簡易檢索 / 詳目顯示

研究生: 達亞
Tatas
論文名稱: 區域及全球空間資料模型於地下水監測與地層下陷防制
Regional and Global Geospatial Data Models for Groundwater Monitoring and Land Subsidence Mitigation
指導教授: 朱宏杰
Chu, Hone-Jay
學位類別: 博士
Doctor
系所名稱: 工學院 - 測量及空間資訊學系
Department of Geomatics
論文出版年: 2023
畢業學年度: 112
語文別: 英文
論文頁數: 148
外文關鍵詞: artificial intelligence, spatial regression, groundwater monitoring, GRACE GWS, inelastic land subsidence, land subsidence mitigation
相關次數: 點閱:101下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報
  • ABSTRACT i ACKNOWLEDGMENTS iii CONTENT v LIST OF TABLES ix LIST OF FIGURES x ABBREVIATIONS xv Chapter 1: Introduction 1 1.1. Motivation 1 1.1.1. Groundwater in global issue 1 1.1.2. Groundwater in a regional issue 3 1.1.3. Groundwater withdrawal volume estimation 4 1.1.4. Land subsidence linked to the extraction of groundwater 5 1.1.5. Geospatial data 5 1.1.6. Technology for geospatial data models 6 1.1.7. Summary of Motivations 7 1.2. Problem Statements 8 1.2.1. Groundwater storage change on the global scale 8 1.2.2. Future hydraulic head estimation 9 1.2.3. Inelastic land subsidence identification 10 1.2.4. Geospatial data and data processing 11 1.3. Objectives 11 1.4. Thesis Structure 13 Chapter 2: Study Area and Flowchart 16 2.1. Study Area 16 2.1.1. Global Map for Estimating Groundwater Storage Change 16 2.1.2. Location Selected for the Estimation of Hydraulic Head 17 2.1.3. Study Area for Elastic and Inelastic Land Subsidence Identification 19 2.2. Flowchart of Research 22 Chapter 3: Data and Methods 25 3.1. Dataset 25 3.1.1. Dataset for Estimation of Groundwater Storage Change 27 3.1.2. Dataset for Estimation of Hydraulic Head in the Next-month 29 3.1.3. Dataset for Identification of Land Subsidence 30 3.2. Groundwater Storage Change Derived by GRACE and GLDAS Approach 31 3.3. Regional Pumping and Rainfall on Groundwater Response 33 3.3.1. The state estimation approach 33 3.3.2. The change estimation approach 33 3.3.3. The spatial regression 34 3.4. Elastic and Inelastic Land Subsidence 36 3.4.1. Stress-strain relation as a stress-strain approach 36 3.4.2. Statistical hydraulic head rule 37 3.4.3. A machine learning approach 38 3.5. Spatial Map Visualization 39 3.6. Performance 40 3.6.1. Root Mean Square Error 40 3.6.2. Correlation 41 3.6.3. Accuracy, Precision, and Recall 41 Chapter 4 Groundwater Storage Change Derived from GRACE and GLDAS 43 4.1. A Global Map Displaying the Change in Groundwater Storage (∆GWS CSR) 43 4.2. Comparison with Groundwater Storage (GWS CSR) in Previous Studies 50 4.3. Groundwater Storage Change for the GSFC Mascons Product Version 52 4.4. Relation Groundwater Storage Change with Land Subsidence 54 4.5. Spatial Gradient of Groundwater Storage Time Series 61 Chapter 5 Future Spatial Groundwater Response Estimation 68 5.1. Spatial Distribution of Monthly Rainfall 68 5.2. The Estimated Volume of Groundwater Extraction 68 5.3. Model Performance 69 5.4. Spatial Hydraulic Head Change Maps 74 5.5. The Correlation among Changes in Hydraulic Head, Local Precipitation, and Pumping Volumes 77 Chapter 6 Spatial Elastic and Inelastic Land Subsidence 83 6.1. Stress-strain Curve in Hydraulic Head and Land Subsidence Relation 83 6.2. Statistical Hydraulic Head Rule 86 6.2.1. The investigation of the threshold value, h*, for land subsidence identification 86 6.2.2. Spatial maps depicting land subsidence obtained from models 89 6.2.3. Validation for the land subsidence classification 92 6.3. Machine Learning Techniques for Spatial Estimation 95 6.3.1. Classifier 96 Most locations recorded threshold values ranging from 0.3 to 0.5. Notably, higher values of n indicate more significant elastic subsidence or uplift. 96 6.3.2. Spatial patterns of inelastic and elastic land subsidence 99 6.4. Comparison of Various Methods 106 7.1. Conclusion 112 7.1.1. Global groundwater storage change map 112 7.1.2. Future hydraulic head estimation 113 7.1.3. Spatial land subsidence mapping 115 7.2. Future Works 116 7.2.1. Global groundwater storage change map 116 7.2.2. Future hydraulic head estimation 117 7.2.3. Spatial land subsidence mapping 117 REFERENCES 118

    Abdullahi, M.G., Garba, I., 2016. Effect of Rainfall on Groundwater Level Fluctuation in Terengganu, Malaysia. J. Remote Sens. GIS 4. https://doi.org/10.4172/2469-4134.1000142
    Alfatinah, A., Chu, H., 2023. Fishing Area Prediction Using Scene-Based Ensemble Models.
