Entire Time Series Research Articles

Spectral analysis of soot dynamics in an aero-engine model combustor

The dynamics of soot evolution in a swirl-stabilized model aero-engine combustor are studied using Large-Eddy Simulation (LES) and state-of-the-art combustion and soot models. The simulated combustor is a dual-swirl combustor with and without secondary oxidation air at a pressure of 3 bar. The comparison with experimental data shows that the simulation accurately captures the gas-phase statistics. Overall, soot statistics are well captured, particularly in the shear layers, although underpredicted in other locations. Three locations in the combustor: Shear Layer (SL), Inner Recirculation Zone (IRZ), and Outer Recirculation Zone (ORZ) are probed to obtain velocity, temperature, and soot volume fraction signals. Lomb–Scargle spectral analysis on the probed signals reveals that soot evolution in the SL is characterized by high-frequency dynamics, whereas in the IRZ and ORZ, it is characterized by low-frequency dynamics. Within the SL, couplings between the soot and flow dynamics are observed, with the soot volume fraction and velocity sharing a common dominant frequency. However, in the IRZ and ORZ, such couplings are not evident. Additionally, the wavelet transform is applied to the probed signals to study the temporal distribution of the frequencies. The analysis indicates that, in the SL, the dominant frequency of the velocity occurs continuously throughout the entire time series, while higher frequencies occur in short bursts. Conversely, for the soot volume fraction, both the dominant frequency and higher frequencies appear in short bursts. In the ORZ and IRZ, the soot volume fraction scalogram shows that low frequencies dominate and occur continuously throughout the time series. Finally, the phase space reconstructions show that the trajectory of the soot dynamics shows a circular pattern, indicative of periodic behavior. The center of attraction remains stationary over a relatively large time scale, suggesting stability in the dynamics as they evolve.

Impact of the El Niño on Fire Dynamics on the African Continent

Several studies investigated the occurrence of fires in Africa with numerical modeling or applied statistics; however, only a few studies focused on the influence of El Niño on the fire risk using a coupled model. The study aimed to assess the influence of El Niño on wildfire dynamics in Africa using the SPEEDY-HYCOM model. El Niño events in the Eastern Tropical Pacific were classified via sea surface temperature (SST) anomaly based on a predefined climatology between 1961 and 2020 for the entire time series of SST, obtaining linear anomalies. The time series of the SST anomalies was created for the region between 5° N and 5° S and 110° W and 170° W. The events were defined in three consecutive 3-month periods as weak, moderate, and strong El Niño conditions. The Meteorological Fire Danger Index (MFDI) was applied to detect fire hazards. The MFDI simulated by the SPEEDY-HYCOM model for three El Niño categories across different lagged months revealed relevant distinctions among the categories. In the case of ‘Weak’, the maximum variability of fire risk observed at time lags (0, -3, -6, and -9 months) was primarily in Congo, Gabon, and Madagascar. The ‘Moderate’ pattern had similar characteristics to ‘Weak’ except for the lag-6 months and its occurrence in the equatorial zone of Africa. ‘Strong’ showed a remarkable impact in East Africa, resulting in high fire risk, regardless of time lags. Precipitation and evaporation simulations (SPEEDY-HYCOM) indicated that El Niño categories in Africa need particular attention in the central, southern, and southeastern regions emphasizing the significance of lag-0 and lag-6 (evaporation) as well as lag-0, lag-6, and lag-9 (precipitation). The SPEEDY-HYCOM coupled model in conjunction with the MFDI was efficient in assessing climate variabilities in Africa during El Niño events. This model allows the analysis and prediction of wildfire risks based on El Niño events, providing crucial information for wildfire management and prevention. Its simulations uncover significant variations in risks among different El Niño categories and lagged months, contributing to the understanding and mitigation of this environmental challenge.

Temporal-Frequency Attention Focusing for Time Series Extrinsic Regression via Auxiliary Task.

