Long-term Series Research Articles

Variation of the Start Date of the Vegetation Growing Season (SOS) and Its Climatic Drivers in the Tibetan Plateau.

Climate change inevitably affects vegetation growth in the Tibetan Plateau (TP). Understanding the dynamics of vegetation phenology and the responses of vegetation phenology to climate change are crucial for evaluating the impacts of climate change on terrestrial ecosystems. Despite many relevant studies conducted in the past, there still remain research gaps concerning the dominant factors that induce changes in the start date of the vegetation growing season (SOS). In this study, the spatial and temporal variations of the SOS were investigated by using a long-term series of the Normalized Difference Vegetation Index (NDVI) spanning from 2001 to 2020, and the response of the SOS to climate change and the predominant climatic factors (air temperature, LST or precipitation) affecting the SOS were explored. The main findings were as follows: the annual mean SOS concentrated on 100 DOY-170 DOY (day of a year), with a delay from east to west. Although the SOS across the entire region exhibited an advancing trend at a rate of 0.261 days/year, there were notable differences in the advancement trends of SOS among different vegetation types. In contrast to the current advancing SOS, the trend of future SOS changes shows a delayed trend. For the impacts of climate change on the SOS, winter Tmax (maximum temperature) played the dominant role in the temporal shifting of spring phenology across the TP, and its effect on SOS was negative, meaning that an increase in winter Tmax led to an earlier SOS. Considering the different conditions required for the growth of various types of vegetation, the leading factor was different for the four vegetation types. This study contributes to the understanding of the mechanism of SOS variation in the TP.

CALOTRITON: a convective boundary layer height estimation algorithm from ultra-high-frequency (UHF) wind profiler data

Abstract. Long time series of observations of atmospheric dynamics and composition are collected at the French Pyrenean Platform for Observation of the Atmosphere (P2OA). Planetary boundary layer depth is a key variable of the climate system, but it remains difficult to estimate and analyse statistically. In order to obtain reliable estimates of the convective boundary layer height (Zi) and to allow long-term series analyses, a new restitution algorithm, named CALOTRITON, has been developed. It is based on the observations of an ultra-high-frequency (UHF) radar wind profiler (RWP) from P2OA with the help of other instruments for evaluation. Estimates of Zi are based on the principle that the top of the convective boundary layer is associated with both a marked inversion and a decrease in turbulence. Those two criteria are respectively manifested by larger RWP reflectivity and smaller vertical-velocity Doppler spectral width. With this in mind, we introduce a new UHF-deduced dimensionless parameter which weighs the air refractive index structure coefficient with the inverse of vertical velocity standard deviation to the power of x. We then search for the most appropriate local maxima of this parameter for Zi estimates with defined criteria and constraints such as temporal continuity. Given that Zi should correspond to fair-weather cloud base height, we use ceilometer data to optimize our choice of the power x and find that x=3 provides the best comparisons. The estimates of Zi by CALOTRITON are evaluated using different Zi estimates deduced from radiosounding according to different definitions. The comparison shows excellent results with a regression coefficient of up to 0.96 and a root-mean-square error of 71 m, which is close to the vertical resolution of the UHF RWP of 75 m, when conditions are optimal. In more complex situations, that is when the atmospheric vertical structure is itself particularly ambiguous, secondary retrievals allow us to identify potential thermal internal boundary layers or residual layers and help to qualify the Zi estimations. Frequent estimate errors are observed nevertheless; for example, when Zi is below the UHF RWP first reliable gate or when the boundary layer begins its transition to a stable nocturnal boundary layer.