    Ali, M.Z., Chu, H.J., Burbey, T.J., 2020. Mapping and predicting subsidence from spatio-temporal regression models of groundwater-drawdown and subsidence observations. Hydrogeol. J. 28, 2865–2876. https://doi.org/10.1007/s10040-020-02211-0
    Bagheri-Gavkosh, M., Hosseini, S.M., Ataie-Ashtiani, B., Sohani, Y., Ebrahimian, H., Morovat, F., Ashrafi, S., 2021. Land subsidence: A global challenge. Sci. Total Environ. 778. https://doi.org/10.1016/j.scitotenv.2021.146193
    Beaudoing, H., M. Rodell, N., 2020. GLDAS Noah Land Surface Model L4 monthly 0.25 x 0.25 degree V2.1 (GLDAS_NOAH025_M) at GES DISC [WWW Document]. https://doi.org/10.5067/SXAVCZFAQLNO
    Bell, J.W., Amelung, F., Ferretti, A., Bianchi, M., Novali, F., 2008. Permanent scatterer InSAR reveals seasonal and long-term aquifer-system response to groundwater pumping and artificial recharge. Water Resour. Res. 44, 1–18. https://doi.org/10.1029/2007WR006152
    Brunsdon, C., Fotheringham, A.S., Charlton, M.E., 1996. Geographically Weighted Regression: A Method for Exploring Spatial Nonstationarity. Geogr. Anal. 28, 281–298. https://doi.org/https://doi.org/10.1111/j.1538-4632.1996.tb00936.x
    Burbey, T.J., 2020. Extensometer forensics: what can the data really tell us? Hydrogeol. J. 28, 637–655. https://doi.org/10.1007/s10040-019-02060-6
    Burbey, T.J., 2016. Stress-strain analyses for aquifer-system characterization.
    Chang, F.J., Chang, L.C., Huang, C.W., Kao, I.F., 2016. Prediction of monthly regional groundwater levels through hybrid soft-computing techniques. J. Hydrol. 541, 965–976. https://doi.org/10.1016/j.jhydrol.2016.08.006
    Chen, J., Famigliett, J.S., Scanlon, B.R., Rodell, M., 2016. Groundwater Storage Changes: Present Status from GRACE Observations. Surv. Geophys. 37, 397–417. https://doi.org/10.1007/s10712-015-9332-4
    Chen, J., Li, J., Zhang, Z., Ni, S., 2014. Long-term groundwater variations in Northwest India from satellite gravity measurements. Glob. Planet. Change 116, 130–138. https://doi.org/10.1016/j.gloplacha.2014.02.007
    Chen, J.L., Wilson, C.R., Tapley, B.D., Blankenship, D.D., Ivins, E.R., 2007. Patagonia Icefield melting observed by Gravity Recovery and Climate Experiment (GRACE). Geophys. Res. Lett. 34, 1–6. https://doi.org/10.1029/2007GL031871
    Chen, K.H., Hwang, C., Tanaka, Y., Chang, P.Y., 2023. Gravity estimation of groundwater mass balance of sandy aquifers in the land subsidence-hit region of Yunlin County, Taiwan. Eng. Geol. 315, 107021. https://doi.org/10.1016/j.enggeo.2023.107021
    Chu, H.J., Ali, M.Z., Tatas, Burbey, T.J., 2021a. Development of spatially varying groundwater-drawdown functions for land subsidence estimation. J. Hydrol. Reg. Stud. 35, 100808. https://doi.org/10.1016/j.ejrh.2021.100808
    Chu, H.J., Ali, M.Z., Tatas, Burbey, T.J., 2021b. Development of spatially varying groundwater-drawdown functions for land subsidence estimation. J. Hydrol. Reg. Stud. 35, 100808. https://doi.org/10.1016/j.ejrh.2021.100808
