5.1 Data and methods uncertainty
The main uncertainties in the analysis of snow cover variability and the
association between snow cover variables and atmospheric indices are
related to 1) the use of 8-day snow cover area instead of daily
products, which also implies the derived DSCD and DPSCD are in fact
8-day products, and 2) cloud-cover interpolation. Drawing links between
these variables and flood hazards in East Kazakhstan also requires an
assumption that snow covered area can be used to represent SWE and snow
cover depletion can be a proxy for snow cover melt and river discharge.
The choice of the 8-day composite product was made to reduce the impact
of cloud cover on the study area. While the main misclassification in
the daily MODIS product occurs between clouds and snow, and can
propagate to the 8-day product (Hall & Riggs, 2007), the accuracy of
the composite product might actually be higher than that of the daily
products (93% on average, Hall & Riggs, 2007) in areas of high cloud
cover, also leading to a higher correlation of the composite product
with streamflow (Zhou et al., 2005). In a study conducted in northern
China, Wang et al. (2009) found an accuracy of 94% for snow mapping for
the composite product at snow depths greater than 4 cm, decreasing to
39% for lower depths and patchy snow cover. These are however likely to
represent a small amount of SWE and therefore have a small impact on the
assessment of water availability. The use of a cloud-interpolation
scheme can also help reduce the cloud-cover related uncertainty (Dietz
et al., 2011; Dong & Menzel, 2016), although our algorithm might
introduce biases by enhancing elevation dependency (see S1, point 3) and
in case of snow melt and subsequent snowfall (see S1, point 4); a
further improvement on cloud cover interpolation accuracy is the use of
in situ temperature and precipitation data (Dong & Menzel, 2016),
although these are not often readily available in remote areas. Small
uncertainties in snow cover variability might also stem from data gap
filling in the original MODIS product in 2001 and 2002 at lower and
higher elevations, respectively. Higher elevations in our study area
might be affected by late spring snow falls after snow melt, which will
not be recorded as DSCD, calculated as the first week of snow
disappearance. However, these events are expected to provide a low
contribution to the SWE.
The association between snow covered area, SWE and stream discharge has
been demonstrated in several studies using the daily or 8-day MODIS
composite product and other SCA datasets. Delbart et al. (2015) found an
uncertainty of 15% in predicting water discharge from SCA between April
and September in four Andean catchments. Further still, Gurung et al.
(2017) found high correlations (> 0.78) at elevations
between 4000 and 6000 m a.s.l. in the Gandaki basin in the Hindu Kush
Himalayas. Using the composite product, Tong et al. (2009) identified a
correlation of 0.84 between cloud-filtered snow cover extent and
streamflow in the Quesnel river basin, Canada.
In this study, the relationship between snow covered area and SWE cannot
be assessed due to the lack of in situ observations in our study area
(Mashtayeva et al., 2016). The regulated nature of the Irtysh River,
i.e. the presence of reservoirs and channels in China and Kazakhstan
also causes dampening of peak flow and decreased runoff variability
downstream (Huang et al., 2012; 2014), preventing attempts at
correlating snow covered depletion rates and timing and discharge.