Maps in: North Africa and West Asia

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Agricultural Resource Capital and Population Density

 

Agricultural Resource Capital and Population Density

A high-potential agricultural resource base can be insufficient for a large rural population, whereas areas with lower potential for agriculture but also lower population densities can be sustainable. This map links agricultural resource poverty to population density.

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Agricultural Resource Capital and Population Density

A high-potential agricultural resource base can be insufficient for a large rural population, whereas areas with lower potential for agriculture but also lower population densities can be sustainable. This map links agricultural resource poverty to population density.

Length of the moisture-limited growing period

 

Length of the moisture-limited growing period

The climatic growing period is calculated by means of a model developed by the Food and Agriculture Organization of the United Nations (FAO, 1978) to estimate the length of growing period under either moisture-limiting or temperature-limiting conditions, or both. Under rainfed conditions, both moisture and temperature can be limited. Under irrigated conditions, only temperature is to be considered a limiting factor.
The moisture-limited growing period is calculated, using a waterbalance approach, as the ratio of actual evapotranspiration (AET) to potential evapotranspiration (PET). If this ratio for any particular month is higher than a user-defined threshold (in this study 0.5), that month is part of a growing period. If it is not, that month is not part of the growing period. The start date of the moisture-limited growing period is obtained from linear interpolation of the AET/PET ratios between the last month that is not part of the growing period, and the first month that is part of the growing period. The end date, inversely, is obtained by linear interpolation of the AET/PET ratios between the last month that is part of the growing period, and the first one that is not part of the growing period.

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Length of the moisture-limited growing period

The climatic growing period is calculated by means of a model developed by the Food and Agriculture Organization of the United Nations (FAO, 1978) to estimate the length of growing period under either moisture-limiting or temperature-limiting conditions, or both. Under rainfed conditions, both moisture and temperature can be limited. Under irrigated conditions, only temperature is to be considered a limiting factor.
The moisture-limited growing period is calculated, using a waterbalance approach, as the ratio of actual evapotranspiration (AET) to potential evapotranspiration (PET). If this ratio for any particular month is higher than a user-defined threshold (in this study 0.5), that month is part of a growing period. If it is not, that month is not part of the growing period. The start date of the moisture-limited growing period is obtained from linear interpolation of the AET/PET ratios between the last month that is not part of the growing period, and the first month that is part of the growing period. The end date, inversely, is obtained by linear interpolation of the AET/PET ratios between the last month that is part of the growing period, and the first one that is not part of the growing period.

Relative change in annual trend precipitation 1901-2007

 

Relative change in annual trend precipitation 1901-2007

This map is based on the Full Data Reanalysis Product Version 4 of the Global Precipitation Climatology Centre (GPCC). It has been obtained by linear regression fitted to the 107-year time series of annual precipitation of each 0.5x0.5 degree grid cell by the least-squares method and subsequent resampling to 0.008333 degree (about 1 km) spatial resolution. The map shows the average relative change between 2007 and 1901 in percent per decade (10-year period) as measured along the trend line.
In spite of the high year-to-year variability, there is a clear and often highly significant trend, which is mostly negative (0-5% decrease/decade), with a positive increase in parts of Central Asia. Only in some of the extremely dry parts of the region, this simple linear model leads to an obvious overestimation of change (areas mapped as having 15 to over 30% relative change of annual precipitation per decade).

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Relative change in annual trend precipitation 1901-2007

This map is based on the Full Data Reanalysis Product Version 4 of the Global Precipitation Climatology Centre (GPCC). It has been obtained by linear regression fitted to the 107-year time series of annual precipitation of each 0.5x0.5 degree grid cell by the least-squares method and subsequent resampling to 0.008333 degree (about 1 km) spatial resolution. The map shows the average relative change between 2007 and 1901 in percent per decade (10-year period) as measured along the trend line.
In spite of the high year-to-year variability, there is a clear and often highly significant trend, which is mostly negative (0-5% decrease/decade), with a positive increase in parts of Central Asia. Only in some of the extremely dry parts of the region, this simple linear model leads to an obvious overestimation of change (areas mapped as having 15 to over 30% relative change of annual precipitation per decade).

