Nonlinear dynamical analysis of the physical processes in the environment

Ispitivan  je  spregnut  sistem  jednačina  za  prognozu  temperature  na površini  i  u  dubljem sloju zemljišta.  Računati  su  Ljapunovljevi eksponenti,  bifurkacioni dijagram, atraktor i analiziran je domen rešenja. Uvedene su nove informacione mere  bazirane na Kolmogorovljevoj kompleksnosti,  za kvantifikaciju  stepena nasumičnosti u vremenskim serijama,.  Nove mere su primenjene na razne serije dobijene merenjem fizičkih faktora životne sredine i pomoću klimatskih modela.Coupled system of prognostic equations for  the  ground surface temperature and  the deeper layer temperature was examind. Lyapunov exponents, bifurcation diagrams, attractor and the domain of solutions were analyzed.  Novel information measures based on Kolmogorov complexity  and used  for the quantification of randomness in time series, were presented.Novel measures were tested on various time series obtained by measuring physical factors of the environment or as the climate model outputs

Nonlinear dynamical analysis of the physical processes in the environment

Ispitivan  je  spregnut  sistem  jednačina  za  prognozu  temperature  na površini  i  u  dubljem sloju zemljišta.  Računati  su  Ljapunovljevi eksponenti,  bifurkacioni dijagram, atraktor i analiziran je domen rešenja. Uvedene su nove informacione mere  bazirane na Kolmogorovljevoj kompleksnosti,  za kvantifikaciju  stepena nasumičnosti u vremenskim serijama,.  Nove mere su primenjene na razne serije dobijene merenjem fizičkih faktora životne sredine i pomoću klimatskih modela.Coupled system of prognostic equations for  the  ground surface temperature and  the deeper layer temperature was examind. Lyapunov exponents, bifurcation diagrams, attractor and the domain of solutions were analyzed.  Novel information measures based on Kolmogorov complexity  and used  for the quantification of randomness in time series, were presented.Novel measures were tested on various time series obtained by measuring physical factors of the environment or as the climate model outputs

Climate predictions: the chaos and complexity in climate models

Some issues which are relevant for the recent state in climate modeling have been considered. A detailed overview of literature related to this subject is given. The concept in modeling of climate, as a complex system, seen through Godel's Theorem and Rosen's definition of complexity and predictability is discussed. It is pointed out to occurrence of chaos in computing the environmental interface temperature from the energy balance equation given in a difference form. A coupled system of equations, often used in climate models is analyzed. It is shown that the Lyapunov exponent mostly has positive values allowing presence of chaos in this systems. The horizontal energy exchange between environmental interfaces, which is described by the dynamics of driven coupled oscillators, is analyzed. Their behavior and synchronization, when a perturbation is introduced in the system, as a function of the coupling parameters, the logistic parameter and the parameter of exchange, was studied calculating the Lyapunov exponent under simulations with the closed contour of N=100 environmental interfaces. Finally, we have explored possible differences in complexities of two global and two regional climate models using their output time series by applying the algorithm for calculating the Kolmogorov complexity.Comment: 21 pages, 6 figures, 1 table; This paper has been submitted to Advances in Meteorolog

Climate change effects and UV-B radiation in the Vojvodina region, Serbia under the SRES-A2

In this article we considered the extreme temperatures, precipitation and UV-B radiation in Vojvodina region, Serbia. We describe the actual climate conditions for the period 1981-2007 and applied a dynamic downscaling technique using the EBU-POM regional coupled climate model under the SRES-A2 scenario to assess the changes for the period 2021-2100. The results indicate that a warmer and drier climate in the Vojvodina region can be expected at the end of the century. Projection of climate indicates to a strong increase in the mean annual minimum temperatures, and much smaller increase in the mean annual maximum temperatures. The increase of both extreme temperatures is predicted to be the highest in the winter and the lowest in the summer. Mean annual precipitation is projected to increase toward the end of the first half of the 21st century and to decrease for the last 30 years of the 21st century. Precipitation amount will be the highest during the winter and spring. The model simulations show that, by the end of this century, annual mean UV-B dose will recover by 5.2%. Recovery will be faster in the first half of the 21st century and more slowly later on. The UV-B doses recovery is expected to be the highest during the autumn and spring

Quantifying the Effects of Drought Using the Crop Moisture Stress as an Indicator of Maize and Sunflower Yield Reduction in Serbia

