{{#titleparts: {{PAGENAME}}|1}} }}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}__TOC__ __NOEDITSECTION__
<div class="page_standard">{{InfoBoxTemplate|Overview={{{Overview|}}}|Application={{{Application|}}}|KeyReference={{{KeyReference|}}}|Reference={{{Reference|}}} }}
{{DisplayKeyPolicyQuestionsTemplate|{{PAGENAME}} }}
{{#ifeq:{{{Description|}}}|||<h2>Introduction</h2>
{{{Description|}}}
}}
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}
</div>


[[Category:FrameworkSummary]]
[[HasTitle::{{PAGENAME}}| ]]

[[HasComponentCode::{{{ComponentCode|}}}|]]
[[HasPageLabel::Introduction page| ]]
[[HasAllReferences::{{PAGENAME}}/References| ]]
</includeonly>

FUND model

2014-05-19T12:09:07Z

JeroenDolmans:

{{ComputerModelTemplate
|Subject=climate change
|Description=The Climate Framework for Uncertainty, Negotiation and Distribution (FUND) is a so-called integrated assessment model (IAM) of climate change. FUND was originally set-up to study the role of international capital transfers in climate policy, but it soon evolved into a test-bed for studying impacts of climate change in a dynamic context, and it is now often used to perform cost-benefit and cost-effectiveness analyses of greenhouse gas emission reduction policies, to study equity of climate change and climate policy, and to support game-theoretic investigations into international environmental agreements.
|ExternalURL=http://www.fund-model.org/
|FrameworkRelation=other
|Component=
}}

Template:ComputerModelTemplate

2014-05-19T12:08:40Z

JeroenDolmans:

<noinclude>
This is the "ComputerModelTemplate" template.
It should be called in the following format:
<pre>
{{ComputerModelTemplate
|Subject=
|Description=
|FrameworkRelation=
|Component=
|Creator=
|ExternalURL=
|Reference=
}}
</pre>
Edit the page to see the template text.
</noinclude><includeonly>
{{#ifexpr: {{#if:{{{FrameworkRelation|}}}|1|0}} or {{#if:{{{Component|}}}|1|0}} or {{#if:{{{Creator|}}}|1|0}}|
<table class="PageWidthTableTemplate">
{{#ifeq:{{{Subject|}}}|||<tr><td class="PageWidthTableFirstCell">'''Subject:'''</td><td class="PageWidthTableRemainderCell">
[[HasSubject::{{{Subject|}}}]]
</td></tr>
}}
{{#ifeq:{{{Description|}}}|||<tr><td class="PageWidthTableFirstCell">'''Description:'''</td><td class="PageWidthTableRemainderCell">
[[HasDescription::{{{Description|}}}]]
</td></tr>
}}
{{#ifeq:{{{FrameworkRelation|}}}|||<tr><td class="PageWidthTableFirstCell">'''Relation with IMAGE framework:'''</td><td class="PageWidthTableRemainderCell">
[[RelationWithIMAGEFramework::{{{FrameworkRelation|}}}]]
</td></tr>
}}
{{#ifeq:{{{Component|}}}|||<tr><td class="PageWidthTableFirstCell">'''Implements:'''</td><td class="PageWidthTableRemainderCell">
{{#arraymap:{{{Component|}}}|;|x|[[IsImplementationOf::x]]|; }}
</td></tr>
}}
{{#ifeq:{{{Creator|}}}|||<tr><td class="PageWidthTableFirstCell">'''Developed by:'''</td>
<td class="PageWidthTableRemainderCell">
{{#arraymap:{{{Creator|}}}|;|x|[[HasCreator::x]]|, }}
</td></tr>

}}
{{#ifeq:{{{ExternalURL|}}}|||<tr><td class="PageWidthTableFirstCell">'''External link:'''</td>
<td class="PageWidthTableRemainderCell">
[[HasURL::{{{ExternalURL|}}}]]
</td></tr>
}}
{{#ifeq:{{{Reference|}}}|||<tr><td class="PageWidthTableFirstCell">'''References:'''</td>
<td class="PageWidthTableRemainderCell">
{{#arraymap:{{{Reference|}}}|;|x|[[HasReference::x]]|; }}
</td></tr>
}}
</table>
|}}
Pages that refer to {{PAGENAME}}:
{{#ask:[[HasSource::{{PAGENAME}}]] OR [[HasExternalModel::{{PAGENAME}}]] OR [[-IsImplementationOf::{{PAGENAME}}]] }}
[[Category:ComputerModelAndDatabase]]
</includeonly>

Template:ComputerModelTemplate

2014-05-19T12:07:43Z

JeroenDolmans:

<noinclude>
This is the "ComputerModelTemplate" template.
It should be called in the following format:
<pre>
{{ComputerModelTemplate
|Subject=
|Description=
|FrameworkRelation=
|Component=
|Creator=
|ExternalURL=
|Reference=
}}
</pre>
Edit the page to see the template text.
</noinclude><includeonly>
{{#ifexpr: {{#if:{{{FrameworkRelation|}}}|1|0}} or {{#if:{{{Component|}}}|1|0}} or {{#if:{{{Creator|}}}|1|0}}|
<table class="PageWidthTableTemplate">
{{#ifeq:{{{Subject|}}}|||<tr><td class="PageWidthTableFirstCell">'''Subject:'''</td><td class="PageWidthTableRemainderCell">
[[HasSubject::{{{Subject|}}}]]
</td></tr>
}}
{{#ifeq:{{{Description|}}}|||<tr><td class="PageWidthTableFirstCell">'''Description:'''</td><td class="PageWidthTableRemainderCell">
[[HasDescription::{{{Description|}}}]]
</td></tr>
}}
{{#ifeq:{{{FrameworkRelation|}}}|||<tr><td class="PageWidthTableFirstCell">'''Relation with IMAGE framework:'''</td><td class="PageWidthTableRemainderCell">
[[RelationWithIMAGEFramework::{{{FrameworkRelation|}}}]]</td></tr>
}}
{{#ifeq:{{{Component|}}}|||<tr><td class="PageWidthTableFirstCell">'''Implements:'''</td><td class="PageWidthTableRemainderCell">
{{#arraymap:{{{Component|}}}|;|x|[[IsImplementationOf::x]]|; }}
</td></tr>
}}
{{#ifeq:{{{Creator|}}}|||<tr><td class="PageWidthTableFirstCell">'''Developed by:'''</td>
<td class="PageWidthTableRemainderCell">
{{#arraymap:{{{Creator|}}}|;|x|[[HasCreator::x]]|, }}
</td></tr>

