Fisher-Shannon analysis of the São Francisco river flow: the influence of dams and reservoirs
DOI:
https://doi.org/10.33448/rsd-v9i10.8852Keywords:
Fisher-shannon analysis; São Francisco river; Reservoirs.Abstract
We investigated how the construction of the Sobradinho and Xingó dams affected the daily streamflow of the São Francisco River, using Fisher - Shannon analysis. The daily streamflow time series of the fluviometric stations Juazeiro / BA and Pão de Açúcar / AL that are located downstream of the Sobradinho and Xingó reservoirs were analyzed for the periods prior to the construction of both reservoirs, after the construction of Sobradinho and before the construction of Xingó, and after the construction of both reservoirs. We applied Fisher-Shannon analysis to streamflow subseries and in moving windows, evaluating differences using the Kruskal-Wallis test. This method simultaneously quantifies the local and global properties of the probability density function of the analyzed signal. We observed that in the natural regime the degree of temporal organization of the streamflow series decreased with an increase in the drainage area. After the construction of Sobradinho the degree of regularity of the streamflow dynamics decreased comparing to the natural regime and after the construction of Xingó we observed a more regular and more organized streamflow dynamics. Thus, the operations of the reservoirs changed the degree of regularity and temporal organization of the streamflow series, as indicated by the entropy and Fisher information values, respectively.
References
ANA. (2020). Agência Nacional de Águas. Recuperado de http://hidroweb.ana.gov.br
Angulo, J. C., Antolín, J., & Sen, K. D. (2008). Fisher–Shannon plane and statistical complexity of atoms. Physics Letters A, 372(5), 670–674.
Barreto, I. D. de C., Stosic, T., Filho, M. C., Delrieux, C., Singh, V. P., Asce, D. M., & Stosic, B. (2020). Complexity Analyses of Sao Francisco River Streamflow : Influence of Dams and Reservoirs, 25(10), 1–8. https://doi.org/10.1061/(ASCE)HE.1943-5584.0001996
Bezerra, B. G., Silva, L. L., Santos e Silva, C. M., & de Carvalho, G. G. (2019). Changes of precipitation extremes indices in São Francisco River Basin, Brazil from 1947 to 2012. Theoretical and Applied Climatology, 135(1–2), 565–576. https://doi.org/10.1007/s00704-018-2396-6
Chai, Y., Yue, Y., Zhang, L., Miao, C., Borthwick, A. G. L., Zhu, B., … Dolman, A. J. (2020). Homogenization and polarization of the seasonal water discharge of global rivers in response to climatic and anthropogenic effects. Science of The Total Environment, 709, 136062.
CHESF. (2015). Companhia Hidrelétrica do Rio São Francisco. Recuperado de https://www.chesf.gov.br
Dembo, A., Cover, T. M., & Thomas, J. A. (1991). Information theoretic inequalities. IEEE Transactions on Information Theory, 37(6), 1501–1518.
Döll, P., Fiedler, K., & Zhang, J. (2009). Global-scale analysis of river flow alterations due to water withdrawals and reservoirs. Hydrology and Earth System Sciences, 13(12), 2413–2432. https://doi.org/10.5194/hess-13-2413-2009
Fang, K., Sivakumar, B., & Woldemeskel, F. M. (2017). Complex networks, community structure, and catchment classification in a large-scale river basin. Journal of Hydrology, 545, 478–493.
Fisher, R. A. (1925). Theory of statistical estimation. In Mathematical Proceedings of the Cambridge Philosophical Society (Vol. 22, pp. 700–725). Cambridge University Press.
Fonseca, E. R., de Ávila Modesto, F., Carneiro, G. C. A., Lima, N. F. S., & de Almeida Monte-Mor, R. C. (2020). Conflitos pelo uso da água na Bacia Hidrográfica do rio São Francisco–Estudos de caso no Estado da Bahia. Research, Society and Development, 9(9), e823997929–e823997929.
Frieden, B. R. (1990). Fisher information, disorder, and the equilibrium distributions of physics. Physical Review A, 41(8), 4265.
Guignard, F., Lovallo, M., Laib, M., Golay, J., Kanevski, M., Helbig, N., & Telesca, L. (2019). Investigating the time dynamics of wind speed in complex terrains by using the Fisher–Shannon method. Physica A: Statistical Mechanics and Its Applications, 523, 611–621. https://doi.org/10.1016/j.physa.2019.02.048
Li, Z., & Zhang, Y.-K. (2008). Multi-scale entropy analysis of Mississippi River flow. Stochastic Environmental Research and Risk Assessment, 22(4), 507–512.
Magilligan, F. J., & Nislow, K. H. (2005). Changes in hydrologic regime by dams. Geomorphology, 71(1–2), 61–78.