Time series extrinsic regression (TSER) aims at predicting numeric values based on the knowledge of the entire time series. The key to solving the TSER problem is to extract and use the most representative and contributed information from raw time series. To build a regression model that focuses on those information suitable for the extrinsic regression characteristic, there are two major issues to be addressed. That is, how to quantify the contributions of those information extracted from raw time series and then how to focus the attention of the regression model on those critical information to improve the model's regression performance. In this article, a multitask learning framework called temporal-frequency auxiliary task (TFAT) is designed to solve the mentioned problems. To explore the integral information from the time and frequency domains, we decompose the raw time series into multiscale subseries in various frequencies via a deep wavelet decomposition network. To address the first problem, the transformer encoder with the multihead self-attention mechanism is integrated in our TFAT framework to quantify the contribution of temporal-frequency information. To address the second problem, an auxiliary task in a manner of self-supervised learning is proposed to reconstruct the critical temporal-frequency features so as to focusing the regression model's attention on those essential information for facilitating TSER performance. We estimated three kinds of attention distribution on those temporal-frequency features to perform auxiliary task. To evaluate the performances of our method under various application scenarios, the experiments are carried out on the 12 datasets of the TSER problem. Also, ablation studies are used to examine the effectiveness of our method.

Versatile and high-accuracy seq2seq model with self-attention mechanism for Li-ion battery characterization

In this paper, we innovatively propose a seq2seq model that incorporates a self-attention mechanism for modeling the external characteristics of Li-ion batteries.During training, an input sequence of shape (4, time_steps) is formed by concatenating the battery voltage at each time step with the external stimuli such as current, temperature, and cumulative capacity at the next time step. The voltage variation at next step will be used as the target sequence of shape (1, time_steps). During inference, the input sequence is iteratively updated by adding the voltage variation obtained at each step to the previous voltage value, allowing us to predict the voltage variation over the entire time series. The seq2seq model employs two heads that utilize different sequence masks, ensuring attention to both historical data and current stimuli. This approach achieves top-tier prediction accuracy on an open dataset.Furthermore, our model demonstrates outstanding versatility and transferability, as it is not constrained by battery type, operating conditions, temperature, or data upload frequency.Lastly, the characteristics of aged batteries can be represented through fine-tuning.

Divergent bleaching and recovery trajectories in reef-building corals following a decade of successive marine heatwaves.

Increasingly frequent marine heatwaves are devastating coral reefs. Corals that survive these extreme events must rapidly recover if they are to withstand subsequent events, and long-term survival in the face of rising ocean temperatures may hinge on recovery capacity and acclimatory gains in heat tolerance over an individual's lifespan. To better understand coral recovery trajectories in the face of successive marine heatwaves, we monitored the responses of bleaching-susceptible and bleaching-resistant individuals of two dominant coral species in Hawai'i, Montipora capitata and Porites compressa, over a decade that included three marine heatwaves. Bleaching-susceptible colonies of P. compressa exhibited beneficial acclimatization to heat stress (i.e., less bleaching) following repeat heatwaves, becoming indistinguishable from bleaching-resistant conspecifics during the third heatwave. In contrast, bleaching-susceptible M. capitata repeatedly bleached during all successive heatwaves and exhibited seasonal bleaching and substantial mortality for up to 3 y following the third heatwave. Encouragingly, bleaching-resistant individuals of both species remained pigmented across the entire time series; however, pigmentation did not necessarily indicate physiological resilience. Specifically, M. capitata displayed incremental yet only partial recovery of symbiont density and tissue biomass across both bleaching phenotypes up to 35 mo following the third heatwave as well as considerable partial mortality. Conversely, P. compressa appeared to recover across most physiological metrics within 2 y and experienced little to no mortality. Ultimately, these results indicate that even some visually robust, bleaching-resistant corals can carry the cost of recurring heatwaves over multiple years, leading to divergent recovery trajectories that may erode coral reef resilience in the Anthropocene.

Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series.

Blood oxygen level dependent (BOLD) MRI time series with maternal hyperoxia can assess placental oxygenation and function. Measuring precise BOLD changes in the placenta requires accurate temporal placental segmentation and is confounded by fetal and maternal motion, contractions, and hyperoxia-induced intensity changes. Current BOLD placenta segmentation methods warp a manually annotated subject-specific template to the entire time series. However, as the placenta is a thin, elongated, and highly non-rigid organ subject to large deformations and obfuscated edges, existing work cannot accurately segment the placental shape, especially near boundaries. In this work, we propose a machine learning segmentation framework for placental BOLD MRI and apply it to segmenting each volume in a time series. We use a placental-boundary weighted loss formulation and perform a comprehensive evaluation across several popular segmentation objectives. Our model is trained and tested on a cohort of 91 subjects containing healthy fetuses, fetuses with fetal growth restriction, and mothers with high BMI. Biomedically, our model performs reliably in segmenting volumes in both normoxic and hyperoxic points in the BOLD time series. We further find that boundary-weighting increases placental segmentation performance by 8.3% and 6.0% Dice coefficient for the cross-entropy and signed distance transform objectives, respectively.