}}
{{#ifeq:{{{ExternalURL|}}}|||<tr><td class="PageWidthTableFirstCell">'''External link:'''</td>
<td class="PageWidthTableRemainderCell">
[[HasURL::{{{ExternalURL|}}}]]</td></tr>
}}
{{#ifeq:{{{Reference|}}}|||<tr><td class="PageWidthTableFirstCell">'''References:'''</td>
<td class="PageWidthTableRemainderCell">
{{#arraymap:{{{Reference|}}}|;|x|[[HasReference::x]]|; }}
</td></tr>
}}
</table>
|}}
Pages that refer to {{PAGENAME}}:
{{#ask:[[HasSource::{{PAGENAME}}]] OR [[HasExternalModel::{{PAGENAME}}]] OR [[-IsImplementationOf::{{PAGENAME}}]] }}
[[Category:ComputerModelAndDatabase]]
</includeonly>

Template:ComputerModelTemplate

2014-05-19T12:06:48Z

JeroenDolmans:

<noinclude>
This is the "ComputerModelTemplate" template.
It should be called in the following format:
<pre>
{{ComputerModelTemplate
|Subject=
|Description=
|FrameworkRelation=
|Component=
|Creator=
|ExternalURL=
|Reference=
}}
</pre>
Edit the page to see the template text.
</noinclude><includeonly>
{{#ifexpr: {{#if:{{{FrameworkRelation|}}}|1|0}} or {{#if:{{{Component|}}}|1|0}} or {{#if:{{{Creator|}}}|1|0}}|
<table class="PageWidthTableTemplate">
{{#ifeq:{{{Subject|}}}|||<tr><td class="PageWidthTableFirstCell">'''Subject:'''</td><td class="PageWidthTableRemainderCell">
[[HasSubject::{{{Subject|}}}]]</td></tr>
}}
{{#ifeq:{{{Description|}}}|||<tr><td class="PageWidthTableFirstCell">'''Description:'''</td><td class="PageWidthTableRemainderCell">
[[HasDescription::{{{Description|}}}]]
</td></tr>
}}
{{#ifeq:{{{FrameworkRelation|}}}|||<tr><td class="PageWidthTableFirstCell">'''Relation with IMAGE framework:'''</td><td class="PageWidthTableRemainderCell">
[[RelationWithIMAGEFramework::{{{FrameworkRelation|}}}]]</td></tr>
}}
{{#ifeq:{{{Component|}}}|||<tr><td class="PageWidthTableFirstCell">'''Implements:'''</td><td class="PageWidthTableRemainderCell">
{{#arraymap:{{{Component|}}}|;|x|[[IsImplementationOf::x]]|; }}</td></tr>
}}
{{#ifeq:{{{Creator|}}}|||<tr><td class="PageWidthTableFirstCell">'''Developed by:'''</td>
<td class="PageWidthTableRemainderCell">
{{#arraymap:{{{Creator|}}}|;|x|[[HasCreator::x]]|, }}</td></tr>

}}
{{#ifeq:{{{ExternalURL|}}}|||<tr><td class="PageWidthTableFirstCell">'''External link:'''</td>
<td class="PageWidthTableRemainderCell">
[[HasURL::{{{ExternalURL|}}}]]</td></tr>
}}
{{#ifeq:{{{Reference|}}}|||<tr><td class="PageWidthTableFirstCell">'''References:'''</td>
<td class="PageWidthTableRemainderCell">
{{#arraymap:{{{Reference|}}}|;|x|[[HasReference::x]]|; }}</td></tr>
}}
</table>
|}}
Pages that refer to {{PAGENAME}}:
{{#ask:[[HasSource::{{PAGENAME}}]] OR [[HasExternalModel::{{PAGENAME}}]] OR [[-IsImplementationOf::{{PAGENAME}}]] }}
[[Category:ComputerModelAndDatabase]]
</includeonly>

FUND model

2014-05-19T12:04:24Z

JeroenDolmans:

{{ComputerModelTemplate
|Subject=climate change
|Description=The Climate Framework for Uncertainty, Negotiation and Distribution (FUND) is a so-called integrated assessment model (IAM) of climate change.

FUND was originally set-up to study the role of international capital transfers in climate policy, but it soon evolved into a test-bed for studying impacts of climate change in a dynamic context, and it is now often used to perform cost-benefit and cost-effectiveness analyses of greenhouse gas emission reduction policies, to study equity of climate change and climate policy, and to support game-theoretic investigations into international environmental agreements.

test
|ExternalURL=http://www.fund-model.org/
|FrameworkRelation=other
|Component=
}}

FUND model

2014-05-19T12:02:30Z

JeroenDolmans:

{{ComputerModelTemplate
|Subject=climate change
|Description=The Climate Framework for Uncertainty, Negotiation and Distribution (FUND) is a so-called integrated assessment model (IAM) of climate change.

FUND was originally set-up to study the role of international capital transfers in climate policy, but it soon evolved into a test-bed for studying impacts of climate change in a dynamic context, and it is now often used to perform cost-benefit and cost-effectiveness analyses of greenhouse gas emission reduction policies, to study equity of climate change and climate policy, and to support game-theoretic investigations into international environmental agreements.

|ExternalURL=http://www.fund-model.org/
|FrameworkRelation=other
|Component=
}}

FUND model

2014-05-19T12:01:38Z

JeroenDolmans:

{{ComputerModelTemplate
|Subject=climate change
|Description=The Climate Framework for Uncertainty, Negotiation and Distribution (FUND) is a so-called integrated assessment model (IAM) of climate change.

FUND was originally set-up to study the role of international capital transfers in climate policy, but it soon evolved into a test-bed for studying impacts of climate change in a dynamic context, and it is now often used to perform cost-benefit and cost-effectiveness analyses of greenhouse gas emission reduction policies, to study equity of climate change and climate policy, and to support game-theoretic investigations into international environmental agreements.

|ExternalURL=http://www.fund-model.org/
|FrameworkRelation=other
|Component=
}}

FUND model

2014-05-19T12:01:13Z

JeroenDolmans:

{{ComputerModelTemplate
|Subject=climate change
|Description=The Climate Framework for Uncertainty, Negotiation and Distribution (FUND) is a so-called integrated assessment model (IAM) of climate change.

FUND was originally set-up to study the role of international capital transfers in climate policy, but it soon evolved into a test-bed for studying impacts of climate change in a dynamic context, and it is now often used to perform cost-benefit and cost-effectiveness analyses of greenhouse gas emission reduction policies, to study equity of climate change and climate policy, and to support game-theoretic investigations into international environmental agreements.
|ExternalURL=http://www.fund-model.org/
|FrameworkRelation=other
|Component=
}}

Template:FrameworkIntroductionTemplate

2014-05-19T11:58:52Z

JeroenDolmans:

<noinclude>
This is the "FrameworkIntroductionTemplate" template.
It should be called in the following format:
<pre>
{{FrameworkIntroductionTemplate
|Overview
|IMAGEComponent
|Application
|ExternalModel
|Reference
}}
</pre>
Edit the page to see the template text.
</noinclude><includeonly> __NOEDITSECTION__
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}__TOC__
{{InfoBoxTemplate|Overview={{{Overview|}}}|IMAGEComponent={{{IMAGEComponent|}}}|Application={{{Application|}}}|ExternalModel={{{ExternalModel|}}}|Reference={{{Reference|}}} }}
<div class="page_standard">{{{Description|}}}
</div>
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}


[[Category:FrameworkIntroduction]]
[[HasTitle::{{PAGENAME}}| ]]

[[HasComponentCode::{{{ComponentCode|}}}|]]
[[HasPageLabel::Setting the stage| ]]
</includeonly>

IMAGE framework

2014-05-19T11:57:17Z

JeroenDolmans:

{{FrameworkIntroductionTemplate
|Overview=Framework overview;
|ExternalModel=FUND model; DICE model; ENV-Linkages model;
|Reference=Zalasiewicz et al., 2010;
|Description=The Introduction to IMAGE framework consists of a number of pages representing various aspects of the framework.