Integrating historical and recent information to understand chondrichthyan dynamics in the central Mediterranean

Chondrichthyans (sharks, rays, and chimaeras) are highly susceptible to the impacts of fisheries due to their vulnerable life-history traits. Over the last 100 years, several cases of local extinction have been documented in heavily fished areas across the Mediterranean Sea. In the Strait of Sicily (SoS), one of the main demersal fishing grounds of the Mediterranean, chondrichthyans constitute a significant component of both commercial and discarded bycatch. In this area, the lack of long-term data series on these species hinders our ability to fully comprehend the extent of changes due to both overfishing and climate variations. Here we aim to use historical data from the end of the 19th century, provided by Döderlein, to uncover evidence of long-term changes in the occurrence and diversity of these fishes. We employ a semi-quantitative approach to compare past data with recent frequency of occurrence estimates, to improve our ability to propose management advice. We report a decline in both the number of species and the frequency of occurrence of sharks and ray species in the study region over the past 150 years. Our findings shed light on the current status of sharks and rays compared to the historical data from the 19th century and highlight the urgent need to develop management strategies to mitigate the impact of harvesting on these vulnerable species.

Verification of tidal trends in Cananeia (SP) and Santos (SP)

Recent studies indicate an imminent global rise in mean sea level. However, comprehending variations in tidal patterns is imperative before analyzing the scenario of extreme events, typically associated with the combination of high tides and intense meteorological influences. This study presents local trends in sea level, amplitude, and phase of the two main tidal components in Cananeia (SP) and Santos (SP), utilizing tide gauge data from records of sufficient length to avoid the need for nodal correction in the tide components. Using more up-to-date tide gauge data allowed the investigation of observed trends. Long-term tidal data series revealed an upward trend in mean level of approximately 4,324 mm/year in Cananeia and 0,538 mm/year in Santos. Both locations were affected by human activities along their estuaries, with Santos experiencing significant variations in the amplitude and phase values of the tidal constituents annually.

Long-term series wetland classification of Guangdong-Hong Kong-Macao Greater Bay Area based on APSMnet

Wetlands play a crucial role in achieving carbon peak and carbon neutrality goals. Exploring spatiotemporal distribution is one of the fundamental task in wetland research. However, existing large-scale and long-term series wetland mapping methods have challenges related to classification accuracy and obtaining inter-annual wetland samples. Therefore, a rapid sample collection and precise classification method are needed to support wetland resource assessment, conservation, and ecological restoration. In this paper, we propose a novel deep learning method suitable for large-scale and long-term series wetland classification. First, we acquire and preprocess the long-term series remote sensing data on the Google Earth Engine (GEE) platform. Second, the maximum wetland extent is extracted using decision trees and the Otsu algorithm (OTSU). Third, the attention and pyramid structure-based multidimensional feature extraction network (APSMnet) and the inter-annual sample migration algorithm (ISM) are proposed for realizing inter-annual wetland classification and wetland change analysis. Experiments conducted on Landsat 7 data of the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 2000 to 2022 demonstrate that the overall accuracy (OA), average accuracy (AA), and kappa coefficient (Kappa) achieved by the proposed wetland classification method are 0.9664, 0.9396, and 0.9570, respectively. Long-term series trend analysis reveals that mangrove/swamp areas experience fluctuations influenced by urbanization and ecological policies, ultimately undergoing restoration and becoming stable. The codes and datasets are made available publicly at https://github.com/louanjun/APSMnet.

The Applicability of the Drought Index and Analysis of Spatiotemporal Evolution Mechanisms of Drought in the Poyang Lake Basin

