Monitoring the spatiotemporal terrestrial water storage changes in the Yarlung Zangbo River Basin by applying the P-LSA and EOF methods to GRACE data

Journal Pre-proof Monitoring the spatiotemporal terrestrial water storage changes in the Yarlung Zangbo River Basin by applying the P-LSA and EOF methods to GRACE data

Hong Zhang, Ling Lei Zhang, Jia Li, Rui Dong An, Yun Deng PII:

S0048-9697(19)36270-9

DOI:

https://doi.org/10.1016/j.scitotenv.2019.136274

Reference:

STOTEN 136274

To appear in:

Science of the Total Environment

Received date:

11 July 2019

Revised date:

16 December 2019

Accepted date:

20 December 2019

Please cite this article as: H. Zhang, L.L. Zhang, J. Li, et al., Monitoring the spatiotemporal terrestrial water storage changes in the Yarlung Zangbo River Basin by applying the P-LSA and EOF methods to GRACE data, Science of the Total Environment (2019), https://doi.org/10.1016/j.scitotenv.2019.136274

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© 2019 Published by Elsevier.

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Monitoring the spatiotemporal terrestrial water storage changes in the Yarlung Zangbo River Basin by applying the P-LSA and EOF methods to GRACE data

Hong Zhanga, Ling Lei Zhanga, *, Jia Lia, Rui Dong Ana and Yun Denga a

State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource & Hydropower,

Sichuan University, Chengdu 610065, China

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(J.L.); [email protected] (R.-D. An);[email protected] (Y.D.)

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E-mail address: [email protected] (H.Z.); [email protected] (L.-L. Z.); [email protected]

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*Corresponding author: Ling Lei Zhang

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State Key Laboratory of Hydraulics and Mountain River Engineering, College of Water Resource & Hydropower,

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Sichuan University, Chengdu 610065, China E-mail address: [email protected] (L.L.Z.)

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Tel.: +86-028-8540-7075

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Monitoring the spatiotemporal terrestrial water storage changes in the Yarlung Zangbo River Basin by applying the P-LSA and EOF methods to GRACE data Abstract: The Yarlung Zangbo River Basin is a regulator of water vapor changes in China and even Asia. To avoid the shortcomings of traditional water resource monitoring methods, this study used Gravity Recovery and Climate Experiment (GRACE) data to monitor the terrestrial water storage anomaly (TWSA) in this river from 2002-2015 with the

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help of the polynomial-least squares approach (P-LSA) and the empirical orthogonal function (EOF). The obtained TWSA

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was compared with hydrometeorological data from several sources to discuss the applicability, uniqueness and response relationship. The results showed that (1) the combination of P-LSA and EOF had strong applicability to explore the TWSA

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in the study area, with R2=0.75 and 0.80, respectively, and could indirectly reflect dry and wet conditions in

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southwestern China. (2) The TWSA revealed significant cyclical and seasonal fluctuations of approximately 12 months

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and increased from upstream to downstream and from north to south, which was discussed for the first time in the

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research area. (3) The EOF method can effectively identify the TWSA principal component and structure (EOF1

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contribution=91.08%) by removing noise and redundancy, which is beneficial for revealing the laws essential for TWSA changes. (4) The TWSA in the studied watershed was unique (i.e., the clearest periodic changes with the best fitting effect (R=0.90); peak, low and peak-low difference values that were 1.82, 1.19 and 1.52 times larger than those of the 8 -1

other rivers; and the largest downward trend of 4.13 mm·a ). (5) Rainfall was the decisive factor influencing the TWSA, with correlation coefficients (R) greater than 0.60. This study enhances our overall understanding of the TWSA in this plateau watershed and provides a scientific basis for optimal water resource management. Keywords: GRACE; TWSA; applicability; uniqueness; response relationship

Journal Pre-proof 1. Introduction Since the beginning of the 21st century, changes in the global water cycle induced by climate changes and anthropogenic factors have disastrously impacted human lives (Rodell et al., 2009; Tiwari et al., 2009). The terrestrial water storage (TWS) is the sum of all forms of water (i.e., surface water, soil water, groundwater and vegetation-bound water) in the direction perpendicular to the Earth’s surface, and the TWS plays an important role in global water and energy cycles (Famiglietti 2004). Under the background of global climate change, the exploration of TWS changes will be

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conducive to the optimal utilization and rational development of water resources.

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The Yarlung Zangbo River Basin is the highest river in the world, as it is located in the interior of the Tibetan Plateau, which is known as "the world's third pole" (Madsen et al., 2016). The water resources in this area are abundant,

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with an annual runoff of approximately 1395.4×108 m3, ranking third in China (Yang et al., 2014). The potential

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hydropower resources (approximately 0.113 billion kw) account for 16.87% of China’s total resources, ranking second in

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the country. Thus, this area is an important strategic reserve and economic development area for water and

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hydropower resources. Additionally, due to the unique geographical environment (high mountains, deep valleys,

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complex terrain, large drop of 5435 m from west to east), this basin is an important channel for the transport of water vapor over the Tibetan Plateau. The climate also exhibits large differences from upstream to downstream and changes from cold and dry to warm and humid. Therefore, this basin is an important regulator of water vapor changes in China, Asia and even the whole world; thus, it has key and far-reaching impacts (Lin et al., 1990; Tian et al., 2001). The exploration of TWS changes in this area has great significance for research on the climate, hydrology and environment. However, due to the unique topographical features, varying climatic conditions and diverse ecosystems, this basin features a limited number of climatic and hydrological stations, which are unevenly distributed, leading to difficulty in TWS monitoring. The Gravity Recovery and Climate Experiment (GRACE) satellites, which were launched on March 17, 2002,

