Peak river flows in Central Asia occur in spring and summer when snow in the high mountains melts. These river flows are of considerable societal importance, as they are critical to both agriculture and water resources in the region. Since river flows are largely determined by snow melt, snow pack conditions at the end of winter provide useful information about river flow totals for the following spring and summer. Unfortunately, sufficiently detailed and accurate snow pack measurements, particularly depth, are difficult to obtain. Previous research, however, has suggested that (i) local winter precipitation can serve as a useful proxy for snow pack and (ii) the local precipitation is influenced by large-scale, potentially predictable climate variability. Here we use 35 years of river flow data for 25 stations in Central Asia to determine the potential to predict the average spring and summer river flow from large-scale climate information in the preceding autumn and winter.

Canonical Correlation Analysis (CCA) is used to extract the relationships between the predictor (e.g., gridded observed Dec-Mar precipitation) and predictand (e.g, Apr-Sep river flow), with Empirical Orthogonal Function filtering applied prior to the CCA. A clear relationship between gridded regional winter precipitation and spring/summer river flows is obtained, with correlations exceeding 0.6 for several of the stations. This relationship can also be obtained using operationally available NCEP/NCAR Climate Data Assimilation System precipitation. The CCA results are regressed to large-scale wind and Sea Surface Temperature (SST) anomalies, to examine the relationship to large-scale climate variability. The winds show changes in the intensity of the westerly flow that impinges on the mountains of the region, consistent with the precipitation anomalies. These changes in regional winds and precipitation are associated with equatorial Pacific SST anomalies. The SST anomalies are similar to the El Nino pattern, but with greater strength in the central Pacific relative to the eastern Pacific: an SST pattern previously shown to affect the Central Asia climate. Moreover, this connection to Pacific SSTs suggests the potential to increase the lead of the river flow prediction even further, and the skill levels for forecasting spring and summer river flows based on SSTs in the previous autumn are also examined.