Poyang Lake, the largest freshwater lake in China, is an important regional water resource and a landmark ecosystem. In recent years, it has experienced a period of prolonged drought. Using appropriate drought indices to describe the drought characteristics of the Poyang Lake Basin (PLB) is of great practical significance in the face of severe drought situations. This article explores the applicability of four drought indices (including the precipitation anomaly index (PJP), standardized precipitation index (SPI), China Z-index (CPZI), and standardized precipitation evapotranspiration index (SPEI)) based on historical facts. A systematic study was conducted on the spatiotemporal evolution patterns of meteorological drought in the PLB based on the optimal drought index. The results show that SPI is more suitable for the description of drought characteristics in the PLB. Meteorological droughts occur frequently in the summer and autumn in the PLB, with the frequency of mild drought being 17.29% and 16.88%, respectively. The impact range of severe drought or worse reached 22.19% and 28.33% of the entire basin, respectively. The probability of drought occurrence in the PLB shows an increasing trend in spring, while in most areas, it shows a decreasing trend in other seasons, with only a slight increase in the upper reaches of the Ganjiang River (UGR). One of the important factors influencing drought in the PLB is atmospheric circulation. The abnormal variation of the Western Pacific Subtropical High was one of the key factors contributing to the severe drought in the PLB in 2022. This study is based on a long-term series of meteorological data and selects the drought index for the PLB. It describes the spatiotemporal distribution characteristics and evolution patterns of drought and investigates the developmental path and influencing factors of drought in typical years. This study provides a reliable scientific basis for similar watershed water resource management.

Screening of representative rainfall event series for long-term hydrological performance evaluation of grassed swales.

Evaluation of the hydrological performance of grassed swales usually needs long-term monitoring data. At present, suitable techniques for simulating the hydrological performance using limited monitoring data are not available. Therefore, current study aims to investigate the relationship between saturated hydraulic conductivity (Ks) fitting results and rainfall characteristics of various events series length. Data from a full-scale grassed swale (Enschede, the Netherlands) were utilized as long-term rainfall event series length (95 rainfall events) on the fitting outcomes. Short-term rainfall event series were extracted from these long-term series and used as input in fitting into a multivariate nonlinear model between Ks and its influencing rainfall indicators (antecedent dry days, temperature, rainfall, rainfall duration, total rainfall, and seasonal factor (spring, summer, autumn, and winter, herein refer as 1, 2, 3, and 4). Comparison of short-term and long-term rainfall event series fitting results allowed to obtain a representative short-term series that leads to similar results with those using long-term series. A cluster analysis was conducted based on the fitting results of the representative rainfall event series with their rainfall event characteristics using average values of influencing rainfall indicators. The seasonal index (average value of seasonal factors) was found to be the most representative short rainfall event series indicator. Furthermore, a Bayesian network was proposed in the current study to predict if a given short-term rainfall event series is representative. It was validated by a data series (58 rainfall events) from another full-scale grassed swale located in Utrecht, the Netherlands. Results revealed that it is quite promising and useful to evaluate the representativeness of short-term rainfall event series used for long-term hydrological performance evaluation of grassed swales.

Long-term recovery dynamics determined by the degree of the disturbance – Ten years tracking of aquatic macroinvertebrate recolonisation after an industrial disaster (Red Sludge Disaster, Hungary)

A ten-year-long examination of macroinvertebrate community recovery was conducted following a catastrophic spill of highly alkaline red sludge (pH >13) into lowland streams. Our primary objective was to compare recovery patterns after coarse- and fine-grain disturbances, focusing on two aspects: i) trend analysis to reveal long-term changes of six community parameters, and ii) variation analyses to assess parameter changes over time. We conducted statistical analysis on long-term data series of macroinvertebrates obtained from quantitative samples collected at four sections with varying degrees of disturbance along the impacted stream sections.We developed a comprehensive theoretical framework comprising a series of sequential phases: Ramp-up, Overshoot, and Oscillation Phases.i) A trend analysis revealed that disturbances show a gradual recovery pattern, while variance analyses showed an asymptotic convergence to an equilibrium.ii) Evaluating these trends across phases unveiled that the initial recovery phase exhibited a steep trajectory, lasting 4–9 months, irrespective of disturbance severity. Coarse-grain disturbances induced a remarkable Overshoot phenomenon across all community metrics. The more severe the disturbance, the greater the height and duration of the Overshoot. Our results suggest that the presence or absence of Overshoot can serve as an indicator for coarse-grain disturbances in the context of large and infrequent disturbances (LID). The entire recovery process lasts for 2.5–3 years irrespective of the severity of the LID.In conclusion, a minimum survey duration of two and half years is deemed imperative to capture the phases of recovery, and changes associated with LID are not expected to extend beyond the three-year threshold. The theoretical framework, including Overshoot parameters, may assist future studies in comparing recovery patterns of different LID types. Furthermore, our theoretical framework is likely to be applicable to other groups of organisms given a sufficiently long monitoring of recovery, influenced also by the length of reproductive cycles.

