CO2 Emissions Associated with Groundwater Storage Depletion in South Korea: Estimation and Vulnerability Assessment Using Satellite Data and Data-Driven Models
Abstract
:1. Introduction
2. Study Area and Data
2.1. Study Area
2.2. Data
3. Methods
3.1. Prediction of GWSA
3.1.1. Machine and Deep Learning Models
3.1.2. Hyperparameter Tuning
3.2. Quantification of CO2 Emission Due to Groundwater Storage Depletion
3.2.1. Trend Analysis of Model-Predicted GWSA
3.2.2. CO2 Emission from Groundwater Storage Depletion
3.3. Determining Groundwater Storage Depletion and CO2 Emission Vulnerability Using Entropy Weight and TOPSIS Method
3.3.1. Selection of Indicators
3.3.2. Determining Weights Using Entropy Weight Method
3.3.3. TOPSIS
4. Results
4.1. Model Performance in GWSA Prediction
4.2. Estimation of CO2 Emission Due to Groundwater Storage Depletion
4.3. Evaluation of Groundwater Storage Depletion and CO2 Emission Vulnerability
5. Discussion
5.1. Groundwater Storage Depletion Using Data-Driven Models Based on Multi-Satellite and Reanalysis Data
5.2. Relationships between Groundwater Storage Depletion-Associated CO2 Emissions and Vulnerability
5.3. Significance and Limitations
6. Conclusions
- Data-driven models based on multi-satellite and reanalysis data can predict spatiotemporal GWSA with relatively high accuracy.
- CO2 emissions from groundwater storage depletion in South Korea were estimated to be 0.216 and 0.202 MMTCO2/year using XGBoost and CNN-LSTM models, respectively.
- Western regions of South Korea are highly or very highly vulnerable to groundwater storage depletion and prone to CO2 emissions.
- A correlation relationship was identified between the co-occurrence of CO2 emissions from groundwater storage depletion and vulnerability to groundwater storage depletion.
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Variables | Data | Properties | Reference |
---|---|---|---|
P | TMPA3B43V7 | 0.25° resolution, January 2003–December 2019 | https://disc.gsfc.nasa.gov/datasets/TRMM_3B43_7/summary, accessed on 20 November 2023 |
T and SM | GLDAS NOAH025_M | 0.25° resolution, January 2003–December 2019 | https://ldas.gsfc.nasa.gov/gldas, accessed on 20 November 2023 |
NDVI and MNDWI | Landsat 5 and 8 | 30m resolution, January 2003–February 2013 (Landsat 5) March 2013–December 2019 (Landsat 8) | https://earthexplorer.usgs.gov, accessed on 20 November 2023 |
TWSA | CSR RL06M | 0.25° resolution, January 2003–April 2017 (GRACE) June 2018–December 2019 (GRACE-FO) | https://grace.jpl.nasa.gov, accessed on 20 November 2023 |
Model | Hyperparameter | Setting Value Range |
---|---|---|
XGBoost | Number of estimators | {1, …, 1000} |
Maximum depth | {3, 5, …, 15} | |
Minimum child weight | {1, 2, …, 9} | |
Subsample | {0.5, 0.6, …, 0.9} | |
Colsample_bytree | {0.5, 0.6, …, 0.9} | |
Gamma | {0, 0.1, …, 0.7} | |
Lambda | {0, …, 100} | |
Alpha | {0, …, 100} | |
Learning rate | {0.001, …, 0.01} | |
Time lags (month) | {1, 2, …, 12} | |
CNN-LSTM | Number of filters | {16, …, 128} |
Number of hidden layers | {1, 2, 3} | |
Hidden unit | {0, …, 100} | |
Dropout | {0.1, 0.2, …, 0.5} | |
Learning rate | {0.001, …, 0.01} | |
Epochs | {1, 2, …, 100} | |
Iteration | {1, 2, …, 100} | |
Time lags (month) | {1, 2, …, 12} |
Effect | Indicators | Source | Period |
---|---|---|---|
Positive | Groundwater use per unit area (m3/year/km2) | Korea Groundwater Annual Report | 2004–2019 |
Annual non-rainy days (day) | Korea Annual Hydrological Report | ||
Cultivation land per unit area (%) | Korean Statistical Information Service | ||
Negative | Annual GWSA (mm/year) | XGBoost and CNN-LSTM models | |
Annual precipitation (mm/year) | Korea Annual Hydrological Report | ||
Water supply ratio (%) | Waterworks Statistics |
Model | Hyperparameter | Optimal Value |
---|---|---|
XGBoost | Number of estimators | 88 |
Maximum depth | 5 | |
Minimum child weight | 5 | |
Subsample | 0.7 | |
Colsample_bytree | 0.8 | |
Gamma | 0.0 | |
Lambda | 1.0 | |
Alpha | 0.0 | |
Learning rate | 0.1 | |
Time lags (month) | 8 | |
CNN-LSTM | Number of filters | 72 |
Number of hidden layers | 2 | |
Hidden unit | 70 (layer 1), 60 (layer 2) | |
Dropout | 0.01 | |
Learning rate | 0.01 | |
Epochs | 60 | |
Iteration | 50 | |
Time lags (month) | 7 |
Sector | CO2 Emission (MMTCO2/Year) | Percentage (%) | |
---|---|---|---|
Energy | 585.408 | 86.064 | |
Industry | 53.690 | 7.893 | |
Agriculture | Rice paddies | 7.070 | 1.039 |
Farmland soil | 5.199 | 0.764 | |
Livestock manure treatment | 4.662 | 0.685 | |
Livestock enteric fermentation | 4.305 | 0.633 | |
Crop residue burning | 0.017 | 0.002 | |
Land use Land use change | Farmland | 2.944 | 0.433 |
Wetland | 0.312 | 0.046 | |
Waste | Landfill | 7.641 | 1.123 |
Burning | 6.499 | 0.956 | |
Wastewater treatment | 1.716 | 0.252 | |
Etc. | 0.516 | 0.076 | |
Groundwater storage depletion | 0.202–0.216 | 0.030–0.032 |
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Seo, J.Y.; Lee, S.-I. CO2 Emissions Associated with Groundwater Storage Depletion in South Korea: Estimation and Vulnerability Assessment Using Satellite Data and Data-Driven Models. Remote Sens. 2024, 16, 3122. https://doi.org/10.3390/rs16173122
Seo JY, Lee S-I. CO2 Emissions Associated with Groundwater Storage Depletion in South Korea: Estimation and Vulnerability Assessment Using Satellite Data and Data-Driven Models. Remote Sensing. 2024; 16(17):3122. https://doi.org/10.3390/rs16173122
Chicago/Turabian StyleSeo, Jae Young, and Sang-Il Lee. 2024. "CO2 Emissions Associated with Groundwater Storage Depletion in South Korea: Estimation and Vulnerability Assessment Using Satellite Data and Data-Driven Models" Remote Sensing 16, no. 17: 3122. https://doi.org/10.3390/rs16173122