Long-term Variability Research Articles

Abundance of trace fossil Phycosiphon incertum in core sections measured using a convolutional neural network

A convolutional neural network (CNN) was used to construct a semantic segmentation model to examine the abundance of Phycosiphon incertum by identifying the trace fossil regions in core section images. The abundance of trace fossils provides information about the past activities of benthic animals affected by paleoenvironmental conditions. To quantify the intensity of bioturbation, it is necessary to extract regions of trace fossils and measure the proportion of bioturbated and the observed area of the outcrop section. In this study, a U-Net-type CNN model was used with residual connections and attention mechanisms to identify the trace fossil Phycosiphon. The model was trained to recognize the relationships between core section images from the International Ocean Discovery Program Expedition 362 Site U1480 and manually annotated trace fossil images. After training, the model successfully classified the pixels of the background, outcrop, and Phycosiphon for core section images other than the training data set. The bioturbation intensity estimated from the image predicted by the model was nearly equal to that from the ground truth image. A long-term (approximately past 10 Myr) variation in Phycosiphon abundance was estimated by applying the model to the core section images at Site U1480. Phycosiphon abundance negatively correlated with the number of sandstone layer intercalations, but it was not affected by the sediment accumulation rates. These findings may reflect resistance of Phycosiphon producers to environmental stress. The model developed in this study can be used for other ichnotaxa to reveal the general tendency of variation in bioturbation intensity and ichnodiversity.

Application of short-term analysis of skin temperature variability in prediction of survival in patients with cirrhosis.

Background: Liver cirrhosis is a complex disorder, involving several different organ systems and physiological network disruption. Various physiological markers have been developed for survival modelling in patients with cirrhosis. Reduction in heart rate variability and skin temperature variability have been shown to predict mortality in cirrhosis, with the potential to aid clinical prognostication. We have recently reported that short-term skin temperature variability analysis can predict survival independently of the severity of liver failure in cirrhosis. However, in previous reports, 24-h skin temperature recordings were used, which are often not feasible in the context of routine clinical practice. The purpose of this study was to determine the shortest length of time from 24-h proximal temperature recordings that can accurately and independently predict 12-month survival post-recording in patients with cirrhosis. Methods: Forty individuals diagnosed with cirrhosis participated in this study and wireless temperature sensors (iButtons) were used to record patients' proximal skin temperature. From 24-h temperature recordings, different length of recordings (30 min, 1, 2, 3 and 6h) were extracted sequentially for temperature variability analysis using the Extended Poincaré plot to quantify both short-term (SD1) and long-term (SD2) variability. These patients were then subsequently followed for a period of 12 months, during which data was gathered concerning any cases of mortality. Results: Cirrhosis was associated with significantly decreased proximal skin temperature fluctuations among individuals who did not survive, across all durations of daytime temperature recordings lasting 1hour or more. Survival analysis showcased 1-h daytime proximal skin temperature time-series to be significant predictors of survival in cirrhosis, whereby SD2, was found to be independent to the Model for End-Stage Liver Disease (MELD) score and thus, the extent of disease severity. As expected, longer durations of time-series were also predictors of mortality for the majority of the temperature variability indices. Conclusion: Crucially, this study suggests that 1-h proximal skin temperature recordings are sufficient in length to accurately predict 12-month survival in patients with cirrhosis, independent from current prognostic indicators used in the clinic such as MELD.

Comprehensive analysis of spatiotemporal variability of rainfall-based extremes and their implications on agriculture in the Upper Ganga Command Area.