Assessing the Gumbel-Logistic Model’s performance in modeling long-term rainfall series in a high-latitude plain region

Assessing the Gumbel-Logistic Model’s performance in modeling long-term rainfall series in a high-latitude plain region

Russian per capita consumption of fish products in raw mass or marketable product weight: how to recalculate back and forth

Aim. Find a simple way to transfer the average per capita consumption of fish products (kg/person/year) from the raw mass to the mass of marketable products and vice versa.Methods. Statistical processing of official data on the consumption of fish products in the USSR and the Russian Federation, published by Rosstat (Russian Federal State Statistics Service) and FAO (Food and Agriculture Organization of the United Nations) for 58 years, was made using the least squares method.Results. Two-parameter equations are found that best describe the relationship between the average per capita consumption of fish products in the mass of raw fish and in the mass of commercial products ready for consumption, as well as coefficients for simplified approximate quick calculations of the mass of consumed raw fish by the mass of commercial products and the mass of consumed commercial products by mass of raw fish.Novelty. Based on the results of the original analysis of the material, a new method for recalculating the Russian per capita consumption of fish products in the mass of raw fish by the mass of consumed commercial products and vice versa was obtained for the first time.Practical significance. Restoration of long-term series of consumption and targets for food security, the efficiency of the functioning of the fishery complex, and the social policy of the state. The article may be useful to economists, marketers, sociologists, medicals, businessmen, officials and politicians.

Reduced wave time series for long-term morphodynamic applications

Shoreline models have usually been recognized by professionals as the most appropriate tool for reproducing the long-term morphodynamic evolution of the shoreline of sandy beaches. Despite their underlying simplifications, the simulation of shoreline evolution at large temporal and spatial scales may imply significant computational efforts. Hence, to reduce computational costs, many approaches aimed to optimize the size of the input wave datasets have been proposed so far. A simplified novel method to reduce long-term offshore wave series is proposed herein. The rationale of the approach is to build reduced series that induce the same morphodynamic effects in the long-term as the ones induced by the whole, and more computationally expensive, original series. The method is conceived to define offshore reduced time series with the same chronological order of the complete series and is able to represent the bi-modal features of the wave climate. In-depth hydrodynamic and morphodynamic parametric analyses have been performed and it has been demonstrated that the method is capable to get reliable reduced offshore wave time series for reproducing the long-term evolution of sandy beaches with decreased computational costs.

Monitoring Suspended Sediment Transport in the Lower Yellow River using Landsat Observations

The spatiotemporal variations in suspended sediment concentration (SSC) in the lower reaches of the Yellow River exhibit significant variability and are influenced by reservoir operations. Understanding the spatiotemporal distribution characteristics of SSC in water holds crucial implications for environmental protection and reservoir operation management. Based on daily-scale SSC monitoring data from four hydrological stations in the lower Yellow River, this study established an SSC remote sensing model applicable to Landsat series satellite data. The independent variable of the model, Rrs(NIR)/(Rrs(G) + Rrs(R) + Rrs(SWIR)), demonstrated sensitivity to water bodies with different SSC values. Distinctive spatiotemporal characteristics in sediment transport were observed across the lower Yellow River. Spatially, the SSC values in the Sanmenxia and Xiaolangdi reservoirs were notably lower than those in other river sections, averaging 1008.42 ± 602.83 mg/L and 1177.89 ± 627.95 mg/L, respectively. Over time, the majority of the river sections (96%) exhibited decreasing trends in SSC during 1984–2022, particularly in the downstream Xiaolangdi reservoir, with average SSC values of 4265.58 ± 1101.77 mg/L in the 1980s and 1840.80 ± 2255.15 mg/L in the 2020s. Seasonal variations in SSC were prominent, with higher summer concentrations, averaging 5536.43 ± 2188.77 mg/L (2020s summer) and 814.11 ± 158.27 mg/L (2020s winter). Reductions in SSC during 1984–2022 primarily occurred in summer, weakening its seasonal variability in the lower Yellow River. Water discharge emerged as a critical factor influencing suspended sediment transport, with SSC increasing in high-water-flow months. Following the construction of the Xiaolangdi reservoir, the relationship between SSC and water discharge at different stations underwent notable alterations. This study enhances our understanding of the spatiotemporal dynamics of suspended sediment transport in the lower Yellow River, providing valuable insights for utilizing long-term Landsat series data in the dynamic monitoring of river sediment transport.

