In this study, we employ a spatial unsupervised classification technique to analyze the spatio-temporal variability of Sea Surface Temperature (SST) in the tropical African zone. The methodology we propose considers both the spatial dimensions of the data and their functional characteristics, distinguishing it from conventional approaches. The results demonstrate noteworthy fluctuations in SST across spatial and temporal scales. This variability signifies a detected anomaly in SST within the study area, which can be attributed to the impacts of climate change.
References
[1]
Tabari, H. (2021) Extreme Value Analysis Dilemma for Climate Change Impact Assessment on Global Flood and Extreme Precipitation. Journal of Hydrology, 593, Article ID: 125932. https://doi.org/10.1016/j.jhydrol.2020.125932
[2]
Perkins-Kirkpatrick, S.E., Stone, D.A., Mitchell, D.M., Rosier, S., King, A.D., Lo, Y.T.E., et al. (2022) On the Attribution of the Impacts of Extreme Weather Events to Anthropogenic Climate Change. Environmental Research Letters, 17, Article ID: 024009. https://doi.org/10.1088/1748-9326/ac44c8
[3]
Ebi, K.L., Vanos, J., Baldwin, J.W., Bell, J.E., Hondula, D.M., Errett, N.A., et al. (2021) Extreme Weather and Climate Change: Population Health and Health System Implications. Annual Review of Public Health, 42, 293-315. https://doi.org/10.1146/annurev-publhealth-012420-105026
[4]
Ury, E.A., Yang, X., Wright, J.P. and Bernhardt, E.S. (2021) Rapid Deforestation of a Coastal Landscape Driven by Sea-Level Rise and Extreme Events. Ecological Applications, 31, e02339. https://doi.org/10.1002/eap.2339
[5]
Mann, J., Foroughirad, V., McEntee, M.H.F., Miketa, M.L., Evans, T.C., Karniski, C., et al. (2021) Elevated Calf Mortality and Long-Term Responses of Wild Bottlenose Dolphins to Extreme Climate Events: Impacts of Foraging Specialization and Provisioning. Frontiers in Marine Science, 8, Article No. 219. https://doi.org/10.3389/fmars.2021.617550
[6]
Johnson, A.J., Shields, E.C., Kendrick, G.A. and Orth, R.J. (2020) Recovery Dynamics of the Seagrass Zostera Marina Following Mass Mortalities from Two Extreme Climatic Events. Estuaries and Coasts, 44, 535-544. https://doi.org/10.1007/s12237-020-00816-y
[7]
Sampaio, E., Santos, C., Rosa, I.C., Ferreira, V., Pörtner, H., Duarte, C.M., et al. (2021) Impacts of Hypoxic Events Surpass Those of Future Ocean Warming and Acidification. Nature Ecology & Evolution, 5, 311-321. https://doi.org/10.1038/s41559-020-01370-3
[8]
Xu, G., Kong, H., Chang, X., Dupont, S., Chen, H., Deng, Y., Hu, M. and Wang, Y. (2021) Gonadal Antioxidant Responses to Seawater Acidification and Hypoxia in the Marine Mussel Mytilus coruscus. Environmental Science and Pollution Research International, 28, 53847-53856.
