Using Phase Annealing to generate surrogate discharge time series

2020 ◽  
Author(s):  
Faizan Anwar ◽  
András Bárdossy
Keyword(s):  
Time Series ◽  
Statistical Significance ◽  
Main Concern ◽  
Discharge Time ◽  
Empirical Copula ◽  
Statistical Sense ◽  
Copula Density ◽  
Spatio Temporal ◽  
Phase Randomization ◽  
Selection Of

<p>Phase randomization and its variants such as the Amplitude-adjusted (AAFT) and the Iterative amplitude adjusted (IAAFT) Fourier transform are used to check statistical significance of a given hypothesis and/or to generate time series that are similar to a reference in some statistical sense. These methods have the drawback of producing incorrect dependence structures e.g. empirical copula density, asymmetries and entropies. Recently, another form of such methods, “Phase Annealing”, was introduced, giving a possibility to generate n-dimensional realizations of a process under given constraint(s). The main concern using this method is the selection of correct objective function(s).</p><p>Here we show discharge time series generation using Phase Annealing with new objective functions. This allowed us to generate time series that are much longer than the reference, which in turn was helpful in establishing better distributions of floods.</p><p>We also show the generation of discharge time series at multiple locations that have the correct spatio-temporal dependences among all the series. Using the results, we generated full distributions of simultaneous extremes at observation locations.</p><p>Further uses may include clustering catchments that are likely to bring floods together and reliability analysis i.e. simulating distributions of failures for a system with many dependent/independent components. Drawbacks using this method are also shown.</p>

Spatio Temporal Variability of Rainfall Across Western Libya from 1979 to 2009

2020 ◽  
Vol 6 (3) ◽  
pp. 235-244
Author(s):  
Ali Salem Eddenjal
Keyword(s):  
Time Series ◽  
Rainfall Variability ◽  
Statistical Significance ◽  
Regional Scale ◽  
Policy Makers ◽  
Rain Gauges ◽  
Spatio Temporal ◽  
Annual Scale ◽  
Increasing Trends ◽  
The Impact

Aims: To understand the links between climate variability and hydrology in western Libya. Background: This study represents the first comprehensive assessment of rainfall variability in western Libya at a regional scale. Objective: To assess temporal and spatial variability of rainfall in western Libya, based on data (1979-2009) from 16 rain gauges. Methods: The non-parametric Mann-Kendall method and Sen’s slop estimator were used to define changes in rainfall series and their statistical significance. Results: Coastal and mountainous time series showed decreasing trends at the annual, autumn, and spring scales, with very few exceptions. Notably, winter showed increasing trends, with the significant values of 1.94 and 0.88 mm/year at Sirt and Nalut, respectively. Desert stations showed increasing trends, especially at the annual scale, with the greatest significant increase on the order of 1.19 mm/year in Ghadames. For the regional rainfall trend analysis, annual, spring and autumn rainfalls decreased in the coastal and mountainous zones, with the highest significant decrease of 1.94 mm/year. Again, winter rainfall showed increasing trend over the whole study domain. Conclusion: Although most time series showed a tendency towards more drier conditions, most of the detected trends were statistically non-significant. This study will provide guidance for policy makers in their future planning to mitigate the impact of drought.

scholarly journals Stochastic simulation of streamflow and spatial extremes: a continuous, wavelet-based approach

2020 ◽  
Vol 24 (8) ◽  
pp. 3967-3982 ◽  
Author(s):  
Manuela I. Brunner ◽  
Eric Gilleland
Keyword(s):  
Water Management ◽  
Time Series ◽  
Wavelet Transform ◽  
Stochastic Simulation ◽  
Kappa Distribution ◽  
Simulation Approach ◽  
Spatial Extremes ◽  
Temporal Model ◽  
Spatio Temporal ◽  
Phase Randomization