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pioneered a new field of remote sensing related to the tracking of the terrestrial water storage anomaly (TWSA). This method not only effectively avoids the disadvantages of large investments, multiple limitations and uncertain results associated with traditional water resource monitoring methods but also significantly improves the precision and spatiotemporal resolution of monitoring data (Seo et al., 2016). Previous studies have mainly focused on global patterns (Sahoo et al., 2011; Lakshmi et al., 2018), typical climatic regions (such as India and Africa (Singh et al., 2017; Anyah et

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al., 2018)), and large-scale watersheds (such as the Amazon and Mississippi River Basin (De Linage et al., 2014; Khaki et

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al., 2017)). Moreover, there has recently been considerable research progress on GRACE-derived TWSAs in the Yarlung

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Zangbo River Basin. For instance, Yang et al. (2009) used GRACE GFZ RL04 data to observe TWSAs from 2003-2007. Xu et

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al. (2013) reported TWSA changes indicated by GRACE CSR RL04 L-2 data over this river during 2005-2010. Lakshmi et al.

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(2018) created a basin water balance formula and compared it to the GRACE-derived TWSA in this river from 2002 to 2014. These studies suggested that GRACE is a very valuable tool for TWSA observations and opens up new ideas for

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TWS research, thereby promoting the booming development of hydrology research.

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Nevertheless, these previous studies had limitations. (1) The differences in GRACE data selection and processing

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resulted in large errors between the derived TWSA and the in situ observed values; (2) the analyses of TWSA were mainly based on traditional descriptive methods, thus resulting in a not sufficiently deep or comprehensive discussion; (3) there was a lack of discussion in the spatial TWS patterns in the study area; (4) the TWSA responses caused by climate and human factors have rarely been studied; (5) there are no studies on the major components of TWSA variations in the study area. Thus, the potential for using GRACE to monitor the TWS in this watershed is far from fully exploited. The polynomial-least squares approach (P-LSA) is a mathematical optimization technique that seeks the best data matching function by minimizing the sum of the squares of errors. This technique can adequately fit ordered pairs of data and has good mathematical theory support and high accuracy (Tian et al., 2012). The method is widely used in

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physics, biology, remote sensing, computer science, hydrology and other scientific fields and is important for determining the relationship between two variables (Xiong et al., 2001; Hughes 2015; Gabriel 2016). The empirical orthogonal function (EOF), also called eigenvector analysis or principal component analysis, was proposed by Karl Pearson (1901). It can effectively identify the most important element and structure in data, remove noise and redundancy, reduce the dimensions of original complex data, and reveal the simple structure hidden within data (Eom

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et al., 2017). At present, this method has mainly been used in the fields of rainfall, temperature and the magnetic field

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(Liew et al., 2010; Naik et al., 2010; Shore et al., 2018), to effectively analyze the changes in the study object under

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different spatiotemporal dimensionalities and identify and strengthen the similarity of signals by using the variance

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contribution.

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To solve the difficulty of TWS monitoring in the Yarlung Zangbo River Basin, reduce errors during data processing, obtain the main spatiotemporal distribution patterns and identify key climatic impact factors, this paper used the latest

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GRACE RL05 monthly data in combination with two statistical methods (i.e., P-LSA and EOF) to derive the

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spatiotemporal TWSA variations. The results were compared with hydrometeorological data from multiple sources to

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discuss the applicability of the method and the unique characteristics of the TWSA in the research basin. This study provides useful guidance and is of great significance for the rational distribution and utilization of water resources.

Fig. 1. Sketch of the Yarlung Zangbo River Basin.

2. Data and Methods

Journal Pre-proof 2.1. Study area The Yarlung Zangbo River Basin, which is the most important watershed on the Qinghai-Tibet Plateau, is an important international river, with an average elevation of more than 4000 m. This basin ranges from 82°01’E to 97°06’E and from 27°40’N to 31°17’N. The main stream of this river in China is approximately 2057 km, and its catchment area covers approximately 2.58×105 km2. The mean annual runoff is 4425 m3/s (Nie et al., 2012). Furthermore, this basin is in a typical subtropical climate zone and is greatly affected by the South Asian summer monsoon (Zhang et al., 2018). The

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water resources are heavily affected by the unique climatic (cold) and topographic features (high altitude).

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2.2. Data

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2.2.1. The GRACE data

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The GRACE satellites provided continuous fully normalized spherical harmonic coefficients (Cml and Sml) of the Earth for 16 years until the launch of the GRACE follow-on (GRACE-FO) satellites on May 23, 2018. In this study, we used

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GRACE data (RL-05, Level-2) from the Center for Space Research (CSR) with a 1°×1° spatial resolution and a monthly

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temporal resolution. The data were obtained from ftp://rz-vm152.gfz-potsdam.de/grace/ and were converted to TWSA in

2.2.2. Comparison data

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the form of equivalent water height (unit: mm) (Zhang et al., 2017).

