Coherent Patterns Research Articles

Power system coherency assessment by the affinity propagation algorithm and distance correlation

This paper assesses the coherency in power systems employing the affinity propagation (AP) algorithm with different distance metrics and quality measurements. This assessment allows determining the appropriate metric to cluster a frequency dataset that possesses coherent patterns. Thanks to the AP method does not require initialization for the number of clusters, its convergence characteristics guaranteed by the optimization process, and its capacity for using different distance metrics as input, the AP is adopted to identify and distinguish such coherent patterns that embody the collective motion of an operative area in a power system. The AP method is a data-driven method that uses an affinity matrix as input, i.e., the square matrix computed with pairwise distances. Since the distance function significantly impacts the quality of the resulting clustering, this contribution evaluates three different distance metrics, distance correlation, and the results are compared using four cluster indexes. The data collection is constituted by a set of frequency signals and the representative objects are the nodes identified as the center of each operative area. This contribution presents experimental results using simulated signals with added noise and real event signals captured by 94 PMUs. We found that our proposed strategy achieves highly competitive results for identifying coherent generator and non-generator buses in large-scale power systems.

Spatial Patterns of Deltaic Deposition/Erosion Revealed by Streaklines Extracted From Remotely‐Sensed Suspended Sediment Concentration

AbstractDeltas are vulnerable landscapes, making it crucial to understand their spatial patterns of deposition/erosion. Here, we used patterns in suspended sediment concentration (SSC) measured by a NASA airborne spectrometer, AVIRIS‐NG, to infer deposition/erosion within Wax Lake Delta, Louisiana. Conceptually, change in SSC within a fluid parcel traveling downstream reflects settling and/or entrainment from the bed. We found that remotely‐sensed SSC displays curvilinear features, which we interpret as streaklines. We developed a semi‐automated technique for extracting streaklines using a cost function based on SSC and its geometric curvature. We measured SSC change along streaklines, which when combined with flow velocities obtained from a hydrodynamic model, allowed us to infer instantaneous deposition/erosion rates. These rates are realistic in magnitude and record coherent spatial patterns across the delta. Our novel method provides a promising avenue for relating spatial patterns of land change to flow conditions over wide areas in vulnerable deltas.

Colorectal cancer concurrent gene signature based on coherent patterns between genomic and transcriptional alterations

BackgroundThe aim of the study was to enhance colorectal cancer prognostication by integrating single nucleotide polymorphism (SNP) and gene expression (GE) microarrays for genomic and transcriptional alteration detection; genes with concurrent gains and losses were used to develop a prognostic signature.MethodsThe discovery dataset comprised 32 Taiwanese colorectal cancer patients, of which 31 were assayed for GE and copy number variations (CNVs) with Illumina Human HT-12 BeadChip v4.0 and Omni 25 BeadChip v1.1. Concurrent gains and losses were declared if coherent manners were observed between GE and SNP arrays. Concurrent genes were also identified in The Cancer Genome Atlas Project (TCGA) as the secondary discovery dataset (n = 345).ResultsThe “universal” concurrent genes, which were the combination of z-transformed correlation coefficients, contained 4022 genes. Candidate genes were evaluated within each of the 10 public domain microarray datasets, and 1655 (2000 probe sets) were prognostic in at least one study. Consensus across all datasets was used to build a risk predictive model, while distinct relapse-free/overall survival patterns between defined risk groups were observed among four out of five training datasets. The predictive accuracy of recurrence, metastasis, or death was between 61 and 86% (cross-validation area under the receiver operating characteristic (ROC) curve: 0.548-0.833) from five independent validation studies.ConclusionThe colorectal cancer concurrent gene signature is prognostic in terms of recurrence, metastasis, or mortality among 1746 patients. Genes with coherent patterns between genomic and transcriptional contexts are more likely to provide prognostication for colorectal cancer.