Abstract. Stochastically generated streamflow time series are used for various water management and hazard estimation applications. They provide realizations of plausible but as yet unobserved streamflow time series with the same temporal and distributional characteristics as the observed data. However, the representation of non-stationarities and spatial dependence among sites remains a challenge in stochastic modeling. We investigate whether the use of frequency-domain instead of time-domain models allows for the joint simulation of realistic, continuous streamflow time series at daily resolution and spatial extremes at multiple sites. To do so, we propose the stochastic simulation approach called Phase Randomization Simulation using wavelets (PRSim.wave) which combines an empirical spatio-temporal model based on the wavelet transform and phase randomization with the flexible four-parameter kappa distribution. The approach consists of five steps: (1) derivation of random phases, (2) fitting of the kappa distribution, (3) wavelet transform, (4) inverse wavelet transform, and (5) transformation to kappa distribution. We apply and evaluate PRSim.wave on a large set of 671 catchments in the contiguous United States. We show that this approach allows for the generation of realistic time series at multiple sites exhibiting short- and long-range dependence, non-stationarities, and unobserved extreme events. Our evaluation results strongly suggest that the flexible, continuous simulation approach is potentially valuable for a diverse range of water management applications where the reproduction of spatial dependencies is of interest. Examples include the development of regional water management plans, the estimation of regional flood or drought risk, or the estimation of regional hydropower potential. Highlights. Stochastic simulation of continuous streamflow time series using an empirical, wavelet-based, spatio-temporal model in combination with the parametric kappa distribution. Generation of stochastic time series at multiple sites showing temporal short- and long-range dependence, non-stationarities, and spatial dependence in extreme events. Implementation of PRSim.wave in R package PRSim: Stochastic Simulation of Streamflow Time Series using Phase Randomization.

scholarly journals Cumulative areawise testing in wavelet analysis and its application to geophysical time series

2015 ◽  
Vol 2 (4) ◽  
pp. 1227-1273 ◽  
Author(s):  
J. A. Schulte
Keyword(s):  
Time Series ◽  
Wavelet Analysis ◽  
Statistical Power ◽  
Signal To Noise Ratio ◽  
Statistical Significance ◽  
Testing Procedure ◽  
Statistical Significance Testing ◽  
Significance Level ◽  
Significance Levels ◽  
Selection Of

Abstract. Statistical significance testing in wavelet analysis was improved through the development of a cumulative areawise test. The test was developed to eliminate the selection of two significance levels that an existing geometric test requires for implementation. The selection of two significance levels was found to make the test sensitive to the chosen pointwise significance level, which may preclude further scientific investigation. A set of experiments determined that the cumulative areawise test has greater statistical power than the geometric test in most cases, especially when the signal-to-noise ratio is high. The number of false positives identified by the tests was found to be similar if the respective significance levels were set to 0.05. The new testing procedure was applied to the time series of the Atlantic Multi-decadal Oscillation (AMO), North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), and Niño 3.4 index. The testing procedure determined that the NAO, PDO, and AMO are consistent with red-noise processes, whereas significant power was found in the 2–7 year period band for the Niño 3.4 index.

Der Sommer und Herbst 2003 aus phänologischer Sicht | Summer and autumn 2003 from a phenological point of view

2004 ◽  
Vol 155 (5) ◽  
pp. 142-145 ◽  
Author(s):  
Claudio Defila
Keyword(s):  
Time Series ◽  
New Record ◽  
Point Of View ◽  
Late Spring ◽  
Mean Deviation ◽  
Leaf Shedding ◽  
The Mean ◽  
Very High ◽  
Selection Of

The record-breaking heatwave of 2003 also had an impact on the vegetation in Switzerland. To examine its influences seven phenological late spring and summer phases were evaluated together with six phases in the autumn from a selection of stations. 30% of the 122 chosen phenological time series in late spring and summer phases set a new record (earliest arrival). The proportion of very early arrivals is very high and the mean deviation from the norm is between 10 and 20 days. The situation was less extreme in autumn, where 20% of the 103 time series chosen set a new record. The majority of the phenological arrivals were found in the class «normal» but the class«very early» is still well represented. The mean precocity lies between five and twenty days. As far as the leaf shedding of the beech is concerned, there was even a slight delay of around six days. The evaluation serves to show that the heatwave of 2003 strongly influenced the phenological events of summer and spring.