Proportion of underweight children(year 2000)

 

Proportion of underweight children(year 2000)

This map shows the percentage of underweight children. As with the map of population density , the uneven pattern of large and small polygons is an artifact due to differences in the size of the statistical units available in different countries and, while offering a reasonable average, does not necessarily allow a point-exact  estimate of this poverty indicator.

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Proportion of underweight children(year 2000)

This map shows the percentage of underweight children. As with the map of population density , the uneven pattern of large and small polygons is an artifact due to differences in the size of the statistical units available in different countries and, while offering a reasonable average, does not necessarily allow a point-exact  estimate of this poverty indicator.

Accessibility  to markets

 

Accessibility to markets

This map shows travel time to cities with at least 50,000 inhabitants as an indicator of accessibility to markets.

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Accessibility to markets

This map shows travel time to cities with at least 50,000 inhabitants as an indicator of accessibility to markets.

Irrigated areas

 

Irrigated areas

This map shows the percentage of land that is irrigated. Whereas irrigation is the ultimate solution for agricultural water shortage, this map makes clear that only a small fraction of the drylands is irrigated, although there are major differences between individual countries (-> Statistical Tables).

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Irrigated areas

This map shows the percentage of land that is irrigated. Whereas irrigation is the ultimate solution for agricultural water shortage, this map makes clear that only a small fraction of the drylands is irrigated, although there are major differences between individual countries (-> Statistical Tables).

Absolute change in annual trend precipitation 1901-2007

 

Absolute change in annual trend precipitation 1901-2007

This map is based on the Full Data Reanalysis Product Version 4 of the Global Precipitation Climatology Centre (GPCC). It has been obtained by linear regression fitted to the 107-year time series of annual precipitation of each 0.5x0.5 degree grid cell by the least-squares method and subsequent resampling to 0.008333 degree (about 1 km) spatial resolution. The map shows the average absolute change in mm/year as measured along the trend line between 1901 and 2007. With some exceptions (e.g. parts of the Black Sea coast and the rim of Central Asia mountains) the trend is negative in most of the region.

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Absolute change in annual trend precipitation 1901-2007

This map is based on the Full Data Reanalysis Product Version 4 of the Global Precipitation Climatology Centre (GPCC). It has been obtained by linear regression fitted to the 107-year time series of annual precipitation of each 0.5x0.5 degree grid cell by the least-squares method and subsequent resampling to 0.008333 degree (about 1 km) spatial resolution. The map shows the average absolute change in mm/year as measured along the trend line between 1901 and 2007. With some exceptions (e.g. parts of the Black Sea coast and the rim of Central Asia mountains) the trend is negative in most of the region.

Drylands and SRT types

 

Drylands and SRT types

This map shows which drylands belong to the SRT2 category (requiring vulnerability and risk reduction) and which ones to the SRT3 category (with possibilities for sustainable intensification). The subdivision is entirely based on the value of the aridity index (ratio of mean annual precipitation over mean annual potential evapotranspiration), with a value below 0.35 for SRT2 areas and equal to or greater than 0.35 for SRT3 areas. This is by necessity a gross simplification as production and livelihood systems’ vulnerability and intensification potential are not only determined by a simple climatic indicator but, perhaps more importantly, by socioeconomic factors as well. However, the latter are difficult to put on a map and there is no denying that in drylands the moisture regime is a major determinant of vulnerability and intensification potential.  To accommodate irrigated areas in very dry areas an intermediate category  (SRT2-irrigated), which may have aspects of both vulnerability (e.g. depleting water resources, salinization) and intensification potential (e.g. water saving technologies, addressing yield gaps).