A novel formulation of RNN-based neural network with real-time updating – An application for dynamic hydraulic fractured shale gas production forecasting

The Recurrent Neural Network (RNN) has found extensive application in production forecasting for fractured reservoirs, particularly excelling at accurately predicting self-regression in late-life prediction for wells with substantial historical data. However, forecasting for new wells or those with limited historical data presents greater challenges. The main difference lies in the insufficiency of historical data, which requires meaningful integration of a continuous production data stream to enhance prediction results. In the context of utilizing real-time data and continuously updating predictions, many existing formulations of RNN-based models in the literature either utilize inputs of sub-sequences instead of the entire sequence or require retraining of the entire model to handle new data and update the predictions. Therefore, we propose a novel prediction framework called Recurrent Updated Forecasting (RUF). This framework considers the entire time series structure, improves accuracy and training efficiency, and allows for prediction updates without retraining. In scenarios without production data, we employ a Radial Basis Function Network (RBFN) to initialize the process. Once more extended production history becomes available, it can be utilized to enhance prediction results. We present a case study based on both synthetic and field data from the Montney shale gas reservoir, considering different production-related features as inputs to predict gas production rates over a three-year period. The proposed method is evaluated using the P10, P50, and P90 values of the testing Root Mean Square Error (RMSE). The results demonstrate that our method outperforms existing approaches, exhibiting outstanding efficiency, reliability, and accuracy.

Mapping surface water dynamics (1985–2021) in the Hudson Bay Lowlands, Canada using sub-pixel Landsat analysis

The fate of the carbon stored in frozen peatlands in Canada's Hudson Bay Lowlands (HBL) depends strongly on water, with wetter conditions favoring long-term storage. Dynamic surface water products generated from historical Landsat data exist to inform surface water trends in the HBL based on binary classifications of land vs water at 30 m spatial resolution. However, the HBL contains many water features smaller than 30 m, including streams and patterned fens that require a sub-pixel mapping approach to fully capture their dynamics. In this paper, we leverage an existing binary dynamic surface water product to generate a spatially and temporally comprehensive Landsat sub-pixel surface water time-series over the HBL. We first generate Landsat Red, NIR and SWIR composites from 1985 to 2021 and evaluate data-driven machine learning and physical models to derive sub-30 m water fractions across the entire mapping domain. Data-driven models were calibrated on 30 m water fractions obtained from 16 of 17 WorldView scene water fraction maps and assessed on the 17th held-out scene in a jackknife validation. Data-driven models were also calibrated by relating scaled Landsat reflectance to water fractions obtained from binary water masks at corresponding scales from 90 to 270 m. Physical models involved simple linear unmixing using a new approach that samples land endmembers in the vicinity of each pixel to represent land reflectance, and a global water endmember sampled beneath permanent water in each image. The assumption of linear scaling was verified at sub-30 m to 270 m resolution for NIR, SWIR and NDVI, which were all highly correlated with water fraction and had no significant differences among water fraction – spectral feature relationships across the full range of scales. Each of 11 different methods and input spectral features were evaluated against WorldView water fractions and ranked based on nine criteria. We found that machine learning using all spectral features including local NIR land endmembers performed best among data-driven approaches, and linear unmixing of the NIR band using local land endmembers performed best among physical. Both best-performing methods were applied to the entire time-series and surface water area trends were calculated and compared with trends obtained from binary water masks. All three methods agree on the direction of surface water change towards wetting, with sub-pixel inter-annual variation agreeing with stream discharge noted in other studies.