(1) Hydrometeorological data

Monthly precipitation (P) and temperature (T) from 29 weather stations in the research basin were obtained from the China Meteorological Data Service Centre (CMDC) (http://data.cma.cn). The meteorological stations are unevenly distributed due to the limitations of manpower resources and the natural environment. Therefore, the spatial distributions of the monthly P and T data were obtained by the inverse distance weighting (IDW) method, and the Takahashi Formula (1) was used to calculate the mean monthly evapotranspiration (ET) (Zhang et al., 2018). ET 

3100·P 3100  1.8·P 2 ·exp(

 34.4·T ) 235  T

(1)

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(2) GLDAS data The Global Land Data Assimilation System (GLDAS) calls 3 land surface process models (i.e., CLM, Noah, and Mosaic) and 1 hydrological model (i.e., VIC) and can export multiple surface state variables (Bonan 2002). In this study, the mean monthly Noah data (Version 1) were adopted (https://hydro1.gesdisc.eosdis.nasa.gov/data/GLDAS_V1/GLDA S_NOAH10_M/), which has advantages of a stable driving field, advanced mode, long time series, and the best

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applicability compared with the 3 other data models (Cheng et al., 2013; Deng et al., 2018; Zhang et al., 2018). The sum

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of the 3 main output variables (i.e., snow water equivalent (SWE), canopy water storage (CWS), and soil water storage

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(SMS)) was calculated. Moreover, to reasonably compare the results of the GRACE-derived TWS, the obtained GLDAS

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data had the same spatial resolution (1°×1°) and temporal resolution (monthly) as the GRACE data. This study adopted similar treatments for the GLDAS-Noah data as used for the GRACE data: the GLDAS-Noah grid data were expanded

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using spherical harmonics, Cml and Sml were truncated to 60, and the Gaussian smoothing function and Gaussian filtering

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radius (500 km) were used to process the expanded spherical harmonic coefficient.

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(3) Observed mean annual runoff data

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There are 17 hydrological stations in the Yarlung Zangbo River Basin. Among them, the Lazi (catchment area: 4

2

5

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4.94×10 km ), Yangcun (catchment area: 1.53×10 km ) and Nuxia (catchment area: 1.90×10 km ) stations control almost the whole basin, and these stations exhibit a certain amount of representativeness. In this study, the mean annual runoff values of the 3 stations from 2003 to 2009 were collected, and the weighted averages were calculated. (4) Total water resource data In this study, the annual mean total water resource for southwestern China from 2002 to 2015 were obtained from the China water resource bulletin (http://www.mwr.gov.cn/sj/tjgb/szygb), which is a statistical report on the amounts of water resources, water storage, water resource development and water quality in China and its subregions in each year.

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(5) TWSA data from other typical watersheds around the world Some previous studies demonstrated that the Yarlung Zangbo River Basin is unique in terms of water and hydropower resources, runoff, the geographical environment and climate conditions. In this study, the GRACE-derived TWSA data from 8 typical watersheds were compared with the data from the Yarlung Zangbo River Basin to explore the unique TWSA characteristics of this watershed (Luo et al., 2012; Ni et al., 2014; Yan et al., 2015; Lakshmi et al., 2018).

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The selection of these rivers was mainly based on 2 principles. (1) Different geographical locations: these rivers are

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evenly distributed across 5 continents and are representative rivers of each continent. (2) Different climate conditions:

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these rivers are mainly distributed in the tropical and temperate zones of both hemispheres, leading to different climate

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conditions. The different locations and climate conditions resulted in unique characteristics in the 8 rivers. The purpose

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of this work is to summarize the common ranges of the TWSA variations and characteristics of the 8 rivers and compare these variations with those in the study area to analyze the unique characteristics of the Yarlung Zangbo River Basin.

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The basic information on these basins is provided in Table S1 (the Supplementary Material).

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2.3. Methods

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2.3.1. Calculation of the TWSA from GRACE data m

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The description below is the basic theory used to transform the GRACE spherical harmonic coefficients (C l and S l) into the TWSA. The change in equivalent mass surface density can be expressed by standard spherical harmonic expansion at any place (θ and φ) (Wahr et al., 2004):  ( ,  ) 

2R ave 3



l

 l 0 m

Pl m (cos )

2l  1  [Clm cos(m )  Slm sin( m )] 1  kl

(2)

where Cml(t) and Sml(t) are the measured time-varying fully normalized spherical harmonic coefficients obtained from GRACE satellites; l and m represent the degree and order of the spherical harmonic coefficients; R represents the m

radius of the Earth; θ is the colatitude (θ=90-latitude of the Earth); φ is the geographic longitude value; P l represents the Legendre function; ρave represents the mean density of the Earth; and Kl represents the load Love number.

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The GRACE data errors in monitoring the TWSA mainly include truncation errors, measurement errors, and leakage errors. Several measures were taken to improve data accuracy and reduce errors: (1) The GRACE Level-2 RL05 data published by CSR were adopted, which improved the models of the gravity background field, ocean and non-ocean tides, polar tide and atmosphere; removed the nontidal atmospheric and oceanic effects; and omitted the long-term change rate corrections for C20, C30, C40, C21 and S21. (2) The Cml and Sml were truncated by a degree and order of 60 to reduce the satellite measurement error. (3) All kinds of tidal effects (e.g., tides, polar tides, and solid tides) and nontidal

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atmospheric and oceanic effects were removed. (4) Nevertheless, the Cml and Sml of the gravity field model can be m

m

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expanded to only a finite order, which inevitably leads to a truncation error. Additionally, the error of (C l and S l) of the

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model increases with increasing order. Therefore, the Gaussian smoothing function Wl (Formula (4)) was implemented

