NP4.1 | Complex system based time series analysis in the geosciences and beyond
EDI

Time series obtained within the different geoscientific disciplines commonly exhibit a large degree of irregularity, complexity and/or nonstationarity. In such cases, the use of classical (linear) concepts for the statistical analysis and modelling of time series (such as power spectra, autoregressive or other linear models) may be insufficient to obtain reliable and correct process-related information from the available data. Conversely, applying emergent concepts developed in fields like dynamical system theory, stochastic processes or computer science may provide useful tools to foster the knowledge discovery from complex geoscientific systems. Many of the corresponding methods from nonlinear time series analysis have meanwhile matured and reached a stage of broad applicability while still undergoing further methodological refinements, extensions and adaptations.

This session brings together researchers developing time series analysis approaches tailored to nonlinear deterministic and/or stochastic dynamical systems with such applying those concepts across the different geoscientific disciplines and beyond. We are confident that methodological knowledge transfer across the different topical fields present at EGU is of utmost relevance to improving our capability, as a community, to derive the most useful pieces of information from the growing amount of available data on various geoscientific phenomena. Therefore, we cordially invite contributions using different types of approaches, including (but not limited to) multi-scale methods for time series, information theoretic concepts, statistical complexity measures, causal inference, state space methods, stochastic process descriptions, etc., addressing recent methodological developments and/or successful applications to time series from any geoscience discipline and beyond.

Co-organized by CL5/ST4
Convener: Reik Donner | Co-conveners: Tommaso Alberti, Giorgia Di Capua, Simone Benella
Orals
| Tue, 25 Apr, 16:15–18:00 (CEST)
 
Room G2
Posters on site
| Attendance Mon, 24 Apr, 14:00–15:45 (CEST)
 
Hall X4
Posters virtual
| Attendance Mon, 24 Apr, 14:00–15:45 (CEST)
 
vHall ESSI/GI/NP
Orals |
Tue, 16:15
Mon, 14:00
Mon, 14:00

Orals: Tue, 25 Apr | Room G2

16:15–16:20
16:20–16:30
|
EGU23-5840
|
NP4.1
|
solicited
|
Highlight
|
On-site presentation
Michel Crucifix, Linda Hinnov, Anne-Christine Da Silva, David De Vleeschouwer, Stephen Meyers, Andrew Parnell, Matthias Sinnesael, Thomas Westerhold, and Sébastien Wouters

Time series analysis of palaeoclimate data is used to identify quasi-periodic changes attributable to astronomical forcing of insolation by Earth’s axial obliquity and precession, and orbital eccentricity, i.e., Milankovitch cycles. Hays et al. (1976) applied time series analysis – including spectral analysis, filtering, tuning and hypothesis testing – on palaeoclimatic data from the most recent 500 Ka of Earth history to demonstrate forcing from these astronomical parameters. The CENOGRID “splice” (Westerhold et al., 2000) has since extended this evidence to 66 Ma. Investigators have also recognised the imprint of Milankovitch cycles in palaeoclimatic records reaching back into the Precambrian. 

Palaeoclimate time series present unique challenges: sample spacing is generally not constant; measured data represent combinations of palaeoenvironmental factors; most problematic of all, palaeoclimate time scales are almost never known with adequate certainty. Important time constraints are provided by geochronology from volcanic ash layers, geomagnetic reversals and selected chemostratigraphic events, but only at isolated, widely spaced points along geologic time, and only extremely rarely do they provide a precision sufficient to determine the time-periodicity of palaeoclimate variations at Milankovitch scales. Investigators must also grapple with uncertainties in celestial mechanics, and in the theory of climate change, sedimentation and alteration. From this collective information, one may choose to investigate mechanisms of climate or environmental change (climate modelling); estimate the chronology and duration of stratigraphic series of palaeoclimate data (cyclostratigraphy); and constrain the celestial mechanics of Earth’s distant past. 

In principle, all of these objectives can be obtained through application of a hierarchical Bayesian model: astronomical forcing -> climate -> environment -> sedimentation -> alteration -> observation. Bayesian theory allows us to reverse all of the arrows and to update information about sedimentation, the environment, climate, and astronomical forcing. However, in Bayesian statistics, expressing a likelihood function is a fundamental step and requires parameterising stochastic quantities. One needs to be clear and explicit about errors. We present an example that considers an explicit-likelihood route for Quaternary data (Carson et al., 2019). In the more distant geologic past, uncertainties about climate and sedimentation are increasingly challenging. Strategies tend to be based on pattern identification by the investigator, with or without numerical techniques. Examples include recognising orbital eccentricity bundling in paleoclimatic data sequences that exhibit precession cycling, and studying the relationships between frequency and amplitudes (Meyers and Malinverno, 2018). We review examples illustrating the relationship between frequency and amplitude together with the supporting theory. 

References: Carson et al., Proc. R. Soc. A (2019), 475, 20180854; Hays et al., Sci. (1976), 194(4270), 1121-1132; Meyers, S.R., Malinverno, A., Proc. Natl. Acad. Sci. U.S.A. (2018), 115(25), 6363-6368; Westerhold et al., Sci. (2020), 369, 1383-1387.

How to cite: Crucifix, M., Hinnov, L., Da Silva, A.-C., De Vleeschouwer, D., Meyers, S., Parnell, A., Sinnesael, M., Westerhold, T., and Wouters, S.: Advances in Bayesian time series analysis of palaeoclimate data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-5840, https://doi.org/10.5194/egusphere-egu23-5840, 2023.

