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Based on in situ soil moisture and river runoff records in the Tarim River basin, usability of the long term Essential Climate Variable (ECV) soil moisture dataset is validated in the arid climatic region of China. The spatio-temporal variation of soil moisture and its possible influencing factors in the 1988–2013 is also preliminary analyzed in the current paper. Results reveal that the ECV soil moisture can capture the large scale dynamics of regional water cycle quite satisfactorily, showing good agreement with in situ observations in their seasonal and interannual variability. In the period of 1988–2013, the ECV soil moisture shows obvious increasing trends in the northwest and the southwest parts of the Tarim River basin, particularly in spring (March–May) and autumn (September–November). Statistical analysis further suggests that the variations of soil moisture in the Tarim River basin are more controlled by precipitation, and temperature is less effective in controlling of soil moisture variations. [ABSTRACT FROM AUTHOR]

Since the 1950s, the population in the arid to hyperarid Tarim River basin has grown rapidly concurrent with an expansion of irrigated agriculture. This threatens the Tarim River basin's natural ecosystems and causes water shortages, even though increased discharges in the headwaters have been observed more recently. These increases have mainly been attributed to receding glaciers and are projected to cease when the glaciers are unable to provide sufficient amounts of meltwater. Under these circumstances water management will face a serious challenge in adapting its strategies to changes in river discharge, which to a greater extent will depend on changes in precipitation. In this paper, we aim to develop accurate seasonal predictions of precipitation to improve water resources management. Possible predictors of precipitation for the Tarim River basin were either downloaded directly or calculated using NCEP/NCAR Reanalysis 1 and NOAA Extended Reconstructed Sea Surface Temperature (SST) V3b data in monthly resolution. To evaluate the significance of the predictors, they were then correlated with the monthly precipitation dataset GPCCv6 extracted for the Tarim River basin for the period 1961 to 2010. Prior to the Spearman rank correlation analyses, the precipitation data were averaged over the subbasins of the Tarim River. The strongest correlations were mainly detected with lead times of four and five months. Finally, an artificial neural network model, namely a multilayer perceptron (MLP), and a multiple linear regression (LR) model were developed each in two different configurations for the Aksu River subbasin, predicting precipitation five months in advance. Overall, the MLP using all predictors shows the best performance. The performance of both models drops only slightly when restricting the model input to the SST of the Black Sea and the Siberian High Intensity (SHI) pointing towards their importance as predictors. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]