Unveiling the climatic origin of streamflow persistence through multifractal analysis of hydro-meteorological datasets of India

References

• Adarsh, S., et al., 2019. Multifractal characterization of meteorological drought in India using detrended fluctuation analysis. International Journal of Climatolology, 39 (11), 4234–4255. doi:10.1002/joc.6070
   Web of Science ®Google Scholar
• Adarsh, S., et al., 2020a. Multifractal description of streamflow and suspended sediment concentration data from Indian river basins. Acta Geophysica, 68 (2), 519–535. doi:10.1007/s11600-020-00407-2
   Web of Science ®Google Scholar
• Adarsh, S., et al., 2020b. Multifractal description of daily rainfall fields over India. Journal of Hydrology, 586, 124913. doi:10.1016/j.jhydrol.2020.124913
   Web of Science ®Google Scholar
• Adarsh, S., et al., 2022. On the detection and attribution of streamflow persistence of rivers in Peninsular India. Acta Geophysica, 70 (3), 1373–1383. doi:10.1007/s11600-022-00800-z
   Web of Science ®Google Scholar
• Adarsh, S., Dharan, S.D., and Anuja, P.K., 2018. Analyzing the hydrologic variability of Kallada River, India using continuous wavelet transform and fractal theory. Water Conservation Science and Engineering, 3 (4), 305–319. doi:10.1007/s41101-018-0060-8
   Google Scholar
• Bassingthwaighte, J.B. and Beyer, R.P., 1991. Fractal correlation in heterogeneous systems. Physica D, 53 (1991), 71–84. doi:10.1016/0167-2789(91)90165-6
   Web of Science ®Google Scholar
• Biswal, B., 2016. Dynamic hydrologic modelling using the zero-parameter Budyko model with instantaneous dryness index. Geophysical Research Letters, 43 (18), 9696–9703. doi:10.1002/2016GL070173
   Web of Science ®Google Scholar
• Brito, A.A., et al., 2018. Cross-correlation in a turbulent flow: analysis of the velocity field using the ρ DCCA coefficient. Europhysics Letters, 123 (2), 20011. doi:10.1209/0295-5075/123/20011
   Google Scholar
• Budyko, M., 1974. Climate and life. New York, NY: Academic Press.
   Google Scholar
• Burgueño, A., et al., 2014. Daily extreme temperature multifractals in Catalonia (NE Spain). Physics Letters A, 378 (11–12), 874–885. doi:10.1016/j.physleta.2014.01.033
   Web of Science ®Google Scholar
• Cadenas, E., et al., 2019. Wind speed variability study based on the Hurst coefficient and fractal dimensional analysis. Energy Science and Engineering, 7 (2), 361–378. doi:10.1002/ese3.277
   Web of Science ®Google Scholar
• Chakraborty, S. and Chattopadhyay, S., 2021. Exploring the Indian summer monsoon rainfall through multifractal detrended fluctuation analysis and the principle of entropy maximization. Earth Science Informatics, 14 (3), 1571–1577. doi:10.1007/s12145-021-00641-2
   Web of Science ®Google Scholar
• Chandrasekaran, S., et al., 2019. An investigation on the relationship between the Hurst exponent and the predictability of a rainfall time series. Meteorological Applications, 16 (3), 511–519. doi:10.1002/met.1784
   Web of Science ®Google Scholar
• Chen, X., Wu, Z., and Huang, N.E., 2010. The time-dependent intrinsic correlation based on the empirical mode decomposition. Advances in Adaptive Data Analysis, 2 (2), 233–265. doi:10.1142/S1793536910000471
   Google Scholar
• Dahlstedt, K. and Jensen, H.J., 2005. Fluctuation spectrum and size scaling of river flow and level. Physica A: Statistical Mechanics and Its Applications, 348, 596–610. doi:10.1016/j.physa.2004.09.039
   Web of Science ®Google Scholar
• Deidda, R., Benzi, R., and Siccardi, F., 1999. Multifractal modelling of anomalous scaling laws in rainfall. Water Resources Research, 35 (6), 1853–1867. doi:10.1029/1999WR900036
   Web of Science ®Google Scholar
• Dey, P. and Mujumdar, P.P., 2018. Multiscale evolution of persistence of rainfall and streamflow. Advances in Water Resources, 121, 285–303. doi:10.1016/j.advwatres.2018.08.018
   Web of Science ®Google Scholar
• Dey, P. and Mujumdar, P., 2022. On the statistical complexity of streamflow. Hydrological Sciences Journal, 67 (1), 40–53. doi:10.1080/02626667.2021.2000991
   Web of Science ®Google Scholar
• Dimitriadis, P., et al., 2021. A global-scale investigation of stochastic similarities in marginal distribution and dependence structure of key hydrological-cycle processes. Hydrology, 8 (2), 59. doi:10.3390/hydrology8020059
   Web of Science ®Google Scholar
• Drożdż, S., et al., 2019. Signatures of the crypto-currency market decoupling from the Forex. Future Internet, 11 (7), 154. doi:10.3390/fi11070154
   Web of Science ®Google Scholar
• García-Marín, A.P., et al., 2015. Delimiting homogeneous regions using the multifractal properties of validated rainfall data series. Journal of Hydrology, 529, 106–119.