Moving away from a scale mismatch: Spatiotemporal modelling of striped shrimp (Pandalus montagui) density in Canada’s subarctic

Mismatches between assessment and ecological processes can increase the likelihood of resource mismanagement, lead to the misinterpretation of population dynamics, and impact decision making. Striped shrimp (Pandalus montagui) are an important part of Canada’s subarctic marine ecosystems but have little historical research or assessment focus. Off northern Canada, striped shrimp are currently assessed separately within three assessment areas, despite these areas forming an open, dynamic system with population mixing throughout. Although assessed using data from a common survey, the differing time series of available data within each area have hampered efforts to consider the dynamics of the stock as one population and create a single biomass index for the area. Consequently, there is a spatial-scale mismatch between the biological resource and the assessment and management scales. To address causes of this scale mismatch, we developed a spatiotemporal model that can accommodate inconsistent survey coverage and can be used to estimate annual biomass of fishable striped shrimp throughout the study area over the entire time series (2005–2022). Meanwhile, to address deficiencies in biological knowledge, we concomitantly explored potential environmental and ecosystem covariates (i.e., bottom temperature, bottom salinity, and predicted potential predator densities) to further scientific understanding of striped shrimp’s relationships with their ecosystem. Our results build a cohesive population-level biomass time series and found fishable striped shrimp densities were related to depth, bottom temperature, and lagged predicted redfish density.

Vegetation Trend Detection Using Time Series Satellite Data as Ecosystem Condition Indicators for Analysis in the Northwestern Highlands of Ethiopia

Vegetation is an essential component of the terrestrial ecosystem and has changed significantly over the last two decades in the Northwestern Highlands of Ethiopia. However, previous studies have focused on the detection of bitemporal change and lacked the incorporation of entire vegetation time series changes, which are considered significant indicators of ecosystem conditions. The Normalized Difference Vegetation Index (NDVI) time series dataset from the Moderate-Resolution Imaging Spectroradiometer (MODIS) is an efficient method for analyzing the dynamics of vegetation change over a lengthy period using remote sensing techniques. This study aimed to utilize time series satellite data to detect vegetation changes from 2000 to 2020 and investigate their links with ecosystem conditions. The time-series satellite processing package (TIMESAT) was used to estimate the seasonal parameter values of NDVI and their correlation across the seasons during the study period. Break Detection for Additive Season and Trend (BFAST) was applied to identify the year of breakpoints, the direction of magnitude, and the number of breakpoints. The results were reported, analyzed, and linked to ecosystem conditions. The overall trend in the study area increased from 0.58 (2000–2004) to 0.65 (2015–2020). As a result, ecosystem condition indicators such as peak value (PV), base value (BV), amplitude (Amp), and large integral (LI) exhibited significant positive trends, particularly for Acacia decurrens plantations, Eucalyptus plantations, and grasslands, but phenology indicator parameters such as start of season (SOS), end of season (EOS), and length of season (LOS) did not show significant trends for almost any vegetation type. The most abrupt changes were recorded in 2015 (24.7%), 2012 (18.6%), and 2014 (9.8%). Approximately 30% of the vegetation changes were positive in magnitude. The results of this study imply that there was an improvement in the ecosystem’s condition following the establishment of the Acacia decurrens plantation. The findings are considered relevant inputs for policymakers and serve as an initial stage for the assessment of the other environmental and climatic implications of Acacia decurrens plantations at the local scale.

Energy demand forecasting using adaptive ARFIMA based on a novel dynamic structural break detection framework

Forecasting energy demand has become increasingly important due to technological advances, especially new power systems and population growth. Accurate predictions of energy demand and supply are crucial for academics and policymakers. Also, energy consumption is non-stationary and dynamic in time, requiring an adaptive forecasting algorithm. This paper presents a new adaptive hybrid approach for energy time series forecasting using statistical methods. Some of the most popular energy demand time series applications are used to demonstrate the superiority of the proposed algorithm, which covers a wide range of critical energy demand forecasting situations at various scales. The paper's objectives are presented in three phases. In Phase I, ARFIMA is used to forecast the entire original time series as a statistical inspector and later for comparison. Although ARFIMA's fractionality feature helps to capture long and short-term memory behavior in time series, to be more adaptive and become close to a nonlinear algorithm that can handle non-stationarity, and Gaussian and non-Gaussian time series closely, a novel dynamic statistical structural break detection framework is developed, and used in phase II to identify the time series' change points and associated time indexes. Since time series are partitioned based on structural change detections, they approach one in terms of the Hurst exponent and exhibit pure long memory, which is ideal for ARFIMA modeling. Phase III captures non-stationary statistical properties and memory characteristics by applying adaptive ARFIMA modeling to segmented time series. Finally, the predicted partitions are concatenated. Various performance assessment metrics, e.g., Mean Absolute Percentage Error (MAPE), tested on the final achieved results show that the proposed adaptive algorithm outperforms the forecasting capabilities of the existing algorithms on the same energy demand time series used in the current paper. MAPE of the proposed approach for the four utilized real-life cases are 1.72%, 1.35%, 0.22%, and 4.4%, respectively. Therefore, the proposed model is a satisfactory method for energy demand forecasting due to its high accuracy.