Maneta, M. P., Torres, M., Wallender, W. W., Vosti, S., Kirby, M., Bassoi, L. H., & Rodrigues, L. N. (2009). Water demand and flows in the São Francisco River Basin (Brazil) with increased irrigation. Agricultural Water Management, 96(8), 1191–1200. https://doi.org/10.1016/j.agwat.2009.03.008
Martin, M. T., Pennini, F., & Plastino, A. (1999). Fisher’s information and the analysis of complex signals. Physics Letters A, 256(2–3), 173–180.
Matteau, M., Assani, A. A., & Mesfioui, M. (2009). Application of multivariate statistical analysis methods to the dam hydrologic impact studies. Journal of Hydrology, 371(1–4), 120–128.
Pfirman, S. L. (2003). Complex environmental systems: synthesis for earth, life, and society in the 21st century: A 10-year outlook for the National Science Foundation. National Science Foundation.
Pierini, J. O., Restrepo, J. C., Lovallo, M., & Telesca, L. (2015). Discriminating between different streamflow regimes by using the fisher-shannon method: An application to the Colombia rivers. Acta Geophysica, 63(2), 533–546. https://doi.org/10.2478/s11600-014-0229-2
Raykar, V. C., & Duraiswami, R. (2006). Fast optimal bandwidth selection for kernel density estimation. In Proceedings of the 2006 SIAM International Conference on Data Mining (pp. 524–528). SIAM.
Rego, C. R. C., Frota, H. O., & Gusmão, M. S. (2013). Multifractality of Brazilian rivers. Journal of Hydrology, 495, 208–215. https://doi.org/10.1016/j.jhydrol.2013.04.046
Richter, B. D., Baumgartner, J. V, Powell, J., & Braun, D. P. (1996). A method for assessing hydrologic alteration within ecosystems. Conservation Biology, 10(4), 1163–1174.
Richter, B. D., & Thomas, G. A. (2007). Restoring environmental flows by modifying dam operations. Ecology and Society, 12(1). https://doi.org/10.5751/ES-02014-120112
Sivakumar, B. (2009). Nonlinear dynamics and chaos in hydrologic systems: Latest developments and a look forward. Stochastic Environmental Research and Risk Assessment, 23(7), 1027–1036. https://doi.org/10.1007/s00477-008-0265-z
Stosic, T., Telesca, L., de Souza Ferreira, D. V., & Stosic, B. (2016). Investigating anthropically induced effects in streamflow dynamics by using permutation entropy and statistical complexity analysis: A case study. Journal of Hydrology, 540, 1136–1145. https://doi.org/10.1016/j.jhydrol.2016.07.034
Telesca, L., & Lovallo, M. (2017). On the performance of Fisher Information Measure and Shannon entropy estimators. Physica A: Statistical Mechanics and Its Applications, 484, 569–576. https://doi.org/10.1016/j.physa.2017.04.184
Telesca, L., Lovallo, M., Hsu, H.-L., & Chen, C.-C. (2011). Analysis of dynamics in magnetotelluric data by using the Fisher–Shannon method. Physica A: Statistical Mechanics and Its Applications, 390(7), 1350–1355.
Tongal, H., Demirel, M. C., & Moradkhani, H. (2017). Analysis of dam-induced cyclic patterns on river flow dynamics. Hydrological Sciences Journal, 62(4), 626–641.
Tonkin, J. D., Merritt, D. M., Olden, J. D., Reynolds, L. V, & Lytle, D. A. (2018). Flow regime alteration degrades ecological networks in riparian ecosystems. Nature Ecology & Evolution, 2(1), 86–93.
Vignat, C., & Bercher, J.-F. (2003). Analysis of signals in the Fisher–Shannon information plane. Physics Letters A, 312(1–2), 27–33.
Woldesenbet, T. A., Elagib, N. A., Ribbe, L., & Heinrich, J. (2017). Hydrological responses to land use/cover changes in the source region of the Upper Blue Nile Basin, Ethiopia. Science of the Total Environment, 575, 724–741.
Zhou, Y., Zhang, Q., Li, K., & Chen, X. (2012). Hydrological effects of water reservoirs on hydrological processes in the East River (China) basin: complexity evaluations based on the multi-scale entropy analysis. Hydrological Processes, 26(21), 3253–3262.
Zhou, Y., Zhang, Q., & Singh, V. P. (2014). Fractal-based evaluation of the effect of water reservoirs on hydrological processes: The dams in the Yangtze River as a case study. Stochastic Environmental Research and Risk Assessment, 28(2), 263–279. https://doi.org/10.1007/s00477-013-0747-5
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2020 Ikaro Daniel Barreto de Carvalho; Eucymara França Nunes Santos; Tatijana Stosic
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
1) Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
2) Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
3) Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.