==Setting the stage==
===Background===
The IMAGE 3.0 framework addresses a set of global environmental issues and sustainability challenges. The most prominent are climate change, land-use change, biodiversity loss, modified nutrient cycles, and water scarcity. These highly complex issues are characterised by long-term dynamics and are either global issues, such as climate change, or manifest in a similar form in many places making them global in character. Typically, these global environmental issues have emerged as human societies have harnessed natural resources to support their development, for instance to provide energy, food, water and shelter.

Economic growth and increasing wealth have brought enormous benefits to human societies but have been accompanied by large-scale changes to the global environment system. These changes result from clearing land, burning biomass, domesticating animals and developing crop production systems, and from human activities that have lead to emissions of air pollutants and greenhouse gases. Mankind’s influence on the state and functioning of the natural environment has steadily increased in degree and spatial scale over the last 150 years.

Science now recognises humans as a geological force ([[Zalasiewicz et al., 2010]]) and suggests that the most recent age should be called the Anthropocene, a man-made era. In the last decades, concern has been growing that the scale of human interaction with the natural environment is reaching levels that could have consequences for the Earth’s capacity to continue supporting an increasing population. For example, the risk that emissions and atmospheric build-up of greenhouse gases from the use of fossil fuels and from other sources will seriously affect global climate.

Key policy challenges are how to avoid or reduce current and future tension between human activity and natural systems. This requires understanding the present ‘state-of-the-world’ as a result of the main drivers in the past. It is also essential to explore how the world could unfold in the future and the implications for human development, including how degradation of natural systems influences opportunities for human development. Then, alternative pathways can be identified, and their merits and downsides assessed to guide policy-making.

To understand these complex, global and long-term issues, and to design effective response strategies, integrated assessment models such as IMAGE 3.0 have been developed. Integrated assessment models cover key processes, ranging from human activities as the primary drivers, to the behaviour of the natural system, and impacts on the natural environment and on human development.

===Integrated environmental assessment===
Integrated assessment models ({{abbrTemplate|IAM}}s) have been developed to describe the key processes in the interaction of human development and the natural environment. IAM methods and tools draw on functional relationships between activities, such as provision of food, water and energy, and the associated impacts. Traditionally, most IAMs focused on climate change and air pollution. More recently, these models have been expanded to assess an increasing number of impacts, such as air and water quality, water scarcity, depletion of non-renewable resources (fossil fuels, phosphorus), and overexploitation of renewable resources (fish stocks, forests). IAMs are designed to provide insight into how driving factors induce a range of impacts, taking into account some of the key feedback and feed-forward mechanisms. To achieve this effectively, IAMs need to be sufficiently detailed to address the problem, yet simple enough to be applicable in assessments, including exploration of uncertainties, and without loss of transparency because of the complex relationships involved (see framework introduction part: [[IMAGE framework introduction/Organisational set-up and scientific quality|Organisational set-up and scientific quality]]).

===Objective and scope of IMAGE===
IMAGE is a comprehensive integrated modelling framework of interacting human and natural systems. Its design relies on intermediate complexity modelling, balancing level of detail to capture key processes and behaviour, and allowing for multiple runs to explore aspects of sensitivity and uncertainty of the complex, interlinked systems (see framework introduction part: [[IMAGE_framework_introduction/A_brief_history_of_IMAGE|A brief history of IMAGE]]).

The objectives of IMAGE are as follows:
* To analyse large-scale and long-term interactions between human development and the natural environment to gain better insight into the processes of global environmental change;
* To identify response strategies to global environmental change based on assessment of options for mitigation and adaption;
* To indicate key interlinkages and associated levels of uncertainty in processes of global environmental change.

IMAGE is often used to explore two types of issues:
* How the future unfolds if no deliberate, drastic changes in prevailing economic, technology and policy developments are assumed, commonly referred to as baseline, business-as-usual, or no-new-policy assessment;
*How policies and measures prevent unwanted impacts on the global environment and human development.

====Baseline Scenario====
The baseline scenario is used to assess the magnitude and relevance of global environmental issues and how they relate to human activities. This is important at the beginning of a policy cycle when an environmental issue arises. The scenario can be used to explore how the future might unfold under business-as-usual, and to assess the costs and foregone opportunities of policy inaction, and to study the impacts on the natural environment of a human development pathway with essentially unaltered practices. To some degree, impacts may be taken into account in an endogenous feedback loop by the integrated assessment procedure. For instance, changes in temperature and precipitation resulting from climate change have an effect on agricultural productivity and water availability. Biophysical feedbacks of this type are part of the IMAGE model, see [[Framework_overview|framework Components]].

====Alternative scenarios====
Often, alternative scenarios explore possible solutions to a problem, such as climate change, by assuming societal and policy responses to the impacts projected under baseline conditions. To this end, alternative cases are developed and implemented in model compatible terms to test how the outcomes change. They also reveal synergies and trade-offs between policy issues. For example, with increasing crop yields, less land is required to grow a given amount of crops, and thus loss of natural areas is reduced to the benefit of ecosystems rich in biodiversity. Carbon emissions from land use are also reduced when less land is converted to agriculture, but fertiliser application may increase to sustain the higher yields with emissions to air, groundwater and surface water as a consequence. Furthermore, higher yields may contribute to lower food prices and thus to reducing undernourishment and hunger to the benefit of human health.

===IMAGE in comparison to other IAMs===
Various types of IAMs have been developed, evolving from different classes of models with a specific disciplinary focus and point of entry. These are discussed briefly in order to identify the position of IMAGE in relation to other IAM models. The common feature of all IAM models is that they all describe a combination of the Human and Earth systems to gain better understanding global environmental problems.

====Detail versus simplification====
As indicated above, a key trade-off in IAMs is detail versus simplification. Sufficient detail is required to include all relevant processes in both the Human and the Earth system according to state-of-the-art knowledge. Simplicity is needed to ensure sufficient transparency in complex model systems, and to explore uncertainties. For instance, a crop growth model with data input on observed, local climate, soil layers and crop variety parameters may perform well at field scale. However, such a model is less suitable for use in an IAM that requires more generic crop growth representation operating as part of a global scale system.