Unsupervised anomaly detection by densely contrastive learning for time series data

Time series data continuously collected by different sensors play an essential role in monitoring and predicting events in many real-world applications, and anomaly detection for time series has received increasing attention during the past decades. In this paper, we propose an anomaly detection method by densely contrasting the whole time series with its sub-sequences at different timestamps in a latent space. Our approach leverages the locality property of convolutional neural networks (CNN) and integrates position embedding to effectively capture local features for sub-sequences. Simultaneously, we employ an attention mechanism to extract global features from the entire time series. By combining these local and global features, our model is trained using both instance-level contrastive learning loss and distribution-level alignment loss. Furthermore, we introduce a reconstruction loss applied to the extracted global features to prevent the potential loss of information. To validate the efficacy of our proposed technique, we conduct experiments on publicly available time-series datasets for anomaly detection. Additionally, we evaluate our method on an in-house mobile phone dataset aimed at monitoring the status of Parkinson’s disease, all within an unsupervised learning framework. Our results demonstrate the effectiveness and potential of the proposed approach in tackling anomaly detection in time series data, offering promising applications in real-world scenarios.

Dual-stage ensemble approach using online knowledge distillation for forecasting carbon emissions in the electric power industry

The electric power industry is the key to achieving the goals of carbon peak and neutrality. Accurate forecasting of carbon emissions in the electric power industry can aid in the prompt adjustment of power generation policies and the early achievement of carbon reduction targets. This study proposes a new approach that combines the decomposition-ensemble paradigm with knowledge distillation to forecast daily carbon emissions. First, seasonal and trend decomposition using locally weighted scatterplot smoothing (STL) is used to decompose the data into three subcomponents. Second, two heterogeneous deep neural network models are jointly trained to predict each subcomponent based on online knowledge distillation. During training, the two models learn and provide feedback to each other. The first model-ensemble stage is performed by synthesizing the predictions for each subcomponent of the two models. Finally, the second model-ensemble stage is performed. The predictions for each subcomponent are integrated using linear addition to obtain the final results. In addition, to avoid leakage of test data caused by decomposing the entire time series, a recursive forecasting strategy is applied. Multistep predictions are obtained by forecasting 7, 15, and 30 days in the future. Experimental results using metaheuristic algorithms to optimize hyperparameters show that the proposed method evaluated on the daily carbon emissions dataset has better forecasting performance than all baselines.

Fitting Early Phases of the COVID-19 Outbreak: A Comparison of the Performances of Used Models.

The COVID-19 outbreak involved a spread of prediction efforts, especially in the early pandemic phase. A better understanding of the epidemiological implications of the different models seems crucial for tailoring prevention policies. This study aims to explore the concordance and discrepancies in outbreak prediction produced by models implemented and used in the first wave of the epidemic. To evaluate the performance of the model, an analysis was carried out on Italian pandemic data from February 24, 2020. The epidemic models were fitted to data collected at 20, 30, 40, 50, 60, 70, 80, 90, and 98 days (the entire time series). At each time step, we made predictions until May 31, 2020. The Mean Absolute Error (MAE) and the Mean Absolute Percentage Error (MAPE) were calculated. The GAM model is the most suitable parameterization for predicting the number of new cases; exponential or Poisson models help predict the cumulative number of cases. When the goal is to predict the epidemic peak, GAM, ARIMA, or Bayesian models are preferable. However, the prediction of the pandemic peak could be made carefully during the early stages of the epidemic because the forecast is affected by high uncertainty and may very likely produce the wrong results.