16:30–16:40
|
EGU23-2400
|
NP4.1
|
ECS
|
On-site presentation
|
Mengyi Gong, Rebecca Killick, Christopher Nemeth, John Quinton, and Jessica Davis

Healthy soil plays a critical role in sustaining biodiversity, maintaining food production, and mitigating climate change through carbon capture. Soil moisture is an important measure of soil health that scientists model via soil drydown curves. The typical modelling process requires manually separating the soil moisture time series into segments representing the drying process and fitting exponential decay models to these segments to obtain an estimation of the key parameters. With the advancement of sensor technology, scientists can now obtain higher frequency measurements over longer periods in a larger number of locations. To enable automatic data processing and to obtain a dynamic view of the soil moisture drydown, a changepoint-based approach is developed to automatically identify structural changes in soil moisture time series.

Specifically, timings of the sudden rises in soil moisture over a long time series are captured and the parameters characterising the drying processes following the sudden rises are estimated simultaneously. An algorithm based on the penalised exact linear time (PELT) method was developed to identify the changepoints and estimate the model parameters. This method can be considered as a complement to the conventional soil moisture modelling. It requires little data pre-processing and can be applied to a soil moisture time series directly. Since each drying segment has its unique parameters, the method also has the potential of capturing any temporal variations in the drying process, thus providing a more comprehensive summary of the data.

The method was applied to the hourly soil moisture time series of nine field sites from the NEON data portal (https://data.neonscience.org/). Distributions and summary statistics of key model parameters, such as the exponential decay rate and the asymptotic soil moisture level, are produced for each field site. Investigating and comparing these quantities from different field sites enables the identification of soil signatures which can reflect the hydrological properties of the soil. Visualising the model parameters as a time series reveals the subtle temporal pattern of the drying process in some field sites. 

How to cite: Gong, M., Killick, R., Nemeth, C., Quinton, J., and Davis, J.: Identifying soil signatures from soil moisture time series via a changepoint-based approach, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-2400, https://doi.org/10.5194/egusphere-egu23-2400, 2023.

16:40–16:50
|
EGU23-4698
|
NP4.1
|
ECS
|
On-site presentation
Mitsuhiro Hirano and Hiroyuki Nagahama

It is known that power law exists in the background of various natural phenomena. One example is the viscoelastic behavior of rocks. In the flow laws of high temperature of rocks, strain rate is in proportion to the power of stress. It can be replaced by the one that relaxation modulus (the ratio of stress to strain) is in proportion to the power of time with fractal dimension as power exponent. From Laplace transform for the relaxation modulus, the distribution of relaxation time (relaxation spectrum) with the power of relaxation time is derived. It indicates the existence of fractal distribution of different relaxation times in material elements in rocks. On the other hand, these strain-relaxation modulus-stress relations can be recaptured as the input-response-output relation in an ideal complex system with the power law of component. When input and output are stochastic with probability functions, the response corresponds to the change in differential geometric structure on a statistical manifold with a point as a probability function. Although previous studies suggested the correspondence between the power exponent (fractal dimension) and the constant (alpha) characterizing invariant geometric structure (alpha-connection), its details have not been discussed yet. In this presentation, we would reveal the correspondence between the power exponent (fractal dimension) and the constant (alpha) based on q-exponential family in information geometry, which is a more general exponential family.

How to cite: Hirano, M. and Nagahama, H.: The power low in information geometry: Attempt from the viscoelastic relaxation of rock, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4698, https://doi.org/10.5194/egusphere-egu23-4698, 2023.

16:50–17:00
|
EGU23-1464
|
NP4.1
|
On-site presentation
Uwe Ehret, Sanika Baste, and Pankaj Dey

Recently, Ehret and Dey (2022) suggested the c-u-curve method to analyze, classify and compare dynamical systems of arbitrary dimension, deterministic or probabilistic, by the two key features uncertainty and complexity. It consists of subdividing the system’s time-trajectory into a number of time slices. For all values in a time slice, the Shannon information entropy is calculated, measuring within-slice variability. System uncertainty is expressed by the mean entropy of all time slices. System complexity is then defined as “uncertainty about uncertainty”, expressed by the entropy of the entropies of all time slices. Calculating and plotting uncertainty u and complexity c for many different numbers of time slices yields the c-u-curve. Systems can be analyzed, compared and classified by the c-u-curve in terms of i) its overall shape, ii) mean and maximum uncertainty, iii) mean and maximum complexity, and iv) its characteristic time scale expressed by the width of the time slice for which maximum complexity occurs.

In our contribution, we will briefly revisit the basic concepts of the c-u-curve method, and then present results from applying it to hydro-meteorological time series of 512 catchments from the CAMELS-US data set (Newman et al., 2015). We will show how c-u-curve properties i) relate to hydro-climatological features, ii) how they can be used for catchment classification, and iii) how the classes compare to existing classifications by Knoben et al. (2018) and Jehn et al. (2020).

References

Ehret, U., and Dey, P.: Technical note: c-u-curve: A method to analyse, classify and compare dynamical systems by uncertainty and complexity, Hydrol. Earth Syst. Sci. Discuss., 2022, 1-12, 10.5194/hess-2022-16, 2022.

Jehn, F. U., Bestian, K., Breuer, L., Kraft, P., and Houska, T.: Using hydrological and climatic catchment clusters to explore drivers of catchment behavior, Hydrol. Earth Syst. Sci., 24, 1081-1100, 10.5194/hess-24-1081-2020, 2020.

Knoben, W. J. M., Woods, R. A., and Freer, J. E.: A Quantitative Hydrological Climate Classification Evaluated With Independent Streamflow Data, Water Resources Research, 54, 5088-5109, https://doi.org/10.1029/2018WR022913, 2018.

