https://doi.org/10.1140/epjs/s11734-025-01636-6
Regular Article
Fractal complexity evaluation for the predictability assessment of daily streamflows of Indian river basins
TKM College of Engineering, 691005, Kollam, India
a adarsh_lce@yahoo.co.in, adarsh1982@tkmce.ac.in
Received:
12
December
2024
Accepted:
15
April
2025
Published online:
5
May
2025
Complexity analysis of hydrologic datasets is an essential prerequisite for improved predictions in a changing environment. This study presents a novel framework for assessing the predictability of the daily streamflows of five major Indian river basins through fractal analysis. First, the stochastic behavior of daily streamflows is assessed based on scaling exponent obtained from degree distribution plot, which showed that 90% of station data considered in the study are stochastic than chaotic in nature. Subsequently, fractal dimension is estimated for the time series which displayed stochasticity. Then the persistence of each series was estimated in terms of Hurst exponent (H) for the complete length along with 14 sub-series stretches. Moreover, indicators like predictability Index (PI) and lagged correlation measures derived from H are used to decipher the predictability of the series. The framework was applied for analyzing the predictability of daily streamflows of 147 stations located within the five river basins. The PI estimates showed that the series not necessarily be predictable even if the total series display long-term persistence (LTP) for its complete length. The segments which displayed abnormal deviation in PI and H are found to be destroying the overall predictability. The insights gained from the study are found to be useful for developing the appropriate models for daily streamflow forecasting.
Supplementary Information The online version contains supplementary material available at https://doi.org/10.1140/epjs/s11734-025-01636-6.
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© The Author(s), under exclusive licence to EDP Sciences, Springer-Verlag GmbH Germany, part of Springer Nature 2025
Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.