Detection of outlying patterns from sparse and irregularly sampled electronic health records data

Within the intensive care unit (ICU), vital signs such as arterial blood pressure (ABP) collected from electronic health records (EHRs) are typically recorded at different and uneven sampling frequencies and are often infrequently measured due to the nature of the medical treatment. Furthermore, from a temporal trajectory perspective, EHR data are likely to be corrupted by outlying patterns that deviate from normal samples in terms of the curves’ magnitude and shape. In this work, we propose a two-stage outlier detection approach for sparse and irregularly sampled (SiS) temporal data using functional data analysis (FDA) tools. In the first stage, an outlier identification measure is defined by a max–min statistic and a clean subset that contains nonoutliers. In the second stage, a multiple hypothesis testing problem is formulated based on the asymptotic distribution of the proposed measure. The simulation-based framework shows that the proposed method is robust to different types of shape and magnitude outliers. The detection results are more accurate than the widely used functional depth methods, especially in extremely sparse settings where the proportion of the observed data points over the entire time series is approximately 10%. Extensive experiments are also conducted on the real-world MIMIC-II dataset, which demonstrate that the method effectively detects clinically meaningful outlying patterns.

Over 500 Days in the Life of the Photosphere of the Type Iax Supernova SN 2014dt

Type Iax supernovae (SNe Iax) are the largest known class of peculiar white dwarf SNe, distinct from normal Type Ia supernovae (SNe Ia). The unique properties of SNe Iax, especially their strong photospheric lines out to extremely late times, allow us to model their optical spectra and derive the physical parameters of the long-lasting photosphere. We present an extensive spectral timeseries, including 21 new spectra, of SN Iax 2014dt from +11 to +562 days after maximum light. We are able to reproduce the entire timeseries with a self-consistent, nearly unaltered deflagration explosion model from Fink et al. using TARDIS, an open source radiative-transfer code. We find that the photospheric velocity of SN 2014dt slows its evolution between +64 and +148 days, which closely overlaps the phase when we see SN 2014dt diverge from the normal spectral evolution of SNe Ia (+90 to +150 days). The photospheric velocity at these epochs, ∼400–1000 km s−1, may demarcate a boundary within the ejecta below which the physics of SNe Iax and normal SNe Ia differ. Our results suggest that SN 2014dt is consistent with a weak deflagration explosion model that leaves behind a bound remnant and drives an optically thick, quasi-steady-state wind creating the photospheric lines at late times. The data also suggest that this wind may weaken at epochs past +450 days, perhaps indicating a radioactive power source that has decayed away.

Temporal and Spatial Variability of Soil-Vegetation Variables in the West Region of Santa Catarina State

The western region of the state of Santa Catarina (SC) has suffered several episodes of drought, thus impacting vegetation, agricultural production and water availability. This study aimed to evaluate the spatial and temporal variability of soil-vegetation parameters in the region. Monthly data of evapotranspiration, precipitation, radiation, air and soil moisture and temperature were obtained from the NASA-funded Famine Early Warning Systems Network (FEWS NET) project, Land Data Assimilation System (FLDAS). The study focused on the western region of SC. Evapotranspiration anomalies showed that the years 2015 and 2020 had the lowest values in the last years (i.e. 1990 and 2022). Precipitation had greater variability in recent years, having months of high rainfall combined with periods of severe drought. An evaluation for the Xanxerê city region showed that the year 2020 presented records of low precipitation, evapotranspiration, soil moisture and NDVI, compared to the entire time series. Spatial correlation of the evapotranspiration controlling variables showed that precipitation, air temperature and humidity, and short-wave radiation presented variable correlations in different regions of SC. Soil moisture, however, had the highest positive correlation in the entire territory, showing that this is the main controlling variable of local evapotranspiration and therefore an indicator of the importance of vegetation transpiration in the latent heat fluxes. Equivalent water thickness obtained from the GRACE satellite show that after a maximum occurred in December 2015 its magnitudes steadily decreased, reaching very low groundwater values in 2022. This trend represents a 3.3 mm decrease per year. These results show that the region is undergoing a change in these soil-vegetation parameters that needs to be better monitored and understood.