Newman, A. J., Clark, M. P., Sampson, K., Wood, A., Hay, L. E., Bock, A., Viger, R. J., Blodgett, D., Brekke, L., Arnold, J. R., Hopson, T., and Duan, Q.: Development of a large-sample watershed-scale hydrometeorological data set for the contiguous USA: data set characteristics and assessment of regional variability in hydrologic model performance, Hydrol. Earth Syst. Sci., 19, 209-223, 10.5194/hess-19-209-2015, 2015.

How to cite: Ehret, U., Baste, S., and Dey, P.: Analyzing and classifying dynamical hydrological systems by uncertainty and complexity with the c-u-curve method, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-1464, https://doi.org/10.5194/egusphere-egu23-1464, 2023.

17:00–17:10
|
EGU23-6728
|
NP4.1
|
On-site presentation
Stéphane Vannitsem

The impact of climate change on weather pattern dynamics over the North Atlantic is explored through the lens of the information theory of forced dissipative dynamical systems.

The predictability problem is first tackled by investigating the evolution of block entropies on observational time series of weather patterns produced by the Met Office, which reveals that predictability is increasing as a function of time in the observations during the 19th century and beginning of the 20th century, while the trend is reversed at the end of the 20th century and beginning of the 21st century. This feature is also investigated in the 15-member ensemble of the UK Met Office CMIP5 model for the 20th and 21st centuries under two climate change scenarios, revealing a wide range of possible evolutions depending on the realization considered, with an overall decrease in predictability in the 21st century for both scenarios.

Lower bounds of the information entropy production are also extracted, providing information on the degree of time asymmetry and irreversibility of the dynamics. The analysis of the UK Met Office model runs suggests that the information entropy production will increase by the end of the 21st century, by a factor of 10% in the Representative Carbon Pathway RCP2.6 scenario and a factor of 30 %–40% in the RCP8.5 one, as compared to the beginning of the 20th century. This allows one to make the conjecture that the degree of irreversibility is increasing, and hence heat production and dissipation will also increase under climate change, corroborating earlier findings based on the analysis of the thermodynamic entropy production.

How to cite: Vannitsem, S.: Weather pattern dynamics over western Europe under climate change: predictability, information entropy and production, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6728, https://doi.org/10.5194/egusphere-egu23-6728, 2023.

17:10–17:20
|
EGU23-6327
|
NP4.1
|
On-site presentation
Suzana Blesic, Milica Tosic, Neda Aleksandrov, Thandi Kapwata, Rajendra Maharaj, and Caradee Wright

We preformed statistical analysis of two sets of malaria incidence time series: of daily admissions from two large public hospitals in Limpopo Province in South Africa (records taken in the period 2002-2017), and of weekly epidemiological reports from five districts in the same province (for the period 2000-2020). We analysed these time series in relation to time series of temperature and rainfall ground or satellite data from the same geographical area.

Firstly, we used wavelet transform (WT) cross-correlation analysis to monitor and characterize coincidences in daily changes of meteorological variables and variations in hospital admissions. All our daily admission records had global wavelet power spectra (WTS) of the power-law type, indicating that they are outputs of complex sets of causes acting on different time scales. We found that malaria in South Africa is a seasonal multivariate event, initiated by co-occurrence of heat and rainfall. We then proceeded to utilize obtained results for the analysis of the weekly cases data, using the WTS superposition of signals rule to discern WTS peaks that are time lags between the onset of combined meteorological drivers and hospital admissions for malaria. We presumed that all these peaks are characteristic times connected to the characteristic periods of development, distribution and survival of either mosquitos, as disease vectors, the pathogens they transmit, or the times needed for human incubation of the disease. Thus, we were able to propose a regression model for the number of admissions (for malaria) cases, and to provide critical values of temperature and rainfall for the initiation of the disease spread.

Finally, using the developed model we investigated how future changes of meteorological variables and their combination can affect malaria dynamics, and thus provide information that can be of use for public health preparedness.

How to cite: Blesic, S., Tosic, M., Aleksandrov, N., Kapwata, T., Maharaj, R., and Wright, C.: Understanding and modeling meteorological drivers of the number of hospital admissions for malaria in South Africa, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-6327, https://doi.org/10.5194/egusphere-egu23-6327, 2023.

17:20–17:30
|
EGU23-7730
|
NP4.1
|
On-site presentation
|
Norbert Marwan and Tobias Braun

The estimation of power spectral density (PSD) of time series is an important task in many quantitative scientific disciplines. However, the estimation of PSD from discrete data, such as extreme event series is challenging. We present a novel approach for the estimation of a PSD of discrete data. Combining the edit distance metric with the Wiener-Khinchin theorem provides a simple yet powerful PSD analysis for discrete time series (e.g., extreme events). This method works directly with the event time series without interpolation. We demonstrate the method's potential on some prototypical examples and on event sequences of atmospheric rivers (AR), narrow filaments of extensive water vapor transport in the lower troposphere. Considering the spatial-temporal event series of ARs over Europe, we investigate the presence of a seasonal cycle as well as periodicities in the multi-annual range for specific regions, likely related to the North-Atlantic Oscillation (NAO).

How to cite: Marwan, N. and Braun, T.: Power spectrum estimation for extreme events data, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7730, https://doi.org/10.5194/egusphere-egu23-7730, 2023.

17:30–17:40
|
EGU23-14863
|
NP4.1
|
Virtual presentation
Ana M. Tarquis, Andres F. Almeida-Ñauñay, Ernesto Sanz, Juan C. Losada, and Rosa M. Benito

Being one of the essential ecosystems, grasslands represent an important ecological area for water and biodiversity conservation. In this line, remote sensing instruments are a helpful tool for assessing vegetation status. The Modified Soil-Adjusted Vegetation Index (MSAVI) time-series are used to monitor drought events and to consider the soil influence in vegetation monitoring. In this sense, Recurrence plots (RPs) techniques have been demonstrated to be one of the most capable tools to unravel the complex dynamics of the time-series analysis. This work highlights the recurrence techniques' benefits in visualising and quantifying vegetation dynamics.

