Analysis of long-term catchment data: a nonlinear perspective

2020 ◽  
Author(s):  
Holger Lange ◽  
Michael Hauhs ◽  
Katharina Funk ◽  
Sebastian Sippel ◽  
Henning Meesenburg
Keyword(s):  
Time Series ◽  
Temporal Resolution ◽  
Forest Growth ◽  
Scaling Behavior ◽  
Permutation Entropy ◽  
Data Sets ◽  
Detailed Model ◽  
Spatial Gradients ◽  
Visibility Graphs

<p>We analyze time series from several forested headwater catchments located adjacent to each other in the Bramke valley, Harz mountains (Germany) which are monitored since decades for hydrology, hydrochemistry and forest growth. The data sets include meteorological variables, runoff rates, streamwater chemical concentrations, and others. The basic temporal resolution is daily for hydrometeorology and two-weekly for streamwater chemistry (in addition, standing biomass of a Norway spruce stand is measured every couple of years).</p><p>A model was calibrated and run for the streamflow from one of the catchments, based on precipitation, temperature and (simulated) evapotranspiration of the growing trees, to elucidate the effect of forest growth on catchment hydrology.</p><p>The catchments exhibit long-term changes and spatial gradients related to atmospheric deposition, management and changing climate. After providing a short multivariate summary of the dataset, we present several nonlinear metrics suitable to detect and quantify subtle changes and to describe different behavior, both between different variables from the same catchment, as well as for the same variable across catchments. The methods include, but are not limited to: Tarnopolski analysis, permutation entropy and complexity, q- and α-complexities, and Horizontal Visibility Graphs.</p><p>The detection of these changes is remarkable, because linear trends have already been removed prior to analysis. Hence, their presence reflects intrinsic changes in the patterns of the time series. The metrics also allow for a detailed model evaluation from a nonlinear perspective.</p><p>An important methodological aspect is the temporal resolution of the time series. We investigate the scaling behavior of the nonlinear metrics through aggregation or decimation to coarser resolutions and conclude on what the scaling behavior may imply for inverse (hydrological) modelling tasks.</p>

scholarly journals Multiple Evidence for Climate Patterns Influencing Ecosystem Productivity across Spatial Gradients in the Venice Lagoon

2021 ◽  
Vol 9 (4) ◽  
pp. 363
Author(s):  
Camilla Bertolini ◽  
Edouard Royer ◽  
Roberto Pastres
Keyword(s):  
Time Series ◽  
Primary Productivity ◽  
Clustering Analysis ◽  
Venice Lagoon ◽  
Spatial Effects ◽  
Spatial Gradients ◽  
Climate Forecasting ◽  
Oxygen Production Rate ◽  
Temporal And Spatial

Effects of climatic changes in transitional ecosystems are often not linear, with some areas likely experiencing faster or more intense responses, which something important to consider in the perspective of climate forecasting. In this study of the Venice lagoon, time series of the past decade were used, and primary productivity was estimated from hourly oxygen data using a published model. Temporal and spatial patterns of water temperature, salinity and productivity time series were identified by applying clustering analysis. Phytoplankton and nutrient data from long-term surveys were correlated to primary productivity model outputs. pmax, the maximum oxygen production rate in a given day, was found to positively correlate with plankton variables measured in surveys. Clustering analysis showed the occurrence of summer heatwaves in 2008, 2013, 2015 and 2018 and three warm prolonged summers (2012, 2017, 2019) coincided with lower summer pmax values. Spatial effects in terms of temperature were found with segregation between confined and open areas, although the patterns varied from year to year. Production and respiration differences showed that the lagoon, despite seasonality, was overall heterotrophic, with internal water bodies having greater values of heterotrophy. Warm, dry years with high salinity had lower degrees of summer autotrophy.

scholarly journals Flexible approach for quantifying average long-term changes and seasonal cycles of tropospheric trace species

2019 ◽  
Author(s):  
David D. Parrish ◽  
Richard G. Derwent ◽  
Simon O'Doherty ◽  
Peter G. Simmonds
Keyword(s):  
Time Series ◽  
Seasonal Cycle ◽  
Linear Trend ◽  
Power Series Expansion ◽  
Mathematical Representation ◽  
Seasonal Cycles ◽  
Data Sets ◽  
Significant Information ◽  
Long Term Changes

