Providing Enhanced Insights into Groundwater Exchange Patterns through Downscaled GRACE Data
Abstract
:1. Introduction
2. Materials
2.1. Study Area
2.2. Data Sources
2.2.1. Groundwater Storage Dataset
2.2.2. Comparison of the Reconstructions between the GRACE and GRACE-FO Data
2.2.3. Ground-Based Measurements
3. Methods
3.1. Equations for Calculating Groundwater Exchange
3.2. Seasonal Trend Decomposition via the Loess Method
3.3. Sen’s Slope and Mann–Kendall Test Methods
3.4. Quantifying the Relative Contributions of Climate and Human Activities to Groundwater Storage Changes
4. Results
4.1. Groundwater Storage Change Patterns
4.2. Groundwater Flux Exchange in 1° Grids
4.3. Groundwater Flux Exchange in Different Administrative Districts
4.4. Groundwater Flux Exchange between Different Hydrogeologic Regions
5. Discussion
5.1. Causes of Groundwater Exchange Changes
5.2. Comparison between Groundwater Flux Exchange and the Groundwater Level Changes
5.3. Contribution of Anthropogenic and Climate Factors
5.4. Limitations of This Research
6. Conclusions
- (1)
- Between 2003 and 2007, groundwater storage in the study area remained relatively stable. However, groundwater storage has consistently decreased since 2008, especially in spring and summer, but increased seasonal rainfall has not led to a corresponding increase in groundwater storage. The decrease in groundwater storage gradually increased in the Piedmont plain, and it became even more severe from south to north.
- (2)
- The groundwater exchange trends in the mountain front area calculated using 1° and 0.05° GWSA data were basically consistent. Naturally, the 0.05° GWSA data could better reflect the characteristics of groundwater exchange in small areas. Groundwater exchange exhibited a decreasing trend from the mountain area to the coastal areas. Groundwater exchange in the western Taihang Mountains was greater than that in the northern Yanshan Mountains and gradually decreased from south to north. From 2003 to 2007, groundwater exchange between the mountainous and plain areas decreased, and since 2008, it has shown an increasing trend. The change in groundwater exchange between the plain areas and coastal areas was relatively small, and the change trend was the opposite of that in the mountain plain area, which may be due to the lag in the lateral horizontal exchange between the mountain areas and plain areas and coastal areas.
- (3)
- The change rate of groundwater exchange between the inner and outer boundaries and the change in the groundwater level difference were not always consistent. This may be attributed to the groundwater level in the monitoring wells being affected by local groundwater extraction or replenishment, resulting in a certain delay between the groundwater level and storage changes.
- (4)
- Anthropogenic activities accounted for more than 90.9% of the decrease in groundwater storage, with an average contribution of 98.2%. Climate factors played a secondary role, with contributions ranging from 0% to 9.1% and an average contribution rate of approximately 1.8%. Compared to human influences, climate change exhibited minimal independent control over groundwater storage variations.
