Arid ecosystems are very sensitive to a variety of physical, chemical and biological degradation processes. Tarim Basin,
the biggest endorheic basin in the Central Asia continent, is considered as one of the least water-endowed regions in the
world and arid and semi-arid environmental conditions are dominant. For the purposes of the convention, arid, semi-arid
and dry sub-humid areas were defined as "areas, other than polar and sub-polar regions, in which the ratio of annual
precipitation to potential evapotranspiration falls within the range from 0.05 to 0.65." In this study, the Aridity Index
(AI), the ratio of precipitation and land surface temperature, was also adopted as the base method for determining dry
land types and thereby delineating boundaries and showing changes of aridity conditions in Tarim Basin. Here,
precipitation is from TRMM/PR, and land surface temperature is from Modis LST. To analyze the spatial and temporal
variations of arid environmental conditions in Tarim basin, we calculated the yearly aridity index (the ratio of total
yearly rainfall to yearly mean Land Surface Temperature) based on the accumulated monthly precipitation and the
monthly Land Surface Temperature in growing season for the period 2000-2009. The results indicated it is possible to
work out an aridity index map with more detailed spatial patterns, which is valuable for identifying human impacts by
associated with vegetation and soil moisture characters.
Hydrological predictions in ungauged or poorly gauged basin are crucial for sustainable water management and
environmental changes study induced by climate change. Application of remote sensing technology has retrieved lots of
spatio-temporal dataset during the past decades for references. In this study, TRMM/PR and MODIS LST data were
introduced to get spatial patterns of precipitation and temperature changes by Empirical Orthogonal Function (EOF)
technique in a mountainous watershed, southern Tianshan. An input variable group was attempted to be constructed for
the Artificial Neural Networks (ANN) to model the stream flow change based on the patterns achieved above. The
results indicate that the spatial variability patterns of meteorology can be well recognized from the remote sensing data
by EOF analysis. The stream flow process can be satisfyingly simulated with input variables captured from the leading
modes during the study period. While, since the probabilistic model was not based on full physical mechanisms, and
often times, also limited by the amount of input data, uncertainties often implicated in the output. As an example, it is
discussed through the rapidly glaciers melting phenomena induced by climate warming, which is expected to cause
change in the flow generation mechanism.
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