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Drylands and SRT types

This map shows which drylands belong to the SRT2 category (requiring vulnerability and risk reduction) and which ones to the SRT3 category (with possibilities for sustainable intensification). The subdivision is entirely based on the value of the aridity index (ratio of mean annual precipitation over mean annual potential evapotranspiration), with a value below 0.35 for SRT2 areas and equal to or greater than 0.35 for SRT3 areas. This is by necessity a gross simplification as production and livelihood systems’ vulnerability and intensification potential are not only determined by a simple climatic indicator but, perhaps more importantly, by socioeconomic factors as well. However, the latter are difficult to put on a map and there is no denying that in drylands the moisture regime is a major determinant of vulnerability and intensification potential.  To accommodate irrigated areas in very dry areas an intermediate category  (SRT2-irrigated), which may have aspects of both vulnerability (e.g. depleting water resources, salinization) and intensification potential (e.g. water saving technologies, addressing yield gaps).

Land use/land cover 1993

 

Land use/land cover 1993

This map shows  an 8-class land use/land cover map, compiled by aggregation of an initial 12-class land use/land cover classification based on image analysis of AVHRR data at 1-km spatial resolution for the period April 1992 to March 1993. The main approach used for building up the classification was a hierarchical decision-tree with sliding thresholds for the annual minimum, maximum and mean NDVI, which varied according to the agroclimatic zone.

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Land use/land cover 1993

This map shows  an 8-class land use/land cover map, compiled by aggregation of an initial 12-class land use/land cover classification based on image analysis of AVHRR data at 1-km spatial resolution for the period April 1992 to March 1993. The main approach used for building up the classification was a hierarchical decision-tree with sliding thresholds for the annual minimum, maximum and mean NDVI, which varied according to the agroclimatic zone.

Biomass response to precipitation variability 1982-2000

 

Biomass response to precipitation variability 1982-2000

This map shows the coefficient of variation of the maximum NDVI for the period 1982-2000, which is an indicator of the fluctuations in agricultural or natural biomass, which do not result from land use change and are related to current climatic variability.
The colors in orange and red express where the variability is the highest. As the CV of the maximum NDVI expresses the response of the vegetation biomass to climatic fluctuations, it is an impact indicator and hence evidence of the presence of ‘hot’ or ‘cool’ spots in different parts of CWANA.
The map shows several current hot spots:

  • North Africa, from Morocco into Tunisia
  • The Sahel, from Mauritania  into Sudan, Eritrea, northern Ethiopia and turning south into Somalia
  • The Fertile Crescent, from northern Syria, Kurdistan, turning southeast into Kuzistan and southern Iran
  • The foothill zone north of the Tien-Shan and Pamir Central Asia mountain ranges
  • The rangelands in the north of Central Asia

 All these areas are characterized by severe droughts, degradation of land, water and vegetation resources, and sometimes famines.

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Biomass response to precipitation variability 1982-2000

This map shows the coefficient of variation of the maximum NDVI for the period 1982-2000, which is an indicator of the fluctuations in agricultural or natural biomass, which do not result from land use change and are related to current climatic variability.
The colors in orange and red express where the variability is the highest. As the CV of the maximum NDVI expresses the response of the vegetation biomass to climatic fluctuations, it is an impact indicator and hence evidence of the presence of ‘hot’ or ‘cool’ spots in different parts of CWANA.
The map shows several current hot spots:

  • North Africa, from Morocco into Tunisia
  • The Sahel, from Mauritania  into Sudan, Eritrea, northern Ethiopia and turning south into Somalia
  • The Fertile Crescent, from northern Syria, Kurdistan, turning southeast into Kuzistan and southern Iran
  • The foothill zone north of the Tien-Shan and Pamir Central Asia mountain ranges
  • The rangelands in the north of Central Asia

 All these areas are characterized by severe droughts, degradation of land, water and vegetation resources, and sometimes famines.

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