Table 1
2.2 Data
2.2.1 Observed precipitation
data
The observed monthly precipitation series was obtained from the Climatic
Research Unit (CRU; Harris, Osborn, Jones, & Lister, 2020). There were
observed 500 CRU sites within the EIB, of which the longest time series
spans from 1820–2021. In this study, 139 precipitation sites spanning a
data series of 1967–2020 were selected, and 123 sites with data series
from 1961–2021 were used for trend analyses. Missing values were
linearly interpolated based on the values in the preceding and
subsequent month or the adjacent sites. The CRU gridded precipitation
dataset (TS v.4.05) (Harris et al., 2020), with a 0.5º × 0.5º
resolution, was also evaluated in this study.
2.2.2 Remote sensing precipitation
data
The Global Precipitation Measurement (GPM) Integrated Multi-satellite
Retrievals (IMERG) Final run v.06 is a level 3 precipitation product
(Huffman, Stocker, Bolvin, Nelkin, & Tan, 2019). This product using
multiple precipitation-relevant satellite passive microwave sensors. The
algorithm was combined with precipitation gauge analyses,
microwave-calibrated infrared satellite estimates, and other
precipitation estimators at a finer spatial resolution (0.1°). The data
series used in this study spanned from June 2006 to December 2020.
2.2.3 GRACE TWSA data
Several versions of TWSA products have been released by several
agencies; this study utilized the RL06 (v.3) GRACE liquid water
equivalent thickness anomaly data series (Landerer & Swenson, 2012)
from the Center for Space Research (CSR) at the University of Texas
(Austin USA), the Geoforschungs Zentrum Potsdam (GFZ), and the Jet
Propulsion Laboratory (JPL) in which the spatial resolution was 1º×1º.
In addition, the GRACE Mascon solutions from JPL (RL06-v2) (Watkins,
Wiese, Yuan, Boening, & Landerer, 2015) and the National Aeronautics
and Space Administration Goddard Space Flight Center (GSFC) (RL06-v.1)
(Loomis, Luthcke, & Sabaka, 2019) were used; the spatial resolutions of
these products were 0.5º×0.5º and 1º×1º, respectively. These five GRACE
products were abbreviated as CSR-v3, GFZ-v3, JPL-v3, JPL-v2, and
GFSC-v1, and they also include the equivalent water thickness. The
monthly series from April 2002 to December 2020 was used in this study.
Due to the lack of observational data for validation, it was difficult
to determine which product was more suitable for the EIB; as such, all
five products were used to derive the TWSA trends. The spherical
harmonic coefficients solution was used in three v3 products, while the
mascon solution was used in the JPL-v2 and GFSC-v1 products.
Post-processing filters were applied to reduce correlated errors. These
solutions provide accurate surface-based gridded information, which may
be well applied to studies on hydrology (Watkins et al., 2015; Save,
Bettadpur, & Tapley, 2016; Loomis et al., 2019). Additional
descriptions of data processing are provided in Loomis et al. (2019),
Save et al. (2016), and Watkins et al. (2015).
2.3 Methods
2.3.1 Simulation of monthly AET
The hydrological budget method is an effective tool to simulate AET in
inland basins. For a closed inland basin, precipitation and AET
represent the hydrologic gains and losses, respectively. According to
the water balance in the basin, the difference between precipitation and
AET is equivalent to water storage changes in the basin; therefore, the
monthly AET may be simulated as:
AETi = Pi –ΔSi (1)
where AETi , Pi , andΔSi are the AET, precipitation, and water storage
changes within a month for a closed basin, respectively, in which the
unit is mm. For the gridded precipitation and TWSA data, the average
data series of each closed basin was calculated based on the area
weighting of each grid in the basin. For grids covered by the basin
boundary, the area weighting of the boundary grid was represented by the
proportion of area within the basin boundary.
In simulating the monthly AET, it is necessary to focus the consistency
of the time period for each variable. Monthly precipitation and AET are
the mean values within a month, computed between the beginning and end
of a month. ΔS is the difference between water storage at the end and
beginning of the month. However, the TWSA data used in this study
represent the mean water storage within a month. Obtaining ΔS from TWSA
is key to the simulation; here, ΔS was calculated as:
\({S}_{i}=(\text{TWSA}_{i+1}-\text{TWSA}_{i-1})/2\) (2)
where TWSA(i+1) in the next month andTWSA(i-1) in the previous month represent water
storage at the end of the simulated month and the beginning of the
simulated month, respectively. The accuracy of this calculation has been
validated by Long et al. (2014).
2.3.2 Trend detection and identification of its main
attribution
methods
The rank-based non-parametric Mann-Kendall (MK) test and trend magnitude
method (Hirsch, Slack, & Smith, 1982) were applied to detect long-term
monotonic trends and their magnitudes. This test is able to handle
non-normality, censoring, data reported as “less-than” values, missing
values, and seasonality; it also has high asymptotic efficiency (Fu,
Charles, Liu, & Yu, 2009). Further details regarding this test were
reported by Xu, Liu, Fu, & Chen (2010). The annual and monthly
precipitation trends, AET, and TWSA were detected in each basin or grid.
Based on the water balance principle, the main factors causing changes
in the AET and TWSA were identified. According to the water source
consumed by the AET, the change in AET was attributed to changes in
precipitation and the consumption of other water supply sources. Based
on the hydrologic budget within a closed basin, the change in the TWSA
was attributed to changes in precipitation and AET. Precipitation and
AET have positive and negative effects on the TWSA; this means an
increase or decrease in precipitation may prompt an increase or decrease
in the TWSA, while an increase or decrease in the AET may trigger a
decrease or increase in the TWSA, respectively. Similarly, precipitation
and potential evapotranspiration (PET) have positive and negative
effects on the AET, respectively. Based on these analyses, the main
attribution of AET and TWSA changes was identified at basin scales. The
contribution of precipitation and other factors to changes in the AET
was semi-quantified by analyzing the magnitude of the trend between AET
and precipitation.