Predictors of precipitation for improved water resources management in the 
Tarim River basin: Creating a seasonal forecast model

Hartmann, H.; Snow, J.; Stein, S.; Su, B.; Zhai, J.; Jiang, T.; Krysanova, Valentina; Kundzewicz, Zbigniew W.

doi:10.1016/j.jaridenv.2015.09.010

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Predictors of precipitation for improved water resources management in the Tarim River basin: Creating a seasonal forecast model

Urheber*innen

Hartmann, H.
External Organizations;

Snow, J.
External Organizations;

Stein, S.
External Organizations;

Su, B.
External Organizations;

Zhai, J.
External Organizations;

Jiang, T.
External Organizations;

/persons/resource/Valentina.Krysanova

Krysanova, Valentina
Potsdam Institute for Climate Impact Research;

/persons/resource/zbyszek

Kundzewicz, Zbigniew W.
Potsdam Institute for Climate Impact Research;

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Zitation

Hartmann, H., Snow, J., Stein, S., Su, B., Zhai, J., Jiang, T., Krysanova, V., Kundzewicz, Z. W. (2016): Predictors of precipitation for improved water resources management in the Tarim River basin: Creating a seasonal forecast model. - Journal of Arid Environments, 125, 31-42.
https://doi.org/10.1016/j.jaridenv.2015.09.010

Zitierlink: https://publications.pik-potsdam.de/pubman/item/item_21382

Zusammenfassung

In recent years, an expansion of irrigated agriculture and rapid population growth have threatened the Tarim River basin's natural ecosystems and caused water shortages. Improving the water resources management in the basin requires accurate seasonal precipitation forecasts. Based on previous research, possible predictors of precipitation were selected and either downloaded directly or calculated using NCEP/NCAR Reanalysis 1 or NOAA Extended Reconstructed Sea Surface Temperature (SST) V3b data. Predictors were correlated with precipitation data, provided by the National Climate Centre of the China Meteorological Administration for the period 1961 to 2010 and averaged over the subbasins of the Tarim River. The Spearman rank correlation analyses with lead times of up to six months (or two seasons) revealed significant (at the 1% level) and strong (ρ ≤ −0.6 or ρ ≥ 0.6) correlations of precipitation in all subbasins with the SST and monsoon indices as well as with the Siberian High Intensity (SHI) and the Westerly Circulation Index (WCI). Lastly, we demonstrate the setup of a forecast model based on a multiple linear regression on the example of the Hotan River subbasin. This model predicts precipitation 5 months in advance with reasonable accuracy in two out of three configurations.