Darusman, T., Murdiyarso, D., Impron, I., Chaniago, I. A., and Lestari, D. P.: Carbon dynamics in rewetted tropical peat swamp forests, Climate, 10, 35,
https://doi.org/10.3390/cli10030035, 2022.
a
da Silva, M. F., Pereira, É. J. d. A. L., da Silva Filho, A. M., de Castro, A. P. N., Miranda, J. G. V., and Zebende, G. F.: Quantifying cross-correlation between Ibovespa and Brazilian blue-chips: The DCCA approach, Physica A, 424, 124–129, 2015. a
da Silva, M. F., Pereira, É. J. d. A. L., da Silva Filho, A. M., de Castro, A. P. N., Miranda, J. G. V., and Zebende, G. F.: Quantifying the contagion effect of the 2008 financial crisis between the G7 countries (by GDP nominal), Physica A, 453, 1–8, 2016. a
Desai, A. R., Murphy, B. A., Wiesner, S., Thom, J., Butterworth, B. J., Koupaei-Abyazani, N., Muttaqin, A., Paleri, S., Talib, A., Turner, J., Mineau, J., Merrelli, A., Stoy, P., and Davis, K.: Drivers of Decadal Carbon Fluxes Across Temperate Ecosystems, J. Geophys. Res.-Biogeo., 127, e2022JG007014,
https://doi.org/10.1029/2022JG007014, 2022.
a
Hirano, T., Segah, H., Kusin, K., Limin, S., Takahashi, H., and Osaki, M.: Effects of Disturbances on the Carbon Balance of Tropical Peat Swamp Forests, Global Change Biol., 18, 3410–3422, 2012. a
Höll, M., Kiyono, K., and Kantz, H.: Theoretical foundation of detrending methods for fluctuation analysis such as detrended fluctuation analysis and detrending moving average, Phys. Rev. E, 99, 033305,
https://doi.org/10.1103/PhysRevE.99.033305, 2019.
a
Hu, K., Ivanov, P. C., Chen, Z., Carpena, P., and Stanley, H. E.: Effect of trends on detrended fluctuation analysis, Phys. Rev. E, 64, 011114,
https://doi.org/10.1103/PhysRevE.64.011114, 2001.
a
Kantelhardt, J. W., Koscielny-Bunde, E., Rego, H. H., Havlin, S., and Bunde, A.: Detecting long-range correlations with detrended fluctuation analysis, Physica A, 295, 441–454, 2001. a
Koutsoyiannis, D.: Climate change, the Hurst phenomenon, and hydrological statistics, Hydrol. Sci. J., 48, 3–24, 2003. a
Liu, J., Bowman, K., Palmer, P. I., Joiner, J., Levine, P., Bloom, A. A., Feng, L., Saatchi, S., Keller, M., Longo, M., Schimel, D., and Wennberg, P. O.: Enhanced Carbon Flux Response to Atmospheric Aridity and Water Storage Deficit During the 2015–2016 El Niño Compromised Carbon Balance Recovery in Tropical South America, AGU Advances, 5, e2024AV001187,
https://doi.org/10.1029/2024AV001187, 2024.
a
Louzada, R. O., Bergier, I., and Assine, M. L.: Landscape changes in avulsive river systems: Case study of Taquari River on Brazilian Pantanal wetlands, Sci. Total Environ., 723, 138067,
https://doi.org/10.1016/j.scitotenv.2020.138067, 2020.
a
Lu, W., Wu, B., Wang, L., and Gao, Y.: Multi-Scale Drought Resilience in Terrestrial Plants: From Molecular Mechanisms to Ecosystem Sustainability, Water, 17, 2516,
https://doi.org/10.3390/w17172516, 2025.
a
Matcharashvili, T. and Prangishvili, A.: Quantifying regularity of the Internet Interdomain Routing based on Border Gateway Protocol (BGP) data bases, in: 2020 International Conference on Electrical, Communication, and Computer Engineering (ICECCE), 1–5, IEEE, 17–18 January 2020, Techno India University, Kolkata, India,
https://doi.org/10.1109/ICCECE48148.2020, ISBN 1728144779, 9781728144771, 2020.
a
Meir, P. and Grace, J.: Scaling relationships for woody tissue respiration in two tropical rain forests, Plant Cell Environ., 25, 963–973, 2002. a
Miller, S. D., Goulden, M. L., Menton, M. C., Da Rocha, H. R., de Freitas, H. C., Figueira, A. M. e. S., and Dias de Sousa, C. A.: Biometric and micrometeorological measurements of tropical forest carbon balance, Ecol. Appl., 14, 114–126, 2004. a
Oliveira Filho, F. M., Guedes, E. F., and Rodrigues, P. C.: Networks analysis of Brazilian climate data based on the DCCA cross-correlation coefficient, PLOS ONE, 18, e0290838,
https://doi.org/10.1371/journal.pone.0290838, 2023.
a
Peng, C.-K., Buldyrev, S. V., Havlin, S., Simons, M., Stanley, H. E., and Goldberger, A. L.: Mosaic organization of DNA nucleotides, Phys. Rev. E, 49, 1685–1689,
https://doi.org/10.1103/PhysRevE.49.1685, 1994.
a,
b,
c
Pobocikova, I., Souza, A. d., Abreu, M. C., Oliveira-Júnior, J. F. d., Santos, C. M. d., Lins, T. M. P., Aristone, F., and Ramos, P. L.: The impacts of climate change on rainfall modeling in the Pantanal of Mato Grosso do Sul, Acta Scientiarum, Technology, 43, e55112,
https://doi.org/10.4025/actascitechnol.v43i1.55112, 2021.
a
Tatli, H. and Menteş, Ş. S.: Detrended cross-correlation patterns between North Atlantic oscillation and precipitation, Theor. Appl. Climatol., 138, 387–397, 2019. a
Teodoro, P. E., de Oliveira-Júnior, J. F., da Cunha, E. R., Correa, C. C. G., Torres, F. E., Bacani, V. M., Gois, G., and Ribeiro, L. P.: Cluster analysis applied to the spatial and temporal variability of monthly rainfall in Mato Grosso do Sul State, Brazil, Meteorol. Atmos. Phys., 128, 197–209, 2016.
a,
b
Vassoler, R. and Zebende, G.: DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity, Physica A, 391, 2438–2443, 2012. a
Yuan, N. and Fu, Z.: Different spatial cross-correlation patterns of temperature records over China: A DCCA study on different time scales, Physica A, 400, 71–79, 2014. a
Zhang, Y., Tan, Z., Song, Q., Yu, G., and Sun, X.: Respiration Controls the Unexpected Seasonal Pattern of Carbon Flux in an Asian Tropical Rain Forest, Atmos. Environ., 44, 3886–3893, 2010. a