The drought in Serbia in the summer of 2017 heavily affected agricultural production, decreasing yields of maize, sunflower, soybean, and sugar beet. Monitoring moisture levels in crops can provide timely information about potential risk within a growing season, thus helping to create an early warning system for various stakeholders. The purpose of this study was to quantify the level of moisture stress in crops during summer and the consequences that it can have on yields. For that, maize and sunflower yield data provided by an agricultural company were used at specific parcels in the Backa region of Vojvodina province (Serbia) for 2017, 2018, 2019, and 2020. The crop moisture level was estimated at each parcel by calculating the normalized difference moisture index (NDMI) from Sentinel-2 data during the summer months (June–July–August). Based on the average NDMI value in July, the new crop moisture stress (CMS) index was introduced. The results showed that the CMS values at a specific parcel could be used for within-season estimation of maize and sunflower yield and the assessment of drought effects. The CMS index was tested for the current growing season of 2022 as an early warning system for yield reduction, demonstrating the potential to be included in a platform for digital agriculture, such as AgroSens, which is operational in Serbia

Nonlinear dynamical analysis of the physical processes in the environment

Ispitivan  je  spregnut  sistem  jednačina  za  prognozu  temperature  na površini  i  u  dubljem sloju zemljišta.  Računati  su  Ljapunovljevi eksponenti,  bifurkacioni dijagram, atraktor i analiziran je domen rešenja. Uvedene su nove informacione mere  bazirane na Kolmogorovljevoj kompleksnosti,  za kvantifikaciju  stepena nasumičnosti u vremenskim serijama,.  Nove mere su primenjene na razne serije dobijene merenjem fizičkih faktora životne sredine i pomoću klimatskih modela.Coupled system of prognostic equations for  the  ground surface temperature and  the deeper layer temperature was examind. Lyapunov exponents, bifurcation diagrams, attractor and the domain of solutions were analyzed.  Novel information measures based on Kolmogorov complexity  and used  for the quantification of randomness in time series, were presented.Novel measures were tested on various time series obtained by measuring physical factors of the environment or as the climate model outputs

Kolmogorov Complexity Based Information Measures Applied to the Analysis of Different River Flow Regimes

We have used the Kolmogorov complexities and the Kolmogorov complexity spectrum to quantify the randomness degree in river flow time series of seven rivers with different regimes in Bosnia and Herzegovina, representing their different type of courses, for the period 1965–1986. In particular, we have examined: (i) the Neretva, Bosnia and the Drina (mountain and lowland parts), (ii) the Miljacka and the Una (mountain part) and the Vrbas and the Ukrina (lowland part) and then calculated the Kolmogorov complexity (KC) based on the Lempel–Ziv Algorithm (LZA) (lower—KCL and upper—KCU), Kolmogorov complexity spectrum highest value (KCM) and overall Kolmogorov complexity (KCO) values for each time series. The results indicate that the KCL, KCU, KCM and KCO values in seven rivers show some similarities regardless of the amplitude differences in their monthly flow rates. The KCL, KCU and KCM complexities as information measures do not “see” a difference between time series which have different amplitude variations but similar random components. However, it seems that the KCO information measures better takes into account both the amplitude and the place of the components in a time series

Quantifying the Effects of Drought Using the Crop Moisture Stress as an Indicator of Maize and Sunflower Yield Reduction in Serbia

The drought in Serbia in the summer of 2017 heavily affected agricultural production, decreasing yields of maize, sunflower, soybean, and sugar beet. Monitoring moisture levels in crops can provide timely information about potential risk within a growing season, thus helping to create an early warning system for various stakeholders. The purpose of this study was to quantify the level of moisture stress in crops during summer and the consequences that it can have on yields. For that, maize and sunflower yield data provided by an agricultural company were used at specific parcels in the Backa region of Vojvodina province (Serbia) for 2017, 2018, 2019, and 2020. The crop moisture level was estimated at each parcel by calculating the normalized difference moisture index (NDMI) from Sentinel-2 data during the summer months (June–July–August). Based on the average NDMI value in July, the new crop moisture stress (CMS) index was introduced. The results showed that the CMS values at a specific parcel could be used for within-season estimation of maize and sunflower yield and the assessment of drought effects. The CMS index was tested for the current growing season of 2022 as an early warning system for yield reduction, demonstrating the potential to be included in a platform for digital agriculture, such as AgroSens, which is operational in Serbia

Novel measures based on the Kolmogorov complexity for use in complex system behavior studies and time series analysis

We propose novel metrics based on the Kolmogorov complexity for use in complex system behavior studies and time series analysis. We consider the origins of the Kolmogorov complexity and discuss its physical meaning. To get better insights into the nature of complex systems and time series analysis we introduce three novel measures based on the Kolmogorov complexity: (i) the Kolmogorov complexity spectrum, (ii) the Kolmogorov complexity spectrum highest value and (iii) the overall Kolmogorov complexity. The characteristics of these measures have been tested using a generalized logistic equation. Finally, the proposed measures have been applied to different time series originating from: a model output (the biochemical substance exchange in a multi-cell system), four different geophysical phenomena (dynamics of: river flow, long term precipitation, indoor 222Rn concentration and UV radiation dose) and the economy (stock price dynamics). The results obtained offer deeper insights into the complexity of system dynamics and time series analysis with the proposed complexity measures