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into Formula (2). Hence, the change in surface density was converted to the equivalent water height (Δh(θ,φ)), which can be used to represent the regional TWSA (Formula (3) (Wahr et al., 1998; Swenson et al., 2002). 2R ave 3 w



l

i 0

m

 

Wl  Pl m (cos ) 

2l  1 [Clm cos(m )  Slm sin( m )] 1  kl

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h ( ,  ) 

(4)

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W0  1  Wl  (1  exp( 2b)) /(1  exp( 2b))  1 / b W  (2l  1)W / b  W l l 1  l 1

(3)

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where b=ln2/(1-cos(r/R), and r represents the filtering radius, where r=500 km was adopted. Additionally, as the monthly gravity models Cml and Sml mostly reflect the composition of Earth's static structure, it was necessary to remove the monthly average TWS for the research period. The missing data during the research period were directly remedied by linear interpolation.

2.3.2. The polynomial-least squares approach (P-LSA) P-LSA is a traditional method to analyze variations in the TWSA and hydrometeorological factors (Diatta and Fink, 2014). First, the polynomial fitting model f(x) was determined according to the observed data {(x i, yi), i=0, 1, 2..., m}. This model can roughly determine the function class according to the knowledge of each subject. Based on a series of TWSA and precipitation values at different times (t1, y1; t2, y2 ... tm, ym), this study found that the relationship between the two

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variables (t and y) was close to that of a sine or cosine function. To understand the variations in the TWSA and precipitation series in this study, the least squares general fitness model was used, as expressed in Formula (5):

f (t )  a1  sin( a 2  t  a 3 )

(5)

where f(t) is the TWSA or precipitation at time t and a1, a2 and a3 are coefficients of the fitness model. Then, based on the least squares principle, the functional relation y=f(t, A) between t and y was obtained to create

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the best approximation or fit of the observed data. f(t, A) is called the fitting model, and A=(a 1, a2..., an) represents a set

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of parameters to be determined. The method involves selecting parameter A to minimize the weighted sum of squares

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of the residuals {ek=yk-f(tk, A)} between the fitting model and the actual observed values at each point. In other words,

m

  (t )( f

m

*

(ti )  yi ) 2  min   (ti )( f (ti )  yi ) 2 ,  (ti )  0

(6)

i 0

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i 0

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f*(t) is calculated to make Formula (6) successful. In this study, the P-LSA was implemented in MATLAB software.

2.3.3. EOF technique

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In this study, we decomposed the spatiotemporal changes in precipitation and TWSA in the Yarlung Zangbo River

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Basin using the EOF technique. The basic principle of this method is to decompose the variables containing p spatial

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points into spatial parts that change over time (EOFS) and temporal parts that do not change over space (principal components, PCS), which can effectively separate data errors and true signals due to the method’s capacity to identify spatial correlations in spatiotemporal data (Wouters et al., 2007; Navarra et al., 2010). The EOF decomposition process is as follows: (1) Establish the matrix X=(xij)=(x1, x2, ..., xj), by assuming there are m spatial points, each spatial point has n observation values at p different time points, each data point is defined as x ij (i T

=1,2...m; j = 1, 2..., n), and the actual spatial field is xj. (2) Establish the covariance matrix (C=XX /p) and decompose it T

orthogonally (C=E^E ). (3) Calculate the eigenvector of XX' according to the order of eigenvalues (λ 1 > λ2 >·· > λm). Then, the spatial domain can be orthogonally decomposed. (4) Obtain the EOF values by the normalization method. (5) Calculate the PCs (PC=E'*Y).

Journal Pre-proof 3. Results and Discussion 3.1. Interannual and intra-annual variability in the GRACE-derived TWSA in the Yarlung Zangbo River Basin Figure 2(a-b) shows the GRACE-derived interannual and intra-annual TWSA changes in the Yarlung Zangbo River Basin that were determined using the P-LSA method. (1) The mean monthly TWSA (Fig. 2(a)) presented clear cyclical fluctuations with periods of approximately 12 months. (2) The TWSAs in summer and autumn were relatively higher

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than those in spring and winter, as shown in Fig. 2(b); the maximum increase occurred in August (97.38 mm), and the -1

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maximum decrease occurred in March (-70.99 mm). (3) An obvious TWSA decreasing trend at a rate of 4.13 mm·a was

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found over the nearly 14 years. Agriculture utilizes the most water resources in the studied basin (accounting for 94.39%

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of the annual mean total water consumption in the basin), and the development of agriculture will lead to a decrease in water quantity each year (Siebert et al., 2013). These 3 characteristics ((1)-(3)) are consistent with the results from Xu et

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al. (2013) and Yang et al. (2009) (Fig. 2(c-d)). The correlation coefficients (R2) of the mean monthly TWSA between this

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study and the 2 previous studies were 0.90 (significant correlation) and 0.77 (high correlation), and the R2 of the

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long-term mean monthly TWSA were 0.96 and 0.98 (significant correlation). (4) The P-LSA results of the mean monthly

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TWSA showed that the fitting model was TWSA(t)=87.45*sin(0.5242t-1.413), where t represents time (month) and 2