Abstract. We present an approach to derive a systematic mathematical representation of the statistically significant features of the average long-term changes and seasonal cycle of concentrations of trace tropospheric species. The results for two illustrative data sets (time series of baseline concentrations of ozone and N2O at Mace Head, Ireland) indicate that a limited set of seven or eight parameter values provides this mathematical representation for both example species. This method utilizes a power series expansion to extract more information regarding the long-term changes than can be provided by oft-employed linear trend analyses. In contrast, the quantification of average seasonal cycles utilizes a Fourier series analysis that provides less detailed seasonal cycles than are sometimes represented as twelve monthly means; including that many parameters in the seasonal cycle representation is not usually statistically justified, and thereby adds unnecessary noise to the representation and prevents a clear analysis of the statistical uncertainty of the results. The approach presented here is intended to maximize the statistically significant information extracted from analyses of time series of concentrations of tropospheric species regarding their mean long-term changes and seasonal cycles, including non-linear aspects of the long-term trends. Additional implications, advantages and limitations of this approach are discussed.

scholarly journals Comparison of GPS tropospheric delays derived from two consecutive EPN reprocessing campaigns from the point of view of climate monitoring

2016 ◽  
Vol 9 (9) ◽  
pp. 4861-4877 ◽  
Author(s):  
Zofia Baldysz ◽  
Grzegorz Nykiel ◽  
Andrzej Araszkiewicz ◽  
Mariusz Figurski ◽  
Karolina Szafranek
Keyword(s):  
Time Series ◽  
Point Of View ◽  
Trend Test ◽  
Data Sets ◽  
Total Delay ◽  
University Of Technology ◽  
Time Period ◽  
Seasonal Signals ◽  
Linear Trends

Abstract. The main purpose of this research was to acquire information about consistency of ZTD (zenith total delay) linear trends and seasonal components between two consecutive GPS reprocessing campaigns. The analysis concerned two sets of the ZTD time series which were estimated during EUREF (Reference Frame Sub-Commission for Europe) EPN (Permanent Network) reprocessing campaigns according to 2008 and 2015 MUT AC (Military University of Technology Analysis Centre) scenarios. Firstly, Lomb–Scargle periodograms were generated for 57 EPN stations to obtain a characterisation of oscillations occurring in the ZTD time series. Then, the values of seasonal components and linear trends were estimated using the LSE (least squares estimation) approach. The Mann–Kendall trend test was also carried out to verify the presence of linear long-term ZTD changes. Finally, differences in seasonal signals and linear trends between these two data sets were investigated. All these analyses were conducted for the ZTD time series of two lengths: a shortened 16-year series and a full 18-year one. In the case of spectral analysis, amplitudes of the annual and semi-annual periods were almost exactly the same for both reprocessing campaigns. Exceptions were found for only a few stations and they did not exceed 1 mm. The estimated trends were also similar. However, for the reprocessing performed in 2008, the trends values were usually higher. In general, shortening of the analysed time period by 2 years resulted in a decrease of the linear trends values of about 0.07 mm yr−1. This was confirmed by analyses based on two data sets.

Time series analysis of long-term data sets of atmospheric mercury concentrations

2004 ◽  
Vol 380 (3) ◽  
pp. 493-501 ◽  
Author(s):  
Christian Temme ◽  
Ralf Ebinghaus ◽  
J�rgen W. Einax ◽  
Alexandra Steffen ◽  
William H. Schroeder
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Atmospheric Mercury ◽  
Data Sets ◽  
Series Analysis ◽  
Term Data

scholarly journals Analysing Hydrometeorological Time Series for Evidence of Climatic Change

1993 ◽  
Vol 24 (2-3) ◽  
pp. 135-150 ◽  
Author(s):  
Geoff Kite
Keyword(s):  
Time Series ◽  
Climatic Change ◽  
Data Sets ◽  
Large Lakes ◽  
Hydrologic Time Series ◽  
Natural Time ◽  
Streamflow Data ◽  
Scientific Attention ◽  
Linear Trends