Spatiotemporal Change Detection of Coastal Wetlands Using Multi-Band SAR Coherence and Synergetic Classification

Synthetic aperture radar (SAR) signal can penetrate clouds and some vegetation canopies in all weather, and therefore, provides an important measurement tool for change detection and sustainable development of coastal wetland environments and ecosystems. However, there are a few quantitative estimations about the spatiotemporal coherence change with multi-band SAR images in complex coastal wetland ecosystems of the Yellow River Delta (YRD). In this study, C-band Sentinel-1 and L-band ALOS-2 PALSAR data were used to detect the spatiotemporal distribution and change pattern of interferometric coherence in the coastal wetlands of the YRD. The results show that the temporal baseline has a greater impact on the interferometric coherence than the perpendicular baseline, especially for short wavelength C-band SAR. Furthermore, the OTSU algorithm was proven to be able to distinguish the changing regions. The coherence mean and standard deviation values of different land cover types varied significantly in different seasons, while the minimum and maximum coherence changes occurred in February and August, respectively. In addition, considering three classical machine learning algorithms, namely naive Bayes (NB), random forest (RF), and multilayer perceptron (MLP), we proposed a method of synergetic classification with SAR coherence, backscatter intensity, and optical images for coastal wetland classification. The multilayer perceptron algorithm performs the best in synergetic classification with an overall accuracy of 98.3%, which is superior to a single data source or the other two algorithms. In this article, we provide an alternative cost-effective method for coastal wetland change detection, which contributes to more accurate dynamic land cover classification and to an understanding of the response mechanism of land features to climate change and human activities.

Topological Maps and Brain Computations From Low to High.

We first briefly summarize data from microelectrode studies on visual maps in non-human primates and other mammals, and characterize differences among the features of the approximately topological maps in the three main sensory modalities. We then explore the almost 50% of human neocortex that contains straightforward topological visual, auditory, and somatomotor maps by presenting a new parcellation as well as a movie atlas of cortical area maps on the FreeSurfer average surface, fsaverage. Third, we review data on moveable map phenomena as well as a recent study showing that cortical activity during sensorimotor actions may involve spatially locally coherent traveling wave and bump activity. Finally, by analogy with remapping phenomena and sensorimotor activity, we speculate briefly on the testable possibility that coherent localized spatial activity patterns might be able to ‘escape’ from topologically mapped cortex during ‘serial assembly of content’ operations such as scene and language comprehension, to form composite ‘molecular’ patterns that can move across some cortical areas and possibly return to topologically mapped cortex to generate motor output there.

Application of Adaptive Neural Network Algorithm Model in English Text Analysis.

Based on the existing optimization neural network algorithm, this paper introduces a simple and computationally efficient adaptive mechanism (adaptive exponential decay rate). By applying the adaptive mechanism to the Adadelta algorithm, it can be seen that AEDR-Adadelta acquires the learning rate dynamically and adaptively. At the same time, by proposing an adaptive exponential decay rate, the number and method of configuring hyperparameters can be reduced, and different learning rates can be effectively obtained for different parameters. The model is based on the encoder-decoder structure and adopts a dual-encoder structure. The transformer encoder is used to extract the context information of the sentence; the Bi-GRU encoder is used to extract the information of the source sentence; and the gated structure is used at the decoder side. The input information is integrated, and each part is matched with different attention mechanisms, which improves the model's ability to extract and analyze relevant features in sentences. In order to accurately capture the coherence features in English texts, an improved subgraph matching algorithm is used to mine frequently occurring subgraph patterns in sentence semantic graphs, which are used to simulate the unique coherence patterns in English texts, and then analyze the overall coherence of English texts. According to the frequency of occurrence of different subgraph patterns in the sentence semantic graph, the subgraphs are filtered to generate frequent subgraph sets, and the subgraph frequency of each frequent subgraph is calculated separately. The overall coherence quality of English text is quantitatively analyzed by extracting the distribution characteristics of frequent subgraphs and the semantic values of subgraphs in the sentence semantic graph. According to the experimental results, the algorithm using the adaptive mechanism can reduce the error of the training set and the test set, improve the classification accuracy to a certain extent, and has a faster convergence speed and better text generalization ability. The semantic coherence diagnosis model of English text in this paper performs well in various tasks and has a good effect on improving the automatic correction system of English composition and providing reference for English teachers' composition correction.

Zircon U–Pb age, whole-rock geochemistry and Nd–Sr–Pb isotope constraints on petrogenesis of the Eocene Zajkan gabbro–monzogranite intrusion, Tarom-Hashtjin magmatic belt, NW Iran