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
Abbreviations
BJ | Beijing |
BD | Baoding |
CD | Chengde |
CZ | Cangzhou |
SJZ | Shijiazhuang |
TS | Tangshan |
ZJK | Zhangjiakou |
HD | Handan |
HS | Hengshui |
LF | Langfang |
QHD | Qinghuangdao |
TJ | Tianjin |
XT | Xingtai |
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Source | Mo et al. (2022) [52] | Humphrey and Gudmundsson (2019) [53] | Deng et al. (2023) [54] | Li et al. (2021) [55] |
---|---|---|---|---|
Study area | Global | Global | Global | Global |
Method | Bayesian convolutional neural network | A simple statistical model that considers the residence times of local TWS datapoints | Summation method, empirical orthogonal function bias correction, and multiple linear regression | Signal separation and detrending |
GRACE data | JPL mascon | JPL mascon | JPL mascon | CSR mascon |
Forcing data | Precipitation, temperature, cumulative water storage change, and ERA5L-derived TWSA | Precipitation and land temperature | Soil moisture, snow depth, precipitation, land temperature, and glacier mass change | Precipitation, land temperature, sea surface temperature, soil moisture, evaporation, runoff, and 17 other tele-connected climate indices |
Time period | April 2002–December 2020 | January 1901–July 2019 | January 1981–June 2020 | July 1979–June 2020 |
Spatial resolution | 1° × 1° | 0.5° × 0.5° | 1° × 1° | 0.5° × 0.5° |
GWSAor Slope | Drivers | Driver Division | Contribution Rate (%) | ||
---|---|---|---|---|---|
GWSAcc Slope | GWSAac Slope | cc | ac | ||
>0 | cc & ac | >0 | >0 | Slopecc/Slopeor | Slopeac/Slopeor |
cc | >0 | <0 | 100 | 0 | |
ac | <0 | >0 | 0 | 100 | |
<0 | cc & ac | <0 | <0 | Slopecc/Slopeor | Slopeac/Slopeor |
cc | <0 | >0 | 100 | 0 | |
ac | >0 | <0 | 0 | 100 |
Parameters | North | West | South | East | ||||||
---|---|---|---|---|---|---|---|---|---|---|
outside (j) | G11 | G19 | G20 | G23 | G29 | G36 | G43 | G25 | G32 | |
inside (i) | G18 | G26 | G27 | G24 | G30 | G37 | G44 | G32 | G33 | |
β | 5 | 5 | 5 | 5 | 5 | 5 | 5 | 0.6 | 0.5 | |
T (m2/d) | 4000 | 800 | 4800 | 5200 | 6000 | 4400 | 4000 | 400 | 100 | |
Syi | 0.06 | 0.04 | 0.08 | 0.08 | 0.15 | 0.06 | 0.05 | 0.04 | 0.03 |
Regions | 2003–2007 | 2008–2014 | 2015–2020 |
---|---|---|---|
North | 190 | 102 * | 142 * |
West | −602 * | 87 * | 517 * |
South | 0 | −1 * | −11 * |
East | −2 * | 0 | −2 * |
Regions | 2003–2007 | 2008–2014 | 2015–2020 |
---|---|---|---|
I | −35 * | 82 * | 117 * |
II | −21 * | 48 * | 59 * |
III | −123 * | 123 * | 203 * |
IV | 3 * | −1 * | −2 * |
Regions | 2003–2007 | 2008–2014 | 2015–2020 | |
---|---|---|---|---|
TM-PP | West | −602 * | 87 * | 517 * |
III | −123 * | 123 * | 203 * | |
TM-PP | −522 * | 94 * | 435 * | |
YM-PP | North | 190 * | 102 * | 142 * |
I | −35 * | 82 * | 117 * | |
II | −21 * | 48 * | 59 * | |
PP-ECP | South | 0 | −1 * | −11 * |
IV | 3 * | −1 * | −2 * | |
PP-ECP | 0 | 0 | 0 | |
EPC-SEA | East | −2 * | 0 | −2 * |
EPC-SEA | 0 | 0 | 0 |
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Sun, J.; Hu, L.; Zhang, J.; Yin, W. Providing Enhanced Insights into Groundwater Exchange Patterns through Downscaled GRACE Data. Remote Sens. 2024, 16, 812. https://doi.org/10.3390/rs16050812
Sun J, Hu L, Zhang J, Yin W. Providing Enhanced Insights into Groundwater Exchange Patterns through Downscaled GRACE Data. Remote Sensing. 2024; 16(5):812. https://doi.org/10.3390/rs16050812
Chicago/Turabian StyleSun, Jianchong, Litang Hu, Junchao Zhang, and Wenjie Yin. 2024. "Providing Enhanced Insights into Groundwater Exchange Patterns through Downscaled GRACE Data" Remote Sensing 16, no. 5: 812. https://doi.org/10.3390/rs16050812
APA StyleSun, J., Hu, L., Zhang, J., & Yin, W. (2024). Providing Enhanced Insights into Groundwater Exchange Patterns through Downscaled GRACE Data. Remote Sensing, 16(5), 812. https://doi.org/10.3390/rs16050812