TWSA represents the corresponding TWSA values. Furthermore, the R of this fitting formula was 0.81, indicating a good fitting effect. Lakshmia et al. (2018) monitored the TWSA in the Ganga-Brahmaputra river system and obtained results that were greatly different from those of this study (Table 1). These differences are not only related to the larger study area than in this study but also possibly related to the differences in choices of GRACE data sources, parameters and data processing methods. Moreover, the TWSA amplitudes and phases obtained by Xu and Yang were larger than those obtained in this study. Xu adopted CSR-RL04 L2 data and a 750 km Gaussian filter radius, while Yang used GFZ-RL04 data and a 400 km Gaussian filter radius. The current GRACE product represents the 5th generation (RL-05), with improvements in the background models (i.e., the Earth system reference framework, the high-frequency atmospheric

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and ocean mass model, and the ocean tidal model) and data processing techniques (e.g., the spatial resolution is better than that of the RL04 data, and the C20 did not require replacement by the C21 laser), making this product more suitable for the study of periodic Earth system changes and trends (Chambers et al., 2006; Bettadpur 2007; Flechtner 2007; Kusche et al., 2009). On the other hand, several measures were taken in this study to reduce the GRACE errors in monitoring the TWSA: (1) The Cml and Sml values were truncated by several different degrees and orders (10, 20, 30, 40,

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50, 60, 70, 80, 90). The results indicated that the differences in TWSA values were very small when the truncation

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ranged from 60 to 90. Tapley (2004) believed that controlling the model order below 90 was reasonable. Chen et al.

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(2005) further indicated that an order larger than 60 would lead to large errors. Therefore, it was concluded that

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controlling Cml and Sml truncation between 60 and 90 degrees/orders was reasonable. In this study, the Cml and Sml were

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truncated by a degree and order of 60. (2) All kinds of tidal effects (e.g., tides, polar tides, and solid tides) and nontidal atmospheric and oceanic effects were removed. (3) The data accuracy was enhanced using Gaussian smoothing and a

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spherical harmonic filter, and a 500 km of the Gaussian filtering radius was adopted. Therefore, the GRACE-derived

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TWSA in this study was close to the actual values.

Fig. 2. The GRACE-derived TWSA for the Yarlung Zangbo River Basin from 2002-2015 (a-b) and corresponding comparisons with TWSA in other previous studies in this basin (c-d): the black dots in panels b and d are the GRACE-derived TWSA values before the treatment by P-LSA.

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In this study, the GRACE-derived TWSA in the Yarlung Zangbo River Basin was compared with that in 8 other typical rivers around the world (Fig. 3). The results showed that the TWSA of most rivers presented obvious monthly and seasonal cyclical fluctuations that were similar to those in the basin studied in this research, except for the Yellow, Congo and Murray-Darling River basins. Moreover, the maximum (August and September) and minimum (January to March) TWSA appeared at similar times in all rivers in the Northern Hemisphere, while the 3 rivers in the Southern

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Hemisphere (the Amazon, Congo and Murray-Darling) differed greatly due to their significant differences in climate.

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However, the TWSA of the Yarlung Zangbo River Basin exhibited unique characteristics. First, the P-LSA results showed 2

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that this basin had the highest fitting effect (R =0.81), indicating the most obvious periodic changes. Second, the

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amplitudes of the TWSA in this basin were also the largest. The TWSA peak, low, peak-low difference and peak/low ratio

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values of the study area were 1.82, 1.19, 1.52 and 1.54 times higher, respectively, than the corresponding values in the 8 other watersheds (Table 2), indicating that the TWSA in the Yarlung Zangbo River Basin exhibited the most dramatic

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annual changes. Third, the TWSAs of the 8 comparison watersheds exhibited both increasing and decreasing trends

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during the study period, while the TWSAs of the Yarlung Zangbo River Basin decreased at the most rapid rate. There are

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3 main reasons for the unique characteristics of the Yarlung Zangbo River Basin. (1) The Yarlung Zangbo River Basin is the highest river in the world (>4000 m) and is a cold region. The water resources in this basin are heavily affected by hydrometeorological factors (R=0.63 (P), 0.60 (ET) and 0.58 (T)). The significant periodic changes in these hydrometeorological conditions generate periodic fluctuations in the TWSA in this watershed. (2) The cold and dry conditions in the upstream area, the warm and humid conditions in the downstream area, the long sunshine duration, the large temperature differences between the morning and evening, and the large seasonal fluctuations in the river runoff caused the amplitudes of the TWSA in this river to vary greatly. (3) The Amazon and Congo River basins in the Southern Hemisphere represent the first and second largest rainforest areas in the world, respectively. High temperatures and many rainfall events occur throughout the year, with no obvious seasonal changes. The

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Murray-Darling River is a typical plain basin with low elevations (>200 m). The large differences in geographical location and climate led to the significant differences in the TWSAs between the Yarlung Zangbo River Basin and the 3 basins in

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the Southern Hemisphere.

Fig. 3. The interannual (a-i) and intra-annual (j) TWSA of the Yarlung Zangbo River Basin and 8 other typical basins.