Considerable scientific attention has been focused on a measured increase in atmospheric CO2 and a suspected corresponding change in climate. Such a change in climate, if it occurred, might be expected to have a magnified effect on hydrologic time series and, indeed, projections have been made of major changes in water resources. If the climatic changes are indeed magnified in hydrologic time series then, by detecting trends in such series, it should be possible to work backwards and identify the causative climatic change. This paper looks at two data sets: 1) long-term temperature, precipitation and streamflow data from sites across Canada and 2) long-term levels of large lakes in Africa and North America. The study assumes that time series may be modelled by trend, periodic, autoregressive and random residual components. The trend component of a time series is generally associated with changes in the structure of the time series caused by cumulative natural or manmade phenomena. Periodicities in natural time series are usually due to astronomical cycles such as the earth's rotation around the sun. Autoregressive components reflect the tendency for an event to be dependent on the magnitude of the previous event(s), a memory effect. The analyses of temperature, precipitation and streamflow data show some significant linear trends but no pattern is apparent. The analyses of longterm lake levels also identify linear trends but these are all explainable without invoking climate change due to greenhouse gases.

scholarly journals The SPARC Data Initiative: comparisons of CFC-11, CFC-12, HF and SF<sub>6</sub> climatologies from international satellite limb sounders

2016 ◽  
Vol 8 (1) ◽  
pp. 61-78 ◽  
Author(s):  
S. Tegtmeier ◽  
M. I. Hegglin ◽  
J. Anderson ◽  
B. Funke ◽  
J. Gille ◽  
...  
Keyword(s):  
Time Series ◽  
Satellite Data ◽  
Lower Stratosphere ◽  
Atmospheric Transport ◽  
Temporal Structure ◽  
Data Sets ◽  
Upper Troposphere ◽  
Mean Fields ◽  
Mean State

Abstract. A quality assessment of the CFC-11 (CCl3F), CFC-12 (CCl2F2), HF, and SF6 products from limb-viewing satellite instruments is provided by means of a detailed intercomparison. The climatologies in the form of monthly zonal mean time series are obtained from HALOE, MIPAS, ACE-FTS, and HIRDLS within the time period 1991–2010. The intercomparisons focus on the mean biases of the monthly and annual zonal mean fields and aim to identify their vertical, latitudinal and temporal structure. The CFC evaluations (based on MIPAS, ACE-FTS and HIRDLS) reveal that the uncertainty in our knowledge of the atmospheric CFC-11 and CFC-12 mean state, as given by satellite data sets, is smallest in the tropics and mid-latitudes at altitudes below 50 and 20 hPa, respectively, with a 1σ multi-instrument spread of up to ±5 %. For HF, the situation is reversed. The two available data sets (HALOE and ACE-FTS) agree well above 100 hPa, with a spread in this region of ±5 to ±10 %, while at altitudes below 100 hPa the HF annual mean state is less well known, with a spread ±30 % and larger. The atmospheric SF6 annual mean states derived from two satellite data sets (MIPAS and ACE-FTS) show only very small differences with a spread of less than ±5 % and often below ±2.5 %. While the overall agreement among the climatological data sets is very good for large parts of the upper troposphere and lower stratosphere (CFCs, SF6) or middle stratosphere (HF), individual discrepancies have been identified. Pronounced deviations between the instrument climatologies exist for particular atmospheric regions which differ from gas to gas. Notable features are differently shaped isopleths in the subtropics, deviations in the vertical gradients in the lower stratosphere and in the meridional gradients in the upper troposphere, and inconsistencies in the seasonal cycle. Additionally, long-term drifts between the instruments have been identified for the CFC-11 and CFC-12 time series. The evaluations as a whole provide guidance on what data sets are the most reliable for applications such as studies of atmospheric transport and variability, model–measurement comparisons and detection of long-term trends. The data sets will be publicly available from the SPARC Data Centre and through PANGAEA (doi:10.1594/PANGAEA.849223).

An integrated deterministic–stochastic approach for forecasting the long-term trajectories of COVID-19

2021 ◽  
pp. 2141001
Author(s):  
Indrajit Ghosh ◽  
Tanujit Chakraborty
Keyword(s):  
Time Series ◽  
Stochastic Approach ◽  
World Population ◽  
Data Sets ◽  
Univariate Time Series ◽  
Reproduction Numbers ◽  
The World ◽  
Long Term Forecasting ◽  
Ar Models

