3 research outputs found
Analysis of Daily Streamflow Complexity by Kolmogorov Measures and Lyapunov Exponent
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 the phenomenological
records. To address this question for daily discharge data recorded during the
period 1989-2016 at twelve gauging stations on Brazos River in Texas (USA), we
use a set of novel quantitative tools: Kolmogorov complexity (KC) with its
derivative associated measures to assess complexity, and Lyapunov time (LT) to
assess predictability. We find that all daily discharge series exhibit long
memory with an increasing downflow tendency, while the randomness of the series
at individual sites cannot be definitively concluded. All Kolmogorov complexity
measures have relatively small values with the exception of the USGS (United
States Geological Survey) 08088610 station at Graford, Texas, which exhibits
the highest values of these 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 tends to decrease
downflow, meaning that larger catchments are generally less influenced by
anthropogenic activity. The correction on randomness of Lyapunov time
(quantifying predictability) is found to be inversely proportional to the
Kolmogorov complexity, which strengthens our conclusion regarding the effect of
anthropogenic activities, considering that KC and LT are distinct measures,
based on rather different techniques
Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France)
10siAnalysis of daily solar irradiation variability and predictability in space and time important for energy resources planning, development, and management. The natural variability
of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and
orography, which introduce additional complexity into the phenomenological records. To address
this question for daily solar irradiation data recorded during the years 2013, 2014 and 201511 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures
and Hamming distance (HAM) and their combination to assess complexity and corresponding
predictability. We find that all half-day (from sunrise to sunset) solar irradiation series exhibit
high complexity. However, all of them can be classified into three groups strongly influenced trade winds that circulate in a “flow around” regime: the windward side (trade winds slow down),
the leeward side (diurnal thermally-induced circulations dominate) and the coast parallel to trade
winds (winds are accelerated due to Venturi effect). We introduce Kolmogorov time (KT) that
quantifies the time span beyond which randomness significantly influences predictability.openopenDragutin T. Mihailović, Miloud Bessafi, Sara Marković, Ilija Arsenić,
Slavica Malinović-Milićević, Patrick Jeanty, Mathieu Delsaut, Jean-Pierre Chabriat,
Nusret Drešković, Anja MihailovićDragutin T., Mihailović; Miloud, Bessafi; Marković, Sara; Ilija, Arsenić; Slavica, Malinović-Milićević; Patrick, Jeanty; Mathieu, Delsaut; Jean-Pierre, Chabriat; Nusret, Drešković; Anja, Mihailovic
Analysis of Solar Irradiation Time Series Complexity and Predictability by Combining Kolmogorov Measures and Hamming Distance for La Reunion (France)
Analysis of daily solar irradiation variability and predictability in space and time is important for energy resources planning, development, and management. The natural variability of solar irradiation is being complicated by atmospheric conditions (in particular cloudiness) and orography, which introduce additional complexity into the phenomenological records. To address this question for daily solar irradiation data recorded during the years 2013, 2014 and 2015 at 11 stations measuring solar irradiance on La Reunion French tropical Indian Ocean Island, we use a set of novel quantitative tools: Kolmogorov complexity (KC) with its derivative associated measures and Hamming distance (HAM) and their combination to assess complexity and corresponding predictability. We find that all half-day (from sunrise to sunset) solar irradiation series exhibit high complexity. However, all of them can be classified into three groups strongly influenced by trade winds that circulate in a “flow around” regime: the windward side (trade winds slow down), the leeward side (diurnal thermally-induced circulations dominate) and the coast parallel to trade winds (winds are accelerated due to Venturi effect). We introduce Kolmogorov time (KT) that quantifies the time span beyond which randomness significantly influences predictability