3.2. Spatial variations in the GRACE-derived TWSA in the Yarlung Zangbo River Basin The spatial distribution of the TWSA in the Yarlung Zangbo River Basin has rarely been studied in the past, which is not conducive to the optimal utilization of water resources, especially during the period of cascade power station planning and construction. As shown in Fig. 4, the long-term annual mean TWSA exhibited great differences among upstream and downstream areas, with annual mean TWSA values of -19.40 mm (upstream), -10.53 mm (midstream) and -10.91 mm (downstream). These results indicated that the TWSA exhibited a decreasing trend throughout the basin,

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which is consistent with the results discussed in section 3.1, indicating that climatic changes and human activities have had a great influence on the TWSA. Additionally, the water resources in the midstream and downstream areas were more abundant than those in the upstream area, and these abundant resources were conducive to the development of the economy and hydropower. In detail, the largest increase was found in the Nianchu River Basin. Relevant studies have shown that precipitation and runoff in the Nianchu River have increased in recent decades (Dunzhu 2015; Yang et al.,

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2012), while the glacier area in this watershed decreased by 18.386 km2 during 1987-2014 (Li et al., 2018). Glaciers

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represented a small proportion of the total water storage in this watershed, so the TWSA of this river showed an overall

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increasing trend, which to some extent verified the applicability of GRACE-derived TWSA estimates in this region.

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Moreover, the TWSA decreased the most in the Lhasa River Basin, indicating that this area experienced extensive water

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shortages. The Lhasa River is the largest tributary of the Yarlung Zangbo River Basin. The climate in this area is relatively mild, the terrain is flat, the soil quality is good, and the plateau flora and fauna, as well as geothermal resources, are

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abundant; hence, this area represents the main food production and living area in Tibet. In addition, a large hydropower

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project called Pangduo was built on this river, leading to a large change in the TWSA due to runoff regulation. Therefore,

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the water storage in this region is insufficient due to these climate changes, engineering adjustments and human factors. In conclusion, climatic factors shape the basic patterns of water dynamics, while geological and human factors influence the ranges and rates of the changes.

Fig. 4. Spatial changes in the long-term mean annual TWSA for the Yarlung Zangbo River Basin from 2003-2014. 3.3. Dominant spatiotemporal patterns of the TWSA as determined by the EOF method

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To further analyze the dominant spatiotemporal variation characteristics of water resources in the Yarlung Zangbo River Basin, this paper used the EOF method to decompose the TWSA. The results showed that the first 3 modes of accumulative variance had contributions totaling 98.77%, which accounted for 91.08%, 6.61% and 1.07% of the total variance, respectively. The spatial distributions (EOF1, EOF2 and EOF3) and temporal variation characteristics (PC1, PC2 and PC3) of the first 3 modes are shown in Fig. 5(a-f). Fig. 5(a-b) shows the first eigenvector (EOF1) and its corresponding time coefficient (PC1) of the TWSA in the

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Yarlung Zangbo River Basin. The high contribution rate of EOF1 (91.08%) and the significant correlation (R2=0.98)

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between PC1 and the TWSA (Fig. 5(g)) indicated that the first mode represented the key spatiotemporal distribution

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pattern of the TWSA in the research basin. In detail, EOF1 showed consistently positive features, indicating that the

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TWSA trend during the research period was highly consistent and significantly homogeneous. Additionally, EOF1 increased from upstream to downstream and from north to south, indicating that the TWSA in the downstream area

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was higher than that in the upstream area and that the TWSA in the south was larger than that in the north of the

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watershed. PC1 presented a drying trend, as indicated by its decreasing trend from 2002 to 2015. The P-LSA result of

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PC1 (Fig. 5(h)) yielded a fitting model of PC1=1546*sin(0.5241t-1.457) with an R2 value of 0.83, where t is time (in months). This result reflected an obvious cycle of 11.98 months, and the significant seasonal changes in PC1 manifested as positive and negative alterations within a year. These decomposed results of EOF1 and PC1 showed effects that were highly consistent with the spatiotemporal distribution of the TWSA discussed in sections 3.1 and 3.2. EOF2 and PC2 (Fig. 5(c-d)) accounted for only 6.61% of the TWSA, which represented several monthly variations in some subregions. The results showed that EOF2 gradually decreased from upstream to downstream, where the upstream area was a positive area, the downstream area was a negative area, and the midstream area was a transitional area. For PC2, the annual changes were significant and implied a slightly increasing trend during the research period. And the EOF3 and PC3 modes (Fig. 5(e-f)) explained 1.07% of the total TWSA, representing a nonsignificant signal.

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At present, some studies have used the EOF method to discuss the spatiotemporal TWSA changes but not in much detail (Eom et al., 2017; Yang et al., 2017). In the Yarlung Zangbo River Basin, this method has rarely been used. Relevant studies have indicated that the EOF method can effectively separate noise and real signals by using the correlation between coefficients (Schmidt et al., 2008; Xavier et al., 2010). Compared with traditional methods, the EOF method in this study can better reduce errors in the GRACE monthly gravity field and retain abundant effective signals (Wouters et

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al., 2007; Navarra et al., 2010). Therefore, the EOF method has little influence on Earth's real geophysical signals and

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does not generate false signals. Thus, it is considered to be an effective method for reducing errors.

Fig. 5. The first 3 major modes of the EOF analysis (a-f) for the GRACE-based TWSA during 2002–2015 (unit:

Journal Pre-proof dimensionless) and the correlation analysis between PC1 and the TWSA (g) and the P-LSA results for the PC1 values (h) in the Yarlung Zangbo River Basin. 3.4. Applicability of the GRACE-derived TWSA in the Yarlung Zangbo River Basin In this study, water resource data from several sources were adopted to verify the applicability of the GRACE-derived TWSA. In general, GRACE data were usually combined with GLDAS data in most previous studies, and the results demonstrated that the water storage changes were consistent to some extent between the 2 datasets

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(Shamsudduha et al., 2012; Lakshmi et al., 2018). The results of this study (Fig. 6 (a-b)) showed that the cycles of the 2 data sources were similar. However, their differences were also obvious: (1) the R was 0.6, indicating that the correlation

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was not high; (2) the phase of the GRACE-derived TWSA exhibited a significant delay compared with that of the

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GLDAS-based water storage; (3) the amplitude of the GLDAS-based TWSA variation was smaller than that of the

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GRACE-derived values; (4) the GRACE-derived TWSA decreased more than the GLDAS-based water storage estimates by 2.90 mm/a. These differences occurred because (1) the GLDAS data mainly reflect the changes in surface water

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(SWE+CWS+SMS), while GRACE data reflect total changes in both surface water and groundwater (Andrew, et al., 2017).