GROUNDWATER TIME SERIES

1970 ◽  
Vol 1 (3) ◽  
pp. 181-205 ◽  
Author(s):  
ERIK ERIKSSON
Keyword(s):  
Time Series ◽  
Point Of View ◽  
Hydrologic Models ◽  
Physical Point ◽  
Run Off ◽  
Fort Collins ◽  
Statistical Sense ◽  
Full Benefit ◽  
Future Work ◽  
Prediction Problems

The term “stochastic hydrology” implies a statistical approach to hydrologic problems as opposed to classic hydrology which can be considered deterministic in its approach. During the International Hydrology Symposium, held 6-8 September 1967 at Fort Collins, a number of hydrology papers were presented consisting to a large extent of studies on long records of hydrological elements such as river run-off, these being treated as time series in the statistical sense. This approach is, no doubt, of importance for future work especially in relation to prediction problems, and there seems to be no fundamental difficulty for introducing the stochastic concepts into various hydrologic models. There is, however, some developmental work required – not to speak of educational in respect to hydrologists – before the full benefit of the technique is obtained. The present paper is to some extent an exercise in the statistical study of hydrological time series – far from complete – and to some extent an effort to interpret certain features of such time series from a physical point of view. The material used is 30 years of groundwater level observations in an esker south of Uppsala, the observations being discussed recently by Hallgren & Sands-borg (1968).

scholarly journals Spatio-temporal selection of reference genes in the two congeneric species of Glycyrrhiza

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Yuping Li ◽  
Xiaoju Liang ◽  
Xuguo Zhou ◽  
Yu An ◽  
Ming Li ◽  
...  
Keyword(s):  
Gene Expression ◽  
Developmental Stage ◽  
Reference Genes ◽  
Developmental Stages ◽  
Individual Factors ◽  
Congeneric Species ◽  
Spatio Temporal ◽  
The Stability ◽  
Different Tissues ◽  
Selection Of

AbstractGlycyrrhiza, a genus of perennial medicinal herbs, has been traditionally used to treat human diseases, including respiratory disorders. Functional analysis of genes involved in the synthesis, accumulation, and degradation of bioactive compounds in these medicinal plants requires accurate measurement of their expression profiles. Reverse transcription quantitative real-time PCR (RT-qPCR) is a primary tool, which requires stably expressed reference genes to serve as the internal references to normalize the target gene expression. In this study, the stability of 14 candidate reference genes from the two congeneric species G. uralensis and G. inflata, including ACT, CAC, CYP, DNAJ, DREB, EF1, RAN, TIF1, TUB, UBC2, ABCC2, COPS3, CS, R3HDM2, were evaluated across different tissues and throughout various developmental stages. More importantly, we investigated the impact of interactions between tissue and developmental stage on the performance of candidate reference genes. Four algorithms, including geNorm, NormFinder, BestKeeper, and Delta Ct, were used to analyze the expression stability and RefFinder, a comprehensive software, provided the final recommendation. Based on previous research and our preliminary data, we hypothesized that internal references for spatio-temporal gene expression are different from the reference genes suited for individual factors. In G. uralensis, the top three most stable reference genes across different tissues were R3HDM2, CAC and TUB, while CAC, CYP and ABCC2 were most suited for different developmental stages. CAC is the only candidate recommended for both biotic factors, which is reflected in the stability ranking for the spatio (tissue)-temporal (developmental stage) interactions (CAC, R3HDM2 and DNAJ). Similarly, in G. inflata, COPS3, R3HDM2 and DREB were selected for tissues, while RAN, COPS3 and CS were recommended for developmental stages. For the tissue-developmental stage interactions, COPS3, DREB and ABCC2 were the most suited reference genes. In both species, only one of the top three candidates was shared between the individual factors and their interactions, specifically, CAC in G. uralensis and COPS3 in G. inflata, which supports our overarching hypothesis. In summary, spatio-temporal selection of reference genes not only lays the foundation for functional genomics research in Glycyrrhiza, but also facilitates these traditional medicinal herbs to reach/maximize their pharmaceutical potential.