We chose a study area in the centre of Spain, where the Mediterranean climate dominates. We selected the MODQ1.V006 product from the MODIS imagery collection, with a spatial resolution of 250x250m. Then, an average MSAVI time series from pixels that met predefined criteria were analysed. RPs and Cross recurrence plots (CRPs) were computed to reveal the dynamics of the time series. Furthermore, diagonal-wise profiles (DWP)  and windowed-cross recurrence plots (WCRPs) were included in the analysis at different time scales. In the end, RPs, CRPs and WCRPs are quantified through the recurrence quantification analysis (RQA).

RPs displayed different patterns depending on the studied time series. Precipitation showed a stochastic dynamic, emphasising the unstable behaviour of Mediterranean rainfalls. On the opposite, temperature revealed a diagonal-like pattern in the RP. This fact pointed out the temperature's seasonal behaviour over time. Concerning MSAVI, RP presented a mixture of both patterns.

CRPs between precipitation and MSAVI showed a delayed consequence of MSAVI to precipitation events. Contrary to precipitation, CRPs between temperature and MSAVI did not show a delayed response in the studied period. WCRPs indicated characteristic phases in the time series, revealing interactions between vegetation and climate and being different between wet and dry seasons.

RPs techniques have been demonstrated to be a valuable instrument for uncovering the complex dynamics between vegetation and climate. Therefore, they should be considered a viable alternative in the vegetation time series analysis.

 

Acknowledgements: The authors acknowledge the support of Clasificación de Pastizales Mediante Métodos Supervisados - SANTO from Universidad Politécnica de Madrid (project number: RP220220C024).

References

Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J.C., Tarquis, A.M., 2022. Recurrence plots for quantifying the vegetation indices dynamics in a semiarid grassland. Geoderma 406, 115488. https://doi.org/10.1016/j.geoderma.2021.115488

Almeida-Ñauñay, A.F., Benito, R.M., Quemada, M., Losada, J.C., Tarquis, A.M., 2021. The Vegetation–Climate System Complexity through Recurrence Analysis. Entropy 23, 559. https://doi.org/10.3390/e23050559

Martín-Sotoca, J.J., Saa-Requejo, A., Moratiel, R., Dalezios, N., Faraslis, I., Tarquis, A.M., 2019. Statistical analysis for satellite-index-based insurance to define damaged pasture thresholds. Nat. Hazards Earth Syst. Sci. 19, 1685–1702. https://doi.org/10.5194/nhess-19-1685-2019

Sanz, E., Saa-Requejo, A., Díaz-Ambrona, C.H., Ruiz-Ramos, M., Rodríguez, A., Iglesias, E., Esteve, P., Soriano, B., Tarquis, A.M., 2021. Normalized Difference Vegetation Index Temporal Responses to Temperature and Precipitation in Arid Rangelands. Remote Sens. 13, 840. https://doi.org/10.3390/rs13050840

How to cite: Tarquis, A. M., Almeida-Ñauñay, A. F., Sanz, E., Losada, J. C., and Benito, R. M.: Windowed recurrence plot approach in semiarid grasslands, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-14863, https://doi.org/10.5194/egusphere-egu23-14863, 2023.

17:40–18:00
|
EGU23-11060
|
NP4.1
|
solicited
|
On-site presentation
Giuseppe Consolini

The dynamics of the Earth's magnetosphere are a tremendously complex system that exhibit nonlinear dynamics in response to variations in the solar wind and interplanetary magnetic field. It has been amply demonstrated in the past that scale-invariant processes define magnetospheric dynamics as measured by geomagnetic indices. Forced and/or Self Organized Criticality, a term coined by T.S. Chang in the 90s, describes the plasma dynamics in the magnetospheric tail region. The multifractal structure of the variations of geomagnetic indices is another distinctive feature of the Earth's magnetospheric response.  Here, we use the joint multifractal measures approach first proposed by Meneveau et al. (1990) and low and high latitude geomagnetic indices  (AE, AL, Sym-H, Asy-H ,etc) to examine the link between the intermittency degrees of high and low latitude dynamics. The findings are examined in regard to the coupling of storms and substorms. 

How to cite: Consolini, G.: Joint-Multifractal Analysis of High and Low Latitude Magnetospheric Dynamics., EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11060, https://doi.org/10.5194/egusphere-egu23-11060, 2023.

Posters on site: Mon, 24 Apr, 14:00–15:45 | Hall X4

X4.86
|
EGU23-8485
|
NP4.1
Anna Albano, Vincenzo Carbone, and Francesco Lamonaca

A Measurement Node for the continuous gravity and tilt observations in an active

geodynamic area of southern Italy: the Calabrian Arc system

Anna Albano1, Vincenzo Carbone1, Francesco Lamonaca2

1Dipartimento di Fisica, Università della Calabria, Arcavacata di Rende (CS), Italy

2Dipartimento di Ingegneria Informatica, Modellistica, Elettronica e Sistemistica,
Università della Calabria, Arcavacata di Rende (CS), Italy

 