    Chu, H.J., Kong, S.J., Chang, C.H., 2018. Spatio-temporal water quality mapping from satellite images using geographically and temporally weighted regression. Int. J. Appl. Earth Obs. Geoinf. 65, 1–11. https://doi.org/10.1016/j.jag.2017.10.001
    Chu, H.J., Lin, C.W., Burbey, T.J., Ali, M.Z., 2020. Spatiotemporal Analysis of Extracted Groundwater Volumes Estimated from Electricity Consumption. Groundwater 58, 962–972. https://doi.org/10.1111/gwat.13008
    Chusnah, W.N., Chu, H.J., Tatas, Jaelani, L.M., 2023. Machine-learning-estimation of high-spatiotemporal-resolution chlorophyll-a concentration using multi-satellite imagery. Sustain. Environ. Res. 33, 1–14. https://doi.org/10.1186/s42834-023-00170-1
    Devi, D., Sophia, S., Boselin Prabhu, S.R., 2021. Deep learning-based cognitive state prediction analysis using brain wave signal, Cognitive Computing for Human-Robot Interaction: Principles and Practices. INC. https://doi.org/10.1016/B978-0-323-85769-7.00017-3
    Doll, P., Schmied, H.M., Schuh, C., Portmann, F.T., Eicker, and A., 2014. Global-scale assessment of groundwater depletion and related groundwater abstractions: Combining hydrological modeling with information from well observations and GRACE satellites. Water Resour. Manag. 50, 5698–5720. https://doi.org/https://doi.org/10.1002/2014WR015595
    Düntsch, I., Gediga, G., 2020. Indices for rough set approximation and the application to confusion matrices. Int. J. Approx. Reason. 118, 155–172. https://doi.org/10.1016/j.ijar.2019.12.008
    Erkens, G., Sutanudjaja, E.H., 2015. Towards a global land subsidence map. Proc. Int. Assoc. Hydrol. Sci. 372, 83–87. https://doi.org/10.5194/piahs-372-83-2015
    Ezquerro, P., Herrera, G., Marchamalo, M., Tomás, R., Béjar-Pizarro, M., Martínez, R., 2014. A quasi-elastic aquifer deformational behavior: Madrid aquifer case study. J. Hydrol. 519, 1192–1204. https://doi.org/10.1016/j.jhydrol.2014.08.040
    Famiglietti, J.S., 2014. The global groundwater crisis. Nat. Clim. Chang. 4, 945–948. https://doi.org/10.1038/nclimate2425
    Famiglietti, J.S., Lo, M., Ho, S.L., Bethune, J., Anderson, K.J., Syed, T.H., Swenson, S.C., De Linage, C.R., Rodell, M., 2011. Satellites measure recent rates of groundwater depletion in California’s Central Valley. Geophys. Res. Lett. 38, 2–5. https://doi.org/10.1029/2010GL046442
    Feng, W., Zhong, M., Lemoine, J.M., Biancale, R., Hsu, H.T., Xia, J., 2013. Evaluation of groundwater depletion in North China using the Gravity Recovery and Climate Experiment (GRACE) data and ground-based measurements. Water Resour. Res. 49, 2110–2118. https://doi.org/10.1002/wrcr.20192
    Foody, G.M., 2023. Challenges in the real world use of classification accuracy metrics: From recall and precision to the Matthews correlation coefficient. PLoS One 18, e0291908. https://doi.org/10.1371/journal.pone.0291908
    Fotheringham, A.S., Brunsdon, C., Charlton, M., 2002. Geographically weighted regression: the analysis of spatially varying relationships. Educ. Psychol. Meas. 31, 1029–1029. https://doi.org/10.1177/001316447103100435
    Galloway, D., Jones, D.R., Ingebritsen, S.E., 2000. Land subsidence in the United States, US Geological Survey Circular.
