2 research outputs found

    Probabilistic-statistical models of the dynamics of climatic changes in the Altai Mountains

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    A probabilistic-statistical parameterization of time series characterizing geological and climatic processes allows determining some regularities by an autocorrelation analysis of signals which differ in nature. The use of the autocorrelation method for analyzing data related to solar and tectonic activity and characterizing the level of stratospheric ozone (total ozone content), hydrothermal regimes (De Martonne aridity index), and wood structure (maximum density of annual rings) allows us to find regularities in time series of various natural processes. Data on the maximum density of Siberian larch trees growing in the Altai Mountains made it possible to calculate the past changes in total ozone content and the aridity index in the Altai Mountains from 1900 to 2014 based on some similarities in the series and a separation of a dendrochronological signal into its main components

    Reconstruction of changes in stratospheric ozone in the taiga forests based of the singular spectral analysis

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    ΠΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½ΠΎΡΡ‚ΡŒ. ΠŸΡ€ΠΎΠ±Π»Π΅ΠΌΠ° сниТСния уровня стратосфСрного ΠΎΠ·ΠΎΠ½Π° ΠΈ интСрСс ΠΊ Π΅Π³ΠΎ ΠΏΡ€ΠΎΡˆΠ»Ρ‹ΠΌ измСнСниям связаны с происходящим ΠΏΡ€ΠΈ этом ΡƒΠ²Π΅Π»ΠΈΡ‡Π΅Π½ΠΈΠ΅ΠΌ Π΄ΠΎΠ·Ρ‹ ΡƒΠ»ΡŒΡ‚Ρ€Π°Ρ„ΠΈΠΎΠ»Π΅Ρ‚ΠΎΠ²ΠΎΠΉ Ρ€Π°Π΄ΠΈΠ°Ρ†ΠΈΠΈ Π² ΠΊΠΎΡ€ΠΎΡ‚ΠΊΠΎΠ²ΠΎΠ»Π½ΠΎΠ²ΠΎΠΌ Π΄ΠΈΠ°ΠΏΠ°Π·ΠΎΠ½Π΅, Π΄ΠΎΡΡ‚ΠΈΠ³Π°ΡŽΡ‰Π΅ΠΉ повСрхности Π—Π΅ΠΌΠ»ΠΈ. РСконструкция ΠΈ ΠΏΡ€ΠΎΠ³Π½ΠΎΠ· Π΄ΠΎΠ»Π³ΠΎΠΏΠ΅Ρ€ΠΈΠΎΠ΄Π½Ρ‹Ρ… ΠΊΠΎΠ»Π΅Π±Π°Π½ΠΈΠΉ исслСдуСмых ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ осущСствлСны ΠΏΡ€ΠΈ ΠΏΠΎΠΌΠΎΡ‰ΠΈ ΠΌΠ΅Ρ‚ΠΎΠ΄Π° мноТСствСнной рСгрСссии ΠΏΠΎ Π΄Π°Π½Π½Ρ‹ΠΌ плотности Π³ΠΎΠ΄ΠΈΡ‡Π½Ρ‹Ρ… ΠΊΠΎΠ»Π΅Ρ† Ρ…Π²ΠΎΠΉΠ½Ρ‹Ρ…. Π§Ρ‚ΠΎΠ±Ρ‹ Ρ€Π°ΡΡˆΠΈΡ€ΠΈΡ‚ΡŒ Π±Π°Π·Ρƒ ΡΠΊΡΠΏΠ΅Ρ€ΠΈΠΌΠ΅Π½Ρ‚Π°Π»ΡŒΠ½Ρ‹Ρ… Π΄Π°Π½Π½Ρ‹Ρ…, проводятся поисковыС Ρ€Π°Π±ΠΎΡ‚Ρ‹ ΠΏΠΎ ΠΈΠ·ΠΌΠ΅Ρ€Π΅Π½ΠΈΡŽ Π½ΠΎΠ²Ρ‹Ρ… характСристик Π³ΠΎΠ΄ΠΈΡ‡Π½Ρ‹Ρ… ΠΊΠΎΠ»Π΅Ρ†, Π° Ρ‚Π°ΠΊΠΆΠ΅ уточняСтся матСматичСский