[9]
Ettinger, N.P., Larson, T.E., Kerans, C., Thibodeau, A.M., Hattori, K.E., Kacur, S.M., et al. (2020) Ocean Acidification and Photic-Zone Anoxia at the Toarcian Oceanic Anoxic Event: Insights from the Adriatic Carbonate Platform. Sedimentology, 68, 63-107. https://doi.org/10.1111/sed.12786
[10]
Pitcher, G.C., Aguirre-Velarde, A., Breitburg, D., Cardich, J., Carstensen, J., Conley, D.J., et al. (2021) System Controls of Coastal and Open Ocean Oxygen Depletion. Progress in Oceanography, 197, Article ID: 102613. https://doi.org/10.1016/j.pocean.2021.102613
[11]
Kroeker, K.J. and Sanford, E. (2022) Ecological Leverage Points: Species Interactions Amplify the Physiological Effects of Global Environmental Change in the Ocean. Annual Review of Marine Science, 14, 75-103. https://doi.org/10.1146/annurev-marine-042021-051211
[12]
Graves, C.A., Powell, A., Stone, M., Redfern, F., Biko, T. and Devlin, M. (2021) Marine Water Quality of a Densely Populated Pacific Atoll (Tarawa, Kiribati): Cumulative Pressures and Resulting Impacts on Ecosystem and Human Health. Marine Pollution Bulletin, 163, Article ID: 111951. https://doi.org/10.1016/j.marpolbul.2020.111951
[13]
Laffoley, D., Baxter, J.M., Amon, D.J., Claudet, J., Hall-Spencer, J.M., Grorud-Colvert, K., et al. (2020) Evolving the Narrative for Protecting a Rapidly Changing Ocean, Post-Covid-19. Aquatic Conservation: Marine and Freshwater Ecosystems, 31, 1512-1534. https://doi.org/10.1002/aqc.3512
[14]
Chauhan, A., Singh, R.P., Dash, P. and Kumar, R. (2021) Impact of Tropical Cyclone “Fani” on Land, Ocean, Atmospheric and Meteorological Parameters. Marine Pollution Bulletin, 162, Article ID: 111844. https://doi.org/10.1016/j.marpolbul.2020.111844
[15]
Salles, R., Mattos, P., Iorgulescu, A.D., Bezerra, E., Lima, L. and Ogasawara, E. (2016) Evaluating Temporal Aggregation for Predicting the Sea Surface Temperature of the Atlantic Ocean. Ecological Informatics, 36, 94-105. https://doi.org/10.1016/j.ecoinf.2016.10.004
[16]
Hernández-Padilla, J.C., Zetina-Rejón, M.J., Arreguín-Sánchez, F., del Monte-Luna, P., Nieto-Navarro, J.T. and Salcido-Guevara, L.A. (2021) Structure and Function of the Southeastern Gulf of California Ecosystem during Low and High Sea Surface Temperature Variability. Regional Studies in Marine Science, 43, Article ID: 101686. https://doi.org/10.1016/j.rsma.2021.101686
[17]
Feng, J., Stige, L.C., Hessen, D.O., Zuo, Z., Zhu, L. and Stenseth, N.C. (2021) A Threshold Sea-Surface Temperature at 14˚C for Phytoplankton Nonlinear Responses to Ocean Warming. Global Biogeochemical Cycles, 35, e2020GB006808. https://doi.org/10.1029/2020gb006808
[18]
Sambah, A.B., Muamanah, A., Harlyan, L.I., Lelono, T.D., Iranawati, F. and Sartimbul, A. (2021) Sea Surface Temperature and Chlorophyll-a Distribution from Himawari Satellite and Its Relation to Yellowfin Tuna in the Indian Ocean. Aquaculture, Aquarium, Conservation & Legislation, 14, 897-909.
[19]
Perry, R.I., Young, K., Galbraith, M., Chandler, P., Velez-Espino, A. and Baillie, S. (2021) Zooplankton Variability in the Strait of Georgia, Canada, and Relationships with the Marine Survivals of Chinook and Coho Salmon. PLOS ONE, 16, e0245941. https://doi.org/10.1371/journal.pone.0245941
[20]
Chen, S., Wu, R. and Chen, W. (2021) Influence of North Atlantic Sea Surface Temperature Anomalies on Springtime Surface Air Temperature Variation over Eurasia in CMIP5 Models. Climate Dynamics, 57, 2669-2686. https://doi.org/10.1007/s00382-021-05826-5
[21]
Frémont, P., Gehlen, M., Vrac, M., Leconte, J., Delmont, T.O., Wincker, P., et al. (2022) Restructuring of Plankton Genomic Biogeography in the Surface Ocean under Climate Change. Nature Climate Change, 12, 393-401. https://doi.org/10.1038/s41558-022-01314-8
[22]
Sheppard, C. (2018) World Seas: An Environmental Evaluation: Volume III: Ecological Issues and Environmental Impacts. Academic Press.
[23]
Kalyan, D., Mandar, N., Mohit, A., Manickam, N., Sambhaji, M. and Baban, I. (2021) Application of Remotely Sensed Sea Surface Temperature for Assessment of Recurrent Coral Bleaching (2014-2019) Impact on a Marginal Coral Ecosystem. Geocarto International, 37, 4483-4508.