The ongoing coronavirus disease 2019 (COVID-19) pandemic is one of the major health emergencies in decades that affected almost every country in the world. As of June 30, 2020, it has caused an outbreak with more than 10 million confirmed infections, and more than 500,000 reported deaths globally. Due to the unavailability of an effective treatment (or vaccine) and insufficient evidence regarding the transmission mechanism of the epidemic, the world population is currently in a vulnerable position. The daily cases data sets of COVID-19 for profoundly affected countries represent a stochastic process comprised of deterministic and stochastic components. This study proposes an integrated deterministic–stochastic approach to forecast the long-term trajectories of the COVID-19 cases for Italy and Spain. The deterministic component of the daily-cases univariate time series is assessed by an extended version of the SIR [Susceptible–Infected–Recovered–Protected–Isolated (SIRCX)] model, whereas its stochastic component is modeled using an autoregressive (AR) time series model. The proposed integrated SIRCX-AR (ISA) approach based on two operationally distinct modeling paradigms utilizes the superiority of both the deterministic SIRCX and stochastic AR models to find the long-term trajectories of the epidemic curves. Experimental analysis based on the proposed ISA model shows significant improvement in the long-term forecasting of COVID-19 cases for Italy and Spain in comparison to the ODE-based SIRCX model. The estimated Basic reproduction numbers for Italy and Spain based on SIRCX model are found to be [Formula: see text] and [Formula: see text], respectively. ISA model-based results reveal that the number of cases in Italy and Spain between 11 May, 2020–9 June, 2020 will be 10,982 (6383–15,582) and 13,731 (3395–29,013), respectively. Additionally, the expected number of daily cases on 9 July, 2020 for Italy and Spain is estimated to be 30 (0–183) and 92 (0–602), respectively.

Consistency of Towing Speed and Sampling Depth for the Continuous Plankton Recorder

1993 ◽  
Vol 73 (4) ◽  
pp. 967-970 ◽  
Author(s):  
G. C. Hays ◽  
A. J. Warner
Keyword(s):  
Time Series ◽  
North Atlantic ◽  
Data Sets ◽  
Sampling Depth ◽  
The North ◽  
The North Sea ◽  
The Mean ◽  
Towing Speed ◽  
Term Data

The mean annual towing speed of the Continuous Plankton Recorder (CPR) varied systematically between 1946 and 1991. By mounting a pressure transducer on the CPR to record towing depth, it was shown, however, that changes in towing speed did not cause a significant change in towing depth, although the mean towing depth (6–7 m, SD=l-7 m, N=77) was shallower than the previously assumed towing depth of 10 m. Thus the observed changes in towing speed are unlikely to have caused discontinuities in the CPR time-series by affecting sampling depth.Long-term data sets play an important role in attempts to understand the causes of fluctua- tions in plankton abundance. The Continuous Plankton Recorder (CPR) survey provides multi- decadal information on plankton abundance in the North Sea and North Atlantic (McGowan, 1990), and is one of the longest standing marine plankton abundance time-series. However, while the CPR time-series has great potential, as with all other data sets spanning many years, questions may be asked regarding the consistency with which the data have been collected and hence the true continuity of the time-series.

scholarly journals Assessing Time Series Reversibility through Permutation Patterns

2018 ◽  
Author(s):  
Massimiliano Zanin ◽  
Alejandro Rodríguez González ◽  
Ernestina Menasalvas Ruiz ◽  
David Papo
Keyword(s):  
Time Series ◽  
Time Reversal ◽  
Financial Time Series ◽  
Statistical Tests ◽  
Fundamental Property ◽  
Permutation Entropy ◽  
Permutation Patterns ◽  
Visibility Graphs ◽  
Time Irreversibility ◽  
Financial Data Analysis

Time irreversibility, i.e. the lack of invariance of the statistical properties of a system under time reversal, is a fundamental property of all systems operating out of equilibrium. Time reversal symmetry is associated with important statistical and physical properties and is related to the predictability of the system generating the time series. Over the past fifteen years, various methods to quantify time irreversibility in time series have been proposed, but these can be computationally expensive. Here we propose a new method, based on permutation entropy, which is essentially parameter-free, temporally local, yields straightforward statistical tests, and has fast convergence properties. We apply this method to the study of financial time series, showing that stocks and indices present a rich irreversibility dynamics. We illustrate the comparative methodological advantages of our method with respect to a recently proposed method based on visibility graphs, and discuss the implications of our results for financial data analysis and interpretation.

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