Evolving Fuzzy Time Series for Spatio-temporal Forecasting in Renewable Energy Systems

2021 ◽  
Author(s):  
Carlos A. Severiano ◽  
Petrônio de Cândido de Lima e Silva ◽  
Miri Weiss Cohen ◽  
Frederico Gadelha Guimarães
Keyword(s):  
Time Series ◽  
Renewable Energy ◽  
Energy Systems ◽  
Fuzzy Time Series ◽  
Renewable Energy Systems ◽  
Spatio Temporal

scholarly journals Tetracycline-Resistant Bacteria Selected from Water and Zebrafish after Antibiotic Exposure

2021 ◽  
Vol 18 (6) ◽  
pp. 3218
Author(s):  
Ana Rita Almeida ◽  
Marta Tacão ◽  
Joana Soares ◽  
Inês Domingues ◽  
Isabel Henriques
Keyword(s):  
Statistical Significance ◽  
Recovery Period ◽  
Antibiotic Resistance Genes ◽  
Antibiotic Use ◽  
Resistant Bacteria ◽  
Antibiotic Resistant ◽  
Potential Impact ◽  
Susceptibility Profiles ◽  
Selection Of ◽  
Significant Mortality

The emergence of antibiotic-resistant pathogens due to worldwide antibiotic use is raising concern in several settings, including aquaculture. In this work, the selection of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs) was evaluated after exposure of zebrafish to oxytetracycline (OTC) for two months, followed by a recovery period. The selection of ARB in water and fish was determined using selective media. The abundance of tetA genes was estimated through qPCR. Higher prevalence of ARB was measured in all samples exposed to the antibiotic when compared to control samples, although statistical significance was only achieved five days after exposure. Isolates recovered from samples exposed to the antibiotic were affiliated with Pseudomonas and Stenotrophomonas. Various antibiotic susceptibility profiles were detected and 37% of the isolates displayed multidrug resistance (MDR). The selection of the tetA gene was confirmed by qPCR at the highest OTC concentration tested. Two MDR isolates, tested using zebrafish embryos, caused significant mortality, indicating a potential impact on fish health and survival. Overall, our work highlights the potential impact of antibiotic contamination in the selection of potential pathogenic ARB and ARGS.

scholarly journals Adjoint-based direct data assimilation of GNSS time series for optimizing frictional parameters and predicting postseismic deformation following the 2003 Tokachi-oki earthquake

2020 ◽  
Vol 72 (1) ◽  
Author(s):  
Masayuki Kano ◽  
Shin’ichi Miyazaki ◽  
Yoichi Ishikawa ◽  
Kazuro Hirahara
Keyword(s):  
Time Series ◽  
Data Assimilation ◽  
Evaluation Method ◽  
Temporal Evolution ◽  
Recovery Process ◽  
Postseismic Deformation ◽  
Coseismic Slip ◽  
Megathrust Earthquakes ◽  
Spatio Temporal ◽  
Gnss Time Series

Abstract Postseismic Global Navigation Satellite System (GNSS) time series followed by megathrust earthquakes can be interpreted as a result of afterslip on the plate interface, especially in its early phase. Afterslip is a stress release process accumulated by adjacent coseismic slip and can be considered a recovery process for future events during earthquake cycles. Spatio-temporal evolution of afterslip often triggers subsequent earthquakes through stress perturbation. Therefore, it is important to quantitatively capture the spatio-temporal evolution of afterslip and related postseismic crustal deformation and to predict their future evolution with a physics-based simulation. We developed an adjoint data assimilation method, which directly assimilates GNSS time series into a physics-based model to optimize the frictional parameters that control the slip behavior on the fault. The developed method was validated with synthetic data. Through the optimization of frictional parameters, the spatial distributions of afterslip could roughly (but not in detail) be reproduced if the observation noise was included. The optimization of frictional parameters reproduced not only the postseismic displacements used for the assimilation, but also improved the prediction skill of the following time series. Then, we applied the developed method to the observed GNSS time series for the first 15 days following the 2003 Tokachi-oki earthquake. The frictional parameters in the afterslip regions were optimized to A–B ~ O(10 kPa), A ~ O(100 kPa), and L ~ O(10 mm). A large afterslip is inferred on the shallower side of the coseismic slip area. The optimized frictional parameters quantitatively predicted the postseismic GNSS time series for the following 15 days. These characteristics can also be detected if the simulation variables can be simultaneously optimized. The developed data assimilation method, which can be directly applied to GNSS time series following megathrust earthquakes, is an effective quantitative evaluation method for assessing risks of subsequent earthquakes and for monitoring the recovery process of megathrust earthquakes.

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