Calabria (southern Italy) is a site of considerable seismic activity related to the ongoing evolution of the Calabrian Arc system, where a complex lithospheric structure is present. For over a century the Calabrian region has been going through a period of relative seismic quietness, yet its seismic hazard is at the highest levels in the Mediterranean basin due to several catastrophic earthquakes present in the historical records. In order to strengthen the geophysical monitoring of this region, a gravity and tilt recording station was set up in the premises of the University of Calabria. The measurement node is composed by the gravimeter G-1089 from LaCoste & Romberg; the tilt-meter Model 714 from Applied Geomechanics; a 6 and ½ digits multimeter Agilent 34970A data acquisition and switching unit used to converts the analog signals of the gravimeter and tilt-meter in the corresponding digital ones; a computer used to store, elaborate and present the signals. The delay among the input channels of the multimeter is evaluated and the optimal configuration is achieved in order to make such a delay negligible for the time correlation of the input signals. The measurement node is positioned on the ground of the cube 41C. Finally, the information about the temperature and the atmospheric pressure is obtained by the nearby environmental station positioned on the roog of cube 41B. The recorded signals should allow to estimate a tidal anomaly, possibly correlated with the difference between some local feature of the lithosphere or geodynamic activity and the corresponding characteristics of the model used to calculate the reference gravity tide. A reliable model of the gravity tide is necessary for accurate processing of discrete absolute and relative gravimetric measurements and to detect in the gravity signals possible components correlated to major seismic activity. The Ocean Tide Load (OTL) effect was accounted for in the determination of the tidal field spectral parameters. The most widespread DDW99/ NH Earth’s model, adopted here as reference, fits the obtained results well enough.

How to cite: Albano, A., Carbone, V., and Lamonaca, F.: A Measurement Node for the continuous gravity and tilt observations in an activegeodynamic area of southern Italy: the Calabrian Arc system, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-8485, https://doi.org/10.5194/egusphere-egu23-8485, 2023.

X4.87
|
EGU23-3968
|
NP4.1
|
ECS
Valentin Kasburg, Alexander Breuer, Martin Bücker, and Nina Kukowski

Strainmeters measure the change in length between two known fixed points and are used primarily to identify and estimate tectonic strain. In addition to tectonic strain, these instruments also record changes in strain caused by other phenomena such as Earth tides and fluctuations of meteorological signals like changes in groundwater levels caused by precipitation, which may be much larger than tectonic strain and thus mask its signals. To avoid meteorological influences as far as possible, horizontal strainmeters often are maintained in galleries such that tectonically induced strain signals are not affected by other sources of noise.

At Moxa Geodynamic Observatory, located in central Germany, two laser strainmeters with a base length of 26 m each are maintained in galleries oriented north-south and east-west. Their resolution is in the nano-scale and sampling rate is 0.1 Hz. Mountain overburden in the gallery is comparatively low at approx. 30 m and in addition, the hydrogeological situation of the subsurface surrounding the observatory, is very heterogeneous. Therefore, amplitudes of meteorological phenomena are still quite high. In order to correct meteorological influences in the recorded strain time-series, they first need to be better understood. For doing so, long time-series - a decade at least – are needed. As the laser strainmeters continuously record since summer 2011, these are now available.

We present the results of weighting various meteorological parameters on the strainmeter recordings by training Long Short Term Memory Networks and perturbing input parameters for the test data. In this way, the contribution of each parameter to the meteorologically induced strain signals can be estimated. This knowledge is subsequently used to eliminate meteorological influences from the time-series recordings of strain.

How to cite: Kasburg, V., Breuer, A., Bücker, M., and Kukowski, N.: Influence of Environmental Parameters on Highly Sensitive Instruments at Moxa Geodynamic Observatory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-3968, https://doi.org/10.5194/egusphere-egu23-3968, 2023.

X4.88
|
EGU23-7450
|
NP4.1
|
ECS
Dominika Staniszewska

The Pieniny Klippen Belt is located in the middle part of the zone between the Inner Carpathians and Outer Carpathians. Researches at the Pieniny Geodynamic Test Field dates back to the 1960s.

Previous geodynamic studies in the area of the Pieniny Klippen Belt  have indicated neotectonic activity. Currently, starting in 2004, a GNSS survey campaign is held annually in early September.

The subject of the study was to check whether the Pieniny Klippen Belt (PKB) shows neotectonic activity in the relation to the surrounding structures - the Podhale Flysh (FP) and the Magura Nape (MN).

This study was based on the  survey of the movement of stations located in the area of the aforementioned three structures, which create the Pieniny Geodynamic Test Field.

The Pieniny Geodynamic Test Field consists of 15 GNSS stations, including 6 stations inside the PKB, 5 stations within the MN and 4 stations within the FP. The whole geodynamic test field is supplemented by 4 GNSS stations located in the Tatra Mountains.

To determine the horizontal movements of the geodynamic units, the results of satellite measurements made between 2004 and 2020 were processed. The coordinates and velocities of the stations were determined in two reference systems - IGb08 and IGb14.

To define the IGb08 and IGb14 systems, 24 EUREF stations (Euref Parmanent GNSS Network) were used. The stations were selected based on the following criteria: location, length of available data and the fewest number of discontinuities. The stations were basign to be located at the shortest distance from the Pieniny Geodynamic Test Field, as well as to be distributed evenly. Data from the CODE Analysis Center was used to process the GNSS data. GNSS datasets were processed using Bernese 5.2 GNSS Software. The adjustment was prefared in two variants due to inconsistencies between the orbits of the satellites and the IGb14 system. The differences between the ITRF2008 and ITRF2014 are quite small and are due to new or updated antenna calibrations.

Then, the obtained velocities were converted to ETRF2014. Station velocities were determined in two ways-analytically, using transformation parameters between the ITRF and ETRF2014 systems for the 2010.0 epoch, and using the EPN CB Coordinate Transformation Tool shared by the EUREF Permanent Network (EPN).

Horizontal coordinates were determined in both short-period solutions - daily and long-period solutions - covering sixteen measurement epochs.

