1 Introduction
In the regions of Central Asia, owing to the arid and continental
climate, snow cover melt provides an important contribution to runoff,
determining the availability of freshwater for the local populations.
Snow can, however, also act as a source of natural hazards, an issue
which can be further exacerbated by climate change and human
intervention (Dietz et al., 2014; Han et al., 2019). In Kazakhstan,
snowmelt represents the primary source of water for agricultural and
industrial use, dwarfing the contributions from liquid precipitation or
groundwater; Kazakh rivers are characterized by large variability in
annual runoff, with the largest annual discharges exceeding average
annual runoff by 5-7 times and more than 75% of annual runoff occurring
in a short period in early spring (Kozhakhmetov and Nikiforova, 2016).
Floods caused by late winter and spring snow melt are one of the most
severe natural hazards in the country, displacing 10-30,000 people each
year and causing on average more than $30 million in damages (UNOCHA,
2016; Guha-Sapir et al., 2018) through loss of infrastructure, crops and
livestock. Floods affect both lowland and mountain rivers, and there is
evidence that the number of extreme events affecting mountain rivers has
increased by 80% in recent years (Kozhakhmetov and Nikiforova, 2016).
This issue is particularly relevant in the East Kazakhstan region, which
was one of the regions most affected by flooding between 1967 and 1990
and the most affected between 1991 and 2015, with over 42 floods
reported (Kozhakhmetov and Nikiforova, 2016).
In spite of the role of snowmelt for the hydrology of Kazakh rivers,
comparatively little is known about large scale snow cover variations
from year to year. While Mashtayeva et al. (2016) analysed the
spatiotemporal distribution of snow depth and snow cover duration in all
Kazakhstan, a detailed analysis of the patterns of snow cover depletion
is necessary for the mitigation of floods, water management strategies
and conservation of river ecosystems. The improvement of short- and
long-term runoff models further requires an assessment of the
relationship between snow cover variability, snowmelt and meteorological
variables (Kuchment and Gelfan, 2007; Kang and Lee, 2014; Kult et al.,
2014) and the prediction of timing and magnitude of peak runoff.
Temperature exerts the greatest control on snow cover extent and
duration (Bednorz, 2004; Hantel et al., 2000; Mote, 2006; Tang et al.,
2017), although the spatial variation of the temperature-snow cover
relationship with elevation is less well understood (Gurung et al.,
2017). In addition, temperature anomalies and snow cover across Eurasia
are related to large-scale atmospheric circulation patterns via feedback
mechanisms (Cohen et al., 2012). Eurasian snow cover variability in
different seasons has been linked with the phases of the North Atlantic
Oscillation (NAO) and Arctic Oscillation (AO), Eurasian and Siberian
pressure patterns and sea ice extent in the Barents-Kara Sea (Cohen &
Barlow, 2005; Wegmann et al., 2015; Ye and Wu, 2017). Further still,
spring snow retreat is also known to influence the East-Asian climate of
the following summer by controlling the strength of the Indian summer
monsoon (Zhang et al., 2017). However, the links between teleconnection
patterns, temperature anomalies and snow cover retreat at the catchment
scale and possible lagged effects of large-scale atmospheric
circulation, which would provide further indications for long-range
forecasts, remain to be fully explored.
The size of the East Kazakhstan region and the sparse network of
meteorological stations (Mashtayeva et al., 2016) means investigations
on snow cover cannot be realistically undertaken using field data, and
leaves remote sensing as the most viable option. Information on snow
cover extent from optical sensors such as MODIS (MODerate resolution
Imaging Spectroradiometer) and AVHRR (Advanced Very-High-Resolution
Radiometer) at moderate spatial resolution (500-1100 m per pixel) and at
daily to weekly time scales has been employed for studying snow cover
variability across Eurasia (Dietz et al., 2012; 2013; 2014). AVHRR data
represent the longest temporal record from 1978 to the present, but
requires additional processing for snow/cloud cover discrimination and
the quality of the observations decreases significantly with the oldest
generation of sensors (Dietz et al., 2014). Recently, the availability
of Sentinel-2 data has opened up the possibility for investigating snow
cover at a much higher spatial resolution (10-20 m) at weekly time
scales (Hollstein et al., 2016), while Sentinel-3 continues the legacy
of MODIS and AVHRR by providing data at moderate resolution (300 m) in
large swaths. However, no long-term (i.e. decadal) records exist for the
Sentinel satellites as the first was only launched in 2015. Alternative
snow depth and snow water equivalent (SWE) datasets are available from
passive microwave sensors (Pulliainen, 2006) or GRACE gravimetric data
(Wang and Russell, 2016), though the spatial resolution of these
products is much coarser, e.g. 25 km for the ‘Globsnow’ product (Luojus
et al., 2013) and 1º for GRACE (Landerer & Swenson, 2012). In addition,
passive microwave data are known to be unsuitable for snow cover
detection in mountainous regions (Luojus et al., 2013), which form a
large part of the study area. In this study, MODIS was chosen as the
main data source, as it provides an easily accessible archive of snow
cover data from 2000 to the present, produced through a robust
methodology and requiring minimal additional processing (Hall & Riggs,
2007).
This study focuses on the analysis of snow cover variability in one of
the main water catchments of Kazakhstan, using the MODIS MOD10A2
dataset. The aims of this study are: i) To investigate patterns of
spring snow cover change in five sub-basins of the Upper Irtysh river
catchment, including the magnitude and timing of early/late peak snow
cover depletion rates and timing of snow cover disappearance at
different elevations, and ii) to investigate the correlations between
variability in snow cover, air temperature and atmospheric pressure
patterns. The Irtysh basin is used as a case study to evaluate the
general applicability of this approach to understanding the linkages
between large scale snow cover change and runoff.