The Climate Changes and the Simulation of the Runoff in the Last 50 years (1961–2010) in the Upper Tarim River Basin of Southern Xinjiang, China

Abstract

The upper Tarim River basin is supporting approximately 50 million people by melting the glaciers and snow, which are highly vulnerable and sensitive to climate change. Therefore, assessing the relative effects of climate change on the runoff of this region is essential not only for understanding the mechanism of hydrological response over the mountainous areas in Southern Xinjiang but also for local water resource management. This study quantitatively investigated the climate change in the mountainous area of the upper Tarim River basin, using the up-to-date “ground-truth” precipitation and temperature data, the Asian Precipitation Highly Resolved Observational Data Integration Towards Evaluation of Water Resources (APHRODITE, 1961–2010, 0.25°) data; analyzed the potential connections between runoff data, observed at Alar station, and the key climatological variables; and discussed the regression models on simulating the runoff based on precipitation and temperature data. The main findings of this study were as follows—(1) both annual precipitation and temperature generally increase at rates of 0.85 mm/year and 0.25 °C/10a, respectively, while the runoff data measured at the Alar station shows fluctuating decreasing trends. (2) There are significant spatial differences in the temporal trends of precipitation; for example, the larger increasing rates of precipitation occur in the Karakoram mountains, while the larger decreasing rates happen in the northwestern Kashgar county. (3) The decreasing trends of temperature mainly occur in Kashgar county and its surrounding areas in summer. (4) Seasonal correlations in precipitation and temperature trends are more significant than those on a monthly and annual scale. (5) The regression model in simulating the runoff in the upper Tarim River basin based on radial basis function (RBF) is better than that based on the least-squares method, with the predictive values based on RBF models significantly better (correlation coefficient, CC ∼ 0.85) than those by least-squares models (CC ∼ 0.75). These findings will provide valuable information to inform environmental scientists and planners on the climate change issues in the upper Tarim River basin of Southern Xinjiang, China, under a semiarid-arid climate.