Another limitation to the level of detail captured in IAMs is the lack of consistent and complete datasets with global coverage.
While models are developed for different purposes, and thus have limited overlap in scope and detail, in practice many hybrid models are in use. As illustrated in Figure below, IAM models are between models with a primary focus on the Earth system (Earth System Models) and models that focus on the Human system such as pure economic models.
{{DisplayFigureLeftOptimalTemplate|Figure1 IFI}}
Within the IAM group clearly different model groups exists, and IMAGE is characterised by relatively detailed biophysical processes and a wide range of environmental indicators. IMAGE 3.0 also includes an economic model to represent the agricultural system, and a process model to describe the energy system, but has less detail on economics and policy instruments than other energy models. In terms of application, many models are designed and used for climate policy analysis, such as [[FUND model|FUND]] and [[DICE model|DICE]], while other models address a broader range of issues. IMAGE was originally designed to assess the global effect of greenhouse gas emissions and now covers a broad range of environmental and sustainability issues.

====Model history====
Another reason for differences between IAM models is their history. Many have evolved from technical process models of energy systems to cover environmental issues, such as air pollution and more frequently also climatic change. Technical process models describe the physical flows of energy from primary resources through conversion processes, and transport and distribution networks to meet specific demands for energy carriers or energy services. The costs associated with the various components are tracked, and relative costs of competing technologies and supply chains determine market share. In fact, one example embedded in the IMAGE framework is the TIMER energy model (Component [[Energy supply and demand]]).

Other IAMs have their roots in economics and have evolved from models assessing the production of economic outputs to contribute to consumer utility by allocating input factors, such as capital, labour and increasingly also energy, materials and natural resources. Substitution between sectors, inputs and commodities produced depends on their relative prices, taking into account policy interventions, such as taxes and subsidies, import regulations and other market and non-market instruments. Economic models include the OECD model [[ENV-Linkages model| ENV-Linkages]] and the model [[MAGNET model]]. The latter has been integrated into IMAGE 3.0 (see Component [[Agricultural economy]]).

While economic models account for consistency between economic sectors, these models tend to treat the economy in terms of material flows, biochemical, physical and ecological processes in a stylised way, which limits their capacity to capture feedback mechanisms of the natural system.

====Geographic detail====

Finally, IAMs can also be distinguished by the level of geographic detail in land-based activities. To address geographical distribution of bio-geochemical and bio-geophysical processes in conjunction with human development, the IMAGE framework has been developed with a high level of geographic detail. IMAGE provides a relatively high level of detail on land-based processes, such as water, carbon and nutrient cycles, and derived indicators for biodiversity loss and flood risks, also in temporal and spatial resolution.
|ComponentCode=IFI
}}

IMAGE framework

2014-05-19T11:57:01Z

JeroenDolmans:

{{FrameworkIntroductionTemplate
|Overview=Framework overview;
|ExternalModel=FUND model; DICE model; ENV-Linkages model;
|Reference=Zalasiewicz et al., 2010;
|Description=The Introduction to IMAGE framework consists of a number of pages representing various aspects of the framework.

==Setting the stage==
===Background===
The IMAGE 3.0 framework addresses a set of global environmental issues and sustainability challenges. The most prominent are climate change, land-use change, biodiversity loss, modified nutrient cycles, and water scarcity. These highly complex issues are characterised by long-term dynamics and are either global issues, such as climate change, or manifest in a similar form in many places making them global in character. Typically, these global environmental issues have emerged as human societies have harnessed natural resources to support their development, for instance to provide energy, food, water and shelter.

Economic growth and increasing wealth have brought enormous benefits to human societies but have been accompanied by large-scale changes to the global environment system. These changes result from clearing land, burning biomass, domesticating animals and developing crop production systems, and from human activities that have lead to emissions of air pollutants and greenhouse gases. Mankind’s influence on the state and functioning of the natural environment has steadily increased in degree and spatial scale over the last 150 years.

Science now recognises humans as a geological force ([[Zalasiewicz et al., 2010]]) and suggests that the most recent age should be called the Anthropocene, a man-made era. In the last decades, concern has been growing that the scale of human interaction with the natural environment is reaching levels that could have consequences for the Earth’s capacity to continue supporting an increasing population. For example, the risk that emissions and atmospheric build-up of greenhouse gases from the use of fossil fuels and from other sources will seriously affect global climate.

Key policy challenges are how to avoid or reduce current and future tension between human activity and natural systems. This requires understanding the present ‘state-of-the-world’ as a result of the main drivers in the past. It is also essential to explore how the world could unfold in the future and the implications for human development, including how degradation of natural systems influences opportunities for human development. Then, alternative pathways can be identified, and their merits and downsides assessed to guide policy-making.

To understand these complex, global and long-term issues, and to design effective response strategies, integrated assessment models such as IMAGE 3.0 have been developed. Integrated assessment models cover key processes, ranging from human activities as the primary drivers, to the behaviour of the natural system, and impacts on the natural environment and on human development.

===Integrated environmental assessment===
Integrated assessment models ({{abbrTemplate|IAM}}s) have been developed to describe the key processes in the interaction of human development and the natural environment. IAM methods and tools draw on functional relationships between activities, such as provision of food, water and energy, and the associated impacts. Traditionally, most IAMs focused on climate change and air pollution. More recently, these models have been expanded to assess an increasing number of impacts, such as air and water quality, water scarcity, depletion of non-renewable resources (fossil fuels, phosphorus), and overexploitation of renewable resources (fish stocks, forests). IAMs are designed to provide insight into how driving factors induce a range of impacts, taking into account some of the key feedback and feed-forward mechanisms. To achieve this effectively, IAMs need to be sufficiently detailed to address the problem, yet simple enough to be applicable in assessments, including exploration of uncertainties, and without loss of transparency because of the complex relationships involved (see framework introduction part: [[IMAGE framework introduction/Organisational set-up and scientific quality|Organisational set-up and scientific quality]]).

===Objective and scope of IMAGE===
IMAGE is a comprehensive integrated modelling framework of interacting human and natural systems. Its design relies on intermediate complexity modelling, balancing level of detail to capture key processes and behaviour, and allowing for multiple runs to explore aspects of sensitivity and uncertainty of the complex, interlinked systems (see framework introduction part: [[IMAGE_framework_introduction/A_brief_history_of_IMAGE|A brief history of IMAGE]]).

The objectives of IMAGE are as follows:
* To analyse large-scale and long-term interactions between human development and the natural environment to gain better insight into the processes of global environmental change;
* To identify response strategies to global environmental change based on assessment of options for mitigation and adaption;
* To indicate key interlinkages and associated levels of uncertainty in processes of global environmental change.

