Abstract
In this chapter, an assimilated deep learning approach is employed to improve understanding of the links between global climate teleconnection patterns (e.g., ENSO) and changes in terrestrial water storage (TWS). To this end, a hybrid deep learning framework was developed to reconstruct climate-driven TWS and to assess the influence of key climatic drivers on the spatio-temporal distribution of TWS. Using South America as a case study to showcase the potential and utility of this framework, the methodology, challenges, and prospect of machine learning in satellite hydrology are also detailed.
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References
Ahmadi E, McLellan B, Mohammadi-Ivatloo B, Tezuka T (2020) The role of renewable energy resources in sustainability of water desalination as a potential fresh-water source: an updated review. Sustainability 12(13). https://doi.org/10.3390/su12135233
Ahmed M, Sultan M, Elbayoumi T, Tissot P (2019) Forecasting GRACE data over the African watersheds using artificial neural networks. Remote Sens 11(15). https://doi.org/10.3390/rs11151769
Aires F, Rossow WB, Chédin A (2002) Rotation of EOFs by the independent component analysis: toward a solution of the mixing problem in the decomposition of geophysical time series. J Atmos Sci 59(1):111–123. https://doi.org/10.1175/1520-0469(2002)059<0111:ROEBTI>2.0.CO;2
Amari S, Murata N, Muller K-R, Finke M, Yang H (1997) Asymptotic statistical theory of overtraining and cross-validation. IEEE Trans Neural Netw 8(5):985–996. https://doi.org/10.1109/72.623200
Ambrizzi T, de Souza EB, Pulwarty RS (2004) The Hadley and Walker regional circulations and associated ENSO impacts on South American seasonal rainfall. Springer Netherlands, Dordrecht, pp 203–235. https://doi.org/10.1007/978-1-4020-2944-8_8
Barnard E, Wessels L (1992) Extrapolation and interpolation in neural network classifiers. IEEE Control Syst Mag 12(5):50–53. https://doi.org/10.1109/37.158898
Bebis G, Georgiopoulos M (1994) Feed-forward neural networks. IEEE Potentials 13(4):27–31. https://doi.org/10.1109/45.329294
Berner E, Berner R (2012) Global environment: water, air, and geochemical cycles, 2nd edn. Princeton University Press, p 464. https://press.princeton.edu/books/hardcover/9780691136783/global-environment
Bond NA, Cronin MF, Freeland H, Mantua N (2015) Causes and impacts of the 2014 warm anomaly in the NE Pacific. Geophys Res Lett 42(9):3414–3420. https://doi.org/10.1002/2015GL063306
Boulanger J, Leloup J, Penalba O et al (2005) Observed precipitation in the Paraná-Plata hydrological basin: long-term trends, extreme conditions and ENSO teleconnections. Clim Dyn 24:393–413. https://doi.org/10.1007/s00382-004-0514-x
Brêda J, de Paiva R, Collischon W et al (2020) Climate change impacts on South American water balance from a continental-scale hydrological model driven by CMIP5 projections. Clim Chang 159:503–522. https://doi.org/10.1007/s10584-020-02667-9
Burke EJ, Brown SJ, Christidis N (2006) Modeling the recent evolution of global drought and projections for the twenty-first century with the Hadley Centre climate model. J Hydrometeorol 7(5):1113–1125. https://doi.org/10.1175/JHM544.1
Cai W, McPhaden M, Grimm A et al (2020) Climate impacts of the El Niño Southern Oscillation on South America. Nat Rev Earth Environ 1(4):215–231. https://doi.org/10.1038/s43017-020-0040-3
Cardoso JF, Souloumiac A (1993) Blind beamforming for non-Gaussian signals. IEE Proc 140(6):362–370
Cavole L M et al (2016) Biological impacts of the 2013–2015 warm-water anomaly in the northeast pacific: winners, losers, and the future. Oceanography 29(2):273–285. https://doi.org/10.5670/oceanog.2016.32