The late Eocene high-K to shoshonitic Zajkan intrusion, situated in the Tarom-Hashtjin magmatic belt (THMB), NW Iran, ranges in composition from gabbro to pyroxene quartz monzodiorite, pyroxene quartz monzonite and monzogranite, and is classified as an I-type metaluminous granitoid. On the primitive mantle-normalised spider diagram, these rocks demonstrate negative high field strength element (HFSE) anomalies, large-ion lithophile element (LILE) enrichment, and positive Pb anomaly, indicating a subduction-related signature. Furthermore, the granitoids are characterised by coherent chondrite-normalised rare earth element patterns with high light rare earth elements/heavy rare earth elements (LREE/HREE) and moderate to weakly negative Eu anomalies (Eu/Eu* = 0.6–0.86). LA-ICP-MS zircon U–Pb dating demonstrates that the Zajkan intrusion crystallised at ca 36.5 Ma. Relatively low to moderate 87Sr/86Sr ratios (0.7046–0.7061) and positive εNd(t) values (1.52–1.88) of the Zajkan samples are consistent with an enriched lithospheric mantle source that was metasomatised by an earlier subducted slab. Geochemical features show that the parental magma of the Zajkan intrusion likely resulted from a partial melting of metasomatised amphibole-bearing mantle source. Sr–Nd isotope modelling is consistent with the Zajkan intrusion formed from a mix of ∼80% of a mantle-derived magma and ∼20% of a juvenile lower crustal-derived magma. Partial melting of the metasomatised mantle may have resulted from late Eocene lithospheric thinning and hot asthenospheric upwelling through slab roll-back or lithospheric delamination. KEY POINTS The late Eocene high-K to shoshonitic Zajkan intrusion is located within the Tarom-Hashtjin magmatic belt, NW Iran. U–Pb zircon dating revealed an age of ca 36.5 Ma for emplacement of the Zajkan intrusion. Sr–Nd isotope modelling is consistent with the Zajkan intrusion formed from a mix of ∼80% of mantle-derived magma and ∼20% of juvenile lower crustal-derived magma.

A Coherent Pattern Mining Algorithm Based on All Contiguous Column Bicluster

Microarray contains a large matrix of information and has been widely used by biologists and bio data scientist for monitoring combinations of genes in different organisms. The coherent patterns in all continuous columns are mined in gene microarray data matrices. It is investigated, in this study, the coherent patterns in all continuous columns in gene microarray data matrix by developing the time series similarity measure for the coherent patterns in all continuous columns, as well as the evaluation function for verifying the proposed algorithm and the corresponding biclusters. The continuous time changes are taken into account in the coherent patterns in all continuous columns, and co-expression patterns in time series are searched. In order to use all the common information between sequences, a similarity measure for the coherent patterns in continuous columns is defined in this paper. To validate the efficiency of the similarity measure to mine biological information at continuous time points, an evaluation function is defined to measure biclusters and an effective algorithm is proposed to mine the biclusters. Simulation experiments are conducted to verify the biological significance of the biclusters, which include synthetic datasets and real gene microarray datasets. The performance of the algorithm is analyzed and the results show that the algorithm is highly efficient.

Discriminant Dynamic Mode Decomposition for Labeled Spatiotemporal Data Collections

Extracting coherent patterns is one of the standard approaches towards understanding spatio-temporal data. Dynamic mode decomposition (DMD) is a powerful tool for extracting coherent patterns, but the original DMD and most of its variants do not consider label information, which is often available as side information of spatio-temporal data. In this work, we propose a new method for extracting distinctive coherent patterns from labeled spatio-temporal data collections, such that they contribute to major differences in a labeled set of dynamics. We achieve such pattern extraction by incorporating discriminant analysis into DMD. To this end, we define a kernel function on subspaces spanned by sets of dynamic modes and develop an objective to take both reconstruction goodness as DMD and class-separation goodness as discriminant analysis into account. We illustrate our method using a synthetic dataset and several real-world datasets. The proposed method can be a useful tool for exploratory data analysis for understanding spatio-temporal data.

Physics informed DMD for periodic Dynamic Induction Control of Wind Farms

Dynamic Induction Control (DIC) is a novel, exciting branch of Wind Farm Control. It makes use of time-varying control inputs to increase wake mixing, and consequently improve the velocity recovery rate of the flow and the power production of downstream turbines. The Pulse and the Helix are two promising DIC strategies that rely on sinusoidal excitations of the collective pitch and individual pitch of the blades, respectively. While their beneficial effects are evident in simulations and wind tunnel tests, we do not yet fully understand the physics behind them. We perform a systematic analysis of the dynamics of pulsed and helicoidal wakes by applying a data-driven approach to the analysis of data coming from Large Eddy Simulations (LES). Specifically, Dynamic Mode Decomposition (DMD) is used to extract coherent patterns from high-dimensional flow data. The periodicity of the excitation is exploited by adding a novel physics informed step to the algorithm. We then analyze the power spectral density of the resulting DMD modes as a function of the Strouhal number for different pitch excitation frequencies and amplitudes. Finally, we show the evolution in time and space of the dominant modes and comment on the recognizable patterns. By focusing on the modes that contribute the most to the flow dynamics, we gather insight on what causes the increased wake recovery rate in DIC techniques. This knowledge can then be used for the optimization of the signal parameters in complex layouts and conditions.