IMAGE is often used to explore two types of issues:
* How the future unfolds if no deliberate, drastic changes in prevailing economic, technology and policy developments are assumed, commonly referred to as baseline, business-as-usual, or no-new-policy assessment;
*How policies and measures prevent unwanted impacts on the global environment and human development.

====Baseline Scenario====
The baseline scenario is used to assess the magnitude and relevance of global environmental issues and how they relate to human activities. This is important at the beginning of a policy cycle when an environmental issue arises. The scenario can be used to explore how the future might unfold under business-as-usual, and to assess the costs and foregone opportunities of policy inaction, and to study the impacts on the natural environment of a human development pathway with essentially unaltered practices. To some degree, impacts may be taken into account in an endogenous feedback loop by the integrated assessment procedure. For instance, changes in temperature and precipitation resulting from climate change have an effect on agricultural productivity and water availability. Biophysical feedbacks of this type are part of the IMAGE model, see [[Framework_overview|framework Components]].

====Alternative scenarios====
Often, alternative scenarios explore possible solutions to a problem, such as climate change, by assuming societal and policy responses to the impacts projected under baseline conditions. To this end, alternative cases are developed and implemented in model compatible terms to test how the outcomes change. They also reveal synergies and trade-offs between policy issues. For example, with increasing crop yields, less land is required to grow a given amount of crops, and thus loss of natural areas is reduced to the benefit of ecosystems rich in biodiversity. Carbon emissions from land use are also reduced when less land is converted to agriculture, but fertiliser application may increase to sustain the higher yields with emissions to air, groundwater and surface water as a consequence. Furthermore, higher yields may contribute to lower food prices and thus to reducing undernourishment and hunger to the benefit of human health.

===IMAGE in comparison to other IAMs===
Various types of IAMs have been developed, evolving from different classes of models with a specific disciplinary focus and point of entry. These are discussed briefly in order to identify the position of IMAGE in relation to other IAM models. The common feature of all IAM models is that they all describe a combination of the Human and Earth systems to gain better understanding global environmental problems.

====Detail versus simplification====
As indicated above, a key trade-off in IAMs is detail versus simplification. Sufficient detail is required to include all relevant processes in both the Human and the Earth system according to state-of-the-art knowledge. Simplicity is needed to ensure sufficient transparency in complex model systems, and to explore uncertainties. For instance, a crop growth model with data input on observed, local climate, soil layers and crop variety parameters may perform well at field scale. However, such a model is less suitable for use in an IAM that requires more generic crop growth representation operating as part of a global scale system.

Another limitation to the level of detail captured in IAMs is the lack of consistent and complete datasets with global coverage.
While models are developed for different purposes, and thus have limited overlap in scope and detail, in practice many hybrid models are in use. As illustrated in Figure below, IAM models are between models with a primary focus on the Earth system (Earth System Models) and models that focus on the Human system such as pure economic models.
{{DisplayFigureLeftOptimalTemplate|Figure1 IFI}}
Within the IAM group clearly different model groups exists, and IMAGE is characterised by relatively detailed biophysical processes and a wide range of environmental indicators. IMAGE 3.0 also includes an economic model to represent the agricultural system, and a process model to describe the energy system, but has less detail on economics and policy instruments than other energy models. In terms of application, many models are designed and used for climate policy analysis, such as [[FUND model|FUND]] and [[DICE model|DICE]], while other models address a broader range of issues. IMAGE was originally designed to assess the global effect of greenhouse gas emissions and now covers a broad range of environmental and sustainability issues.

====Model history====
Another reason for differences between IAM models is their history. Many have evolved from technical process models of energy systems to cover environmental issues, such as air pollution and more frequently also climatic change. Technical process models describe the physical flows of energy from primary resources through conversion processes, and transport and distribution networks to meet specific demands for energy carriers or energy services. The costs associated with the various components are tracked, and relative costs of competing technologies and supply chains determine market share. In fact, one example embedded in the IMAGE framework is the TIMER energy model (Component [[Energy supply and demand]]).

Other IAMs have their roots in economics and have evolved from models assessing the production of economic outputs to contribute to consumer utility by allocating input factors, such as capital, labour and increasingly also energy, materials and natural resources. Substitution between sectors, inputs and commodities produced depends on their relative prices, taking into account policy interventions, such as taxes and subsidies, import regulations and other market and non-market instruments. Economic models include the OECD model [[ENV-Linkages model| ENV-Linkages]] and the model [[MAGNET model]]. The latter has been integrated into IMAGE 3.0 (see Component [[Agricultural economy]]).

While economic models account for consistency between economic sectors, these models tend to treat the economy in terms of material flows, biochemical, physical and ecological processes in a stylised way, which limits their capacity to capture feedback mechanisms of the natural system.

====Geographic detail====
Finally, IAMs can also be distinguished by the level of geographic detail in land-based activities. To address geographical distribution of bio-geochemical and bio-geophysical processes in conjunction with human development, the IMAGE framework has been developed with a high level of geographic detail. IMAGE provides a relatively high level of detail on land-based processes, such as water, carbon and nutrient cycles, and derived indicators for biodiversity loss and flood risks, also in temporal and spatial resolution.
|ComponentCode=IFI
}}

IMAGE framework

2014-05-19T11:56:11Z

JeroenDolmans:

{{FrameworkIntroductionTemplate
|Overview=Framework overview;
|ExternalModel=FUND model; DICE model; ENV-Linkages model;
|Reference=Zalasiewicz et al., 2010;
|Description=The Introduction to IMAGE framework consists of a number of pages representing various aspects of the framework.

==Setting the stage==
===Background===
The IMAGE 3.0 framework addresses a set of global environmental issues and sustainability challenges. The most prominent are climate change, land-use change, biodiversity loss, modified nutrient cycles, and water scarcity. These highly complex issues are characterised by long-term dynamics and are either global issues, such as climate change, or manifest in a similar form in many places making them global in character. Typically, these global environmental issues have emerged as human societies have harnessed natural resources to support their development, for instance to provide energy, food, water and shelter.

Economic growth and increasing wealth have brought enormous benefits to human societies but have been accompanied by large-scale changes to the global environment system. These changes result from clearing land, burning biomass, domesticating animals and developing crop production systems, and from human activities that have lead to emissions of air pollutants and greenhouse gases. Mankind’s influence on the state and functioning of the natural environment has steadily increased in degree and spatial scale over the last 150 years.

Science now recognises humans as a geological force ([[Zalasiewicz et al., 2010]]) and suggests that the most recent age should be called the Anthropocene, a man-made era. In the last decades, concern has been growing that the scale of human interaction with the natural environment is reaching levels that could have consequences for the Earth’s capacity to continue supporting an increasing population. For example, the risk that emissions and atmospheric build-up of greenhouse gases from the use of fossil fuels and from other sources will seriously affect global climate.

