Analysis of daily streamflow complexity by Kolmogorov measures and Lyapunov exponent
Аутори
Mihailović, Dragutin T.Nikolić-Đorić, Emilija
Arsenić, Ilija
Malinović-Milićević, Slavica
Singh, Vijay P.
Stošić, Tatijana
Stošić, Borko
Чланак у часопису (Објављена верзија)
Метаподаци
Приказ свих података о документуАпстракт
Analysis of daily streamflow variability in space and time is important for water resources planning, development, and management. The natural variability of streamflow is being complicated by anthropogenic influences and climate change, which may introduce additional complexity into streamflow records. To address the complexity in streamflow, daily discharge data recorded during the period 1989–2016 at twelve gauging stations on Brazos River in Texas (USA) were used to derive a set of novel quantitative tools: Kolmogorov complexity (KC) and its derivative-associated measures to assess complexity, and Lyapunov time (LT) to assess predictability. It was found that all daily discharge series exhibited long memory with an increasing down-flow tendency, while the randomness of the series at individual sites could not be definitively concluded. All Kolmogorov complexity measures had relatively small values with the exception of the USGS (United States Geological Survey) 08088610 station at ...Graford, Texas, which exhibited the highest values of the complexity measures. This finding may
be attributed to the elevated effect of human activities at Graford, and proportionally lesser effect at other stations. In addition, complexity tended to decrease downflow, meaning that larger catchments were generally less influenced by anthropogenic activities. The correction on randomness of Lyapunov time (quantifying predictability) was found to be inversely proportional to the Kolmogorov complexity, which strengthened our conclusion regarding the effect of anthropogenic activities, considering that KC and LT were distinct measures, based on rather different techniques.
Кључне речи:
Streamflow time series / Kolmogorov complexity based measures / Lyapunov exponent / Lyapunov time / Kolmogorov time / Predictability of streamflow time seriesИзвор:
Physica A: Statistical Mechanics and its Applications, 2019, 525, 290-303Издавач:
- The Netherlands : Elsevier B.V.
Финансирање / пројекти:
- Истраживање климатских промена и њиховог утицаја на животну средину - праћење утицаја, адаптација и ублажавање (RS-MESTD-Integrated and Interdisciplinary Research (IIR or III)-43007)
DOI: 10.1016/j.physa.2019.03.041
ISSN: 0378-4371 (Print); 1873-2119 (Online)
WoS: 000474503900026
Scopus: 2-s2.0-85063746210
Институција/група
Географски институт „Јован Цвијић“ САНУ / Geographical Institute Jovan Cvijić SASATY - JOUR AU - Mihailović, Dragutin T. AU - Nikolić-Đorić, Emilija AU - Arsenić, Ilija AU - Malinović-Milićević, Slavica AU - Singh, Vijay P. AU - Stošić, Tatijana AU - Stošić, Borko PY - 2019 UR - https://dais.sanu.ac.rs/123456789/13303 AB - Analysis of daily streamflow variability in space and time is important for water resources planning, development, and management. The natural variability of streamflow is being complicated by anthropogenic influences and climate change, which may introduce additional complexity into streamflow records. To address the complexity in streamflow, daily discharge data recorded during the period 1989–2016 at twelve gauging stations on Brazos River in Texas (USA) were used to derive a set of novel quantitative tools: Kolmogorov complexity (KC) and its derivative-associated measures to assess complexity, and Lyapunov time (LT) to assess predictability. It was found that all daily discharge series exhibited long memory with an increasing down-flow tendency, while the randomness of the series at individual sites could not be definitively concluded. All Kolmogorov complexity measures had relatively small values with the exception of the USGS (United States Geological Survey) 08088610 station at Graford, Texas, which exhibited the highest values of the complexity measures. This finding may be attributed to the elevated effect of human activities at Graford, and proportionally lesser effect