Cazenave A, Chen J (2010) Time-variable gravity from space and present-day mass redistribution in the earth system. Earth Planet Sci Lett 298(3):263–274. https://doi.org/10.1016/j.epsl.2010.07.035
Chen J, Cazenave A, Dahle C et al (2022) Applications and challenges of GRACE and GRACE follow-on satellite gravimetry. Surv Geophys 43:305–3455. https://doi.org/10.1007/s10712-021-09685-x
Chen J, Tapley B, Rodell M, Seo K-W, Wilson C, Scanlon BR, Pokhrel Y (2020) Basin-scale river runoff estimation from grace gravity satellites, climate models, and in situ observations: a case study in the amazon basin. Water Resour Res 56(10):e2020WR028032. https://doi.org/10.1029/2020WR028032
Chiew FHS (2006) Estimation of rainfall elasticity of streamflow in Australia. Hydrol Sci J 51(4):613–625. https://doi.org/10.1623/hysj.51.4.613
Chu H-J, Chang L-C (2009) Optimal control algorithm and neural network for dynamic groundwater management. Hydrol Process
Church JA, Gregory JM et al (2001) Changes in sea level, pp 639–694. IPCC. https://www.ipcc.ch/site/assets/uploads/2018/03/WGI_TAR_full_report.pdf on 26th August 2022
Chylek P, Klett J, Dubey M et al (2016) The role of Atlantic multi-decadal oscillation in the global mean temperature variability. Clim Dyn 47:3271–3279. https://doi.org/10.1007/s00382-016-3025-7
Collins M, Chandler R, Cox P et al (2012) Quantifying future climate change. Nat Clim Chang 2:403–409. https://doi.org/10.1038/nclimate1414
Dai H, MacBeth C (1997) Effects of learning parameters on learning procedure and performance of a BPNN. Neural Netw 10(8):1505–1521. https://doi.org/10.1016/S0893-6080(97)00014-2
de Marsily G, Abarca-del Rio R (2016) Water and food in the twenty-first century. Surv Geophys 37:503–527. https://doi.org/10.1007/s10712-015-9335-1
De Pryck K (2021) Intergovernmental expert consensus in the making: the case of the summary for policy makers of the IPCC 2014 synthesis report. Glob Environ Polit 21(1):108–129. https://doi.org/10.1162/glep_a_00574
Delpla I, Jung A-V, Baures E, Clement M, Thomas O (2009) Impacts of climate change on surface water quality in relation to drinking water production. Environ Int 35(8):1225–1233. https://doi.org/10.1016/j.envint.2009.07.001
DiNezio P, Tierney J (2013) The effect of sea level on glacial Indo-Pacific climate. Nat Geosci 44:485–491. https://doi.org/10.1038/ngeo1823
Döll P, Flörke M (2005) Global-scale estimation of diffuse groundwater recharge. Frankfurt Hydrology Paper 03, Institute of Physical Geography, Frankfurt University, Frankfurt am Main, Germany. Retrieved from chrome-extension://efaidnbmnnnibpcajpcglclefindmkaj/https://d-nb.info/1054768056/34 on 26th August 2022
Famiglietti JS, Cazenave A, Eicker A, Reager JT, Rodell M, Velicogna I (2015) Satellites provide the big picture. Science 349(6249):684–685. https://doi.org/10.1126/science.aac9238
Fausett L, Elwasif W (1994) Predicting performance from test scores using backpropagation and counter propagation. In: Proceedings of 1994 IEEE international conference on neural networks (ICNN’94), vol 5, pp 3398–3402. https://doi.org/10.1109/ICNN.1994.374782
Feng S, Kang S, Huo Z, Chen S, Mao X (2008) Neural networks to simulate regional ground water levels affected by human activities. Groundwater 46(1):80–90. https://doi.org/10.1111/j.1745-6584.2007.00366.x
Ferreira VG, Montecino HD, Ndehedehe CE, del Rio RA, Cuevas A, de Freitas SRC (2019a) Determining seasonal displacements of Earth’s crust in South America using observations from space-borne geodetic sensors and surface-loading models. Earth, Planets Space 71(1):84. https://doi.org/10.1186/s40623-019-1062-2