To check the validity of the adjustment, a comparison of the velocities calculated for the reference stations with the EUREF model was performed.

The velocities of stations located in the Pieniny Geodynamic Test Field were also compared with those obtained in a study done in 2016.

The realized comparison of calculations allowed us to conclude that the performed alignment does not deviate from the solutions presented in the model and in the previous study.

The obtained results shows the tectonic activity of the Pieniny Klippen Belt and surrounding units. Horizontal point movements are small, i.e. 0.2 - 0.7mm/year, although changes in the position of points show a linear character. The trend in the direction of these changes and their magnitude is also preserved.

How to cite: Staniszewska, D.: Geodynamic studies in the Pieniny Klippen  Belt in  2004-2020, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-7450, https://doi.org/10.5194/egusphere-egu23-7450, 2023.

X4.89
|
EGU23-4919
|
NP4.1
Hiroyuki Matsumoto, Hiroaki Kajikawa, and Eiichiro Araki

It is postulated that a pressure gauge has potential for detection of vertical crustal deformation associated with plate convergence since the measurement resolution is higher than the expected deformation. However, it has been long known that the sensor drift being a few of hPa (cm) per year or larger rate is identified in the long-term pressure observation at the seafloor. We have investigated the sensor drift of pressure gauges pressurized by a pressure balance in the laboratory. Two types of pressure gauges were examined; one is quartz resonant pressure gauges which are traditionally used for in-situ pressure observations and the other is silicon resonant pressure gauges which can be used for oceanographic observations in the future. Full scale of all pressure gauges examined in the present experiment is 70 MPa. Pressure calibration curves were obtained by applying the standard pressure from zero to full scale to characterize hysteresis and repeatability of pressure gauges. Comparing pressure calibration curves under the different temperature condition, only zero offset is changed for the tested quartz pressure gauges, whereas both zero offset and span are changed for some silicon pressure gauges. Then, static pressure of 20 MPa equivalent to 2000 m water depth is applied to the pressure gauges simultaneously for a period of approximately 100 days under the low temperature condition. Some silicon pressure gauges were pressurized under the normal temperature condition. Pressure calibrations were conducted about 50 times repeatedly by providing the standard pressure of 20 MPa using the pressure balance during the experiment. Differences between the standard pressure and the sensor’s output over time were calculated to evaluate the sensor drift. The results suggest that the lower ambient temperature can contribute to the shorter relaxation time (i.e., the elapsed time to disappear initial abrupt change) and the smaller sensor drift (i.e., the linear trend) in the both types of pressure gauges. The sensor drift between the quartz and the silicon pressure gauges were comparable except for the specific silicon pressure gauges. It is noted that the quartz pressure gauges are more sensitive to temperature than the silicon pressure gauges in the present experiment.

How to cite: Matsumoto, H., Kajikawa, H., and Araki, E.: Long-term sensor drift of pressure gauges characterized by a pressure balance, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-4919, https://doi.org/10.5194/egusphere-egu23-4919, 2023.

X4.90
|
EGU23-9762
|
NP4.1
Eliza Teodorescu, Marius Echim, Jay Johnson, and Costel Munteanu

Intermittency is a property of turbulent astrophysical plasmas, such as the solar wind, that implies non-uniformity in the transfer rate of energy carried by non-linear structures from large to small scales. We evaluate the intermittency level of the turbulent magnetic field measured by the Parker Solar Probe in the slow solar wind in the proximity of the Sun, at about 0.17 AU, during the probe’s first encounter. A quantitative measure of the intermittency of a time-series can be deduced based on the normalized forth order moment of the probability distribution functions, the flatness parameter. We observe that when dividing the data into contiguous samples of various lengths, from three to twenty-four hours, flatness differs significantly from sample to sample, suggestive of alternating intermittency-free time intervals with highly intermittent samples. In order to describe this variability, we apply an elaborate statistical test tailored to identify nonlinear dynamics in a time series which involves the construction of surrogate data that eliminate all nonlinear correlations contained in the dynamics of the signal but are otherwise consistent with an “underlying” linear process, i.e. the null hypothesis that we want to falsify. If a discriminating statistic for the original signal, such as the flatness parameter, is found to be significantly different than that of the ensemble of surrogates, then the null hypothesis is not valid, and we can conclude that the computed flatness reliably reflects the intermittency level of the underlying non-linear processes. We determine that non-stationarity of the time-series strongly influences the flatness of both the data and surrogates and the null hypothesis cannot be falsified. The intermittency level detected in such cases reflects the effects of isolated and, maybe, statistically not meaningful events, consequently, we stress upon the importance of careful data selection and evaluating the significance of the evaluated discriminating statistic.

How to cite: Teodorescu, E., Echim, M., Johnson, J., and Munteanu, C.: Estimating intermittency significance by means of surrogate data: implications for stationarity, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-9762, https://doi.org/10.5194/egusphere-egu23-9762, 2023.

X4.91
|
EGU23-16971
|
NP4.1
Richard Dewey, Martin Scherwath, Steve Milahy, Martin Heesemann, Fabio De Leo, Lanfranco Muzi, Kohen Bauer, and Kim Juniper

Long time series observations in the ocean are rare. In the Northeast Pacific, Ocean Networks Canada (ONC) of the University of Victoria operates a number of permanent cabled ocean observatories. The first was installed in 2006, and they have successfully produced many interdisciplinary high-resolution time series over the years, the longest being over 16 years in duration. The cabled observatories operated by ONC include the VENUS coastal observatory and the NEPTUNE off-shore deep-sea observatory. Each observatory has several sites where an observatory node provides continuous power and high bandwidth communications to a wide range of ocean and geophysical sensors. Various long high-resolution time series will be presented and the assessment of climate, decadal, inter-seasonal, annual, and even daily cycles, variations, and signals will be discussed. Such long time series, including environmental baselines, are key for evaluating physicochemical and biological change in the oceans in response to natural variations and climate change. In this way, recent efforts to leverage our time series data in robust monitoring, measurement, reporting, and verification (M2RV) frameworks in the context of different marine carbon dioxide removal (mCDR) approaches, will also be presented.