Seismic Waveform‐Coherence Controlled by Earthquake Source Dimensions

Rupture size is a fundamental earthquake source parameter that is challenging to infer independently from far-field seismological observations. Here, we develop a novel observational constraint on source size based on the decay rate of wavefield coherence across a seismic array. For a given earthquake, waveform coherence decays with increasing interstation distance or, more precisely, with increasing projection difference defined as the difference between the takeoff vectors associated to the two stations projected along the rupture direction. We find that coherence generally falls off with projection difference faster for earthquakes of larger magnitudes. The magnitude dependence of the coherence decay rate can be explained by a finite source effect: larger source sizes cause larger differences of phase delays between waves arriving from different parts of the rupture at different stations, hence a stronger spatial variability of the wavefield, resulting in a breakdown of waveform coherence. Assuming a 1-D Haskell's source model, the rupture size can be estimated from the coherence decay rate. We apply this method to USArray data of earthquakes in three subduction zones, the Sea of Okhotsk, South America and Japan. The source sizes inferred from the coherence decay patterns are consistent with scaling relations intermediate between width-saturated L-models and quasi equi-dimensional rupture models. Our observation captures a unique pattern of array waveform coherence and demonstrates the potential of utilizing waveform coherence to study earthquake source parameters.

The Rational Appropriateness of Group-Based Pride.

This article seeks to analyze the conditions in which group-based pride is rationally appropriate. We first distinguish between the shape and size of an emotion. For the appropriate shape of group-based pride, we suggest two criteria: the distinction between group-based pride and group-based hubris, and between we-mode and I-mode sociality. While group-based hubris is inappropriate irrespective of its mode due to the arrogant, contemptuous, and other-derogating character of this emotion, group-based pride in the we-mode is appropriate in terms of shape if it is felt over an achievement to which the group members collectively committed themselves. For the same reason, members of I-mode groups can feel appropriately proud of the achievement of their group if they have collectively contributed to it. Instead, group-based pride by mere private identification with a successful group can be rationally appropriate if it manifests the person’s reduced-agency ideal and is also part of a coherent pattern of rationally interconnected emotions focused on the same ideal. Moreover, we suggest that pride in the success of one’s family member or a close friend is typically felt over the rise of social status that one group member’s success grants to the group. However, social status cannot be valued for its own sake as this undermines the values upon which social status is founded. Instead, direct or indirect causal contribution to the success of one’s child, friend, or student can warrant group-based pride, which may be justified on the basis of shared values without causal contribution as well. Finally, regarding the size of group-based pride, members of we-mode groups are warranted to experience and express more intense pride than members of I-mode groups. Moreover, the proper intensity of this emotion depends on the particular other(s) to whom the expression is directed. Finally, criteria of appropriate size don’t apply to shared group-based pride as sharing increases the intensity of emotion by default.

High impact compound events in Australia

Many natural disasters in Australia occur as a result of meteorological drivers occurring in combination. These Compound Events (CEs) amplify risk to socio-economic and ecological systems relative to single meteorological events. We undertake a bivariate assessment for two hazard combinations important in Australia: heatwaves and drought, and strong winds and heavy precipitation. We use observations and reanalysis data to build a high-resolution climatology spanning the period from 1980 to 2014 and identify the north-western Australian coast as a region particularly prone to the joint occurrence of wind and precipitation extremes. Heatwaves and drought frequently co-occur multiple times per year over most of Australia with shorter return periods in the tropical north. We show that CMIP6 models capture the return periods of both CEs well. We then examine projected changes in both CEs under future conditions (2066–2 100) under a moderate and high emission scenario. All CMIP6 models project decreases in the return periods (i.e. more frequent events) of co-occurring heatwaves and droughts with the south of Australia particularly affected. Northern Australia is projected to become more at risk from wind and precipitation extremes while southern Australia experiences lower risk. The high level of skill in simulating the two CEs examined, combined with the coherent spatial patterns projected by the CMIP6 models suggest there is merit in using these models to examine the large-scale risk of CEs in the future, and that these can be used to support national scale planning for the changing risk of CEs.