Ferreira VG, Ndehedehe CE, Montecino HC, Yong B, Yuan P, Abdalla A, Mohammed AS (2019b) Prospects for imaging terrestrial water storage in South America using daily GPS observations. Remote Sens 11(6). https://doi.org/10.3390/rs11060679
Fletcher D, Goss E (1993) Forecasting with neural networks: an application using bankruptcy data. Inf Manag 24(3):159–167. https://doi.org/10.1016/0378-7206(93)90064-Z
Goethals P, Dedecker A, Gabriels W et al (2007) Applications of artificial neural networks predicting macroinvertebrates in freshwaters. Aquat Ecololgy 41:491–508. https://doi.org/10.1007/s10452-007-9093-3
Good SP, Noone D, Bowen G (2015) Hydrologic connectivity constrains partitioning of global terrestrial water fluxes. Science 349(6244):175–177. https://doi.org/10.1126/science.aaa5931
Gormsen E (1997) The impact of tourism on coastal areas. GeoJournal 42:39–54. https://doi.org/10.1023/A:1006840622450
Grimm AM (2003) The El Niño Impact on the summer monsoon in Brazil: regional processes versus remote influences. J Clim 16(2):263–280. https://doi.org/10.1175/1520-0442(2003)016<0263:TENIOT>2.0.CO;2
Grimm AM, Ambrizzi T (2009) Teleconnections into South America from the tropics and extratropics on interannual and intraseasonal timescales. Springer Netherlands, Dordrecht, pp 159–191. https://doi.org/10.1007/978-90-481-2672-9_7
Hayashi A, Akimoto K, Tomoda T et al (2013) Global evaluation of the effects of agriculture and water management adaptations on the water-stressed population. Mitig Adapt Strat Glob Chang 18:591–618. https://doi.org/10.1007/s11027-012-9377-3
House-Peters LA, Chang H (2011) Urban water demand modeling: review of concepts, methods, and organizing principles. Water Resour Res 47(5). https://doi.org/10.1029/2010WR009624
Huang Z, Yuan X, Liu X (2021) The key drivers for the changes in global water scarcity: water withdrawal versus water availability. J Hydrol 601:126658. https://doi.org/10.1016/j.jhydrol.2021.126658
Huffman GJ, Adler RF, Bolvin DT, Gu G, Nelkin EJ, Bowman KP, Hong Y, Stocker EF, Wolff DB (2007) The TRMM multisatellite precipitation analysis (TMPA): quasi-global, multiyear, combined-sensor precipitation estimates at fine scales. J Hydrometeorol 8:38–55. https://doi.org/10.1175/JHM560.1
Humphrey V, Rodell M, Eicker A (2023) Using satellite-based terrestrial water storage data: a review. Surv Geophys. https://doi.org/10.1007/s10712-022-09754-9
Ilin A, Valpola H, Oja E (2005) Semiblind source separation of climate data detects El Nino as the component with the highest inter annual variability. In: Proceedings of the IEEE international joint conference on neural networks, vol 3, pp 1722–1727. https://doi.org/10.1109/IJCNN.2005.1556139
Ivits E, Horion S, Fensholt R, Cherlet M (2014) Drought footprint on European ecosystems between 1999 and 2010 assessed by remotely sensed vegetation phenology and productivity. Glob Chang Biol 20(2):581–593. https://doi.org/10.1111/gcb.12393
Jury WA, Vaux HJ (2007) The emerging global water crisis: managing scarcity and conflict between water users. Advances in agronomy, vol 95, pp 1–76. https://doi.org/10.1016/S0065-2113(07)95001-4
Kalu I, Ndehedehe CE, Okwuashi O, Eyoh AE (2021) Assessing freshwater changes over Southern and Central Africa (2002–2017). Remote Sens 13(13). https://doi.org/10.3390/rs13132543
Kalu I, Ndehedehe CE, Okwuashi O, Eyoh AE, Ferreira VG (2022a) An assimilated deep learning approach to identify the influence of global climate on hydrological fluxes. J Hydrol 614:128498. https://doi.org/10.1016/j.jhydrol.2022.128498
Kalu I, Ndehedehe CE, Okwuashi O, Eyoh AE, Ferreira VG (2022b) A new modelling framework to assess changes in groundwater level. J Hydrol: Reg Stud 43:101185. https://doi.org/10.1016/j.ejrh.2022.101185