How tool combinations in different pipeline versions affect the outcome in RNA-seq analysis.

Data analysis tools are continuously changed and improved over time. In order to test how these changes influence the comparability between analyses, the output of different workflow options of the nf-core/rnaseq pipeline were compared. Five different pipeline settings (STAR+Salmon, STAR+RSEM, STAR+featureCounts, HISAT2+featureCounts, pseudoaligner Salmon) were run on three datasets (human, Arabidopsis, zebrafish) containing spike-ins of the External RNA Control Consortium (ERCC). Fold change ratios and differential expression of genes and spike-ins were used for comparative analyses of the different tools and versions settings of the pipeline. An overlap of 85% for differential gene classification between pipelines could be shown. Genes interpreted with a bias were mostly those present at lower concentration. Also, the number of isoforms and exons per gene were determinants. Previous pipeline versions using featureCounts showed a higher sensitivity to detect one-isoform genes like ERCC. To ensure data comparability in long-term analysis series it would be recommendable to either stay with the pipeline version the series was initialized with or to run both versions during a transition time in order to ensure that the target genes are addressed the same way.

Natural cycles in the Northern Baikal region

The authors of the article present the results of an analysis of the cyclicity of long-term numerical series of meteorological parameters (average annual air temperature, total annual precipitation) and six longterm numerical series of short-lived animal species from different taxonomic groups: ground beetles, small rodents and passerine birds living in similar biotopes of the low-mountain part of Barguzinsky ridge (territory of the Barguzin State Natural Biosphere Reserve). The cyclic components of long-term observation series lasting 33-67 years were studied by Fourier spectral analysis. Periodograms and cyclic sequences of model species coincide in most cases. In all species, high-frequency 2-3 year cycles predominate, while mid-frequency 4-8 year and low-frequency 9-19 year cycles are of secondary importance. Statistically significant correlations between cyclicity in the long-term abundance series of model animal species and the cyclicity of annual precipitation and average annual temperature have been identified. Reliable correlations between the cyclicity of Wolf numbers and the cyclicality of meteorological parameters are shown. Solar activity has an indirect effect on the cyclicity of natural processes in the Northern Baikal region.

Multidecadal fishers’ knowledge reveals overexploitation of sharks in southeastern Brazil

Abstract Assessing the impacts caused by fisheries requires long-term data series and continuous landing monitoring, which are still scarce in several low and middle-income countries. Alternative approaches, such as fishers’ local ecological knowledge (LEK) have been employed to assess the history of marine resources and overcome the challenges of missing data. We documented temporal changes over the last 60 years in small-scale fisheries in Arraial do Cabo, Brazil. Interviews with 155 fishers revealed the capture of six shark species (one genus), all under extinction risk according to the global and the national Red Lists. Among these, Carcharhinus brevipinna, Carcharhinus plumbeus, and Isurus oxyrinchus are commercially important. While LEK suggests an increase in fishing efforts, with new fishing grounds emerging after 2006, the number of individuals caught has decreased over time. Historical shark exploitation has led to substantial declines in the catches of C. plumbeus and C. brevipinna, which have rare occurrences in the region. Although landing data are critical information for building local fisheries management plans and setting conservation strategies, monitoring in the region is discontinuous and should be prioritized by public policies.

