6 Conclusion
In this study, we explored multi-annual (2000-2017) snow cover
variability in the upper course of the Irtysh, a large transboundary
river basin of Central Asia, which is significantly impacted by spring
snowmelt floods. Given the scarcity of direct runoff observations in the
region, we analysed snow covered area as a means to understand the
seasonal and inter-annual variability of snow hydrology, to provide
valuable information in the context of flood forecasting. The analysis
was carried out using 8-day MODIS snow cover composites, both within the
five major tributary basins and the Upper Irtysh as a whole. Cloud
filtered data were used to investigate: 1) the timing of snow cover
disappearance, using the day of year when snow cover disappears from
different 500 m elevation ranges within each basin (DSCD), 2) peak snow
cover depletion, the day of year of maximum areal snow cover depletion
in a basin (DPSCD) and 3) peak snow cover depletion rate in each basin
in each year (PSCDR expressed in
km2d-1). In addition, we employed
ERA-Interim reanalysis data, local weather stations and atmospheric
circulation indices to investigate potential meteorological drivers of
the observed snow cover depletion patterns.
The analysis of snow cover variability in the Upper Irtysh basin points
to large inter-annual and inter-basin differences, for both DSCD and
DPSCD, with the largest, low-lying basin showing the earliest dates and
providing the largest snowmelt contribution to runoff. DPSCD generally
occurs between mid-March and April, depending on the basin, while DSCD
occurs later during spring. No clear trends over the 2000-2017 study
period were identified in any basin for either variable. In contrast,
PSCDR, which peaked at over 5900 km2day-1 in 2017, displays a weak increasing trend for
the Upper Irtysh basin over the same period. In view of their relevance
for flood hazards, the existence of longer-term trends, as well as the
causes of the increase in PSCDR, should be addressed in further
research.
Multi-year fluctuations in peak snow cover depletion and snow cover
disappearance are controlled by spring temperature anomalies and
regional atmospheric patterns detected by weather stations and
reanalysis data in the Upper Irtysh basin. The atmospheric patterns that
best correlate with temperatures and snow cover variables are the winter
Arctic Oscillation and spring Siberian High indices. Among the two, the
winter Arctic Oscillation shows the strongest correlation with DPSCD,
especially in the larger, low-lying basins (-0.71), while lower
correlations are seen in the smaller mountain basins, where topographic
variability likely plays an important role.
The ability of the winter Arctic Oscillation index to predict a
relatively late or early timing of the spring peak snow cover depletion
rate in parts of Eurasia makes it a potentially useful tool for
long-term forecasts, and for use in early-warning flood prevention
schemes. However, the lack of empirical runoff data in the Upper Irtysh
basin complicates validation of the feasibility and effectiveness of
this approach. Despite the lack of validation, our approach is based on
readily available data and can therefore be applied to similar
catchments where snowmelt is the dominant source of runoff, providing
meaningful insights into the interactions between large scale snow cover
depletion patterns and streamflow, for informing water management
strategies. A denser network of ground based observations or different
remote sensing approaches could be employed to further assess the
influence of the Arctic Oscillation in modulating snow cover depletion
at the catchment scale.