Key policy challenges are how to avoid or reduce current and future tension between human activity and natural systems. This requires understanding the present ‘state-of-the-world’ as a result of the main drivers in the past. It is also essential to explore how the world could unfold in the future and the implications for human development, including how degradation of natural systems influences opportunities for human development. Then, alternative pathways can be identified, and their merits and downsides assessed to guide policy-making.

To understand these complex, global and long-term issues, and to design effective response strategies, integrated assessment models such as IMAGE 3.0 have been developed. Integrated assessment models cover key processes, ranging from human activities as the primary drivers, to the behaviour of the natural system, and impacts on the natural environment and on human development.

===Integrated environmental assessment===
Integrated assessment models ({{abbrTemplate|IAM}}s) have been developed to describe the key processes in the interaction of human development and the natural environment. IAM methods and tools draw on functional relationships between activities, such as provision of food, water and energy, and the associated impacts. Traditionally, most IAMs focused on climate change and air pollution. More recently, these models have been expanded to assess an increasing number of impacts, such as air and water quality, water scarcity, depletion of non-renewable resources (fossil fuels, phosphorus), and overexploitation of renewable resources (fish stocks, forests). IAMs are designed to provide insight into how driving factors induce a range of impacts, taking into account some of the key feedback and feed-forward mechanisms. To achieve this effectively, IAMs need to be sufficiently detailed to address the problem, yet simple enough to be applicable in assessments, including exploration of uncertainties, and without loss of transparency because of the complex relationships involved (see framework introduction part: [[IMAGE framework introduction/Organisational set-up and scientific quality|Organisational set-up and scientific quality]]).

===Objective and scope of IMAGE===
IMAGE is a comprehensive integrated modelling framework of interacting human and natural systems. Its design relies on intermediate complexity modelling, balancing level of detail to capture key processes and behaviour, and allowing for multiple runs to explore aspects of sensitivity and uncertainty of the complex, interlinked systems (see framework introduction part: [[IMAGE_framework_introduction/A_brief_history_of_IMAGE|A brief history of IMAGE]]).

The objectives of IMAGE are as follows:
* To analyse large-scale and long-term interactions between human development and the natural environment to gain better insight into the processes of global environmental change;
* To identify response strategies to global environmental change based on assessment of options for mitigation and adaption;
* To indicate key interlinkages and associated levels of uncertainty in processes of global environmental change.

IMAGE is often used to explore two types of issues:
* How the future unfolds if no deliberate, drastic changes in prevailing economic, technology and policy developments are assumed, commonly referred to as baseline, business-as-usual, or no-new-policy assessment;
*How policies and measures prevent unwanted impacts on the global environment and human development.

====Baseline Scenario====
The baseline scenario is used to assess the magnitude and relevance of global environmental issues and how they relate to human activities. This is important at the beginning of a policy cycle when an environmental issue arises. The scenario can be used to explore how the future might unfold under business-as-usual, and to assess the costs and foregone opportunities of policy inaction, and to study the impacts on the natural environment of a human development pathway with essentially unaltered practices. To some degree, impacts may be taken into account in an endogenous feedback loop by the integrated assessment procedure. For instance, changes in temperature and precipitation resulting from climate change have an effect on agricultural productivity and water availability. Biophysical feedbacks of this type are part of the IMAGE model, see [[Framework_overview|framework Components]].

====Alternative scenarios====
Often, alternative scenarios explore possible solutions to a problem, such as climate change, by assuming societal and policy responses to the impacts projected under baseline conditions. To this end, alternative cases are developed and implemented in model compatible terms to test how the outcomes change. They also reveal synergies and trade-offs between policy issues. For example, with increasing crop yields, less land is required to grow a given amount of crops, and thus loss of natural areas is reduced to the benefit of ecosystems rich in biodiversity. Carbon emissions from land use are also reduced when less land is converted to agriculture, but fertiliser application may increase to sustain the higher yields with emissions to air, groundwater and surface water as a consequence. Furthermore, higher yields may contribute to lower food prices and thus to reducing undernourishment and hunger to the benefit of human health.

===IMAGE in comparison to other IAMs===
Various types of IAMs have been developed, evolving from different classes of models with a specific disciplinary focus and point of entry. These are discussed briefly in order to identify the position of IMAGE in relation to other IAM models. The common feature of all IAM models is that they all describe a combination of the Human and Earth systems to gain better understanding global environmental problems.

====Detail versus simplification====
As indicated above, a key trade-off in IAMs is detail versus simplification. Sufficient detail is required to include all relevant processes in both the Human and the Earth system according to state-of-the-art knowledge. Simplicity is needed to ensure sufficient transparency in complex model systems, and to explore uncertainties. For instance, a crop growth model with data input on observed, local climate, soil layers and crop variety parameters may perform well at field scale. However, such a model is less suitable for use in an IAM that requires more generic crop growth representation operating as part of a global scale system.

Another limitation to the level of detail captured in IAMs is the lack of consistent and complete datasets with global coverage.
While models are developed for different purposes, and thus have limited overlap in scope and detail, in practice many hybrid models are in use. As illustrated in Figure below, IAM models are between models with a primary focus on the Earth system (Earth System Models) and models that focus on the Human system such as pure economic models.
{{DisplayFigureLeftOptimalTemplate|Figure1 IFI}}
Within the IAM group clearly different model groups exists, and IMAGE is characterised by relatively detailed biophysical processes and a wide range of environmental indicators. IMAGE 3.0 also includes an economic model to represent the agricultural system, and a process model to describe the energy system, but has less detail on economics and policy instruments than other energy models. In terms of application, many models are designed and used for climate policy analysis, such as [[FUND model|FUND]] and [[DICE model|DICE]], while other models address a broader range of issues. IMAGE was originally designed to assess the global effect of greenhouse gas emissions and now covers a broad range of environmental and sustainability issues.

====Model history====
Another reason for differences between IAM models is their history. Many have evolved from technical process models of energy systems to cover environmental issues, such as air pollution and more frequently also climatic change. Technical process models describe the physical flows of energy from primary resources through conversion processes, and transport and distribution networks to meet specific demands for energy carriers or energy services. The costs associated with the various components are tracked, and relative costs of competing technologies and supply chains determine market share. In fact, one example embedded in the IMAGE framework is the TIMER energy model (Component [[Energy supply and demand]]).

Other IAMs have their roots in economics and have evolved from models assessing the production of economic outputs to contribute to consumer utility by allocating input factors, such as capital, labour and increasingly also energy, materials and natural resources. Substitution between sectors, inputs and commodities produced depends on their relative prices, taking into account policy interventions, such as taxes and subsidies, import regulations and other market and non-market instruments. Economic models include the OECD model [[ENV-Linkages model| ENV-Linkages]] and the model [[MAGNET model]]. The latter has been integrated into IMAGE 3.0 (see Component [[Agricultural economy]]).

While economic models account for consistency between economic sectors, these models tend to treat the economy in terms of material flows, biochemical, physical and ecological processes in a stylised way, which limits their capacity to capture feedback mechanisms of the natural system.