Π°ΠΏΠΏΠ°Ρ€Π°Ρ‚ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊ ΠΏΡ€Π΅Π΄Π²Π°Ρ€ΠΈΡ‚Π΅Π»ΡŒΠ½ΠΎΠΉ ΠΎΠ±Ρ€Π°Π±ΠΎΡ‚ΠΊΠΈ Π΄Π°Π½Π½Ρ‹Ρ…, Π°Π½Π°Π»ΠΈΠ·ΠΈΡ€ΡƒΡŽΡ‚ΡΡ связи Π±ΠΈΠΎΠΈΠ½Π΄ΠΈΠΊΠ°Ρ‚ΠΎΡ€ΠΎΠ² с атмосфСрными ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€Π°ΠΌΠΈ, ΠΈ Ρ€Π°Π·Ρ€Π°Π±Π°Ρ‚Ρ‹Π²Π°ΡŽΡ‚ΡΡ Π½ΠΎΠ²Ρ‹Π΅ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΈΡ… ΠΏΡ€ΠΎΠ³Π½ΠΎΠ·Π°. РСконструкция историчСских ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ атмосфСрных характСристик ΠΌΠΎΠΆΠ΅Ρ‚ Π±Ρ‹Ρ‚ΡŒ рассмотрСна Π² контСкстС пСрспСктивы искусствСнного восстановлСния лСсных рСсурсов. ЦСль: Ρ€Π°ΡΡΠΌΠΎΡ‚Ρ€Π΅Ρ‚ΡŒ ΠΌΠ΅Ρ‚ΠΎΠ΄ΠΈΠΊΡƒ рСконструкции ΠΎΠ±Ρ‰Π΅Π³ΠΎ содСрТания ΠΎΠ·ΠΎΠ½Π° Π½Π° основС Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½ΠΎΠΉ ΠΌΠ½ΠΎΠ³ΠΎΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚Π½ΠΎΠΉ сингулярной ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΎΡ‚ΠΊΠ»ΠΈΠΊΠ° дрСвСсины Π³ΠΎΠ΄ΠΈΡ‡Π½Ρ‹Ρ… ΠΊΠΎΠ»Π΅Ρ† Ρ…Π²ΠΎΠΉΠ½Ρ‹Ρ… Π½Π° измСнСния атмосфСрных ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ². ΠžΠ±ΡŠΠ΅ΠΊΡ‚Ρ‹. Π’ Ρ‚Π΅Ρ‡Π΅Π½ΠΈΠ΅ сСзона роста Ρƒ Π΄Π΅Ρ€Π΅Π²ΡŒΠ΅Π² формируСтся дрСвСсная структура Π³ΠΎΠ΄ΠΈΡ‡Π½Ρ‹Ρ… ΠΊΠΎΠ»Π΅Ρ† со свойствами ΠΊΠΎΠΌΠΏΠΎΠ·ΠΈΡ†ΠΈΠΎΠ½Π½ΠΎΠ³ΠΎ ΠΌΠ°Ρ‚Π΅Ρ€ΠΈΠ°Π»Π°. КаТдая ΠΈΠ· ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚ Π³ΠΎΠ΄ΠΈΡ‡Π½ΠΎΠ³ΠΎ ΠΊΠΎΠ»ΡŒΡ†Π°: углСродсодСрТащая ΠΌΠ°Ρ‚Ρ€ΠΈΡ†Π°, Π²ΠΎΠ΄Π° ΠΈ углСкислый Π³Π°Π·, содСрТит ΠΎΡ‚ΠΊΠ»ΠΈΠΊΠΈ Π½Π° измСнСния условий ΠΎΠΊΡ€ΡƒΠΆΠ°ΡŽΡ‰Π΅ΠΉ срСды. На основС ΠΌΠ½ΠΎΠ³ΠΎΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ суммарныС ΠΎΡ‚ΠΊΠ»ΠΈΠΊΠΈ Π³ΠΎΠ΄ΠΈΡ‡Π½Ρ‹Ρ… ΠΊΠΎΠ»Π΅Ρ†, ΠΏΠΎΠ»ΡƒΡ‡Π΅Π½Π½Ρ‹Π΅ ΠΏΡ€ΠΈ ΠΏΠΎΠΌΠΎΡ‰ΠΈ ΠΏΡ€ΠΎΠ³Ρ€Π°ΠΌΠΌΠ½ΠΎΠ³ΠΎ обСспСчСния Caterpillar SSA 3.40, ΠΌΠΎΠ³ΡƒΡ‚ Π±Ρ‹Ρ‚ΡŒ ΠΈΡΠΏΠΎΠ»ΡŒΠ·ΠΎΠ²Π°Π½Ρ‹ для рСконструкции ΠΈΠ·ΠΌΠ΅Π½Π΅Π½ΠΈΠΉ ΠΎΠ±Ρ‰Π΅Π³ΠΎ содСрТания ΠΎΠ·ΠΎΠ½Π° Π² Π·ΠΎΠ½Π°Ρ… с ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ условиями роста Ρ…Π²ΠΎΠΉΠ½Ρ‹Ρ… Π² ΠΎΡ‚Π»ΠΈΡ‡ΠΈΠ΅ ΠΎΡ‚ Π·ΠΎΠ½ с Π΄ΠΎΠΌΠΈΠ½ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅ΠΌ Ρ‚Π΅ΠΌΠΏΠ΅Ρ€Π°Ρ‚ΡƒΡ€Π½ΠΎΠ³ΠΎ Ρ„Π°ΠΊΡ‚ΠΎΡ€Π°. ΠœΠ΅Ρ‚ΠΎΠ΄Ρ‹: дСндрохронологичСский ΠΌΠ΅Ρ‚ΠΎΠ΄; сингулярный ΡΠΏΠ΅ΠΊΡ‚Ρ€Π°Π»ΡŒΠ½Ρ‹ΠΉ Π°Π½Π°Π»ΠΈΠ·; экономСтричСскиС ΠΌΠ΅Ρ‚ΠΎΠ΄Ρ‹ Π°Π½Π°Π»ΠΈΠ·Π° Π²Ρ€Π΅ΠΌΠ΅Π½Π½Ρ‹Ρ… рядов; ΠΈΠ½Ρ‚Π΅Π»Π»Π΅ΠΊΡ‚ΡƒΠ°Π»ΡŒΠ½Ρ‹ΠΉ Π°Π½Π°Π»ΠΈΠ· Π΄Π°Π½Π½Ρ‹Ρ…; ΠΈΠΌΠΈΡ‚Π°Ρ†ΠΈΠΎΠ½Π½ΠΎΠ΅ ΠΌΠΎΠ΄Π΅Π»ΠΈΡ€ΠΎΠ²Π°Π½ΠΈΠ΅. Π Π΅Π·ΡƒΠ»ΡŒΡ‚Π°Ρ‚Ρ‹. Π”ΠΎΡΡ‚ΠΎΠ²Π΅Ρ€Π½ΠΎΡΡ‚ΡŒ Ρ€Π°Π·Ρ€Π°Π±ΠΎΡ‚Π°Π½Π½ΠΎΠΉ ΠΌΠΎΠ΄Π΅Π»ΠΈ ΠΎΡ‚ΠΊΠ»ΠΈΠΊΠΎΠ² Π³ΠΎΠ΄ΠΈΡ‡Π½Ρ‹Ρ… ΠΊΠΎΠ»Π΅Ρ† Ρ…Π²ΠΎΠΉΠ½Ρ‹Ρ… Π½Π° измСнСния атмосфСрных ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² подтвСрТдаСтся Π½Π° Π²Ρ‹Π±ΠΎΡ€ΠΊΠ΅ эргодичных Ρ…Ρ€ΠΎΠ½ΠΎΠ»ΠΎΠ³ΠΈΠΉ ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚ дрСвСсины. ΠšΡ€ΠΈΡ‚Π΅Ρ€ΠΈΠΉ Π”Π°Ρ€Π±ΠΈΠ½Π°-Уотсона позволяСт Π²Ρ‹Π΄Π΅Π»ΠΈΡ‚ΡŒ Π³Ρ€ΡƒΠΏΠΏΡƒ Ρ…Ρ€ΠΎΠ½ΠΎΠ»ΠΎΠ³ΠΈΠΉ, Π² модСлях ΠΊΠΎΡ‚ΠΎΡ€Ρ‹Ρ… отсутствуСт автокоррСляция Π²ΠΎΠ·ΠΌΡƒΡ‰Π΅Π½ΠΈΠΉ. ИспользованиС ΠΌΠΎΠ΄Π΅Π»ΠΈ суммарных ΠΎΡ‚ΠΊΠ»ΠΈΠΊΠΎΠ² для Ρ‚Ρ€Π΅Ρ… ΠΊΠΎΠΌΠΏΠΎΠ½Π΅Π½Ρ‚ дрСвСсины сущСствСнно ΠΏΠΎΠ²Ρ‹ΡˆΠ°Π΅Ρ‚ Π΄ΠΎΡΡ‚ΠΎΠ²Π΅Ρ€Π½ΠΎΡΡ‚ΡŒ рСконструкции исслСдуСмых атмосфСрных ΠΏΠ°Ρ€Π°ΠΌΠ΅Ρ‚Ρ€ΠΎΠ² для Ρ‚Π°Π΅ΠΆΠ½ΠΎΠΉ Π·ΠΎΠ½Ρ‹ с ΠΎΠΏΡ‚ΠΈΠΌΠ°Π»ΡŒΠ½Ρ‹ΠΌΠΈ условиями для Π³ΠΎΠ΄ΠΈΡ‡Π½ΠΎΠ³ΠΎ прироста Π΄Π΅Ρ€Π΅Π²ΡŒΠ΅Π². Π”Π°Π½Π½Ρ‹Π΅ рСконструкции ΠΎΠ±Ρ‰Π΅Π³ΠΎ содСрТания ΠΎΠ·ΠΎΠ½Π° для Ρ‚Π°Π΅ΠΆΠ½ΠΎΠΉ Π·ΠΎΠ½Ρ‹ Вомского области ΠΏΠΎΠ·Π²ΠΎΠ»ΡΡŽΡ‚ ΡΠ΄Π΅Π»Π°Ρ‚ΡŒ Π²Ρ‹Π²ΠΎΠ΄, Ρ‡Ρ‚ΠΎ, нСсмотря Π½Π° рост уровня ΠΎΠ·ΠΎΠ½Π° Π² стратосфСрС, измСнСния ΠΎΠ±Ρ‰Π΅Π³ΠΎ содСрТания ΠΎΠ·ΠΎΠ½Π° Π½Π΅ Π²Π΅Ρ€Π½ΡƒΠ»ΠΈΡΡŒ ΠΊ своим срСдним историчСским значСниям, ΡƒΡ€ΠΎΠ²Π΅Π½ΡŒ Π£Π€-Π’ ΠΏΠΎ-ΠΏΡ€Π΅ΠΆΠ½Π΅ΠΌΡƒ высок, Π½ΠΎ, Ρ‚Π΅ΠΌ Π½Π΅ ΠΌΠ΅Π½Π΅Π΅, тСрритория ΠΈ соврСмСнный ΠΏΠ΅Ρ€ΠΈΠΎΠ΄ ΡΠ²Π»ΡΡŽΡ‚ΡΡ благоприятными для лСсопосадок.The relevance. The problem of reducing the level of stratospheric ozone and the interest in its past changes are associated with the increase in the dose of ultraviolet radiation in the short-wave range reaching the Earth's surface. Reconstruction and prediction of longperiod fluctuations of the studied parameters can be carried out using the method of multiple regression according to the density of annual growth rings of conifers. In order to expand the experimental data base, exploratory work is being carried out to measure new characteristics of annual growth rings, as well as the mathematical apparatus of data preprocessing techniques is being refined, the connections of bioindicators with atmospheric parameters are