Kalu I, Ndehedehe CE, Okwuashi O, Eyoh AE, Ferreira VG (2023) Reconstructing terrestrial water storage anomalies using convolution-based support vector machine. J Hydrol: Reg Stud 46:101326. https://doi.org/10.1016/j.ejrh.2023.101326
Kanellopoulos I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18(4):711–725. https://doi.org/10.1080/014311697218719
Kayano MT, Capistrano VB (2014) How the Atlantic multidecadal oscillation (AMO) modifies the ENSO influence on the South American rainfall. Int J Climatol 34(1):162–178. https://doi.org/10.1002/joc.3674
Kundzewicz ZW, Mata LJ, N. W. A et al (2008) The implications of projected climate change for freshwater resources and their management. Hydrol Sci J 53(1):3–10. https://doi.org/10.1623/hysj.53.1.3
Lenard MJ, Alam P, Madey GR (1995) The application of neural networks and a qualitative response model to the auditor’s going concern uncertainty decision. Decis Sci 26(2):209–227. https://doi.org/10.1111/j.1540-5915.1995.tb01426.x
Li Y, Huang C, Ding L, Li Z, Pan Y, Gao X (2019) Deep learning in bioinformatics: introduction, application, and perspective in the big data era. Methods 166:4–21. https://doi.org/10.1016/j.ymeth.2019.04.008
Liano K (1996) Robust error measure for supervised neural network learning with outliers. IEEE Trans Neural Netw 7(1):246–250. https://doi.org/10.1109/72.478411
Litjens G, Kooi T, Bejnordi BE et al (2017) A survey on deep learning in medical image analysis. Med Image Anal 42:60–88. https://doi.org/10.1016/j.media.2017.07.005
Liu H, Wang J (2011) Integrating independent component analysis and principal component analysis with neural network to predict Chinese stock market. Math Probl Eng 2011(382659):15. https://doi.org/10.1155/2011/382659
Lu R, Dong B (2005) Impact of Atlantic sea surface temperature anomalies on the summer climate in the western North Pacific during 1997–1998. J Geophys Res: Atmos 110(D16). https://doi.org/10.1029/2004JD005676
Maier HR, Dandy GC (2000) Application of artificial neural networks to forecasting of surface water quality variables: issues, applications and challenges. Artif Neural Netw Hydrol 287–309. https://doi.org/10.1007/978-94-015-9341-0_15
Maier HR, Jain A, Dandy GC, Sudheer K (2010) Methods used for the development of neural networks for the prediction of water resource variables in river systems: current status and future directions. Environ Model Softw 25(8):891–909. https://doi.org/10.1016/j.envsoft.2010.02.003
Masters T (1993) Practical neural network recipies in C++. https://doi.org/10.1016/C2009-0-22399-3
Merrifield MA, Thompson PR, Lander M (2012) Multidecadal sea level anomalies and trends in the western tropical Pacific. Geophys Res Lett 39(13). https://doi.org/10.1029/2012GL052032
Milly P, Dunne K, Vecchia A (2005) Global pattern of trends in streamflow and water availability in a changing climate. Nature 438:347–350. https://doi.org/10.1038/nature04312
Montazerolghaem M, Vervoort W, Minasny B, McBratney A (2016) Long-term variability of the leading seasonal modes of rainfall in south-eastern Australia. Weather Clim Extrem 13:1–14. https://doi.org/10.1016/j.wace.2016.04.001
Mu Q, Zhao M, Running SW (2011) Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens Environ 115(8):1781–1800. https://doi.org/10.1016/j.rse.2011.02.019
Mudigonda M, Ram P, Kashinath K et al (2021) Deep learning for detecting extreme weather patterns, chapter 12. Wiley, pp 161–185
Muringai RT, Mafongoya PL, Lottering R (2021) Climate change and variability impacts on sub-Saharan African fisheries: a review. Rev Fish Sci Aquac 29(4):706–720. https://doi.org/10.1080/23308249.2020.1867057
