Investigation of precipitation concentration and trends and their potential drivers in the major river basins of Central Asia

Abstract

Understanding temporal and spatial changes in precipitation has far-reaching implications for watershed development and risk assessment under global climate change. In our study, temporal and spatial variability in precipitation concentration (PC) in Central Asia (CA) from 1980 to 2017, measured with the precipitation concentration index (PCI) and Gini Coefficient (GC), was analyzed using interpolated precipitation from the Climate Prediction Center (CPC) and National Centers for Environmental Prediction (NCEP). The results showed that: (1) interpolated precipitation increased significantly, with significant mutations (P < .5) mainly concentrated between 2000 and 2010; (2) the spatial distribution of the CPC PCI was opposite to that of the NCEP PCI, and fluctuations in CPC GC were larger than that of the NCEP, which indicated that the precipitation data from NCEP was more uniform over the year; (3) for two of three interpolation methods, CPC PC displayed significant mutations in southern CA between 1985 and 2005, while significant (P < .05) mutations in NCEP PC were relatively concentrated in southern CA between 1987 and 2005; (4) although the El Niño Modoki Index (EMI), Pacific Decadal Oscillation (PDO), Nino 3.4, and Southern Oscillation Index (SOI) displayed resonance relationships with CPC PCI, there were no significant abrupt changes in the resonance correlation. In the NCEP data, EMI, PDO, Nino 3.4, and North Atlantic Oscillation (NAO) led to mutations in the resonance relationship with PCI. Our research proved that the change in PC in CA had closely relationship with some teleconnection indices.