Utilizing homogenized observation records and reconstructed time series data to estimate recent trends in Mid-Atlantic soil moisture scarcity

The Mid-Atlantic region of the USA has experienced increasing annual precipitation amounts in recent decades, along with more frequent extreme events of greater magnitude. Unlike many US regions that have suffered increasing drought conditions from higher evapotranspiration demand, positive trends in the Mid-Atlantic accumulated precipitation are greater than the recent increases in reference evapotranspiration. The temporal correlation between precipitation events and soil moisture capacity is essential for determining how the nature of drought has changed in the region. This analysis has shown that soil moisture scarcity has declined in nine of ten subregions of the Mid-Atlantic that were analyzed from 1985 to 2019. Two algorithms were deployed to draw this conclusion: Climatol enabled the use of the FAO-56 Penman-Monteith equation on daily observation station data for which complete records were unavailable, and the second algorithm calculated soil moisture levels on a daily basis, more accurately capturing drought conditions than common methods using weekly or monthly summaries. Although the declining drought trends were not statistically significant, a result of more extreme events and higher evapotranspiration rates, the inclusion of direct data from an expanded set of locations provides greater clarity from the trends, allowing policymakers and landowners to anticipate changes in future Mid-Atlantic irrigation water demand.

El Niño Index Prediction Using Deep Learning with Ensemble Empirical Mode Decomposition

El Niño is an important quasi-cyclical climate phenomenon that can have a significant impact on ecosystems and societies. Due to the chaotic nature of the atmosphere and ocean systems, traditional methods (such as statistical methods) are difficult to provide accurate El Niño index predictions. The latest research shows that Ensemble Empirical Mode Decomposition (EEMD) is suitable for analyzing non-linear and non-stationary signal sequences, Convolutional Neural Network (CNN) is good at local feature extraction, and Recurrent Neural Network (RNN) can capture the overall information of the sequence. As a special RNN, Long Short-Term Memory (LSTM) has significant advantages in processing and predicting long, complex time series. In this paper, to predict the El Niño index more accurately, we propose a new hybrid neural network model, EEMD-CNN-LSTM, which combines EEMD, CNN, and LSTM. In this hybrid model, the original El Niño index sequence is first decomposed into several Intrinsic Mode Functions (IMFs) using the EEMD method. Next, we filter the IMFs by setting a threshold, and we use the filtered IMFs to reconstruct the new El Niño data. The reconstructed time series then serves as input data for CNN and LSTM. The above data preprocessing method, which first decomposes the time series and then reconstructs the time series, uses the idea of symmetry. With this symmetric operation, we extract valid information about the time series and then make predictions based on the reconstructed time series. To evaluate the performance of the EEMD-CNN-LSTM model, the proposed model is compared with four methods including the traditional statistical model, machine learning model, and other deep neural network models. The experimental results show that the prediction results of EEMD-CNN-LSTM are not only more accurate but also more stable and reliable than the general neural network model.

The extreme warmth of the Central European spring in 2018 and its effects on fruit ripening phenology in Austria – a 251 year perspective

<p>The exceptional warmth of spring and early summer of 2018 caused the earliest beginning of fruit ripening dates in Austria since 1946 of black elder and red currant, the second earliest of apricot, as well as the shortest period between the beginning of flowering and fruit ripening for all three species (same as 1956 for red currant). These phenological extremities of the 2018 spring correspond with the highest Austrian preseason (temperatures before the phenological event) April/May/June average since 1768.</p><p>In order to put the spring of 2018 into a long term perspective, the above mentioned phenological time series were extended back to 1768 by the much longer homogenised HISTALP temperature time series. This was achieved by multiple regression driven by preseason mean monthly temperatures. In order to accommodate for the uncertainty of the regression model, the lower (5%) and upper (95%) bounds of the confidence intervals were added to the reconstructed time series. Even when considering the lower bounds, the 2018 entry date of black elder beginning of fruit ripening remains the earliest since 1768. The 2018 entry date of apricot comes fourth (after 1811, 1794, 1797 and same as 1822) and that of red currant third (after 1811 and 1794). In order to evaluate the phenological variability since 1970 a 11 year moving average and a 41 year moving trend were calculated for the combined time series consisting of the modelled (from 1768 to 1945) and observed (from 1946 – 2018) sections. Neither the level of the 11 year averages nor the level of the 41 year trend values since 1970 have occurred during any other period since 1768.</p><p>These results contribute to the discussion of the temperature sensitivity of phenological phases. In spite of the unprecedented spring and early summer temperature level our phenological data do not indicate that lower bounds of the time period between flowering and fruit ripening have yet been reached. The fruit ripening phenology of the mid latitudes is still sensitive enough to faithfully record temperature trends and extreme events supplementing the instrumental record.</p>

Integrating the Continuous Wavelet Transform and a Convolutional Neural Network to Identify Vineyard Using Time Series Satellite Images