   Web of Science ®Google Scholar
• Gentine, P., et al., 2012. Interdependence of climate, soil, and vegetation as constrained by the Budyko curve. Geophysical Research Letters, 39 (19). doi:10.1029/2012GL053492
   PubMed Web of Science ®Google Scholar
• Ghosh, S. and Mujumdar, P.P., 2007. Nonparametric methods for modelling GCM and scenario uncertainty in drought assessment. Water Resources Research, 43 (7). doi:10.1029/2006WR005351
   PubMed Web of Science ®Google Scholar
• Hargreaves, G.H. and Samani, Z.A., 1985. Reference crop evapotranspiration from temperature. Applied Engineering in Agriculture, 1 (2), 96–99. doi:10.13031/2013.26773
   Google Scholar
• Hirpa, F.A., Gebremichael, M., and Over, T.M., 2010. River flow fluctuation analysis: effect of watershed area. Water Resources Research, 46 (12). doi:10.1029/2009WR009000
   PubMed Web of Science ®Google Scholar
• Huang, Y.-X., et al., 2009. Analysis of daily river flow fluctuations using empirical mode decomposition and arbitrary order Hilbert spectral analysis. Journal of Hydrology, 373 (1–2), 103–111. doi:10.1016/j.jhydrol.2009.04.015
   Web of Science ®Google Scholar
• Huang, Y.-X., 2009. Arbitrary order hilbert spectral analysis definition and application to fully developed turbulence and environmental time-series. Thesis (PhD). University of Lille France.
   Google Scholar
• Hurst, H.E., 1951. The long-term storage capacity of reservoir. Transactions of the American Society of Civil Engineers 116, Paper 2447, Published in 1950 as Proceedings-Separate No. 11.
   Google Scholar
• Hurst, H.E., Black, R.P., and Simaika, Y.M., 1965. Long-term storage: an experimental study. London: Constable.
   Google Scholar
• Ihlen, E.A.F., 2012. Introduction to multifractal detrended fluctuation analysis in Matlab. Frontiers in Physiology, 3, 3. doi:10.3389/fphys.2012.00141
   PubMed Web of Science ®Google Scholar
• Jiang, Z.Q., and Zhou, W.X., 2011. Multifractal detrending moving-average cross-correlation analysis. Physical Review E, 84, 016106. doi:10.1103/PhysRevE.84.016106.
   Web of Science ®Google Scholar
• Jovanovic, T., et al., 2016. Effect of urbanization on the long-term persistence of streamflow records. Physica A: Statistical Mechanics and Its Applications, 447, 208–221. doi:10.1016/j.physa.2015.12.024
   Web of Science ®Google Scholar
• Kantelhardt, J.W., et al., 2002. Multifractal detrended fluctuation analysis of nonstationary time series. Physica A: Statistical Mechanics and Its Applications, 316 (1–4), 87–114. doi:10.1016/S0378-4371(02)01383-3
   Web of Science ®Google Scholar
• Kantelhardt, J.W., et al., 2006. Long-term persistence and multifractality of precipitation and river runoff records. Journal of Geophysical Research, 111 (D1), D01106. doi:10.1029/2005JD005881
   Web of Science ®Google Scholar
• Koscielny-Bunde, E., et al., 2006. Long-term persistence and multifractality of river runoff records: detrended fluctuation studies. Journal of Hydrology, 322 (1–4), 120–137. doi:10.1016/j.jhydrol.2005.03.004
   Web of Science ®Google Scholar
• Koutsoyiannis, D., 2020. Revisiting the global hydrological cycle: is it intensifying? Hydrology and Earth System Sciences, 24 (8), 3899–3932. doi:10.5194/hess-24-3899-2020
   Web of Science ®Google Scholar
• Kwapień, J., Oświęcimka, P., and Drożdż, S., 2015. Detrended fluctuation analysis made flexible to detect range of cross-correlated fluctuations. Physical Review E, 92 (5), 052815. doi:10.1103/PhysRevE.92.052815