Naghibi S, Pourghasemi H, Dixon B (2016) GIS-based groundwater potential mapping using boosted regression tree, classification and regression tree, and random forest machine learning models in Iran. Environ Monit Assess 188(44):1–27. https://doi.org/10.1007/s10661-015-5049-6
Ndehedehe C (2022a) Global freshwater systems. Springer International Publishing, Cham, pp 19–32
Ndehedehe C (2022b) Groundwater from space. Springer International Publishing, Cham, pp 211–230
Ndehedehe C (2022c) Remote sensing hydrology. Springer International Publishing, Cham, pp 3–17
Ndehedehe C (2022d) Satellite geodetic missions. Springer International Publishing, Cham, pp 53–70
Ndehedehe C, Awange J, Agutu N, Kuhn M, Heck B (2016a) Understanding changes in terrestrial water storage over West Africa between 2002 and 2014. Adv Water Resour 88:211–230. https://doi.org/10.1016/j.advwatres.2015.12.009
Ndehedehe CE (2019) The water resources of tropical West Africa: problems, progress and prospect. Acta Geophys 67(2):621–649. https://doi.org/10.1007/s11600-019-00260-y
Ndehedehe CE, Ferreira VG (2020a) Assessing land water storage dynamics over Southern America. J Hydrol 580:124339. https://doi.org/10.1016/j.jhydrol.2019.124339
Ndehedehe CE, Ferreira VG (2020b) Identifying the footprints of global climate modes in time-variable gravity hydrological signals. Clim Chang 159:481–502. https://doi.org/10.1007/s10584-019-02588-2
Ndehedehe CE, Agutu NO, Okwuashi OH, Ferreira VG (2016b) Spatio-temporal variability of droughts and terrestrial water storage over Lake Chad Basin using independent component analysis. J Hydrol 540:106–128. https://doi.org/10.1016/j.jhydrol.2016.05.068
Ndehedehe CE, Awange J, Kuhn M, Agutu N, Fukuda Y (2017) Climate teleconnections influence on West Africa’s terrestrial water storage. Hydrol Process 31(18):3206–3224. https://doi.org/10.1002/hyp.11237
Ndehedehe CE, Awange JL, Agutu NO, Okwuashi O (2018) Changes in hydro-meteorological conditions over tropical West Africa (\(1980-2015\)) and links to global climate. Glob Planet Chang 162:321–341. https://doi.org/10.1016/j.gloplacha.2018.01.020
Ndehedehe CE, Ferreira VG, Onojeghuo AO, Agutu NO, Emengini E, Getirana A (2020) Influence of global climate on freshwater changes in Africa’s largest endorheic basin using multi-scaled indicators. Sci Total Environ 737:139643. https://doi.org/10.1016/j.scitotenv.2020.139643
Ndehedehe CE, Ferreira VG, Agutu NO, Onojeghuo AO, Okwuashi O, Kassahun HT, Dewan A (2021) What if the rains do not come? J Hydrol 595:126040. https://doi.org/10.1016/j.jhydrol.2021.126040
Ndehedehe CE, Ferreira VG, Adeyeri OE, Correa FM, Usman M, Oussou FE, Kalu I, Okwuashi O, Onojeghuo AO, Getirana A, Dewan A (2023) Global assessment of drought characteristics in the Anthropocene. Resour, Environ Sustain 12:100105. https://doi.org/10.1016/j.resenv.2022.100105
Ni S, Chen J, Wilson CR, Li J, Hu X, Fu R (2017) Global terrestrial water storage changes and connections to ENSO events. Surv Geophys. https://doi.org/10.1007/s10712-017-9421-7
Nourani V, Molajou A, Najafi H, Danandeh Mehr A (2019) Emotional ANN (EANN): a new generation of neural networks for hydrological modeling in IoT. Springer International Publishing, Cham, pp 45–61. https://doi.org/10.1007/978-3-030-04110-6_3
Odorico P, Chiarelli DD, Rosa L et al (2020) The global value of water in agriculture. Proc Natl Acad Sci 117(36):21985–21993. https://doi.org/10.1073/pnas.2005835117
OECD (2015) Drying wells, rising stakes: towards sustainable agricultural groundwater use. OECD Studies on WaterOECD Publishing, Paris. https://doi.org/10.1787/9789264238701-en
