Data Availability Statement
Supporting data for this study are openly available in MOD11A2
MODIS/Terra Land Surface Temperature and the Emissivity 8-Day L3 Global
1km SIN Grid athttp://doi.org/10.5067/MODIS/MOD11A2.006, reference number (NASA LP DAAC, 2019) and MYD11B2
MODIS/Aqua Land Surface Temperature and the Emissivity 8-day L3 Global
6km SIN Grid athttp://doi.org/10.5067/MODIS/MYD11B2.006, reference number (NASA LP DAAC, 2019)
1.
Introduction
Water
temperature of inland water bodies is a critical parameter that governs
an array of aquatic ecosystem functions that involve physical, chemical,
and biological processes (Li, Chen, & Zhang, 2015; Ma et al., 2016).
Inland water temperature is regulated by multiple interacting factors,
and excessive lake water warming has severe ecological consequences
(Winslow, Read, Hansen, Rose, & Robertson, 2017; Kraemer, Mehner, &
Adrian, 2017). Adrian et al. reported that some of the largest lakes
across the world have shown notable temperature increases due to climate
change (Adrian et al., 2009; Schmidt et al., 2018). For example, the
temperature of Lake Baikal (Russia) has increased by approximately 1.2
°C since 1946 (Dörnhöfer & Oppelt, 2016). Lake temperature reflects its
morphology, watershed conditions and hydrological dynamics, and variably
influences the biology of resident aquatic organisms (Wetzel, 2001;
Parastatidis, Mitraka, Chrysoulakis, & Abrams, 2017; Song et al.,
2016). Conventional approaches for measuring lake water temperature
using in situ sensors and data loggers provide information that
is temporally continuous but spatially limited. These limitations
therefore hinders widespread application of conventional approaches for
capturing temperature variation at large spatial scales (Parastatidis et
al., 2017). Yet, synoptic information is needed to capture thermal
heterogeneity in large lakes, examine patterns of thermal variation, and
explain fundamental biophysical and chemical processes in these water
bodies (Hook, Vaughan, Tonooka, & Schladow, 2007; Ke & Song, 2014).
Water temperature of lakes is primarily influenced by their absorption
of solar energy in a process affected by an array of physical, chemical
and hydro-morphologic properties of lake ecosystems (Li et al., 2015;
Woolway et al., 2018). In this respect, the surface temperature of lake
water is highly dynamic, as it changes seasonally and diurnally due to
variation in air temperature and the insulation effect of snow/ice (Li
et al., 2015; Parastatidis et al., 2017; Song et al., 2016; Zhang et
al., 2014). The amount of sunlight absorbed by water increases
exponentially with the distance it travels through the water column,
particularly for radiation wavelengths shorter than 750 nm (Schmidt et
al., 2018; Song et al., 2016). The high specific heat of water enables
the dissipation of light energy and its accumulation as heat in the
water column. However, retention of that energy depends on multiple
factors (wind speed, currents and other water movements, watershed
geomorphology) influencing its distribution within a lake system, and
the change rate between water input and discharge through the
tributaries (Gorham, 1964). Thus, the pattern of thermal evolution and
stratification influences fundamentally the cycling of physical and
chemical components of lakes, which in turn drives primary productivity
and decomposition processes (Wetzel, 2001).
Thermal remote sensing methods for monitoring lake water surface
temperature (LWST) circumvent problems of accessibility to lakes in
remote areas and so provide a synoptic context for evaluating
relationships between landscape features and water thermal
characteristics(Kraemer et al., 2017; Moukomla & Blanken., 2016; Zhong,
Notaro, & Vavrus, 2018). Thermal infrared (TIR) remote sensing can
reliably map LWST and its circulation patterns in lakes using various
satellite sensors (Ke & Song, 2014). Of those, the Advanced Very High
Resolution Radiometer (AVHRR) and Moderate Resolution Imaging
Spectroradiometer (MODIS) on board Terra/Aqua have been widely used for
monitoring lake surface temperatures (Zhang et al., 2014; Phillips,
Saylor, Kaye, & Gibert, 2016). Landsat-Thematic Mapper(TM)/ Enhanced
Thematic Mapper Plus (ETM+)/Operational Land Imager (OLI) and Advanced
Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data are
commonly used to retrieve thermal features of inland and coastal waters
(Kraemer et al., 2017; Trumpickas, Shuter, Minns, & Cyr, 2015).
Although LWST studies have been conducted at various scales around the
world, the distribution pattern of LWST for Eurasia lakes has yet to be
analyzed. Investigations about the relationship between LWST and factors
regulating its spatiotemporal variations have been reported (Kraemer et
al., 2017; Parastatidis et al., 2017; Trumpickas et al., 2015). The
overall distribution pattern of LWST for Eurasia lakes deserves
investigation (Zhang, Xie, Kang, Yi, & Ackley, 2011; Yuan et al.,
2015).
In this study, 3,840 composites of MODIS imagery data covering the
terrestrial area of Eurasia were processed and analyzed. The
characteristics of LWST for 1,098 lakes that are at least 25
km2 in size were examined to determine spatial
associations with climatic, landscape and hydrologic conditions. The
objectives of this study were to (1) examine LWST variation of lakes at
the Eurasian continental scale based on time-series MODIS LST data for
the period 2001-2015; (2) examine intra-annual rates of temperature
change of some typical large lakes in Eurasia; and (3) evaluate
potential factors that contribute to variations in LWST.
2. Materials and Methods
2.1 Criteria of lakes selection
In the pre-satellite remote sensing stage, three types of independent
approaches were generally used to obtain a global lake census: (1) a
lake-type approach based on the origin of lakes, (2) an extrapolation of
known regional censuses, and (3) a climatic approach based on lake
distribution in homogeneous temperature and runoff environments
(Meybeck, 2011). With respect to the last approach, it links lake
distribution with five major climatic biomes, i.e., deglaciated regions
(52%), temperate regions (13%), dry and arid regions (25%), deserts
(1%), and wet tropics (9%). This approach has clear merits in
combining temperature and surface-water runoff (Wetzel, 2001).
With the advent of satellite remote sensing, a global census of lake
distribution and thermal characterization has become possible (Downing
et al., 2006). Using MODIS LST products at a nominal spatial resolution
of 1 km2, this study extracted lakes and water
reservoirs larger than 25 km2 in Eurasia from the
Global Lakes and Reservoirs Database (Lehner & Döll, 2004); this
yielded a total of 1,098 water bodies
(Figure 1). Among those, 861 lakes
and 237 water reservoirs were in Asia and Europe, respectively. It
should be noted that some lakes or reservoirs greater than 25
km2 might not have been selected if one of their
dimension is smaller than 1-km, the spatial resolution of the MODIS LST
product. Such elongated water bodies would likely to be located along
shorelines. The selected 1,098 lakes cover 0.63 million
km2, which is about 44.3% of the world’s total lake
areas (Lehner & Döll, 2004).