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Estimating Groundwater Storage Anomalies in Songliao Plain Using GRACE〇Dan FENG, Kenji TANAKA, KHUJANAZAROV Temur, Kazuaki YOROZU, Yoshiya TOUGE

1.Introduction

GRACE data

Groundwater, which accounts for 99% of liquid

We used the Gravity Recovery and Climate

freshwater on Earth, serves as a crucial water source,

Experiment (GRACE) data and GRACE-FO data.

supplying a quarter of human water consumption. It

Release 06 of terrestrial water storage variations fromsupplies drinking water for 50% of the population.

spherical harmonics solutions. The surface massThe utilization of groundwater is divided amongchange data (terrestrial water storage anomalies

agriculture (69%), domestic use (22%), and industry-TWSA)are available from March 2002 to June 2017,(9%). In the recent 20 years, it has been extensive

July 2018 to December 2020. The baseline is Jan 2005

groundwater

exploitation

into Dec 2010.

drivenpopulation

growth and high intensity

byChina,principally

Land surface Models

agriculture. Songliao Plain covers an area of overWe used two land surface models: NOAH

256,288 square kilometers which located in Northeast(version 2.1) and SiBUC model. The time range isChina. It is one of the important crop production areasfrom March 2002 to December 2020. The resolution iswhere Agriculture as the main water user is

1° × 1°. Soil moisture, snow water equivalents,

responsible for 80% of total groundwater pumping.

surface runoff and canopy water storage weresimulated in the NOAH model. Soil moisture, snow
water equivalents, surface storage were simulated inthe SiBUC model.
Precipitation data
The Daily Observational Precipitation data isfrom NOAA. The location of stations are displayed inFigure 1.
3.Methods
The basic approach to deriving groundwater

Fig.1 The location of Songliao Plain.

anomalies (GWSA) estimates involves subtracting

The objective of this study is to exploring themonthly anomalies of hydrologic water storage

following questions:components anomalies from TWSA. The GWSA

（1）How does groundwater variation for decades?

(based on NOAH and SiBUC model) were calculated（2）What are the key features of temporalas follows:variability in GWSA?
（ 3 ） How do land surface processes correlate withgroundwater changes?
2.DataGWSANOAH = TWSAGRACE − SMSANOAH − SWEANOAH − SRANOAH − CWSANOAH （1）GWSASiBUC = TWSAGRACE − SMSASiBUC − SWEASiBUC − SSASiBUC（2）Where TWSA is terrestrial water storage
anomalies; GWSA is groundwater storage anomalies;

SSA is surface storage anomalies; SMSA is soilThe

moisture storage anomalies; SWEA is snow water

uncertainty with standard deviation =1.98 to 8.16

equivalent anomalies; CWSA is canopy water storage

cm and 3.40 to 6.42 cm.

anomalies; SSA is surface runoff anomalies.

GWSASiBUC andGWSANOAH have

theGWSA were plummeted in 2011 ( La Niña

The uncertainty data in TWSA was from GRACE

period).The annual cumulative precipitation decreased

which depends on latitude and smoothing radius.

significantly and the mean temperature was lower in

Uncertainty in the other hydrologic water storage

2011. In the spring of 2012, the Songliao Plain faced acomponents are taken as the standard deviation,

severe drought event. Due to the previous year ’s lessassuming independence between component errors.

precipitation, the amount of snowmelt was less thanUncertainty in GWSA was calculated as follows:

normal. The TWSA, GWSA and SMSA showed

σNOAH−GWSA =�2����−����� + �2NOAH−���� + �2NOAH−���� + �2NOAH−��� + �2NOAH−���� （3）σSiBUC−GWSA =�2����−����� + �2SiBUC−���� + �2SiBUC−���� + �2SiBUC−���（4）

For exploring the relative importance of key

extremely low values in the beginning of 2012. In therainy season of 2013, the Songliao Plain experienced
elevated

precipitation

which

partialrecoveryfeatures of temporal variability in GWSA ， the time

groundwater aquifers. The drought events continued

series of storage components can be decomposed

after 2014, groundwater still remained low level.

using the Seasonal Trend decomposition using Loess

Regarding spatial variations, GWSA has been at low(STL)[1]. This approach is based on decomposing

negative values for a long time in Harbin. It could begridded time series of monthly equivalent water height

related to high-intensity agricultural. It was found that

into long-term, seasonal, and subseasonal components.

dry and paddy lands in Harbin have increased since

We further decomposed the long-term component into

2010 due to global warming.

linear trend and inter-annual by Theil-Sen estimator[2].

We found that temporal variability in GWSA is(5)dominated by long-term trends in Songliao Plain. The���� = �linear trend + ������−������ + ��������� +������������

4.Results

The GWSASiBUC and GWSANOAH all show
significant downward trends in the Songliao Plain forseasonal amplitudes(maximum minus minimum range)
are 0.5 cm and 0.6 cm with GWSASiBUC andGWSANOAH.

decades.Specifically, groundwater depletion estimated

The GWS change is moderately correlated withfrom SiBUC and NOAH with −0.045 ± 0.348 cm/year

the TWS change (RNOAH=0.52,RSiBUC=0.37). The TWS

and −0.091 ± 0.360 cm/year.

change is moderately correlated with the SMS change(RNOAH=0.57, RSiBUC=0.57).
5.Conclusion
Generally good agreement between GWSASiBUC
and GWSANOAH in time series. Both are tracking thedynamics of GWS which increases during wet periods
and decreases during drought. Both datasets show
long ‐ term declining trends in Songliao Plain. SMS

Fig.2 GWSA based on SiBUC and NOAH models.
Uncertainty (grey bands based on the propagatederrors from TWS and other water storage errors)(Lower panels) The Annual cumulative precipitation
anomaly (CPA).

and GWS play important roles in the TWS.
References[1]Cleveland, R. B., Cleveland, W. S., McRae, J. E., & Terpenning, I. (1990). STL: A seasonal‐trend
decomposition procedure based on loess. Journal Official Statistics, 6(1), 3–73
[2]Humphrey, Vincent & Gudmundsson, Lukas & Seneviratne, Sonia. (2016). Assessing Global Water
Storage Variability from GRACE: Trends, Seasonal Cycle, Subseasonal Anomalies and Extremes.
Surveys in Geophysics. 37. 1-39.