[24]
Kaur, S., Kumar, P., Weller, E. and Young, I.R. (2021) Positive Relationship between Seasonal Indo-Pacific Ocean Wave Power and SST. Scientific Reports, 11, Article No. 17419. https://doi.org/10.1038/s41598-021-97047-3
[25]
Penland, C. and Magorian, T. (1993) Prediction of Niño 3 Sea Surface Temperatures Using Linear Inverse Modeling. Journal of Climate, 6, 1067-1076. https://doi.org/10.1175/1520-0442(1993)006<1067:ponsst>2.0.co;2
[26]
Penland, C. and Matrosova, L. (1998) Prediction of Tropical Atlantic Sea Surface Temperatures Using Linear Inverse Modeling. Journal of Climate, 11, 483-496. https://doi.org/10.1175/1520-0442(1998)011<0483:potass>2.0.co;2
[27]
Autret, E. (2014) Analyse de champs de température de surface de la mer à partir d’observations satellite multi-sources. Theses, Télécom Bretagne; Université de Rennes 1.
[28]
Abdul Azeez, P., Raman, M., Rohit, P., Shenoy, L., Jaiswar, A.K., Mohammed Koya, K., et al. (2020) Predicting Potential Fishing Grounds of Ribbonfish (Trichiuruslepturus) in the North-Eastern Arabian Sea, Using Remote Sensing Data. International Journal of Remote Sensing, 42, 322-342. https://doi.org/10.1080/01431161.2020.1809025
[29]
Tonelli, M., Signori, C.N., Bendia, A., Neiva, J., Ferrero, B., Pellizari, V., et al. (2021) Climate Projections for the Southern Ocean Reveal Impacts in the Marine Microbial Communities Following Increases in Sea Surface Temperature. Frontiers in Marine Science, 8, Article ID: 636226. https://doi.org/10.3389/fmars.2021.636226
[30]
Lou, R.R., et al. (2021) Application of Machine Learning in Ocean Data. In: Xu, C.S., Ed., Multimedia Systems, Springer, 1815-1824.
[31]
Zanna, L. (2012) Forecast Skill and Predictability of Observed Atlantic Sea Surface Temperatures. Journal of Climate, 25, 5047-5056. https://doi.org/10.1175/jcli-d-11-00539.1
[32]
Hou, M. (2022) Mori-Zwanzig Formalism Based Reduced-Order Modeling for Decision-Making in Marine Autonomy. PhD Thesis, Georgia Institute of Technology.
[33]
Yang, W., Wikle, C.K., Holan, S.H. and Wildhaber, M.L. (2013) Ecological Prediction with Nonlinear Multivariate Time-Frequency Functional Data Models. Journal of Agricultural, Biological, and Environmental Statistics, 18, 450-474. https://doi.org/10.1007/s13253-013-0142-1
[34]
Aguilera-Morillo, M.C., Durbán, M. and Aguilera, A.M. (2016) Prediction of Functional Data with Spatial Dependence: A Penalized Approach. Stochastic Environmental Research and Risk Assessment, 31, 7-22. https://doi.org/10.1007/s00477-016-1216-8
[35]
Ndiaye, M., Dabo-Niang, S., Ngom, P., Thiam, N., Fall, M. and Brehmer, P. (2020) Nonparametric Prediction for Spatial Dependent Functional Data: Application to Demersal Coastal Fish off Senegal. In: Manou-Abi, S.M., Dabo-Niang, S. and Salone, J.-J., Eds., Mathematical Modeling of Random and Deterministic Phenomena, ISTE Ltd., 31-51.