    Gleeson, T., Cuthbert, M., Ferguson, G., Perrone, D., 2020. Global Groundwater Sustainability, Resources, and Systems in the Anthropocene. Annu. Rev. Earth Planet. Sci. 48, 431–463. https://doi.org/10.1146/annurev-earth-071719-055251
    Gong, G., Mattevada, S., O’Bryant, S.E., 2014. Comparison of the accuracy of kriging and IDW interpolations in estimating groundwater arsenic concentrations in Texas. Environ. Res. 130, 59–69. https://doi.org/10.1016/j.envres.2013.12.005
    Gong, H., Pan, Y., Zheng, L., Li, X., Zhu, L., Zhang, C., Huang, Z., Li, Z., Wang, H., Zhou, C., 2018. Long-term groundwater storage changes and land subsidence development in the North China Plain (1971–2015). Hydrogeol. J. 26, 1417–1427. https://doi.org/10.1007/s10040-018-1768-4
    Guppy, L., Uyttendaele, P., Villholth, K.G., Smakhtin, V., 2018. Groundwater and Sustainable Development Goals: Analysis of Interlinkages. UNU-INWEH Report Series, Issue 04. United Nations University Institute for Water, Environment and Health. Hamilton, Canada 26.
    Guzy, A., Malinowska, A.A., 2020. State of the art and recent advancements in the modelling of land subsidence induced by groundwater withdrawal. Water (Switzerland) 12. https://doi.org/10.3390/w12072051
    He, Q., Zhang, Z., Yi, C., 2008. 3D fluorescence spectral data interpolation by using IDW. Spectrochim. Acta - Part A Mol. Biomol. Spectrosc. 71, 743–745. https://doi.org/10.1016/j.saa.2007.11.041
    Herrera-García, G., Ezquerro, P., Tomas, R., Béjar-Pizarro, M., López-Vinielles, J., Rossi, M., Mateos, R.M., Carreón-Freyre, D., Lambert, J., Teatini, P., Cabral-Cano, E., Erkens, G., Galloway, D., Hung, W.C., Kakar, N., Sneed, M., Tosi, L., Wang, H., Ye, S., 2021. Mapping the global threat of land subsidence. Science (80-. ). 371, 34–36. https://doi.org/10.1126/science.abb8549
    Huang, B., Wu, B., Barry, M., 2010. Geographically and temporally weighted regression for modeling spatio-temporal variation in house prices. Int. J. Geogr. Inf. Sci. 24, 383–401. https://doi.org/10.1080/13658810802672469
    Huang, F., Liu, D., Tan, X., Wang, J., Chen, Y., He, B., 2011. Explorations of the implementation of a parallel IDW interpolation algorithm in a Linux cluster-based parallel GIS. Comput. Geosci. 37, 426–434. https://doi.org/10.1016/j.cageo.2010.05.024
    Huang, Z., Pan, Y., Gong, H., Yeh, P.J. ‐F., Li, X., Zhou, D., Zhao, W., 2015. Subregional‐scale groundwater depletion detected by GRACE for both shallow and deep aquifers in North China Plain. Geophys. Res. Lett. 42, 1791–1799. https://doi.org/10.1002/2014GL062498
    Humphrey, V., Rodell, M., Eicker, A., 2023. Using Satellite-Based Terrestrial Water Storage Data: A Review. Surv. Geophys. 44, 1489–1517. https://doi.org/10.1007/s10712-022-09754-9
    Hung, W., Hwang, C., Sneed, M., Chen, Y., Chu, C., Lin, S., 2021. Measuring and Interpreting Multilayer Aquifer‐System Compactions for a Sustainable Groundwater‐System Development. Water Resour. Res. 57. https://doi.org/10.1029/2020WR028194
    Hung, W.C., Hwang, C., Chang, C.P., Yen, J.Y., Liu, C.H., Yang, W.H., 2010. Monitoring severe aquifer-system compaction and land subsidence in Taiwan using multiple sensors: Yunlin, the southern Choushui river Alluvial fan. Environ. Earth Sci. 59, 1535–1548. https://doi.org/10.1007/s12665-009-0139-9
    Hung, W.C., Hwang, C., Liou, J.C., Lin, Y.S., Yang, H.L., 2012. Modeling aquifer-system compaction and predicting land subsidence in central Taiwan. Eng. Geol. 147–148, 78–90. https://doi.org/10.1016/j.enggeo.2012.07.018