   Web of Science ®Google Scholar
• Labat, D., et al., 2011. Scaling behaviour of the fluctuations in streamflow at the outlet of karstic watersheds, France. Journal of Hydrology, 410 (3–4), 162–168. doi:10.1016/j.jhydrol.2011.09.010
   Web of Science ®Google Scholar
• Ledvinka, O. and Recknagel, T., 2020. Long-term persistence in discharge time series of mountainous catchments in the Elbe River basin. Proceedings of International Association of Hydrological Sciences, 383, 135–140. doi:10.5194/piahs-383-135-2020
   Google Scholar
• Li, E., et al., 2015. Multifractal detrended fluctuation analysis of streamflow in the Yellow River basin, China. Water, 7 (12), 1670–1686. doi:10.3390/w7041670
   Web of Science ®Google Scholar
• Lian, J., Dang, L., and Ma, C., 2016. Hydrologic modification and vanishing self-organized criticality of river flows caused by dam operations. Journal of Hydrologic Engineering, 21 (12). doi:10.1061/(ASCE)HE.1943-5584.0001458
   Web of Science ®Google Scholar
• Livina, V., et al., 2007. Temporal scaling comparison of real hydrological data and model runoff records. Journal of Hydrology, 336 (1–2), 186–198. doi:10.1016/j.jhydrol.2007.01.014
   Web of Science ®Google Scholar
• Mandelbrot, B., 1982. The fractal geometry of nature. New York: WH Freeman Publishers.
   Google Scholar
• Markonis, Y., et al., 2018. Global estimation of long-term persistence in annual river runoff. Advances in Water Resources, 113, 1–12. doi:10.1016/j.advwatres.2018.01.003
   Web of Science ®Google Scholar
• Markonis, Y. and Koutsoyiannis, D., 2016. Scale-dependence of persistence in precipitation records. Nature Climate Change, 6 (4), 399–401. doi:10.1038/nclimate2894
   Web of Science ®Google Scholar
• Mudelsee, M., 2007. Long memory of rivers from spatial aggregation. Water Resources Research, 43 (1). doi:10.1029/2006WR005721
   PubMed Web of Science ®Google Scholar
• Muzy, J.F., Bacry, E., and Arneodo, A., 1993. Multifractal formalism for fractal signals: The structure-function approach versus the wavelet-transform modulus-maxima method. Physical Review E, 47, 875–884.
   PubMed Web of Science ®Google Scholar
• Nayak, A.K., Biswal, B., and Sudheer, K.P., 2021a. A novel framework to determine the usefulness of satellite-based soil moisture data in streamflow prediction using dynamic Budyko model. Journal of Hydrology, 595, 125849. doi:10.1016/j.jhydrol.2020.125849
   Web of Science ®Google Scholar
• Nayak, A.K., Biswal, B., and Sudheer, K.P., 2021b. Role of hydrological model structure in the assimilation of soil moisture for streamflow prediction. Journal of Hydrology, 598, 126465. doi:10.1016/j.jhydrol.2021.126465
   Web of Science ®Google Scholar
• Onyutha, C., et al., 2019. How well do climate models reproduce variability in observed rainfall? A case study of the Lake Victoria basin considering CMIP3, CMIP5 and CORDEX simulations. Stochastic Environmental Research and Risk Assessment, 33 (3), 687–707. doi:10.1007/s00477-018-1611-4
   Web of Science ®Google Scholar
• Onyutha, C., 2021a. Graphical-statistical method to explore variability of hydrological time series. Hydrology Research, 52 (1), 266–283. doi:10.2166/nh.2020.111
   Web of Science ®Google Scholar
• Onyutha, C., 2021b. Trends and variability of temperature and evaporation over the African continent: relationships with precipitation. Atmósfera, 34 (3), 267–287.