[36]
Jiménez-Cordero, A. and Maldonado, S. (2020) Automatic Feature Scaling and Selection for Support Vector Machine Classification with Functional Data. Applied Intelligence, 51, 161-184. https://doi.org/10.1007/s10489-020-01765-6
[37]
Carrizosa, E., Molero-Río, C. and Romero Morales, D. (2021) Mathematical Optimization in Classification and Regression Trees. TOP, 29, 5-33. https://doi.org/10.1007/s11750-021-00594-1
[38]
Richards, J.A. (2022) Clustering and Unsupervised Classification. In: Richards, J.A., Ed., Remote Sensing Digital Image Analysis, Springer International Publishing, 369-401. https://doi.org/10.1007/978-3-030-82327-6_9
[39]
Levantesi, S., Nigri, A. and Piscopo, G. (2022) Clustering-Based Simultaneous Forecasting of Life Expectancy Time Series through Long-Short Term Memory Neural Networks. International Journal of Approximate Reasoning, 140, 282-297. https://doi.org/10.1016/j.ijar.2021.10.008
[40]
Sørensen, H., Goldsmith, J. and Sangalli, L.M. (2013) An Introduction with Medical Applications to Functional Data Analysis. Statistics in Medicine, 32, 5222-5240. https://doi.org/10.1002/sim.5989
[41]
Ieva, F. and Paganoni, A.M. (2016) Risk Prediction for Myocardial Infarction via Generalized Functional Regression Models. Statistical Methods in Medical Research, 25, 1648-1660. https://doi.org/10.1177/0962280213495988
[42]
Elliott, J.M. (1993) The Self-Thinning Rule Applied to Juvenile Sea-Trout, Salmo Trutta. The Journal of Animal Ecology, 62, 371-379. https://doi.org/10.2307/5368
[43]
Yen, J.D.L., Thomson, J.R., Paganin, D.M., Keith, J.M. and Mac Nally, R. (2014) Function Regression in Ecology and Evolution: Free. Methods in Ecology and Evolution, 6, 17-26. https://doi.org/10.1111/2041-210x.12290
[44]
Di Battista, T., Fortuna, F. and Maturo, F. (2016) Parametric Functional Analysis of Variance for Fish Biodiversity Assessment. Journal of Environmental Informatics, 28, 101-109. https://doi.org/10.3808/jei.201600348
[45]
Caruso, G. and Fortuna, F. (2021) Mediterranean Diet Patterns in the Italian Population: A Functional Data Analysis of Google Trends. In: Soitu, D., et al., Eds., Decisions and Trends in Social Systems, Springer International Publishing, 63-72. https://doi.org/10.1007/978-3-030-69094-6_6
[46]
Bolger, T. and Connolly, P.L. (1989) The Selection of Suitable Indices for the Measurement and Analysis of Fish Condition. Journal of Fish Biology, 34, 171-182. https://doi.org/10.1111/j.1095-8649.1989.tb03300.x
[47]
Lorenzen, K. (1996) The Relationship between Body Weight and Natural Mortality in Juvenile and Adult Fish: A Comparison of Natural Ecosystems and Aquaculture. Journal of Fish Biology, 49, 627-642. https://doi.org/10.1111/j.1095-8649.1996.tb00060.x
[48]
Giraldo, R., Delicado, P. and Mateu, J. (2010) Ordinary Kriging for Function-Valued Spatial Data. Environmental and Ecological Statistics, 18, 411-426. https://doi.org/10.1007/s10651-010-0143-y
[49]
Torres, J.M., Nieto, P.J.G., Alejano, L. and Reyes, A.N. (2011) Detection of Outliers in Gas Emissions from Urban Areas Using Functional Data Analysis. Journal of Hazardous Materials, 186, 144-149. https://doi.org/10.1016/j.jhazmat.2010.10.091
[50]
Dabo-Niang, S., Yao, A., Pischedda, L., Cuny, P. and Gilbert, F. (2009) Spatial Mode Estimation for Functional Random Fields with Application to Bioturbation Problem. Stochastic Environmental Research and Risk Assessment, 24, 487-497. https://doi.org/10.1007/s00477-009-0339-6
[51]
Curceac, S., Ternynck, C., Ouarda, T.B.M.J., Chebana, F. and Niang, S.D. (2019) Short-Term Air Temperature Forecasting Using Nonparametric Functional Data Analysis and SARMA Models. Environmental Modelling & Software, 111, 394-408. https://doi.org/10.1016/j.envsoft.2018.09.017
[52]
Boudreault, J., St-Hilaire, A., Chebana, F. and Bergeron, N.E. (2021) Modelling Fish Physico-Thermal Habitat Selection Using Functional Regression. Journal of Ecohydraulics, 6, 105-120. https://doi.org/10.1080/24705357.2020.1840313
[53]