    Jang, C.S., Chen, S.K., Lin, C.C., 2008. Using multiple-variable indicator kriging to assess groundwater quality for irrigation in the aquifers of the Choushui River alluvial fan. Hydrol. Process. 22, 4477–4489. https://doi.org/10.1002/hyp
    Jing, W., Zhang, P., Zhao, X., 2019. A comparison of different GRACE solutions in terrestrial water storage trend estimation over Tibetan Plateau. Sci. Rep. 9, 1–10. https://doi.org/10.1038/s41598-018-38337-1
    Ku, C.Y., Liu, C.Y., 2023. Modeling of land subsidence using GIS-based artificial neural network in Yunlin County, Taiwan. Sci. Rep. 13, 1–17. https://doi.org/10.1038/s41598-023-31390-5
    Kumar, D., Bhattacharjya, R.K., 2021. GRNN Model for prediction of groundwater fluctuation in the state of Uttarakhand of India using GRACE data under limited bore well data. J. Hydroinformatics 23(3), 567–588. https://doi.org/10.2166/hydro.2021.108
    Landerer, F.W., Swenson, S.C., 2012. Accuracy of scaled GRACE terrestrial water storage estimates 48, 1–11. https://doi.org/10.1029/2011WR011453
    Legchenko, A., Miège, C., Koenig, L.S., Forster, R.R., Miller, O., Solomon, D.K., Schmerr, N., Montgomery, L., Ligtenberg, S., Brucker, L., 2018. Estimating water volume stored in the south-eastern Greenland firn aquifer using magnetic-resonance soundings. J. Appl. Geophys. 150, 11–20. https://doi.org/10.1016/j.jappgeo.2018.01.005
    Li, J., Zhu, L., Gong, H., Zhou, J., Dai, Z., Li, X., Wang, H., Zoccarato, C., Teatini, P., 2022. Unraveling elastic and inelastic storage of aquifer systems by integrating fast independent component analysis and a variable preconsolidation head decomposition method. J. Hydrol. 606, 127420. https://doi.org/10.1016/j.jhydrol.2021.127420
    Li, Y., Gong, H., Zhu, L., Li, X., 2017. Measuring spatiotemporal features of land subsidence, groundwater drawdown, and compressible layer thickness in Beijing Plain, China. Water (Switzerland) 9. https://doi.org/10.3390/w9010064
    Liljedahl, L.C., Meierbachtol, T., Harper, J., van As, D., Näslund, J.O., Selroos, J.O., Saito, J., Follin, S., Ruskeeniemi, T., Kontula, A., Humphrey, N., 2021. Rapid and sensitive response of Greenland’s groundwater system to ice sheet change. Nat. Geosci. 14, 751–755. https://doi.org/10.1038/s41561-021-00813-1
    Lin, C., Hsu, J., Lee, Y., Wu, C., Lin, Y., 2016. Estimation of Agricultural Groundwater Usage by Well Pumping Efficiency and Electric Consumption.
    Liu, C.H., Pan, Y.W., Liao, J.J., Huang, C.T., Ouyang, S., 2004. Characterization of land subsidence in the Choshui River alluvial fan, Taiwan. Environ. Geol. 45, 1154–1166. https://doi.org/10.1007/s00254-004-0983-6
    Liu, C.W., Jang, C.S., Chen, S.C., 2002. Three-dimensional spatial variability of hydraulic conductivity in the Choushui River alluvial fan, Taiwan. Environ. Geol. 43, 48–56. https://doi.org/10.1007/s00254-002-0648-2
    Long, D., Chen, X., Scanlon, B.R., Wada, Y., Hong, Y., Singh, V.P., Chen, Y., Wang, C., Han, Z., Yang, W., 2016. Have GRACE satellites overestimated groundwater depletion in the Northwest India Aquifer? Sci. Rep. 6, 1–11. https://doi.org/10.1038/srep24398
    Loomis, B.D., Luthcke, S.B., Sabaka, T.J., 2019a. Regularization and error characterization of GRACE mascons. J. Geod. 93, 1381–1398. https://doi.org/10.1007/s00190-019-01252-y
    Loomis, B.D., Rachlin, K.E., Luthcke, S.B., 2019b. Improved Earth Oblateness Rate Reveals Increased Ice Sheet Losses and Mass-Driven Sea Level Rise. Geophys. Res. Lett. 46, 6910–6917. https://doi.org/10.1029/2019GL082929