at other stations. In addition, complexity tended to decrease downflow, meaning that larger catchments were generally less influenced by anthropogenic activities. The correction on randomness of Lyapunov time (quantifying predictability) was found to be inversely proportional to the Kolmogorov complexity, which strengthened our conclusion regarding the effect of anthropogenic activities, considering that KC and LT were distinct measures, based on rather different techniques. PB - The Netherlands : Elsevier B.V. T2 - Physica A: Statistical Mechanics and its Applications T1 - Analysis of daily streamflow complexity by Kolmogorov measures and Lyapunov exponent SP - 290 EP - 303 VL - 525 DO - 10.1016/j.physa.2019.03.041 UR - https://hdl.handle.net/21.15107/rcub_dais_13303 ER -
@article{ author = "Mihailović, Dragutin T. and Nikolić-Đorić, Emilija and Arsenić, Ilija and Malinović-Milićević, Slavica and Singh, Vijay P. and Stošić, Tatijana and Stošić, Borko", year = "2019", abstract = "Analysis of daily streamflow variability in space and time is important for water resources planning, development, and management. The natural variability of streamflow is being complicated by anthropogenic influences and climate change, which may introduce additional complexity into streamflow records. To address the complexity in streamflow, daily discharge data recorded during the period 1989–2016 at twelve gauging stations on Brazos River in Texas (USA) were used to derive a set of novel quantitative tools: Kolmogorov complexity (KC) and its derivative-associated measures to assess complexity, and Lyapunov time (LT) to assess predictability. It was found that all daily discharge series exhibited long memory with an increasing down-flow tendency, while the randomness of the series at individual sites could not be definitively concluded. All Kolmogorov complexity measures had relatively small values with the exception of the USGS (United States Geological Survey) 08088610 station at Graford, Texas, which exhibited the highest values of the complexity measures. This finding may be attributed to the elevated effect of human activities at Graford, and proportionally lesser effect at other stations. In addition, complexity tended to decrease downflow, meaning that larger catchments were generally less influenced by anthropogenic activities. The correction on randomness of Lyapunov time (quantifying predictability) was found to be inversely proportional to the Kolmogorov complexity, which strengthened our conclusion regarding the effect of anthropogenic activities, considering that KC and LT were distinct measures, based on rather different techniques.", publisher = "The Netherlands : Elsevier B.V.", journal = "Physica A: Statistical Mechanics and its Applications", title = "Analysis of daily streamflow complexity by Kolmogorov measures and Lyapunov exponent", pages = "290-303", volume = "525", doi = "10.1016/j.physa.2019.03.041", url = "https://hdl.handle.net/21.15107/rcub_dais_13303" }
Mihailović, D. T., Nikolić-Đorić, E., Arsenić, I., Malinović-Milićević, S., Singh, V. P., Stošić, T.,& Stošić, B.. (2019). Analysis of daily streamflow complexity by Kolmogorov measures and Lyapunov exponent. in Physica A: Statistical Mechanics and its Applications The Netherlands : Elsevier B.V.., 525, 290-303. https://doi.org/10.1016/j.physa.2019.03.041 https://hdl.handle.net/21.15107/rcub_dais_13303
Mihailović DT, Nikolić-Đorić E, Arsenić I, Malinović-Milićević S, Singh VP, Stošić T, Stošić B. Analysis of daily streamflow complexity by Kolmogorov measures and Lyapunov exponent. in Physica A: Statistical Mechanics and its Applications. 2019;525:290-303. doi:10.1016/j.physa.2019.03.041 https://hdl.handle.net/21.15107/rcub_dais_13303 .
Mihailović, Dragutin T., Nikolić-Đorić, Emilija, Arsenić, Ilija, Malinović-Milićević, Slavica, Singh, Vijay P., Stošić, Tatijana, Stošić, Borko, "Analysis of daily streamflow complexity by Kolmogorov measures and Lyapunov exponent" in Physica A: Statistical Mechanics and its Applications, 525 (2019):290-303, https://doi.org/10.1016/j.physa.2019.03.041 ., https://hdl.handle.net/21.15107/rcub_dais_13303 .