Escabias, M., Aguilera, A.M. and Valderrama, M.J. (2004) Modeling Environmental Data by Functional Principal Component Logistic Regression. Environmetrics, 16, 95-107. https://doi.org/10.1002/env.696
[54]
Ignaccolo, R., Ghigo, S. and Bande, S. (2012) Functional Zoning for Air Quality. Environmental and Ecological Statistics, 20, 109-127. https://doi.org/10.1007/s10651-012-0210-7
[55]
Xu, B., Luo, L. and Lin, B. (2016) A Dynamic Analysis of Air Pollution Emissions in China: Evidence from Nonparametric Additive Regression Models. Ecological Indicators, 63, 346-358. https://doi.org/10.1016/j.ecolind.2015.11.012
[56]
Xiao, W. and Hu, Y. (2018) Functional Data Analysis of Air Pollution in Six Major Cities. Journal of Physics: Conference Series, 1053, Article ID: 012131. https://doi.org/10.1088/1742-6596/1053/1/012131
[57]
Zhou, P., Sang, H., Jin, L. and Lee, W.J. (2017) Application of Statistical Methods to Predict Production from Liquid-Rich Shale Reservoirs. Proceedings of the 5th Unconventional Resources Technology Conference, Austin, 24-26 July 2017, 2999-3017. https://doi.org/10.15530/urtec-2017-2694668
[58]
Anifowose, F., Adeniye, S., Abdulraheem, A. and Al-Shuhail, A. (2016) Integrating Seismic and Log Data for Improved Petroleum Reservoir Properties Estimation Using Non-Linear Feature-Selection Based Hybrid Computational Intelligence Models. Journal of Petroleum Science and Engineering, 145, 230-237. https://doi.org/10.1016/j.petrol.2016.05.019
[59]
Jorge, M. and Romano, E. (2016) Advances in Spatial Functional Statistics. Stochastic Environmental Research and Risk Assessment.
[60]
Mateu, J. and Romano, E. (2017) Advances in Spatial Functional Statistics.
[61]
Ndiaye, M., Dabo-Niang, S. and Ngom, P. (2022) Nonparametric Prediction for Spatial Dependent Functional Data under Fixed Sampling Design. Revista Colombiana de Estadística, 45, 391-428. https://doi.org/10.15446/rce.v45n2.98957
[62]
Ruiz-Medina, M.D. (2011) Spatial Autoregressive and Moving Average Hilbertian Processes. Journal of Multivariate Analysis, 102, 292-305. https://doi.org/10.1016/j.jmva.2010.09.005
[63]
Ruiz-Medina, M.D. and Espejo, R.M. (2012) Spatial Autoregressive Functional Plug-In Prediction of Ocean Surface Temperature. Stochastic Environmental Research and Risk Assessment, 26, 335-344. https://doi.org/10.1007/s00477-012-0559-z
[64]
Ruiz-Medina, M.D., Anh, V.V., Espejo, R.M., Angulo, J.M. and Frías, M.P. (2013) Least-Squares Estimation of Multifractional Random Fields in a Hilbert-Valued Context. Journal of Optimization Theory and Applications, 167, 888-911. https://doi.org/10.1007/s10957-013-0423-4
[65]
Hörmann, S., Kidziński, Ł. and Hallin, M. (2014) Dynamic Functional Principal Components. Journal of the Royal Statistical Society Series B: Statistical Methodology, 77, 319-348. https://doi.org/10.1111/rssb.12076
[66]
Mateu, J. and Giraldo, R. (2021) Geostatistical Functional Data Analysis: Theory and Methods. John Wiley and Sons.
[67]
Cheam, A.S.M., Marbac, M. and McNicholas, P.D. (2017) Model-Based Clustering for Spatiotemporal Data on Air Quality Monitoring. Environmetrics, 28, e2437. https://doi.org/10.1002/env.2437
[68]
Delaigle, A. and Hall, P. (2010) Defining Probability Density for a Distribution of Random Functions. The Annals of Statistics, 38, 1171-1193. https://doi.org/10.1214/09-aos741
[69]
Hörmann, S. and Kokoszka, P. (2012) Supplement to “Consistency of the Mean and the Principal Components of Spatially Distributed Functional Data”.
[70]
Antoniadis, A. and Oppenheim, G. (2012) Wavelets and Statistics, Volume 103. Springer Science & Business Media.
[71]
Ramsay, J.O. (2004) Functional Data Analysis. Encyclopedia of Statistical Sciences, 4.
[72]
Ramsay, J.O. and Silverman, B.W. (2005) Principal Components Analysis for Functional Data. In: Ramsay, J.O. and Silverman, B.W., Eds., Functional Data Analysis, Springer, 147-172.
[73]
Ramsay, J.O and Silverman, B.W. (2007) Applied Functional Data Analysis: Methods and Case Studies. Springer, 191 p.