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(2) The amount of groundwater in the Yarlung Zangbo River Basin is very large, accounting for 21.5% of the river's total

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water resources, leading to large errors between GRACE and GLDAS (Jia, et al., 2008). (3) The large delay dues to that the GRACE-derived current monthly TWSA contains the rainfall information of only the previous month, while rainfall infiltration and evaporation requires a certain time cycle (Wang et al., 2012). Therefore, due to the unique geographical and climatic conditions and the abundance and impact of groundwater, the method that utilizes GLDAS data to verify the applicability of the GRACE-derived TWSA is not suitable in the Yarlung Zangbo River Basin. The maximum and minimum GRACE-derived TWSA values were observed in 2004 and 2015, respectively, which were the same as the water resource changes previously measured in southwestern China (China water resources bulletin, 2018). This result showed that the TWSA changes observed in this watershed could indirectly reflect the wet and dry situations in southwestern China. Moreover, the annual mean decrease in water resources in southwestern

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China was 5.05 mm·a , which is slightly higher than the decrease in the GRACE-derived TWSA in this study, indicating that the decrease in the TWSA in the research basin contributed greatly to the decrease in water resources in southwestern China. In addition, the comparison (Fig. 6(c)) between the annual mean GRACE-derived TWSA and the observed annual mean runoff anomaly presented consistent change characteristics and similar decreasing trends from 2003-2009 and were highly correlated (R2=0.75 (Fig. 6(d)). Furthermore, compared with the observed runoff, the annual mean PC1 (Fig.

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6(e)) also presented very similar change characteristics and decreasing trends. However, the R 2 between the 2 datasets

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was 0.80 (Fig. 6(f)), which was higher than that for the GRACE-derived TWSA. Hence, the GRACE TWSA processed by the

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EOF method has higher applicability than that without EOF processing in the studied watershed. The values from the

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EOF method were all dimensionless, while the P-LSA provided true TWSA values. In conclusion, based on GRACE data, the combination of the P-LSA and EOF methods to monitor the TWSA in the Yarlung Zangbo River Basin was suitable at

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an annual time scale. In this basin, where observation data are difficult to obtain, we should take measures to obtain

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mean monthly data to further verify the applicability of this method in a future study.

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Fig. 6. Comparison of the TWSA with other water resource data in the Yarlung Zangbo River Basin. (a-b) Comparison of the TWSA values based on GRACE and GLDAS data. (c-d) Comparison between GRACE-derived TWSA and the annual

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runoff anomaly. (e-f) Comparison between the annual runoff anomaly and PC1. 3.5. Hydrometeorological response to TWSA changes Many studies have indicated that precipitation (P), temperature (T) and evapotranspiration (ET) are the main factors that influence the regional water cycle and regional water security (Reager et al., 2014; Ndehedehe et al., 2018). Table S2 (Supplementary Material) shows that the R values between the TWSA and P were all higher than those between the TWSA and the 2 other hydrometeorological factors at 4 time scales, indicating that P is the decisive factor influencing the TWSA in the studied basin. Many other studies, such as those in the Tarim River Basin (Yang et al., 2015) and Urmia Lake (Chaudhari et al., 2018), have also demonstrated that rainfall change is the primary factor leading to changes in the TWSA. Fig. 7 shows the interannual and intra-annual changes in P and the first 3 major modes of the EOF

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decomposition for the Yarlung Zangbo River Basin. On the one hand, Fig. 7(a-b) reveals that P presented obvious signs of periodicity and seasonality, which are very similar to the TWSA changes. During the rainy period, the TWSA distinctly increased, and a significant decrease in the TWSA was observed during the dry period. Moreover, the time when the TWSA peak values appeared each year exhibited some degree of hysteresis, whereas no such trend was observed for P. The reason for this difference may be because the formation of the TWSA is a complex process (including soil

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interception, surface and subsurface runoff, rainfall, ET and infiltration) that requires a relatively long time to reach peak

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values. Additionally, the greatest rainfall was concentrated from July to September each year, with the same period

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accounting for 79% of the TWSA. The least rainfall occurred from February to April, accounting for 44% of the TWSA.

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These results further revealed that rainfall has the greatest impact on the TWSA. On the other hand, the first 3 modes of

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P determined by the EOF method accounted for 80.49%, 4.93% and 3.83% of the total variance, respectively, indicating that the distribution pattern of EOF1 was the main distribution pattern. In detail, EOF1 (Fig. 7c) represented consistently

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positive values throughout the region. That is, the P remained in the same phase change throughout the research

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period. Moreover, the P change rates in the middle and downstream areas were obviously higher than those in the

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upstream area, and the corresponding PC1 (Fig. 7d) decreased, representing a declining trend of P from 2002-2015. This result was consistent with the EOF1 and PC1 changes in the TWSA presented in section 3.3, which further indicated that P was the key influencing factor of the TWSA changes in the basin. Many studies have noted that anthropogenic alterations also strongly contribute to water resource changes (Chaudhari et al., 2018). Therefore, the impact of human activities on the TWSA should be discussed in more depth.