An integrated method to develop high temporal Landsat NDVI using free and open-source software (FOSS) GIS application

Landsat image series are long-term times series observing the earth’s surface at a relatively fine spatial resolution of 30 m on the visible and infrared channels, which can be used to monitor vegetation dynamics using band ratios such as NDVI. However, due to cloud cover, having Landsat NDVI at a high temporal resolution is impossible. This is especially found in high-humidity regions such as Indonesia, particularly in the rainy season. Whereas some studies, such as long-term phenological observations and detailed land use/land cover change detection, may require Landsat NDVI with a high temporal resolution. One of the higher temporal resolution images with similar spectral characteristics to Landsat is MODIS, which can be used to fill the gap; however, it may have a coarser spatial resolution. Thus, some methods have been developed to blend Landsat and MODIS images to create images with both Landsat spatial detail and MODIS high temporal resolution. Unfortunately, the blending methods are usually not straightforward, where complex steps are required to find and combine several techniques with advanced computational skills. This study, therefore, aims to develop an integrated method to create high temporal Landsat NDVI using free and open-source software (FOSS) GIS application, which is easily accessible and applicable.

ПЕРСПЕКТИВЫ ИСПОЛЬЗОВАНИЯ СПУТНИКОВОЙ ИНФОРМАЦИИ ДЛЯ ОПРЕДЕЛЕНИЯ УРОВНЯ ВОДЫ КРУПНЫХ ОЗЕР И ВОДОХРАНИЛИЩ (НА ПРИМЕРЕ ВОДНЫХ ОБЪЕКТОВ РОССИИ)

Over the past two decades, the volume and quality of information received from satellites to determine the water level of large lakes and reservoirs have significantly increased. At the same time, due to insufficient accuracy, the results of satellite measurements need an adjustment based on the comparison with ground-based observations. The latter not only allow evaluating errors in satellite data, but also serve as a kind of a benchmark for the development and adaptation of the methods of their correction. Such a methodology is being developed for several years jointly by the Laboratory of Space Geophysical and Oceanographic Studies (LEGOS, France) and the State Hydrological Institute. The paper analyzes the causes and sources of errors in satellite and ground data, the techniques for comparing and analyzing long-term series of these data, and the methods for adapting the results of satellite observations in order to improve their accuracy. The directions of further development of satellite altimetry are considered to increase the reliability of satellite data on the water level of inland water bodies.

Method of Underwater Acoustic Signal Denoising Based on Dual-Path Transformer Network

The presence of natural ambient noise interferes with the system for locating and identifying underwater targets. This paper suggests that a Dual-Path Transformation Network (DPTN) reduces ambient noise in underwater acoustic signals. First, the input acoustic signals’ higher-order non-linear features are extracted using a multi-scale convolutional encoder neural network. Second, sub-vectors with the same length are created according to the time dimension from the higher-order non-linear features. The sub-vectors are stitched together to form a three-dimensional tensor. Third, a neural network transformer based on the feed-forward network is constructed. Further, to capture long-term series features and separate the target signal from the noisy signals, the three-dimensional tensor is used as the input of the transformer-based masking network. Finally, overlap-add and transpose are used to obtain discernible target signals. The experimental results verify the effectiveness of the proposed underwater acoustic signal denoising algorithm and demonstrate that the proposed DPRN method can obtain higher output signal-to-noise ratio (SNR) and the scale-invariant signal-to-noise ratio (SI-SNR) compared with the other classical algorithms.

TEDformer: Temporal Feature Enhanced Decomposed Transformer for Long-term Series Forecasting

TEDformer: Temporal Feature Enhanced Decomposed Transformer for Long-term Series Forecasting

DFINet: Dual-way Feature Interaction Network for Long-Term Series Forecasting

DFINet: Dual-way Feature Interaction Network for Long-Term Series Forecasting