Okwuashi O, Ndehedehe C (2017) Tide modelling using support vector machine regression. J Spat Sci 62(1):29–46. https://doi.org/10.1080/14498596.2016.1215272
Okwuashi O, Ndehedehe CE (2020) Deep support vector machine for hyperspectral image classification. Pattern Recognit 103:107298. https://doi.org/10.1016/j.patcog.2020.107298
Okwuashi O, Ndehedehe CE, Olayinka DN, Eyoh A, Attai H (2021) Deep support vector machine for PolSAR image classification. Int J Remote Sens 42(17):6498–6536. https://doi.org/10.1080/01431161.2021.1939910
Patuwo E, Hu MY, Hung MS (1993) Two-group classification using neural networks. Decis Sci 24(4):825–845. https://doi.org/10.1111/j.1540-5915.1993.tb00491.x
Phillips T, Nerem RS, Fox-Kemper B, Famiglietti JS, Rajagopalan B (2012) The influence of ENSO on global terrestrial water storage using GRACE. Geophys Res Lett 39(L16705):2012. https://doi.org/10.1029/2012GL052495
Piramuthu S, Shaw MJ, Gentry JA (1994) A classification approach using multi-layered neural networks. Decis Support Syst 11(5):509–525. https://doi.org/10.1016/0167-9236(94)90022-1
Ponti MA, Ribeiro LSF, Nazare TS, Bui T, Collomosse J (2017) Everything you wanted to know about deep learning for computer vision but were afraid to ask. In: 30th SIBGRAPI conference on graphics, patterns and images tutorials (SIBGRAPI-T), pp 17–41. https://doi.org/10.1109/SIBGRAPI-T.2017.12
Qadir NU (2020) Influence of principal component analysis as a data conditioning approach for training multilayer feedforward neural networks with exact form of Levenberg-Marquardt algorithm. Glob J Technol Optim 11:1–6
Reboita MS, Ambrizzi T et al (2021) Impacts of teleconnection patterns on South America climate. Ann N Y Acad Sci 1504(1):116–153. https://doi.org/10.1111/nyas.14592
Robinson B, Cohen JS, Herman JD (2020) Detecting early warning signals of long-term water supply vulnerability using machine learning. Environ Model Softw 131:104781. https://doi.org/10.1016/j.envsoft.2020.104781
Rodell M, Famiglietti JS, Wiese DN, Reager JT, Beaudoing HK, Landerer FW, Lo MH (2018a) Emerging trends in global freshwater availability. Nature 557(7707):651–659. https://doi.org/10.1038/s41586-018-0123-1
Rodell M, Famiglietti JS, Wiese DN, Reager JT, Beaudoing HK, Landerer FW, Lo M-H (2018b) Emerging trends in global freshwater availability. Nature 557:651–659. https://doi.org/10.1038/s41586-018-0123-1
Rodell M, Famiglietti J, Wiese D, Reager J, Beaudoing H, Landerer F, Lo M (2019) Emerging trends in global freshwater availability. Nature 557(7739). https://doi.org/10.1038/s41586-018-0831-6
Rogers LL, Dowla FU (1994) Optimization of groundwater remediation using artificial neural networks with parallel solute transport modeling. Water Resour Res 30(2):457–481. https://doi.org/10.1029/93WR01494
Rosenzweig CG, Casassa D, Karoly A, Imeson C et al (2007) Assessment of observed changes and responses in natural and managed systems. Climate change 2007: impacts, adaptation and vulnerability. In: Hanson (eds) Contribution of working group II to the fourth assessment report of the intergovernmental panel on climate change. Cambridge University Press, Cambridge, UK, pp 79–131. Accessed from https://www.pubs.giss.nasa.gov/abs/ro02900h.html on 26th August 2022
Sajjad H, Ghaffar, (2019) Observed, simulated and projected extreme climate indices over Pakistan in changing climate. Theor Appl Climatol 137:255–281. https://doi.org/10.1007/s00704-018-2573-7
Savitzky A, Golay MJE (1964) Soothing and differentiation of data by simplified least squares procedures. Anal Chem 36(8):1627–1639
Shah M, Javed M, Abunama T (2021) Proposed formulation of surface water quality and modelling using gene expression, machine learning, and regression techniques. Environ Sci Pollut Res 28:13202–13220. https://doi.org/10.1007/s11356-020-11490-9