   Web of Science ®Google Scholar
• Oświecimka, P., et al., 2014. Detrended cross-correlation analysis consistently extended to multifractality. Physical Review E, 89 (2), 23305. doi:10.1103/PhysRevE.89.023305
   Web of Science ®Google Scholar
• Pai, D.S., et al., 2015. Analysis of the daily rainfall events over India using a new long period (1901–2010) high resolution (0.25° × 0.25°) gridded rainfall data set. Climate Dynamics, 45 (3–4), 755–776. doi:10.1007/s00382-014-2307-1
   Web of Science ®Google Scholar
• Pal, S., et al., 2020. Hurst exponent approach through rescaled range analysis to study the time series of summer monsoon rainfall over northeast India. Theoretical and Applied Climatology, 142 (1–2), 581–587. doi:10.1007/s00704-020-03338-6
   Web of Science ®Google Scholar
• Pandey, G., Lovejoy, S., and Schertzer, D., 1998. Multifractal analysis of daily river flows including extremes for basins of five to two million square kilometres, one day to 75 years. Journal of Hydrology, 208 (1–2), 62–81. doi:10.1016/S0022-1694(98)00148-6
   Web of Science ®Google Scholar
• Papacharalampous, G. and Tyralis, H., 2020. Hydrological time series forecasting using simple combinations: big data testing and investigations on one-year ahead river flow predictability. Journal of Hydrology, 590, 125205. doi:10.1016/j.jhydrol.2020.125205
   Web of Science ®Google Scholar
• Pathirana, A., Herath, S., and Yamada, T., 2003. Estimating rainfall distributions at high temporal resolutions using a multifractal model. Hydrology and Earth System Sciences, 7 (5), 668–679. doi:10.5194/hess-7-668-2003
   Web of Science ®Google Scholar
• Patnaik, S., Sharma, V.C., and Biswal, B., 2019. Evaluation of an instantaneous dryness index-based calibration-free continuous hydrological model in India. Hydrology Research, 50 (3), 915–924. doi:10.2166/nh.2019.081
   Web of Science ®Google Scholar
• Peng, C.-K., et al., 1994. Mosaic organization of DNA nucleotides. Physical Review E, 49 (2), 1685–1689. doi:10.1103/PhysRevE.49.1685
   PubMed Web of Science ®Google Scholar
• Perdios, A. and Langousis, A., 2020. Revisiting the statistical scaling of annual discharge maxima at daily resolution with respect to the basin size in the light of rainfall climatology. Water, 12 (2), 610. doi:10.3390/w12020610.
   Web of Science ®Google Scholar
• Piao, L. and Fu, Z., 2016. Quantifying distinct associations on different temporal scales: comparison of DCCA and Pearson methods. Scientific Reports, 6 (1), 36759. doi:10.1038/srep36759
   PubMed Web of Science ®Google Scholar
• Podobnik, B. and Stanley, H.E., 2008. Detrended cross-correlation analysis: a new method for analyzing two nonstationary time series. Physical Review Letters, 100 (8), 84102. doi:10.1103/PhysRevLett.100.084102
   PubMed Web of Science ®Google Scholar
• Rego, C.R.C., Frota, H.O., and Gusmão, M.S., 2013. Multifractality of Brazilian rivers. Journal of Hydrology, 495, 208–215. doi:10.1016/j.jhydrol.2013.04.046
   Web of Science ®Google Scholar
• Rickards, N., et al., 2020. Understanding future water challenges in a highly regulated Indian river basin—modelling the impact of climate change on the hydrology of the Upper Narmada. Water, 12 (6), 1762. doi:10.3390/w12061762
   Web of Science ®Google Scholar
• Sagarika, S., Kalra, A., and Ahmad, S., 2014. Evaluating the effect of persistence on long-term trends and analyzing step changes in streamflows of the continental United States. Journal of Hydrology, 517, 36–53. doi:10.1016/j.jhydrol.2014.05.002
   Web of Science ®Google Scholar
• Sankaran, A., et al., 2021. Spatiotemporal variability of multifractal properties of fine resolution daily gridded rainfall fields over India. Natural Hazards, 106 (3), 1951–1979. doi:10.1007/s11069-021-04523-0
   Web of Science ®Google Scholar
• Schertzer, D., et al., 2010. No monsters, no miracles: in nonlinear sciences hydrology is not an outlier! Hydrological Sciences Journal, 55 (6), 965–979. doi:10.1080/02626667.2010.505173
   Web of Science ®Google Scholar
• Schertzer, D., Hubert, P., and Lovejoy, S., 2007. Scaling, multifractals and predictions in ungauged basins: where we have been, where we are going? Predictions in ungauged basins: PUB kick-off. Proceedings of the PUB Kick-Off Meeting Held in Brasilia, 20–22 November 2002, 309, 92–101.
