Variability and Predictability of Central Asia River Flows: Antecedent Winter Precipitation and Large-Scale Teleconnections

Abstract

Abstract Warm season river flows in central Asia, which play an important role in local water resources and agriculture, are shown to be closely related to the regional-scale climate variability of the preceding cold season. The peak river flows occur in the warm season (April–August) and are highly correlated with the regional patterns of precipitation, moisture transport, and jet-level winds of the preceding cold season (November–March), demonstrating the importance of regional-scale variability in determining the snowpack that eventually drives the rivers. This regional variability is, in turn, strongly linked to large-scale climate variability and tropical sea surface temperatures (SSTs), with the circulation anomalies influencing precipitation through changes in moisture transport. The leading pattern of regional climate variability, as resolved in the operationally updated NCEP–NCAR reanalysis, can be used to make a skillful seasonal forecast for individual river flow stations. This ability to make predictions based on regional-scale climate data is of particular use in this data-sparse area of the world. The river flow is considered in terms of 24 stations in Uzbekistan and Tajikistan available for 1950–85, with two additional stations available for 1958–2003. These stations encompass the headwaters of the Amu Darya and Syr Darya, two of the main rivers of central Asia and the primary feeders of the catastrophically shrinking Aral Sea. Canonical correlation analysis (CCA) is used to forecast April–August flows based on the period 1950–85; cross-validated correlations exceed 0.5 for 10 of the stations, with a maximum of 0.71. Skill remains high even after 1985 for two stations withheld from the CCA: the correlation for 1986–2002 for the Syr Darya at Chinaz is 0.71, and the correlation for the Amu Darya at Kerki is 0.77. The forecast is also correlated to the normalized difference vegetation index (NDVI); maximum values exceed 0.8 at 8-km resolution, confirming the strong connection between hydrology and growing season vegetation in the region and further validating the forecast methodology.