    Loomis, B.D., Rachlin, K.E., Wiese, D.N., Landerer, F.W., Luthcke, S.B., 2020. Replacing GRACE/GRACE-FO C30 With Satellite Laser Ranging: Impacts on Antarctic Ice Sheet Mass Change. Geophys. Res. Lett. 47, 1–7. https://doi.org/10.1029/2019GL085488
    Meng, J., 2021. Raster data projection transformation based-on Kriging interpolation approximate grid algorithm. Alexandria Eng. J. 60, 2013–2019. https://doi.org/10.1016/j.aej.2020.12.006
    Mohamed, A., Abdelrady, A., Alarifi, S.S., Othman, A., 2023. Geophysical and Remote Sensing Assessment of Chad’s Groundwater Resources. Remote Sens. 15. https://doi.org/10.3390/rs15030560
    Motagh, M., Djamour, Y., Walter, T.R., Wetzel, H.U., Zschau, J., Arabi, S., 2007. Land subsidence in Mashhad Valley, northeast Iran: Results from InSAR, levelling and GPS. Geophys. J. Int. 168, 518–526. https://doi.org/10.1111/j.1365-246X.2006.03246.x
    Ouatiki, H., Boudhar, A., Leblanc, M., Fakir, Y., Chehbouni, A., 2022. When climate variability partly compensates for groundwater depletion: An analysis of the GRACE signal in Morocco. J. Hydrol. Reg. Stud. 42, 101177. https://doi.org/10.1016/j.ejrh.2022.101177
    Oulaya, B., Aissa, B., Salim, O., 2019. Secure transfer of color images using horizontal and vertical scan. Trait. du Signal 36, 45–51. https://doi.org/10.18280/ts.360106
    Pan, Y., Zhang, C., Gong, H., Yeh, P.J.-F., Shen, Y., Guo, Y., Huang, Z., Li, X., 2017. Detection of human-induced evapotranspiration using GRACE satellite observations in the Haihe River basin of China. Geophys. Res. Lett. 44, 190–199. https://doi.org/10.1002/2016GL071287
    Patra, S.R., Chu, H.-J., Tatas, 2023. Regional groundwater sequential forecasting using global and local LSTM models. J. Hydrol. Reg. Stud. 47, 101442. https://doi.org/10.1016/j.ejrh.2023.101442
    Planning for groundwater sustainability...pdf, n.d.
    Powers, D.M.W., 2020. Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. https://doi.org/10.9735/2229-3981
    Qi, P., Zhang, G., Xu, Y.J., Wang, L., Ding, C., Cheng, C., 2018. Assessing the influence of precipitation on shallow groundwater table response using a combination of singular value decomposition and cross-wavelet approaches. Water (Switzerland) 10. https://doi.org/10.3390/w10050598
    Rahadian, H., Bandong, S., Widyotriatmo, A., Joelianto, E., 2023. Image encoding selection based on Pearson correlation coefficient for time series anomaly detection. Alexandria Eng. J. 82, 304–322. https://doi.org/10.1016/j.aej.2023.09.070
    Rahaman, M., Thakur, B., Kalra, A., Ahmad, S., 2019. Modeling of GRACE-Derived Groundwater Information in the Colorado River Basin. Hydrology 6, 19. https://doi.org/10.3390/hydrology6010019
    Rateb, A., Scanlon, B.R., Pool, D.R., Sun, A., Zhang, Z., Chen, J., Clark, B., Faunt, C.C., Haugh, C.J., Hill, M., Hobza, C., McGuire, V.L., Reitz, M., Müller Schmied, H., Sutanudjaja, E.H., Swenson, S., Wiese, D., Xia, Y., Zell, W., 2020. Comparison of Groundwater Storage Changes From GRACE Satellites With Monitoring and Modeling of Major U.S. Aquifers. Water Resour. Res. 56, 1–19. https://doi.org/10.1029/2020WR027556
    Richard Peltier, W., Argus, D.F., Drummond, R., 2018. Comment on “An Assessment of the ICE-6G_C (VM5a) Glacial Isostatic Adjustment Model” by Purcell et al. J. Geophys. Res. Solid Earth 123, 2019–2028. https://doi.org/10.1002/2016JB013844
    Richter, A., Groh, A., Horwath, M., Ivins, E., Marderwald, E., Hormaechea, J.L., Perdomo, R., Dietrich, R., 2019. The rapid and steady mass loss of the Patagonian Icefields throughout the GRACE era: 2002-2017. Remote Sens. 11, 2002–2017. https://doi.org/10.3390/rs11080974