Enhancement of Partially Coherent Diffractive Images Using Generative Adversarial Network

We present a deep learning-based generative model for the enhancement of partially coherent diffractive images. In lensless coherent diffractive imaging, a highly coherent X-ray illumination is required to image an object at high resolution. Non-ideal experimental conditions result in a partially coherent X-ray illumination, lead to imperfections of coherent diffractive images recorded on a detector, and ultimately limit the capability of lensless coherent diffractive imaging. The previous approaches, relying on the coherence property of illumination, require preliminary experiments or expensive computations. In this article, we propose a generative adversarial network (GAN) model to enhance the visibility of fringes in partially coherent diffractive images. Unlike previous approaches, the model is trained to restore the latent sharp features from blurred input images without finding coherence properties of illumination. We demonstrate that the GAN model performs well with both coherent diffractive imaging and ptychography. It can be applied to a wide range of imaging techniques relying on phase retrieval of coherent diffraction patterns.

Blood-Coated Sensor for High-Throughput Ptychographic Cytometry on a Blu-ray Disc.

The Blu-ray drive is an engineering masterpiece that integrates disc rotation, pickup head translation, and three lasers in a compact and portable format. Here, we integrate a blood-coated image sensor with a modified Blu-ray drive for high-throughput cytometric analysis of various biospecimens. In this device, samples are mounted on the rotating Blu-ray disc and illuminated by the built-in lasers from the pickup head. The resulting coherent diffraction patterns are then recorded by the blood-coated image sensor. The rich spatial features of the blood-cell monolayer help down-modulate the object information for sensor detection, thus forming a high-resolution computational biolens with a theoretically unlimited field of view. With the acquired data, we develop a lensless coherent diffraction imaging modality termed rotational ptychography for image reconstruction. We show that our device can resolve the 435 nm line width on the resolution target and has a field of view only limited by the size of the Blu-ray disc. To demonstrate its applications, we perform high-throughput urinalysis by locating disease-related calcium oxalate crystals over the entire microscope slide. We also quantify different types of cells on a blood smear with an acquisition speed of ∼10,000 cells per second. For in vitro experiments, we monitor live bacterial cultures over the entire Petri dish with single-cell resolution. Using biological cells as a computational lens could enable new intriguing imaging devices for point-of-care diagnostics. Modifying a Blu-ray drive with the blood-coated sensor further allows the spread of high-throughput optical microscopy from well-equipped laboratories to citizen scientists worldwide.

The Diurnal Cycle of Precipitation over Lake Titicaca Basin Based on CMORPH

This paper examines the diurnal cycle of precipitation (DCP) over Lake Titicaca basin (LTb) during the summertime months based on the high spatial–temporal resolution (8 × 8 km2 and hourly) estimates of the Climate Prediction Center Morphing technique (CMORPH). This analysis was carried out using observations from rain gauges (RgSENAMHI) as a reference for the period 2002 to 2013. The accuracy of the CMORPH product was tested with graphical comparisons and several statistical metrics, such as correlation coefficient, bias, and root mean square error. Spatial maps of these metrics and of the diurnal cycle were developed to assess the spatial dependency in the CMORPH accuracy over the LTb. On average, 43% of the total RgSENAMHI variation was explained by the CMORPH. The correlation between the CMORPH and RgSENAMHI amounts was positive over the southeastern and northern LTb and negative in the central and southern LTb. An underestimation bias was observed over most of the LTb areas, and an overestimation bias was observed at some stations (e.g., Lagunillas, Isla Suana, and Desaguadero stations). The total bias decreased when approaching the lake attaining its minimum value over the mountains consistent with previous studies. Overall, the CMORPH was able to capture the spatial patterns of rainfall over the LTb. Over the surrounding lake area, the plateau, and high mountain areas, precipitation peaks were in the late afternoon, while over low areas, such as the valleys and Lake Titicaca, it peaked around midnight to early morning. This result suggests that the DCP is closely related to the local circulation resulting from a response due to solar radiation and the complex orography. On the other hand, the high resolution CMORPH technique can depict finer regional details, such as the less coherent phase pattern over a few regions.