How to cite: Dewey, R., Scherwath, M., Milahy, S., Heesemann, M., De Leo, F., Muzi, L., Bauer, K., and Juniper, K.: Ocean Networks Canada: Long-term ocean observing on a Northeast Pacific cabled ocean observatory, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-16971, https://doi.org/10.5194/egusphere-egu23-16971, 2023.

X4.92
|
EGU23-11340
|
NP4.1
|
ECS
Kalamkas Yessimkhanova and Mátyás Gede

Presently, climate change is an urging topic and mapping the effects of climate change is a crucial part. According to the evaluation by United Nations Development Programme, more than half of the territory of Kazakhstan exposed ecological crisis as drought, extreme weather events, fires and others. In this regard, this study is important for conducting research on both observation and visualization of the boundaries change of climatic zones on the land area of Kazakhstan. The Köppen climate classification was applied as a reference. In particular, such variables as temperature and precipitation were used for climatic zones classification. Extensive database of Google Earth Engine spatial analysis platform allows to leverage climate reanalysis datasets for many decades. Although, World Metereological Organization recommends considering 30-year period to witness the climate change, there is limited data access for the region of interest. Thus, only 21-year time frame was analyzed, specifically, time range between 2000 and 2021. Results are presented as time-series maps of classified climate zones and may benefit other researchers on their projects related to climate change.

How to cite: Yessimkhanova, K. and Gede, M.: Observing climate zones boundaries change: Kazakhstan's case study, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11340, https://doi.org/10.5194/egusphere-egu23-11340, 2023.

X4.93
|
EGU23-12070
|
NP4.1
Reik Donner, Dominik Diedrich, Sven Praast, and Giorgia Di Capua

Statistical associations between variables of interest are commonly assessed by applying similarity measures like Pearson correlation to corresponding observational time series. Most traditional measures focus on continuous variables and their associated complete variability, while there is a vast amount of practical examples where only times with specific conditions (e.g. extreme events) are of interest. For the latter cases, concepts like event synchronization strength or event coincidence rates have been introduced as proper similarity measures, and have proven their broad applicability across many areas of research. However, recent work has shown that such event based similarity measures may have conceptual as well as practical limitations when studying co-occurrence statistics between temporally clustered or extended events that do not meet the common assumption of serially uncorrelated point processes.

In this work, we introduce and discuss a straightforward extension of event coincidence analysis (ECA) to studying statistical associations between sequences of persistent events, which we tentatively call interval coincidence analysis (InCA). Here, each event of interest corresponds to a well-defined time interval, and the discrete counts of event co-occurrences in ECA are replaced by the fractions of time during which event intervals in two sequences mutually overlap. A statistical significance test for the obtained interval coincidence rates is realized by block bootstrapping event and non-event intervals, retaining the event duration and waiting time distributions of the persistent events in both sequences.

We demonstrate the practical potentials of InCA, as well as its similarities and differences with ECA, for a specific case study on atmospheric dynamics. Specifically, we apply both methods to studying the likelihood of co-occurrences between boreal summer (June to August) heatwaves in different parts of the Northern hemisphere and hemispheric anomalies of the atmospheric circulation, such as a jet stream pattern exhibiting two distinct wind bands known as double-jet. Our analysis reveals large-scale regions of markedly elevated likelihood of co-occurrences over Northern Europe, Central to Eastern Siberia, Northeastern Canada as well as the Middle East, Eastern China, the Southwestern and Northeastern United States and Northwest Africa, indicating a particular vulnerability of those regions to the presence of double-jet patterns.

How to cite: Donner, R., Diedrich, D., Praast, S., and Di Capua, G.: Quantifying statistical associations among persistent events: Interval coincidence analysis between Northern hemisphere heatwaves and different types of circulation anomalies, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12070, https://doi.org/10.5194/egusphere-egu23-12070, 2023.

X4.94
|
EGU23-12560
|
NP4.1
|
ECS
Guruprem Bishnoi, Reik Donner, Chandrika Thulaseedharan Dhanya, and Rakesh Khosa

The Indian summer monsoon (ISM), which accounts for the majority of India’s yearly rainfall, has a significant influence on the nation’s economy. Understanding monsoonal dynamics is a challenge because of the related small-scale processes and their spatiotemporal complexity. Nevertheless, in the past decades, complex networks have become a key mathematical tool in the analysis of complex systems like the monsoon. However, multi-scale interactions and the coupling between rainfall and atmospheric circulation have remained underrepresented in the corresponding functional network studies. In this study, we exploit coupled rainfall networks to investigate simultaneous interactions of rainfall with other atmospheric variables. Firstly, rainfall networks are investigated by considering various network measures. Secondly, a coupled network is developed based on several atmospheric variables and their point-wise correlation with rainfall fields. Furthermore, the contrasts between the rainfall network and its coupled equivalent are emphasized. By comparison, the resulting coupled network includes both horizontal and vertical interconnections of the spatially enclosed time sequences, representing both the inherent structure of a single meteorological variable and the interaction structure with rainfall fields. It is expected to help with understanding the dynamics of monsoonal rainfall. This study, therefore, demonstrates the application of a complex network approach to studying highly dynamic phenomena such as the ISM. Our results are anticipated to provide the scientific community with new insights into how the interplay of the atmospheric systems leads to the heavy rainfall episodes that take place during the ISM.