Finally, IAMs can also be distinguished by the level of geographic detail in land-based activities. To address geographical distribution of bio-geochemical and bio-geophysical processes in conjunction with human development, the IMAGE framework has been developed with a high level of geographic detail. IMAGE provides a relatively high level of detail on land-based processes, such as water, carbon and nutrient cycles, and derived indicators for biodiversity loss and flood risks, also in temporal and spatial resolution.

====Geographic detail====
|ComponentCode=IFI
}}

IMAGE framework

2014-05-19T11:55:34Z

JeroenDolmans:

{{FrameworkIntroductionTemplate
|Overview=Framework overview;
|ExternalModel=FUND model; DICE model; ENV-Linkages model;
|Reference=Zalasiewicz et al., 2010;
|Description=The Introduction to IMAGE framework consists of a number of pages representing various aspects of the framework.

==Setting the stage==
===Background===
The IMAGE 3.0 framework addresses a set of global environmental issues and sustainability challenges. The most prominent are climate change, land-use change, biodiversity loss, modified nutrient cycles, and water scarcity. These highly complex issues are characterised by long-term dynamics and are either global issues, such as climate change, or manifest in a similar form in many places making them global in character. Typically, these global environmental issues have emerged as human societies have harnessed natural resources to support their development, for instance to provide energy, food, water and shelter.

Economic growth and increasing wealth have brought enormous benefits to human societies but have been accompanied by large-scale changes to the global environment system. These changes result from clearing land, burning biomass, domesticating animals and developing crop production systems, and from human activities that have lead to emissions of air pollutants and greenhouse gases. Mankind’s influence on the state and functioning of the natural environment has steadily increased in degree and spatial scale over the last 150 years.

Science now recognises humans as a geological force ([[Zalasiewicz et al., 2010]]) and suggests that the most recent age should be called the Anthropocene, a man-made era. In the last decades, concern has been growing that the scale of human interaction with the natural environment is reaching levels that could have consequences for the Earth’s capacity to continue supporting an increasing population. For example, the risk that emissions and atmospheric build-up of greenhouse gases from the use of fossil fuels and from other sources will seriously affect global climate.

Key policy challenges are how to avoid or reduce current and future tension between human activity and natural systems. This requires understanding the present ‘state-of-the-world’ as a result of the main drivers in the past. It is also essential to explore how the world could unfold in the future and the implications for human development, including how degradation of natural systems influences opportunities for human development. Then, alternative pathways can be identified, and their merits and downsides assessed to guide policy-making.

To understand these complex, global and long-term issues, and to design effective response strategies, integrated assessment models such as IMAGE 3.0 have been developed. Integrated assessment models cover key processes, ranging from human activities as the primary drivers, to the behaviour of the natural system, and impacts on the natural environment and on human development.

===Integrated environmental assessment===
Integrated assessment models ({{abbrTemplate|IAM}}s) have been developed to describe the key processes in the interaction of human development and the natural environment. IAM methods and tools draw on functional relationships between activities, such as provision of food, water and energy, and the associated impacts. Traditionally, most IAMs focused on climate change and air pollution. More recently, these models have been expanded to assess an increasing number of impacts, such as air and water quality, water scarcity, depletion of non-renewable resources (fossil fuels, phosphorus), and overexploitation of renewable resources (fish stocks, forests). IAMs are designed to provide insight into how driving factors induce a range of impacts, taking into account some of the key feedback and feed-forward mechanisms. To achieve this effectively, IAMs need to be sufficiently detailed to address the problem, yet simple enough to be applicable in assessments, including exploration of uncertainties, and without loss of transparency because of the complex relationships involved (see framework introduction part: [[IMAGE framework introduction/Organisational set-up and scientific quality|Organisational set-up and scientific quality]]).

===Objective and scope of IMAGE===
IMAGE is a comprehensive integrated modelling framework of interacting human and natural systems. Its design relies on intermediate complexity modelling, balancing level of detail to capture key processes and behaviour, and allowing for multiple runs to explore aspects of sensitivity and uncertainty of the complex, interlinked systems (see framework introduction part: [[IMAGE_framework_introduction/A_brief_history_of_IMAGE|A brief history of IMAGE]]).

The objectives of IMAGE are as follows:
* To analyse large-scale and long-term interactions between human development and the natural environment to gain better insight into the processes of global environmental change;
* To identify response strategies to global environmental change based on assessment of options for mitigation and adaption;
* To indicate key interlinkages and associated levels of uncertainty in processes of global environmental change.

IMAGE is often used to explore two types of issues:
* How the future unfolds if no deliberate, drastic changes in prevailing economic, technology and policy developments are assumed, commonly referred to as baseline, business-as-usual, or no-new-policy assessment;
*How policies and measures prevent unwanted impacts on the global environment and human development.

====Baseline Scenario====
The baseline scenario is used to assess the magnitude and relevance of global environmental issues and how they relate to human activities. This is important at the beginning of a policy cycle when an environmental issue arises. The scenario can be used to explore how the future might unfold under business-as-usual, and to assess the costs and foregone opportunities of policy inaction, and to study the impacts on the natural environment of a human development pathway with essentially unaltered practices. To some degree, impacts may be taken into account in an endogenous feedback loop by the integrated assessment procedure. For instance, changes in temperature and precipitation resulting from climate change have an effect on agricultural productivity and water availability. Biophysical feedbacks of this type are part of the IMAGE model, see [[Framework_overview|framework Components]].

====Alternative scenarios====
Often, alternative scenarios explore possible solutions to a problem, such as climate change, by assuming societal and policy responses to the impacts projected under baseline conditions. To this end, alternative cases are developed and implemented in model compatible terms to test how the outcomes change. They also reveal synergies and trade-offs between policy issues. For example, with increasing crop yields, less land is required to grow a given amount of crops, and thus loss of natural areas is reduced to the benefit of ecosystems rich in biodiversity. Carbon emissions from land use are also reduced when less land is converted to agriculture, but fertiliser application may increase to sustain the higher yields with emissions to air, groundwater and surface water as a consequence. Furthermore, higher yields may contribute to lower food prices and thus to reducing undernourishment and hunger to the benefit of human health.

===IMAGE in comparison to other IAMs===
Various types of IAMs have been developed, evolving from different classes of models with a specific disciplinary focus and point of entry. These are discussed briefly in order to identify the position of IMAGE in relation to other IAM models. The common feature of all IAM models is that they all describe a combination of the Human and Earth systems to gain better understanding global environmental problems.

====Detail versus simplification====
As indicated above, a key trade-off in IAMs is detail versus simplification. Sufficient detail is required to include all relevant processes in both the Human and the Earth system according to state-of-the-art knowledge. Simplicity is needed to ensure sufficient transparency in complex model systems, and to explore uncertainties. For instance, a crop growth model with data input on observed, local climate, soil layers and crop variety parameters may perform well at field scale. However, such a model is less suitable for use in an IAM that requires more generic crop growth representation operating as part of a global scale system.

