1 INTRODUCTION
Inland basins, also referred to as endorheic basins, are defined as
regions where runoff in the basin has no direct hydraulic connection
with the ocean; this means that inland basin runoff is landlocked from
the ocean. This runoff eventually enters inland lakes or is taken up by
evapotranspiration. These regions are one of the most sensitive to
climate change and human activities (Huang, Xu, Guan, Wang, & Guo,
2016; Wang et al., 2018). The Eurasia inland basin (EIB) is the largest
inland basin in the world; its accounts for more than one-third of the
global inland basin area and spans 20 countries. As the climate in EIBs
is characterized by extremely low precipitation and high evaporation,
the hydrology and ecosystem of the EIB are sensitive to changes in
precipitation, actual evapotranspiration (AET), and water storage.
Therefore, changes in and attribution analyses of terrestrial (or total)
water storage (TWS) and AET in the EIB are hugely important to water
resource management, ecosystem health and sustainable agricultural
irrigation in Eurasia.
The Gravity Recovery and Climate Experiment (GRACE) is able to
accurately estimate monthly TWS changes in basins that are larger than
approximately 200 000 and 100 000 km2 at low and high
latitudes, respectively (Rodell et al., 2018). Wang et al. (2018) found
that TWS was decreasing in global endorheic basins, using the GRACE TWS
product; this TWS decline in the EIB contributed to 70% of the global
decline. Based on three GRACE Mascon products, Rodell et al. (2018)
analyzed TWS trends in 34 regions from 2002 to 2016; they categorized
the drivers of this change as natural inter-annual variability,
unsustainable groundwater consumption, climate change, and combinations
of these factors.
In terms of basin scale research, there are inconsistent results on the
main causes of water storage change, with opposing conclusions for some
basins. For example, some studies have suggested that the primary cause
for the water level decline in the Aral Sea was increased water
consumption from enhanced human activities, particularly irrigation and
damming (Yang et al., 2020). However, irrigation diversions increased
from 1992 to 2005 and decreased from 2005 to 2016 (Wang et al., 2020).
Thus, it was concluded that although irrigation diversion plays a
dominant role in this water storage decline, its influence has been
gradually weakening (Jia, Lia, Li, & Huang, 2020). Another study
reported that the mountain lakes in the source region of the Aral Sea
was expanding, which was mainly caused by increased glacial melt induced
by elevating air temperature (Zheng et al., 2019). Several studies have
shown that increasing evaporation rates play a dominant role in the
water level decline in the Caspian Sea (Chen et al., 2017). Arpe,
Molavi-Arabshahi and Leroy (2020) found that the decrease in net
precipitation over the sea and the mean annual inflow contributed 57%
and 43% to this water level decline, respectively. Other studies
attributed the water level decline to increased evapotranspiration or
two major earthquakes in 2000 (Elguindi & Giorgi, 2006; Ozyavas, Khan,
& Casey, 2010).
In addition to the water level declines in the Aral and Caspian Seas, a
decline in TWS was found to occur in other basins in the EIB, including
the Iran (Joodaki, Wahr, & Swenson, 2014; Khaki et al., 2018; Moghim,
2020), Tarim (Yang, Xia, Zhan, Qiao, & Wang, 2017; Xu et al., 2019) and
Turpan basins (Xu et al., 2019). The main cause for this decline in TWS
in the Tarim basin is decreased precipitation (Yang et al., 2017; Xu et
al., 2019). However, it was found that glacial retreat and increased
water resource consumption from human activities also has an influence
on TWS decline. The main factors attributed to the TWS decline in Iran
differed in previous studies. Joodaki et al. (2014) found that this TWS
decline was highest in the Middle East, where human activities where
human activities was largely responsible for this decline. Khaki et al.
(2018) found that the TWS of Iran was continuing to decrease, despite
removing the influence of human activities. As such, this decline in TWS
is likely to be influenced by a combination of human activities and
climate change.
Increasing trends were also detected for water levels or TWS in several
basins within the EIB, including water levels at Balkhash Lake (Duan et
al., 2020) and Issyk-Kul Lake (Alifujiang, Abuduwaili, Ma, Samat, &
Groll, 2017) and the TWS in the Qaidam (Bibi, Wang, Li, Zhang, & Chen,
2019; Meng, Su, Li, & Tong, 2019) and Qiangtang Plateau (Meng et al.,
2019; Liu, Yao, & Wang, 2019) basins. Despite these research findings,
there are relatively few studies that have examined the attributes of
the underlying factors driving these trends.
There have also been contradictory conclusions about the TWS trends
within the same region. For example, Wang et al. (2020) detected a
decreasing TWS for 2002–2016 using one total water storage anomalies
(TWSA) product in the Gansu Hexi Corridor basin. Cao, Nan, Cheng and
Zhang (2018) found that the basin TWS significantly increased during
2002–2013 using another TWSA product. Thus, these opposing conclusions
may be a result of researchers utilizing different datasets.
In summary, there continue to be inconsistent conclusions regarding
changes in TWS and the main drivers of these changes in the EIB and its
sub-basins. This demonstrates the need for further studies on these TWS
changes and the main impact factors for these regions. The hydrologic
budget (i.e., hydrologic gains and losses) is an effective method to
conduct analyses at the basin scale (Liu, Yao, Huang, Wu, & Liu, 2014;
Liu et al., 2016; Reager et al, 2016; Liu, Wang, & Yao, 2018). Unlike
exorheic basins, which include AET and runoff in hydrologic losses,
inland basins only include AET. In other words, the hydrologic budget in
inland basins may only be expressed by precipitation, AET, and TWS. As
such, this method is more suitable for the analysis of TWS changes and
its main attribution in inland basins.
This study used multi-source data on the EIB and its closed basins to
achieve three key objectives: 1) simulate the monthly AET series of each
inland basin using the hydrologic budget method; 2) detect the
spatiotemporal characteristics of annual and monthly precipitation, TWS,
and AET using a non-parametric test method in each basin; and 3)
identify the main attributions of AET and TWSA changes using the water
balance principle in each closed basin.