Temperature variability over Dokriani glacier region, Western Himalaya, India

Long-term climate records which help decipher past climate variability and its impact are scarce in the tough terrain of the Himalayan region. Therefore, in order to fill the climate data gap and understand the glacier climate linkage, we developed a 231 year long (1785–2015 CE) March–June temperature record using ring-width chronology of Himalayan fir (Abies pindrow (Royle ex D.Don) Royle) for the Din Gad valley, Dokriani glacier region, Uttarkashi, Uttarakhand, in the Western Himalaya. The Din Gad, originating from the Dokriani glacier, is a meltwater river contributing to Bhagirathi catchment in the headwaters of the socio-economically vital Ganga River. The 21-year running mean of the temperature record showed 1978–1998 CE as the coldest period followed by 1925–1945 CE, and 1890–1910 CE as the warmest period followed by 1946–1966 CE over the entire time series. The reconstruction matches well with tree-ring based temperature records available from the Garhwal Himalaya. It also shows similarity to tree-ring based temperature reconstructions from the Western Himalaya, Nepal, Tibetan Plateau and Bhutan, thus displaying a regional scale climate signal. The low frequency fluctuation patterns of the March–June temperature also matches with Asia and Northern hemisphere temperature records. Reconstructed March–June temperature record showed a statistically negligible warming temperature trend during 1901–1989 CE in the 20th century. It, however, captured a warming spike from 1990s CE which continues rising into the 21st century, which is also evident in the Northern hemisphere temperature record. Moreover, temperature rise is not anomalous in the past 231 years and well within range of the rest of the series. The present temperature record exclusively from the glacier region revealed a strong linkage with the benchmark Dokriani glacier's winter mass balance (November–April) revealing mass loss (gain) episodes occurred in warm (cool) phases. This first such record from the glacier valleys in Ganga headwaters would be of great value at providing insight into past climate variability and glacier behaviour with respect to climate change in long term perspective, and thus would provide valuable information for water resource management in light of climate change.

Change in monarch winter abundance over the past decade: A Red List perspective

Abstract Assessing invertebrate species for the IUCN Red List under Criterion A frequently involves fitting an appropriate statistical model to available abundance data and calculating a 10‐year change (TYC) estimate from predicted abundances. When the rate of change has not been constant across the entire time series, models that accommodate variable change rates are recommended. The monarch butterfly (Danaus plexippus) was recently added to the IUCN Red List (A2ab Endangered) based on analysis of data on winter abundances in Mexico and the western USA between 1993 and 2020. TYC estimates in the monarch assessment came from models that assumed constant change rates. We conducted a Bayesian analysis of the same data using models that accommodated variable change rates and used those models to compute TYC estimates. Our results suggested that monarch population change rates have not been constant over the study period. The analysis yielded a model‐averaged TYC estimate of +5.23%, which was not statistically distinguishable from 0% and was considerably different from values of −46% and −72% in the assessment. The Bayesian posterior probability of a TYC value below −30% (A2ab Vulnerable) was 0.15 and that of a TYC value below −50% (A2ab Endangered) was 0.03. We recommend that researchers evaluating monarch conservation status consider using abundance models with variable change rates, as models with constant change rates may not accurately predict the trajectory of monarch abundances into the future.

QUANTITATIVE ESTIMATES OF DIRECT PYROGENIC CARBON EMISSIONS IN FORESTS OF RUSSIA ACCORDING TO REMOTE MONITORING DATA 2021

This paper presents the statistics of direct wildfire carbon emission estimates during the wildfires of 2021 on forest lands of Russia using long-term satellite data. In 2021, the area affected by forest wildfires was 9.3 million ha, while carbon emissions amounted to 66.4 MtC. Said values are almost two points higher than the long-term average values. A comparison of similar indicators for twenty years allowed us to conclude that said year was anomalous with respect to the entire time series, similar to the wildfire seasons of 2003 and 2012. A period or interval for recurrence of three anomalous wildfire seasons is nine years. The reason for the recurrence of anomalous wildfire seasons is yet to be found. At the same time, the forest areas affected by wildfires, and direct carbon and other greenhouse gas emissions in anomalous wildfire years decreased from 127.2 MtC (3.7 times) in 2003 to 83.8 MtC (2.4 times) in 2012, and to 66.4 MtC (1.9 times) in 2021.