Shahamiri SR (2021) Speech vision: an end-to-end deep learning-based dysarthric automatic speech recognition system. IEEE Trans Neural Syst Rehabil Eng 29:852–861. https://doi.org/10.1109/TNSRE.2021.3076778
Singh SP, Kumar A, Darbari H, Singh L, Rastogi A, Jain S (2017) Machine translation using deep learning: an overview. In: International conference on computer, communications and electronics (comptelix), pp 162–167. https://doi.org/10.1109/COMPTELIX.2017.8003957
Sun AY (2013) Predicting groundwater level changes using GRACE data. Water Resour Res 49(9):5900–5912. https://doi.org/10.1002/wrcr.20421
Swenson S, Yeh PJ-F, Wahr J, Famiglietti J (2006) A comparison of terrestrial water storage variations from GRACE with in situ measurements from Illinois. Geophys Res Lett 33(16). https://doi.org/10.1029/2006GL026962
Tapley B, Bettadpur S, Watkins M, Reigber C (2004) The gravity recovery and climate experiment: mission overview and early results. Geophys Res Lett 31:1–4. https://doi.org/10.1029/2004GL019920
Theis FJ, Gruber P, Keck IR, Meyer-bäse A, Lang EW (2005) Spatiotemporal blind source separation using double-sided approximate joint diagonalization. In: Proc, EUSIPCO
Tourian MJ, Reager JT, Sneeuw N (2018) The total drainable water storage of the Amazon River Basin: a first estimate using GRACE. Water Resour Res 54(5):3290–3312. https://doi.org/10.1029/2017WR021674
Trenberth KE (1997) The definition of El Niño. Bull Am Meteorol Soc 78(12):2771–2777
Usman M, Ndehedehe CE, Farah H, Manzanas R (2021) Impacts of climate change on the streamflow of a large river basin in the Australian tropics using optimally selected climate model outputs. J Clean Prod 315:128091. https://doi.org/10.1016/j.jclepro.2021.128091
Vergara W, Deeb A, Valencia A, Bradley R, Francou B, Zarzar A, Grünwaldt A, Haeussling S (2007) Economic impacts of rapid glacier retreat in the Andes. Eos, Trans Am Geophys Union 88(25):261–264
Walczak S, Cerpa N (1999) Heuristic principles for the design of artificial neural networks. Inf Softw Technol 41(2):107–117. https://doi.org/10.1016/S0950-5849(98)00116-5
Wang F (1994) The use of artificial neural networks in a geographical information system for agricultural land-suitability assessment. Environ Plan A: Econ Space 26(2):265–284. https://doi.org/10.1068/a260265
Weigend AS, Huberman BA, Rumelhart DE (1990) Predicting the future: a connectionist approach. Int J Neural Syst 01(03):193–209. https://doi.org/10.1142/S0129065790000102
Wiese DN, Landerer FW, Watkins MM (2016) Quantifying and reducing leakage errors in the JPL RL05M GRACE mascon solution. Water Resour Res 52(9):7490–7502. https://doi.org/10.1002/2016WR019344
Xu T, Valocchi AJ (2015) Data-driven methods to improve baseflow prediction of a regional groundwater model. Comput Geosci 85:124–136. https://doi.org/10.1016/j.cageo.2015.05.016
Young T, Hazarika D, Poria S, Cambria E (2018) Recent trends in deep learning based natural language processing [review article]. IEEE Comput Intell Mag 13(3):55–75. https://doi.org/10.1109/MCI.2018.2840738
Yu Q, Wang S, He H, Yang K, Ma L, Li J (2021) Reconstructing grace-like TWS anomalies for the Canadian landmass using deep learning and land surface model. Int J Appl Earth Obs Geoinf 102:102404. https://doi.org/10.1016/j.jag.2021.102404
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Ndehedehe, C. (2023). Assimilated Deep Learning to Assess Terrestrial Hydrology. In: Hydro-Climatic Extremes in the Anthropocene. Springer Climate. Springer, Cham. https://doi.org/10.1007/978-3-031-37727-3_7
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