    Rodell, M., Famiglietti, J.S., 2002. The potential for satellite-based monitoring of groundwater storage changes using GRACE: The High Plains aquifer, Central US. J. Hydrol. 263, 245–256. https://doi.org/10.1016/S0022-1694(02)00060-4
    Rodell, M., Houser, P.R., Jambor, U., Gottschalck, J., Mitchell, K., Meng, C.J., Arsenault, K., Cosgrove, B., Radakovich, J., Bosilovich, M., Entin, J.K., Walker, J.P., Lohmann, D., Toll, D., 2004a. The Global Land Data Assimilation System. Bull. Am. Meteorol. Soc. 85, 381–394. https://doi.org/10.1175/BAMS-85-3-381
    Rodell, M., Houser, P.R., Mitchell, K., Meng, J., Oceanic, N., 2004b. The Global Land Data Assimilation System. https://doi.org/10.1175/BAMS-85-3-381
    Rodell, M., Velicogna, I., Famiglietti, J.S., 2009. Satellite-based estimates of groundwater depletion in India. Nature 460, 999–1002. https://doi.org/10.1038/nature08238
    Sakumura, C., Bettadpur, S., Bruinsma, S., 2014. Ensemble prediction and intercomparison analysis of GRACE time-variable gravity field models. Geophys. Res. Lett. 41, 1389–1397. https://doi.org/10.1002/2013GL058632
    Sauti, N.S., Daud, M.E., Kaamin, M., 2020. Construction of an Integrated social vulnerability index to identify spatial variability of exposure to seismic hazard in Pahang, Malaysia. Int. J. Des. Nat. Ecodynamics 15, 365–372. https://doi.org/10.18280/ijdne.150310
    Save, H., Bettadpur, S., Tapley, B.D., 2016. High‐resolution CSR GRACE RL05 mascons 7547–7569. https://doi.org/10.1002/2016JB013007.
    Scanlon, B.R., Zhang, Z., Save, H., Sun, A.Y., Schmied, H.M., Van Beek, L.P.H., Wiese, D.N., Wada, Y., Long, D., Reedy, R.C., Longuevergne, L., Döll, P., Bierkens, M.F.P., 2018. Global models underestimate large decadal declining and rising water storage trends relative to GRACE satellite data. Proc. Natl. Acad. Sci. U. S. A. 115, E1080–E1089. https://doi.org/10.1073/pnas.1704665115
    Singh, Anil Kumar, Jasrotia, A.S., Taloor, A.K., Kotlia, B.S., Kumar, V., Roy, S., Ray, P.K.C., Singh, K.K., Singh, Anoop Kumar, Sharma, A.K., 2017. Estimation of quantitative measures of total water storage variation from GRACE and GLDAS-NOAH satellites using geospatial technology. Quat. Int. 444, 191–200. https://doi.org/10.1016/j.quaint.2017.04.014
    Singh, V.K., Pandey, H.K., Singh, S.K., 2023. Groundwater storage change estimation using GRACE data and Google Earth Engine: A basin scale study. Phys. Chem. Earth 129, 103297. https://doi.org/10.1016/j.pce.2022.103297
    Sorkhabi, O.M., Asgari, J., Amiri Simkooei, A., 2021. Analysis of Greenland mass changes based on GRACE four-dimensional wavelet decomposition. Remote Sens. Lett. 12, 499–509. https://doi.org/10.1080/2150704X.2021.1903608
    Spokas, K., Graff, C., Morcet, M., Aran, C., 2003. Implications of the spatial variability of landfill emission rates on geospatial analyses. Waste Manag. 23, 599–607. https://doi.org/10.1016/S0956-053X(03)00102-8
    Sun, P., Guo, C., Wei, D., 2022. GRACE Data Explore Moho Change Characteristics Beneath the South America Continent near the Chile Triple Junction. Remote Sens. 14, 1–24. https://doi.org/10.3390/rs14040924
    Tatas, Chu, H.-J., Burbey, T.J., 2021. Estimating future (next-month’s) spatial groundwater response from current regional pumping and precipitation rates. J. Hydrol. 127160. https://doi.org/10.1016/j.jhydrol.2021.127160
    Tatas, Chu, H.J., Burbey, T.J., Lin, C.W., 2023. Mapping regional subsidence rate from electricity consumption-based groundwater extraction. J. Hydrol. Reg. Stud. 45, 101289. https://doi.org/10.1016/j.ejrh.2022.101289