Another limitation to the level of detail captured in IAMs is the lack of consistent and complete datasets with global coverage.
While models are developed for different purposes, and thus have limited overlap in scope and detail, in practice many hybrid models are in use. As illustrated in Figure below, IAM models are between models with a primary focus on the Earth system (Earth System Models) and models that focus on the Human system such as pure economic models.
{{DisplayFigureLeftOptimalTemplate|Figure1 IFI}}
Within the IAM group clearly different model groups exists, and IMAGE is characterised by relatively detailed biophysical processes and a wide range of environmental indicators. IMAGE 3.0 also includes an economic model to represent the agricultural system, and a process model to describe the energy system, but has less detail on economics and policy instruments than other energy models. In terms of application, many models are designed and used for climate policy analysis, such as [[FUND model|FUND]] and [[DICE model|DICE]], while other models address a broader range of issues. IMAGE was originally designed to assess the global effect of greenhouse gas emissions and now covers a broad range of environmental and sustainability issues.

====Model history====
Another reason for differences between IAM models is their history. Many have evolved from technical process models of energy systems to cover environmental issues, such as air pollution and more frequently also climatic change. Technical process models describe the physical flows of energy from primary resources through conversion processes, and transport and distribution networks to meet specific demands for energy carriers or energy services. The costs associated with the various components are tracked, and relative costs of competing technologies and supply chains determine market share. In fact, one example embedded in the IMAGE framework is the TIMER energy model (Component [[Energy supply and demand]]).

Other IAMs have their roots in economics and have evolved from models assessing the production of economic outputs to contribute to consumer utility by allocating input factors, such as capital, labour and increasingly also energy, materials and natural resources. Substitution between sectors, inputs and commodities produced depends on their relative prices, taking into account policy interventions, such as taxes and subsidies, import regulations and other market and non-market instruments. Economic models include the OECD model [[ENV-Linkages model| ENV-Linkages]] and the model [[MAGNET model]]. The latter has been integrated into IMAGE 3.0 (see Component [[Agricultural economy]]).

While economic models account for consistency between economic sectors, these models tend to treat the economy in terms of material flows, biochemical, physical and ecological processes in a stylised way, which limits their capacity to capture feedback mechanisms of the natural system.

====Geographic detail====
Finally, IAMs can also be distinguished by the level of geographic detail in land-based activities. To address geographical distribution of bio-geochemical and bio-geophysical processes in conjunction with human development, the IMAGE framework has been developed with a high level of geographic detail. IMAGE provides a relatively high level of detail on land-based processes, such as water, carbon and nutrient cycles, and derived indicators for biodiversity loss and flood risks, also in temporal and spatial resolution.

|ComponentCode=IFI
}}

Template:FrameworkIntroductionTemplate

2014-05-19T11:48:55Z

JeroenDolmans:

<noinclude>
This is the "FrameworkIntroductionTemplate" template.
It should be called in the following format:
<pre>
{{FrameworkIntroductionTemplate
|Overview
|IMAGEComponent
|Application
|ExternalModel
|Reference
}}
</pre>
Edit the page to see the template text.
</noinclude><includeonly> __NOEDITSECTION__
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}__TOC__
{{InfoBoxTemplate|Overview={{{Overview|}}}|IMAGEComponent={{{IMAGEComponent|}}}|Application={{{Application|}}}|ExternalModel={{{ExternalModel|}}}|Reference={{{Reference|}}} }}
<div class="page_standard">{{{Description|}}}</div>
{{ContentPartsTemplate|PagePartHeader=Parts of {{PAGENAME}}|subpage=0 }}


[[Category:FrameworkIntroduction]]
[[HasTitle::{{PAGENAME}}| ]]

[[HasComponentCode::{{{ComponentCode|}}}|]]
[[HasPageLabel::Setting the stage| ]]
</includeonly>

Land cover and land use

2014-03-31T10:37:08Z

JeroenDolmans:

{{SimpleComponentTemplate
|ComponentCode=LCU
|Application=Roads from Rio+20 (2012) project;
|InputVar=Crop fraction in agricultural area - grid; Grassland, agricultural, and abandoned area - grid; Potential natural vegetation - grid;
|OutputVar=Land cover, land use - grid; Land for bio-energy - grid; Land supply;
|Description=<h2>Interaction between the human system and environmental system</h2>
There are several ways by which the human system directly influences the earth system. Land allocation and atmospheric emissions (see the component [[Emissions]]) form two of the most important factors, others include water extraction, and water and soil pollution.

===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.
{{DisplayFigureLeftOptimalTemplate|Policy intervention figure LCU|plain}}
As observed in other baseline scenarios, in the [[Roads from Rio+20 (2012) project|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 the figure on the left. <br clear=all>
|FrameworkElementType=state component
}}
[[Page has default form::SimpleComponentForm| ]]

Welcome to IMAGE 3.2 Documentation

2014-03-31T10:18:39Z

JeroenDolmans:

__NOTOC__
[[File:Logo.png|left|link=|alt=IMAGE logo]]__NOEDITSECTION__
==IMAGE 3 Documentation==
IMAGE is an Integrated Model to Assess the Global Environment.

The IMAGE model is developed by the IMAGE team under the authority of PBL Netherlands Environmental Assessment Agency.

===What is IMAGE?===
IMAGE is an ecological-environmental framework that simulates the environmental consequences of human activities worldwide. It represents interactions between society, the biosphere and the climate system to assess sustainability issues like climate change, biodiversity and human well-being. The objective of the version of IMAGE described here (version {{#show:CurrentVersion |?HasCurrentVersion}} released in 2013) is to explore the long-term dynamics and impacts of global change as the result of interacting demographic, technological, economic, social, cultural and political factors.

===Navigate through the site ===
* [[IMAGE_framework introduction|Introduction]] to framework and Integrated Assessment Modelling.
* [[IMAGE framework summary|Framework summary]] of drivers, models, impacts and policy responses.
* [[Framework_overview|Framework overview]] links to all components,e.g. drivers, core models, impact models and policy responses.
* [[Policy_interventions_overview|Policy interventions overview]] links to policy interventions pages of the models.
* [[Dataflow overview|Dataflow overview]] links to the input/output and models
* [[Applications_overview|Projects/applications overview]] links to projects where IMAGE framework has been applied.

===Availability of data results from the framework===
We publish a viewer that gives access to resulting scenario indicators (>200) of some projects. See the [[Download]] page.

At IIASA [[URL to database]] IMAGE scenario results are part of the model set. At this site you can view and compare many indicators.

===Availability of models===
Most of the models are not available as downloadable software at the moment. They are too complex and rely on use by experts.

===The IMAGE 3.0 book===
The [[IMAGE 3.0 book]] is the origin of the content of this site. On this site we extend and actualise the information on IMAGE framework.