Grape is an economic crop of great importance and is widely cultivated in China. With the development of remote sensing, abundant data sources strongly guarantee that researchers can identify crop types and map their spatial distributions. However, to date, only a few studies have been conducted to identify vineyards using satellite image data. In this study, a vineyard is identified using satellite images, and a new approach is proposed that integrates the continuous wavelet transform (CWT) and a convolutional neural network (CNN). Specifically, the original time series of the normalized difference vegetation index (NDVI), enhanced vegetation index (EVI), and green chlorophyll vegetation index (GCVI) are reconstructed by applying an iterated Savitzky-Golay (S-G) method to form a daily time series for a full year; then, the CWT is applied to three reconstructed time series to generate corresponding scalograms; and finally, CNN technology is used to identify vineyards based on the stacked scalograms. In addition to our approach, a traditional and common approach that uses a random forest (RF) to identify crop types based on multi-temporal images is selected as the control group. The experimental results demonstrated the following: (i) the proposed approach was comprehensively superior to the RF approach; it improved the overall accuracy by 9.87% (up to 89.66%); (ii) the CWT had a stable and effective influence on the reconstructed time series, and the scalograms fully represented the unique time-related frequency pattern of each of the planting conditions; and (iii) the convolution and max pooling processing of the CNN captured the unique and subtle distribution patterns of the scalograms to distinguish vineyards from other crops. Additionally, the proposed approach is considered as able to be applied to other practical scenarios, such as using time series data to identify crop types, map landcover/land use, and is recommended to be tested in future practical applications.

Source reconstruction of broadband EEG/MEG data using the frequency-adaptive broadband (FAB) beamformer

A beamformer enhances the signal from a voxel of interest by minimising interference from all other locations represented in the sensor covariance matrix. However, the presence of narrowband oscillations in EEG/MEG implies that the spatial structure of the covariance matrix, and hence also the optimal beamformer, depends on the frequency. The frequency-adaptive broadband (FAB) beamformer introduced here exploits this fact in the Fourier domain by partitioning the covariance matrix into cross-spectra corresponding to different frequencies. For each frequency bin, an individual spatial filter is constructed. This assures optimal noise suppression across the frequency spectrum. After applying the spatial filters in the frequency domain, the broadband source signal is recovered using the inverse Fourier transform. MEG simulations using artificial data and real resting-state measurements were used to compare the FAB beamformer to the LCMV beamformer and MNE. The FAB beamformer significantly outperforms both methods in terms of the quality of the reconstructed time series. To our knowledge, the FAB beamformer is the first beamforming approach tailored for the analysis of broadband neuroimaging data. Due to its frequency-adaptive noise suppression, the reconstructed source time series is suited for further time-frequency or connectivity analysis in source space.

Topological segregation of functional networks increases in developing brains

A growing literature conceptualises human brain development from a network perspective, but it remains unknown how functional brain networks are refined during the preschool years. The extant literature diverges in its characterisation of functional network development, with little agreement between haemodynamic- and electrophysiology-based measures. In children aged from 4 to 12 years, as well as adults, age appropriate magnetoencephalography was used to estimate unbiased network topology, using minimum spanning tree (MST) constructed from phase synchrony between beamformer-reconstructed time-series. During childhood, network topology becomes increasingly segregated, while cortical regions decrease in centrality. We propose a heuristic MST model, in which a clear developmental trajectory for the emergence of complex brain networks is delineated. Our results resolve topological reorganisation of functional networks across temporal and special scales in youth and fill a gap in the literature regarding neurophysiological mechanisms of functional brain maturation during the preschool years.

Geomagnetic field declination: from decadal to centennial scales

Declination annual mean time series longer than 1 century provided by 24 geomagnetic observatories worldwide, together with 5 Western European reconstructed declination series over the last 4 centuries, have been analyzed in terms of the frequency constituents of the secular variation at inter-decadal and sub-centennial timescales of 20–35 and 70–90 years. Observatory and reconstructed time series have been processed by several types of filtering, namely Hodrick–Prescott, running averages, and Butterworth. The Hodrick–Prescott filtering allows us to separate a quasi-oscillation at a decadal timescale, which is assumed to be related to external variations and called the 11-year constituent, from a long-term trend. The latter has been decomposed into two other oscillations called inter-decadal and sub-centennial constituents by applying a Butterworth filtering with cutoffs at 30 and 73 years, respectively. The analysis shows that the generally accepted geomagnetic jerks occur around extrema in the time derivative of the trend and coincide with extrema in the time derivative of the 11-year constituent. The sub-centennial constituent is traced back to 1600 in the five 400-year-long time series and seems to be a major constituent of the secular variation, geomagnetic jerks included.

Geomagnetic field declination: from decadal to centennial scales

Declination annual means time-series longer than a century provided by 24 geomagnetic observatories world-wide, together with 5 Western European reconstructed declination series over the last four centuries have been analyzed in terms of frequency constituents of the secular variation at inter-decadal and sub-centennial time-scales of 20–35 and, respectively, 70–90 years. Observatory and reconstructed time-series have been processed by several types of filtering, namely Hodrick-Prescott, running averages, and Butterworth. The Hodrick-Prescott filtering allows to separate a quasi-oscillation at decadal time scale, supposed to be related to external variations and called ’11-year constituent’, from a long-term trend. The latter has been decomposed in two other oscillations, called ‘inter-decadal’ and ‘sub-centennial’ constituents by applying a Butterworth filtering with cutoffs at 30 and 73 years, respectively. The analysis shows that the generally accepted geomagnetic jerks occur around extrema in the time derivative of the trend and coincide with extrema in the time derivative of the 11-year constituent. The sub-centennial constituent is traced back to 1600, in the five 400-year long time-series, and shows to be a major constituent of the secular variation, geomagnetic jerks included.