Biogeographic deconstruction of phylogenetic and functional diversity provides insights into the formation of regional assemblages

Evolutionary history and environmental filtering shape the phylogenetic and functional structure of regional assemblages. However, detecting the footprint of such eco‐evolutionary drivers is challenging because these may often counter each other's signature. Here, we examined whether a biogeographic deconstruction approach of phylogenetic (PD) and functional diversity (FD) patterns may help in identifying eco‐evolutionary signals in extant regional assemblages. As model system, we used forest understorey angiosperms found in three regions of Italy (Alpine, Mediterranean, Continental). We quantified PD and FD of all species inhabiting the three regions (regional assemblages). Then, we computed PD and FD for the subsets of species restricted to each region (biogeographic elements), also examining diversity patterns of species found across the three regions (widespread element). We used aboveground and belowground traits capturing major plant functions to calculate FD. Additionally, we assessed FD patterns decoupled from phylogeny. We found that species restricted to climatically harsh regions (Alpine and Mediterranean elements) were phylogenetically and functionally clustered, whereas widespread species were characterised by overdispersion. Species confined to the climatically intermediate (Continental) region were randomly sorted. By including all species occurring within a region, the patterns found for the region‐restricted species blurred. Phylogenetically decoupled FD patterns were qualitatively similar to non‐decoupled ones with the exception of the Alpine element, where we detected a clear signature of functional differentiation between closely related species. This suggests that recent speciation events contributed to shaping the Alpine flora. Compared to the belowground compartment, aboveground traits showed a more coherent pattern with that of all‐trait FD – likely because most biomass is allocated aboveground in forest understoreys. This biogeographic deconstruction study illustrates which type of eco‐evolutionary insights can be gained by implementing multifaceted and integrated approaches at the macroecological scale.

Engineered Cu-PEN Composites at the Nanoscale: Preparation and Characterisation.

As polymeric materials are already used in many industries, the range of their applications is constantly expanding. Therefore, their preparation procedures and the resulting properties require considerable attention. In this work, we designed the surface of polyethylene naphthalate (PEN) introducing copper nanowires. The surface of PEN was transformed into coherent ripple patterns by treatment with a KrF excimer laser. Then, Cu deposition onto nanostructured surfaces by a vacuum evaporation technique was accomplished, giving rise to nanowires. The morphology of the prepared structures was investigated by atomic force microscopy and scanning electron microscopy. Energy dispersive spectroscopy and X-ray photoelectron spectroscopy revealed the distribution of Cu in the nanowires and their gradual oxidation. The optical properties of the Cu nanowires were measured by UV-Vis spectroscopy. The sessile drop method revealed the hydrophobic character of the Cu/PEN surface, which is important for further studies of biological responses. Our study suggests that a combination of laser surface texturing and vacuum evaporation can be an effective and simple method for the preparation of a Cu/polymer nanocomposite with potential exploitation in bioapplications; however, it should be borne in mind that significant post-deposition oxidation of the Cu nanowire occurs, which may open up new strategies for further biological applications.

Groundwater response to climate variability in Mediterranean type climate zones with comparisons of California (USA) and Portugal

Aquifers are a fundamental source of freshwater, yet they are particularly vulnerable in coastal regions with Mediterranean type climate, due to both climatic and anthropogenic pressures. This comparative study examines the interrelationships between ocean-atmosphere teleconnections, groundwater levels and precipitation in coastal aquifers of California (USA) and Portugal. Piezometric and climate indices (1989–2019) are analyzed using singular spectral analysis and wavelet transform methods. Singular spectral analysis identifies signals consistent with the six dominant climate patterns: the Pacific Decadal Oscillation (PDO), the El Niño-Southern Oscillation (ENSO), and the Pacific/North American Oscillation (PNA) in California, and the North Atlantic Oscillation (NAO), the Eastern Atlantic Oscillation (EA) and the Scandinavian Pattern (SCAND) in Portugal. Lower-frequency oscillations have a greater influence on hydrologic patterns, with PDO (52.75%) and NAO (46.25%) on average accounting for the largest amount of groundwater level variability. Wavelet coherences show nonstationary covariability between climate patterns and groundwater levels in distinct period bands: 4–8 years for PDO, 2–4 years for ENSO, 1–2 years for PNA, 5–8 years for NAO, 2–4 years for EA and 2–8 years for SCAND. Wavelet coherence patterns also show that coupled climate patterns (NAO+ EA– and paired PDO and ENSO phases) are associated with major drought periods in both the Mediterranean climate zones.

In preprints: the problem of producing precise patterns.

In preprints: the problem of producing precise patterns.