   Google Scholar
• Schertzer, D. and Lovejoy, S., 1987. Physical modelling and analysis of rain and clouds by aniso–tropic scaling multiplicative processes. Journal of Geophysical Research, 92, 9693–9714. doi:10.1029/JD092iD08p09693
   Web of Science ®Google Scholar
• Srivastava, A.K., Rajeevan, M., and Kshirsagar, S.R., 2009. Development of a high resolution daily gridded temperature data set (1969–2005) for the Indian region. Atmospheric Science Letters, 10 (4), 249–254 n/a–n/a. doi:10.1002/asl.232
   Web of Science ®Google Scholar
• Sudheer, K.P., Srinivasan, K., and Narasimhan, B., 2019. Morphological study of Krishna and Tungabhadra basins using remote sensing technique. IIT Madras, Report of the project funded by Central Water Commission Ministry of Water Resources Government of India.
   Google Scholar
• Szolgayova, E., et al., 2014. Factors influencing long range dependence in streamflow of European rivers. Hydrological Processes, 28 (4), 1573–1586. doi:10.1002/hyp.9694
   Web of Science ®Google Scholar
• Tan, X. and Gan, T.Y., 2017. Multifractality of Canadian precipitation and streamflow. International Journal of Climatology, 37, 1221–1236. doi:10.1002/joc.5078
   Web of Science ®Google Scholar
• Tatli, H., 2015. Detecting persistence of meteorological drought via the Hurst exponent. Meteorological Applications, 22 (4), 763–769. doi:10.1002/met.1519
   Web of Science ®Google Scholar
• Tchiguirinskai, I., et al., 2007. Potential of multifractal modelling of ungauged basins predictions in ungauged basins: PUB kick-off. Proceedings of the PUB Kick-Off Meeting Held in Brasilia, 20–22 November 2002, 309, 98–308.
   Google Scholar
• Tessier, Y., et al., 1996. Multifractal analysis and modelling of rainfall and river flows and scaling, causal transfer functions. Journal of Geophysical Research, 101, 26427–26440. doi:10.1029/96JD01799
   Web of Science ®Google Scholar
• Torrence, C. and Compo, G.P., 1998. A practical guide to wavelet analysis. Bulletin American Meteorological Society, 79, 61–78. doi:10.1175/1520-0477(1998)079<0061:APGTWA>2.0.CO;2
   Web of Science ®Google Scholar
• Vassoler, R.T. and Zebende, G.F., 2012. DCCA cross-correlation coefficient apply in time series of air temperature and air relative humidity. Physica A: Statistical Mechanics and Its Applications, 391 (7), 2438–2443. doi:10.1016/j.physa.2011.12.015
   Web of Science ®Google Scholar
• Villarini, G. and Wasko, C., 2021. Humans, climate and streamflow. Nature Climate Change, 11 (2021), 725–726. doi:10.1038/s41558-021-01137-z
   Web of Science ®Google Scholar
• Wang, X., et al., 2016. Influence of sub-daily variation on multi-fractal detrended fluctuation analysis of wind speed time series. PLoS ONE, 11 (1), e0146284. doi:10.1371/journal.pone.0146284
   PubMed Web of Science ®Google Scholar
• Watorek, M., et al., 2019. Multifractal cross-correlations between the world oil and other financial markets in 2012–2017. Energy Economics, 81, 874–885. doi:10.1016/j.eneco.2019.05.015
   Web of Science ®Google Scholar
• Zebende, G.F., 2011. DCCA cross-correlation coefficient: quantifying level of cross-correlation. Physica A: Statistical Mechanics and Applications, 390, 614–618. doi:10.1016/j.physa.2010.10.022
   Web of Science ®Google Scholar
• Zhang, Q., et al., 2008. Multifractal detrended fluctuation analysis of streamflow series of the Yangtze River basin, China. Hydrological Processes, 22 (26), 4997–5003. doi:10.1002/hyp.7119
   Web of Science ®Google Scholar
• Zhang, Q., et al., 2009. Multifractal analysis of streamflow records of the East River basin (Pearl River), China. Physica A: Statistical Mechanics and Its Applications, 388 (6), 927–934. doi:10.1016/j.physa.2008.11.025
   Web of Science ®Google Scholar
• Zhang, X., et al., 2019. A modified multifractal detrended fluctuation analysis (MFDFA) approach for multifractal analysis of precipitation in dongting lake basin, China. Water, 11 (5), 891. doi:10.3390/w11050891
   Web of Science ®Google Scholar
• Zhou, W.-X., 2008. Multifractal detrended cross-correlation analysis for two non-stationary signals. Physical Review E, 77 (6), 066211. doi:10.1103/PhysRevE.77.066211
   PubMed Web of Science ®Google Scholar
• Zhou, Y., Zhang, Q., and 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. doi:10.1007/s00477-013-0747-5
   Web of Science ®Google Scholar