 

 

How to cite: Bishnoi, G., Donner, R., Dhanya, C. T., and Khosa, R.: Understanding monsoonal rainfall patterns with a complex network approach , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-12560, https://doi.org/10.5194/egusphere-egu23-12560, 2023.

Posters virtual: Mon, 24 Apr, 14:00–15:45 | vHall ESSI/GI/NP

vEGN.1
|
EGU23-10009
|
NP4.1
Anna Wawrzaszek, Renata Modzelewska, Agata Krasińska, Agnieszka Gil, and Vasile Glavan

We perform a systematic and comparative analysis of the fractal dimension estimators as a proxy for data complexity. In particular, we focus on the analysis of the horizontal geomagnetic field components registered by four stations (Belsk, Hel, Sodankylä and Hornsund) at various latitudes during the period of 22 August–1 September, when the 26 August 2018 geomagnetic storm appeared. To identify the fractal scaling and to compute the fractal dimension, we apply and compare three selected methods: structure function scaling, Higuchi, and detrended fluctuation analysis. The obtained results show the temporal variation of the fractal dimension of horizontal geomagnetic field components, revealing differences between their irregularity (complexity). Moreover, the values of fractal dimension seem to be sensitive to the change of physical conditions related to interplanetary shock, the coronal mass ejection, the corotating interaction region, and the high-speed stream passage during the storm development. Especially, a significant decrease in the fractal dimension for all stations is observed immediately following the interplanetary shock, which was not straightforwardly visible in the geomagnetic field components data.

How to cite: Wawrzaszek, A., Modzelewska, R., Krasińska, A., Gil, A., and Glavan, V.: 26 August 2018 Geomagnetic Storm: Fractal Analysis of Earth Magnetic Field , EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-10009, https://doi.org/10.5194/egusphere-egu23-10009, 2023.

vEGN.2
|
EGU23-11304
|
NP4.1
Dorothee Rebscher, Finnegan G. Reichertz, and Senecio Schefer

Underground research laboratories provide advantageous conditions to observe a broad range of various rock parameters to characterise rock matrix and geological features, and to enhance knowledge of their dynamic behaviour, all under relatively undisturbed conditions. One of their favorable features is that the overburden protects against large environmental changes, although those influences cannot be mitigated in full. Especially for long term investigations, a holistic observation of ambient environmental parameters is necessary on the local scale and beyond.

The Swiss Mont Terri rock laboratory is situated in the Jura Mountains about 250 m below the surface. Starting in 1996, the international Mont Terri Consortium has conducted about 150 experiments in the native Opalinus Clay. Embedded in several of the ongoing in situ experiments, platform tiltmeters assist in the often interdisciplinary investigations. Two different types of biaxial instruments with resolutions of 0.1 urad and better than nrad are distributed throughout the laboratory, together forming a small, growing array, with the first tiltmeters installed in April 2019.

Tiltmeters observe the direct local deformation, they are exposed to near field but also far field impacts. Known local influences are mainly temperature, air pressure, and humidity. In Mont Terri, all of these parameters are registered directly at the location of the tilt sensors with the same relatively high sampling of once every few seconds. In addition, Mont Terri's comprehensive database imparts valuable complementing information. However, the detected deformation pattern is also influenced on a much larger spatial scale, e.g. far field, extensive changes in weather patterns, earth tides, and teleseismic events.

Therefore, to allow detection, identification, and realistic interpretation of complex signal responses on different spatial scales, it is mandatory to distinguish transient and long term signals, natural and anthropogenic disturbances. Their understanding is essential for the evaluation of stability and the safety of a rock laboratory for the benefit of its personnel and visitors. Obviously, long term, continuous data series require long term commitments. But the efforts pays off, not the least, as decade-long deformation studies contribute to multifaceted technical and scientific aspects of long term behavior of barrier rocks, and these are relevant for the exploitation of the deep geological subsurface such as nuclear waste disposal, geological storage of carbon dioxide, use of geothermal energy, or inter-seasonal thermal energy storage.

How to cite: Rebscher, D., Reichertz, F. G., and Schefer, S.: Long term investigations at the Mont Terri rock laboratory of tilt and their near and far field influences, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-11304, https://doi.org/10.5194/egusphere-egu23-11304, 2023.

vEGN.3
|
EGU23-13601
|
NP4.1
|
ECS
|
Moritz Haas, Bedartha Goswami, and Ulrike von Luxburg

Climate networks have become a popular tool for detecting complex structures in spatio-temporal data. However, they require to estimate correlation values on many edges based on limited and noisy time series. Consequently any constructed network likely contains false and missing edges. To measure how severely and in which ways estimated networks are distorted by statistical errors, we simulate time-dependent isotropic random fields on the sphere. We comprehensively present several patterns of distortion in local as well as global network characteristics and demonstrate which network construction methods enhance statistical robustness. When the data has a locally coherent correlation structure, spurious link bundle teleconnections and spurious high-degree clusters have to be expected. Anisotropic estimation variance can also induce severe biases into empirical networks. We validate all our findings with ERA5 reanalysis data. Finally, we explain why commonly applied resampling procedures  are insufficient for evaluating statistical significance of network structures, and introduce a new ensemble construction framework that aims to alleviate most of the discussed shortcomings.

How to cite: Haas, M., Goswami, B., and von Luxburg, U.: Empirical Distortions in Climate Networks, EGU General Assembly 2023, Vienna, Austria, 23–28 Apr 2023, EGU23-13601, https://doi.org/10.5194/egusphere-egu23-13601, 2023.