Land degradation/Description: Difference between revisions

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* soil texture (sand and clay content).
* 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:
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:
: [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_1,p_2… p_5'']. These explanatory factors are:
: temperature,  
: temperature,  
: precipitation,  
: precipitation,  
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* 0.3 for pasture, and  
* 0.3 for pasture, and  
* 0.0 for natural vegetation.  
* 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
FORMULA 1
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
FORMULA 2
The weight w_px for landscape property p is calculated as:
FORMULA 3
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
FORMULA 4
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.


Land cover is characterised by a remotely sensed NDVI map. The soil property v at location x with soil s is estimated as
( v_x ) ̂=v_ls+w_x*(v_hs- v_ls)
with w_x being a weight w∈ [0..1] that determines where v is in the range [v_ls..v_hs ]. Different explanatory factors represented by the landscape properties determine w. The weight at location x is calculated as w_x= ∑_(p=1)^5▒w_px . The weight w_px for landscape property p is calculated as:
:c_pv≥0: w_px=c_pv*  ((p_x-p_ls ))/((p_hs- p_ls ) )
:c_pv<0: w_px=-c_pv*  ((p_hs-p_x ))/((p_hs- p_ls ) )
In which c_pv is a constant that indicates the relative importance of the landscape property p for a soil property v. The sign of c_pv indicates whether there is a positive or negative relationship between the landscape property and the soil property. When ∑_(p=1)^n▒〖|c_(pv ) |=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.  
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.


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.
}}
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Revision as of 13:05, 18 May 2014

link to framework components overview

Parts of Land degradation/Description

  1. Introduction page
  2. Model description
  3. Policy issues
  4. Data, uncertainty and limitations
  5. Overview of references
Component is implemented in:
Components:
Related IMAGE components
Projects/Applications
Models/Databases
Key publications
References
Two approaches to assess land degradation in IMAGE 3.0
Flowchart Land degradation. See also the Input/Output Table on the introduction page.

Model description of Land degradation

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