Monitoring Agricultural Drought in the Near East - Monitoring and Predicting Agricultural Drought: A Global Study - page 220

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ENSO influence on sea-surface temperatures in the northern Atlantic and
on late-season (March-April) precipitation levels in northwestern Africa.
Particularly in Morocco, west and north of the Atlas Mountains, late-
season precipitation tends to be below normal during warm ENSO years.
A major research need is to investigate whether the NAO, like ENSO, is
a coupled ocean-atmosphere phenomenon as opposed to a random atmo-
spheric phenomenon. If the NAO were a coupled phenomenon, it would
enhance the use of an NAO index in drought early warning systems (Cullen
and deMenocal, 2000). However, as Iglesias (2001) points out, the large
variability of climate in the region, spanning time scales from the intrasea-
sonal to the decadal, poses particular challenges for the management of
agriculture. It is therefore expected that, even if this research leads to a
successful outcome, the contribution of seasonal forecasts to the stabiliza-
tion of agricultural production in the Near East will be relatively modest
as compared to other management strategies adapted to highly variable
climates.

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Sp atialization of Drought in Data-Insufficient Areas

The Near East is a region with relatively sparse and heterogeneous cli-
matic data coverage. With the exception of Turkey, which has a good and
homogenous coverage throughout the country, most climatic stations in
the region are concentrated in coastal and agriculturally important areas.
Rangeland (arid) areas and deserts are very poorly covered. Due to the
pressure of the population increase, these areas are becoming increasingly
important from an ecosystem function perspective. As the region is also di-
verse in terms of landscapes and topography, temperature regimes are not
uniform, which needs to be taken into consideration while using drought
indicators based on the water balance.
In such environments characterized by high spatial variations in mois-
ture and temperature regimes, the delineation of drought can be consider-
ably improved by advanced methods of spatial interpolation. Several statis-
tical techniques are now available that make use of digital elevation models
(DEM) to improve the spatialization of climatic parameters (De Pauw et al.,
2000). In view of the strong linkages between climatic variables (especially
temperature, but also rainfall, humidity, and sunshine) and topography,
the most promising techniques for spatialization in climatology are multi-
variate approaches because the latter permit the use of terrain variables as
auxiliary variables in the interpolation process. In contrast to the climatic
target variables themselves, which are only known for a limited number of
sample points, terrain variables have the advantage that they can be known
for all locations in between, which increases the precision of the interpo-
lated climatic variables significantly. Co-kriging (e.g., Bogaert et al., 1995)
and co-splining (e.g., Hutchinson, 1995) are methods that in most cases
lead to excellent interpolations. ICARDA has successfully combined the
co-splining approach of Hutchinson with the GTOPO30 digital elevation

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