being analyzed and new models of their prediction are being developed. Reconstruction of historical changes in atmospheric characteristics can be considered in the context of the prospect of artificial restoration of forest resources. The aim of the research is to discuss a technique for reconstructing the total ozone content based on a multicomponent singular spectral model of the response of conifers annual growth rings. Objects. During the growing season, trees form a woody structure of annual growth rings with the properties of a composite material. Each of the components of the annual growth rings such as a carbon-containing matrix, water and carbon dioxide contains responses to changes in environmental conditions. On the basis of a multicomponent model, the total responses of annual rings obtained using the Caterpillar SSA 3.40 software can be used to reconstruct changes in the total ozone content in zones with optimal coniferous growth conditions, as opposed to zones with dominance of the temperature factor. Methods: dendrochronological method; singular spectral analysis; econometric methods of time series analysis; data mining; simulation modeling. Results. The reliability of the model of responses of coniferous annual growth rings to changes in atmospheric parameters is confirmed by a sample of ergodic chronologies of wood components. The Durbin-Watson statistic makes it possible to identify a group of chronologies in whose models there is no autocorrelation of perturbations. The use of the cumulative response model for three wood components significantly increases the reliability of the reconstruction of the studied atmospheric parameters for the taiga zone with optimal conditions for annual tree growth. The data of the reconstruction of the total ozone content for the taiga zone of the Tomsk region allow us to conclude that despite the increase in the ozone level in the stratosphere, the changes in the total ozone content have not returned to their average historical values, the level of UV-B is still high, but nevertheless the territory and the modern period are favorable for forest plantations
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