Identifying Seasonal Coherence in Global Lake Surface Water Temperature

Boyang Sun

doi:10.54097/hgrgcg35

Authors

Boyang Sun

DOI:

https://doi.org/10.54097/hgrgcg35

Keywords:

Seasonal Pattern; Cluster Analysis; Time Series; Generalized Additive Model; Generalized Linear Model.

Abstract

This project examines seasonal water temperature patterns in thirty large lakes worldwide from 2003 to 2012. Analyzing and identifying seasonal patterns can help understand lake water temperature changes better and predict future trends. The seasonal pattern of different lakes is decomposed by a generalized additive model (GAM) and Seasonal and Trend decomposition using Loess (STL), and 30 lakes are clustered and classified according to the results, which shows that different lakes have the same seasonal pattern of water temperature. The reason for the classification is explained using latitude, longitude, and elevation. This study contributes to a deeper understanding of the seasonal patterns of lakes of the same type while distinguishing the differences in latitude, longitude, and elevation of different types of lakes.

Downloads

Download data is not yet available.

References

[1] Amidon, E.L., Sharpnack, D.A. and Russell, R.M. (1971) The development of an Earth Resources Information System using aerial photographs and digital computers. Berkeley: University of California.

[2] Bonifazi, F. et al. (2013) ‘Glutathione transferase-A2 S112T polymorphis predicts survival, transplant-related mortality, busulfan and bilirubin blood levels after allogeneic stem cell transplantation’, Haematologica, 99(1), pp. 172–179. doi:10.3324/haematol.2013.089888.

[3] Bruggen, V. et al. (2018) ‘Structural validity of the World Assumption Scale’, Journal of Traumatic Stress, 31(6), pp. 816–825. doi:10.1002/jts.22348.

[4] Canova, F. and Hansen, B.E. (1995) ‘Are seasonal patterns constant over time? A test for seasonal stability’, Journal of Business & Economic Statistics, 13(3), p. 237. doi:10.2307/1392184.

[5] Courault, D., Seguin, B. and Olioso, A. (2005) ‘Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches’, Irrigation and Drainage Systems, 19(3–4), pp. 223–249. doi:10.1007/s10795-005-5186-0.

[6] Courault, D., Seguin, B. and Olioso, A. (2005) ‘Review on estimation of evapotranspiration from remote sensing data: From empirical to numerical modeling approaches’, Irrigation and Drainage Systems, 19(3–4), pp. 223–249. doi:10.1007/s10795-005-5186-0.

[7] de Brogniez, D. et al. (2014) ‘A map of the topsoil organic carbon content of Europe generated by a generalized additive model’, European Journal of Soil Science, 66(1), pp. 121–134. doi:10.1111/ejss.12193.

[8] Dunteman, G.H. and Ho, M.-H.R. (2006) An introduction to generalized linear models. Thousand Oaks, CA: Sage Publications.

[9] Efron, B. (1988) ‘Logistic regression, survival analysis, and the kaplan-meier curve’, Journal of the American Statistical Association, 83(402), pp. 414–425. doi:10.1080/01621459.1988.10478612.

[10] Gens, R. and Domingos, P. (2012) Discriminative learning of sum-product networks. Available at: https://homes.cs.washington.edu/~pedrod/papers/nips12.pdf (Accessed: 22 August 2023).

[11] Hamerly, G. and Elkan, C. (2003) Learning the K in K-means | proceedings of the 16th international ... Available at: https://dl.acm.org/doi/10.5555/2981345.2981381 (Accessed: 21 August 2023).

[12] Hastie, T., Friedman, J. and Tisbshirani, R. (2017) The elements of Statistical Learning: Data Mining, Inference, and prediction. New York: Springer.

[13] Hastie, T.J. and Tibshirani, R.J. (2017) ‘Generalized additive models’, Generalized Additive Models, pp. 136–173. doi:10.1201/9780203753781-6.

[14] Huber, P.J. (1987) ‘Experiences with three-dimensional scatterplots’, Journal of the American Statistical Association, 82(398), pp. 448–453. doi:10.1080/01621459.1987.10478447.

[15] Hubert, L.J. (2004) ‘Hierarchical Cluster Analysis’, Encyclopedia of Statistical Sciences [Preprint]. doi:10.1002/0471667196.ess0947.

[16] Johnson, V.E. and Albert, J.H. (1999) ‘Regression models for ordinal data’, Ordinal Data Modeling, pp. 126–157. doi:10.1007/0-387-22702-4_4.

[17] Križanić, S. (2020) ‘Educational data mining using cluster analysis and decision tree technique: A case study’, International Journal of Engineering Business Management, 12, p. 184797902090867. doi:10.1177/1847979020908675.

[18] Lang, S. et al. (2010) ‘Earth observation (eo)-basedex postassessment of Internally Displaced Person (IDP) camp evolution and population dynamics in Zam Zam, Darfur’, International Journal of Remote Sensing, 31(21), pp. 5709–5731. doi:10.1080/01431161.2010.496803.

[19] Lausch, A. et al. (2016) ‘Understanding Forest Health with remote sensing -part I—a review of spectral traits, processes and remote-sensing characteristics’, Remote Sensing, 8(12), p. 1029. doi:10.3390/rs8121029.

[20] Mann, M.E. et al. (2000) ‘Global temperature patterns in past centuries: An interactive presentation’, Earth Interactions, 4(4), pp. 1–1. doi:10.1175/1087-3562(2000)004<0001:gtpipc>2.3.co;2.

[21] Montgomery, K. (2006) ‘Variation in temperature with altitude and latitude’, Journal of Geography, 105(3), pp. 133–135. doi:10.1080/00221340608978675.

[22] Ozesmi, S.L. and Bauer, M.E. (2014) Satellite remote sensing of wetlands - wetlands ecology and management, SpringerLink. Available at: https://link.springer.com/article/10.1023/A:1020908432489

[23] Russell, K.G. (2018) ‘Generalized linear models’, Design of Experiments for Generalized Linear Models, pp. 1–20. doi:10.1201/9780429057489-1.

[24] Stow, C.A. et al. (2015) ‘Long-term and seasonal trend decomposition of Maumee River nutrient inputs to western Lake Erie’, Environmental Science & Technology, 49(6), pp. 3392–3400. doi:10.1021/es5062648.

[25] Stroud, I. and Xirouchakis, P.C. (2000) ‘STL and extensions’, Advances in Engineering Software, 31(2), pp. 83–95. doi:10.1016/s0965-9978(99)00046-0.

[26] Ward, J.H. (1963) ‘Hierarchical grouping to optimize an objective function’, Journal of the American Statistical Association, 58(301), pp. 236–244. doi:10.1080/01621459.1963.10500845.

[27] Zhang, H. (1998) ‘Bayesian Cart Model Search: Comment’, Journal of the American Statistical Association, 93(443), p. 948. doi:10.2307/2669833.