    Tiwari, V.M., Wahr, J., Swenson, S., 2009. Dwindling groundwater resources in northern India, from satellite gravity observations. Geophys. Res. Lett. 36, 1–5. https://doi.org/10.1029/2009GL039401
    Tsai, J.P., Chen, Y.W., Chang, L.C., Kuo, Y.M., Tu, Y.H., Pan, C.C., 2015. High recharge areas in the Choushui River alluvial fan (Taiwan) assessed from recharge potential analysis and average storage variation indexes. Entropy 17, 1558–1580. https://doi.org/10.3390/e17041558
    Tung, H., Hu, J.C., 2012. Assessments of serious anthropogenic land subsidence in Yunlin County of central Taiwan from 1996 to 1999 by Persistent Scatterers InSAR. Tectonophysics 578, 126–135. https://doi.org/10.1016/j.tecto.2012.08.009
    Tzampoglou, P., Ilia, I., Karalis, K., Tsangaratos, P., Zhao, X., Chen, W., 2023. Selected Worldwide Cases of Land Subsidence Due to Groundwater Withdrawal. Water 15, 1094. https://doi.org/10.3390/w15061094
    Varade, S.R., Patel, J.N., 2018. Estimation of change in groundwater storage and draft from rainfall to achieve good irrigation practices. ISH J. Hydraul. Eng. 24, 100–104. https://doi.org/10.1080/09715010.2017.1360159
    Wang, G. ya, Zhu, J. qi, You, G., Yu, J., Gong, X. long, Li, W., Gou, F. gang, 2017. Land rebound after banning deep groundwater extraction in Changzhou, China. Eng. Geol. 229, 13–20. https://doi.org/10.1016/j.enggeo.2017.09.006
    Wang, S.J., Lee, C.H., Hsu, K.C., 2015. A technique for quantifying groundwater pumping and land subsidence using a nonlinear stochastic poroelastic model. Environ. Earth Sci. 73, 8111–8124. https://doi.org/10.1007/s12665-014-3970-6
    Wang, W., 2019. Site selection of fire stations in cities based on geographic information system and fuzzy analytic hierarchy process. Ing. des Syst. d’Information 24, 619–626. https://doi.org/10.18280/isi.240609
    World Bank, 2012. India Groundwater: a Valuable but Diminishing Resource [WWW Document]. URL https://www.worldbank.org/en/news/feature/2012/03/06/india-groundwater-critical-diminishing (accessed 4.24.23).
    Xu, L., Chen, N., Zhang, X., Chen, Z., 2019. Spatiotemporal Changes in China’s Terrestrial Water Storage From GRACE Satellites and Its Possible Drivers. J. Geophys. Res. Atmos. 124, 11976–11993. https://doi.org/10.1029/2019JD031147
    Yeh, H.F., Lee, C.H., Chen, J.F., Chen, W.P., 2007. Estimation of groundwater recharge using water balance model. Water Resour. 34, 153–162. https://doi.org/10.1134/S0097807807020054
    Yu, H.L., Chu, H.J., 2010. Understanding space-time patterns of groundwater system by empirical orthogonal functions: A case study in the Choshui River alluvial fan, Taiwan. J. Hydrol. 381, 239–247. https://doi.org/10.1016/j.jhydrol.2009.11.046
    Yu, W., Gong, H., Chen, B., Zhou, C., Zhang, Q., 2021. Combined grace and mt-insar to assess the relationship between groundwater storage change and land subsidence in the beijing-tianjin-hebei region. Remote Sens. 13. https://doi.org/10.3390/rs13183773
    Zhou, J., Sun, H., Xu, J., Zhang, W., 2016. Estimation of local water storage change by space- and ground-based gravimetry. J. Appl. Geophys. 131, 23–28. https://doi.org/10.1016/j.jappgeo.2016.05.007
    Zhu, C., Li, W., 2023. Comparison of GRACE/GRACE-FO Spherical Harmonic Coefficient and Mascon Products in Explaining the Influence of South-to-North Water Transfer Project on Water Reserves in the North China Plain. Water (Switzerland) 15. https://doi.org/10.3390/w15132343

    無法下載圖示 校內:2029-01-11公開
    校外:2029-01-11公開
    電子論文尚未授權公開,紙本請查館藏目錄
    QR CODE