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Fig. 7. Time series of intra-annual (a), interannual (b) P changes, and the first 3 major modes of the EOF analysis

4. Conclusions

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(c-h) of P in the Yarlung Zangbo River Basin from 2002-2015 (unit: dimensionless).

Dynamic TWSA monitoring is of great significance for studying regional water resource changes and water balances. Based on GRACE data, the spatiotemporal variations in the TWSA in the Yarlung Zangbo River Basin were comprehensively analyzed in depth by combining the P-LSA and EOF methods. The main conclusions are as follows: (1) The combination of P-LSA and EOF to explore the dynamic changes in the GRACE-derived TWSA has strong 2

applicability in the Yarlung Zangbo River Basin. A. The results were highly correlated (R =0.75, 0.80) with the observed annual mean runoff in the studied basin and indirectly reflected dry and wet conditions in southwestern China. B. The TWSA presented significant cyclical fluctuations of approximately 12 months, with an obvious decreasing trend of 4.13

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mm·a . C. The GRACE data were used for the first time to explore the spatial distribution of water storage in the research basin. The TWSA increased from upstream to downstream areas and from north to south. The annual mean TWSA increased the most in the Nianchu River and decreased the most in the Lhasa River. (2) The EOF method can effectively identify the principal component and structure of the data by removing noise and redundancy, which is beneficial for revealing the essential TWSA change laws. The results demonstrated that EOF1

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and its corresponding PC1 accounted for 91.08% of the total variance, which represented the key spatiotemporal

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distribution pattern of the TWSA in the research basin.

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(3) Compared with the TWSA values of 8 other typical global rivers, which roughly reflect the common TWSA

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ranges and characteristics of global rivers, the TWSA of the Yarlung Zangbo River Basin had obvious unique characteristics. Specifically, the Yarlung Zangbo River Basin featured obvious periodic changes with the best least

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squares fitting effect (R2=0.81); peak, low, and peak-low difference values that were 1.82, 1.19 and 1.52 times larger

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than those of the 8 other rivers; and the largest downward trend (4.13 mm·a-1).

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(4) Rainfall is the decisive factor affecting the TWSA. The more P there is, the greater the TWSA will be. However,

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the TWSA exhibited a slight delay, due to its complexity which required a long time to reach the final values. In conclusion, the application of GRACE data is of great significance for water resource management and global climate change research. The P-LSA and EOF methods have been demonstrated to be effective, which make the results deeper and more comprehensive. However, due to human and systematic errors, there are still some deficiencies that must be improved. For example, there is a lack of effective data or methods to verify the GRACE-derived TWSA in the studied basin. The groundwater storage of this river and the impact of human activities on the TWSA should be discussed further. With the successful launch of the GRACE-FO mission, the observation accuracy will be 10 times that of the GRACE mission, which will help us obtain a more accurate picture of the global water cycle.

Acknowledgments

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This work was funded by the National Natural Science Foundation of China [grant numbers 91547211] and the National Key R&D Program of China [grant numbers 2016YFC0502200]. The authors would like to thank the China National Meteorological Data Service Center for providing meteorological data and the Center for Space Research (CSR) for providing GRACE data. The work of these scientists is the basis for our investigations.

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Table 1 The P-LSA results of TWSA from different studies in the Yarlung Zangbo River Basin Cycle (month)

Amplitude (mm)

Phase (°)

This study-GRACE

11.98

87.45

154.52

Xu’s study

12.02

269.30

162.69

Yang’s study

11.92

251.60

166.90

Lakshmia’s study

11.93

32.85

-178.66

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TWSA datasets

Yellow Yangtze

Heihe

Murray-

Danube Mississippi

River

120.33

66.06

14.67

Low values

-69.28

-45.70

-13.41

Peak-low

189.61

111.77

28.08

Peak/Low

-1.74

-1.45

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-1.09

Amazon Darling

25.10

26.19

39.27

23.14

20.14

51.03

-18.89

-22.83

-28.46

-22.91

-11.69

-58.45

43.99

49.02

67.73

46.05

31.82

109.48

-1.33

-1.15

-1.38

-1.01

-1.72

-0.87

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Peak values

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Zangbo

Congo

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Yarlung Values

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Table 2 Comparison of the interannual TWSA between the Yarlung Zangbo River Basin and other typical basins

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Conflict of Interest Statement no conflicts of interest exist in the submission of this manuscript. The authors listed have read and approved this version of the article, and care has been taken to ensure the integrity of the work. I would like to declare on behalf of my co-authors that the work described is original research that has

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not been published previously and is not under consideration for publication elsewhere, in whole or

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in part. It is not submitted to any other journal.

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Graphical abstract

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Highlights

(1) GRACE-derived TWSA can supplement the limited hydrological data and enhance understanding of the Yarlung Zangbo River Basin. (2) EOF was the first time been used in this river, to identify the TWSA principal components by removing noise and redundancy.

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(3) P-LSA and EOF combination improved precision of the GRACE-TWSA (R2=0.75 and 0.80), which has strong

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localized applicability.

(4) TWSA in the study river was unique in terms of the period, extremum and trends compared with 8 other typical

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rivers.

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(5) Rainfall was the decisive factor influencing the TWSA.