The agricultural sector in India primarily relieson monsoon rains, making it necessary for agricultural water managers to study variations in water availability by analyzing rainfall patterns. This information is critical for deciding cropping patterns and irrigation approaches, managing water resources, and improving crop water productivity. The long-term spatiotemporal variability over the command area, India Meteorological Department (IMD) based selection of thresholds for extremes indices, and thorough analysis of the consequences of extreme on agriculture make the study unique. These aspects collectively enhance the perception of the climate-agriculture relationship, which is essential for promoting agriculture resilience under changing rainfall patterns. This type of research is very limited in the literature. Therefore, the present study focuses on comprehensively analyzing the spatiotemporal variability of rainfall and extreme precipitation indices and their implications on agriculture. The study is carried out in the UpperGanga Command Area (UGCA), India's oldest irrigation canal system, by using long-term (1901-2021) high-resolution (0.25 × 0.25°) daily rainfall gridded data obtained from IMD, Pune. Eight different extreme rainfall indices are used to assess spatiotemporal variations. The Mann-Whitney-Pettitt test identified 1970 as a significant change point. Rainfall trends and extreme indices for pre- and post-1970 periods were examined using the non-parametric modified Mann-Kendall test. The results show significant increases in rainfall trends for the annual, monsoon, and kharif seasons from 1901 to 1970, but significant decreases were observed during 1971-2021. This difference resulted in a noticeable decrease in average precipitation in the latter period, approximately 62mm less than in 1901-1970. Similarly, extreme precipitation indices, including the number of rainy days (NxRainy), consecutive dry days (CWD), simple daily intensity index (SDII), and total precipitation (PRCPTOT), exhibited increasing trends in 1901-1970, but they showed significant decreasing trends in 1971-2021. In addition, there is a strong positive correlation between these indices and total precipitation. The study also found that geographical factors influence these trends, with all indices, except SDII, strongly correlated with latitude and elevation, but not with longitude. The implications of these negative rainfall and extreme rainfall trends were further analyzed, and the results indicate a significant impact on the cropping patterns in the study area. The present research findings will be beneficial for regional water resource managers and policymakers in better understanding the existing trends in rainfall distribution over the UGCA.

Fasting glucose variability and risk of dementia in Parkinson's disease: a 9-year longitudinal follow-up study of a nationwide cohort.

Diabetes is associated with an increased risk of Parkinson's disease dementia (PDD); however, it is unknown whether this association is dependent on continuous hyperglycemia, hypoglycemic events, or glycemic variability. We aimed to investigate the relationship between visit-to-visit fasting glucose variability and PDD development in patients with Parkinson's disease (PD). Using data from the Korean National Health Insurance Service, we examined 9,264 patients aged ≥40 years with de novo Parkinson's disease (PD) who underwent ≥3 health examinations and were followed up until December 2019. Glucose variability was measured using the coefficient of variation, variability independent of the mean, and average real variability. Fine and Gray competing regression analysis was performed to determine the effect of glucose variability on incident PDD. During the 9.5-year follow-up period, 1,757 of 9,264 (19.0%) patients developed PDD. Patients with a higher visit-to-visit glucose variability had a higher risk of future PDD. In the multivariable adjusted model, patients with PD in the highest quartile (subdistribution hazard ratio [SHR] = 1.50, 95% CI 1.19 to 1.88), quartile 3 (SHR = 1.29, 95% CI 1.02 to 1.62), and quartile 2 (SHR = 1.30, 95% CI 1.04 to 1.63) were independently associated with a higher risk of PDD than those in the lowest quartile. We highlighted the effect of long-term glucose variability on the development of PDD in patients with PD. Furthermore, our findings suggest that preventive measures for constant glucose control may be necessary to prevent PDD.

Long-term Variations in the Orbital Period of Hot Jupiters from Transit-timing Analysis Using TESS Survey Data

Many hot Jupiters may experience orbital decays, which are manifested as long-term transit-timing variations. We have analyzed 7068 transits from the Transiting Exoplanet Survey Satellite (TESS) for a sample of 326 hot Jupiters. These new mid-transit-time data allow us to update ephemerides for these systems. By combining the new TESS transit-timing data with archival data, we searched for possible long-term variations in the orbital period in these hot Jupiters using a linear and a quadratic ephemeris model. We identified 26 candidates that exhibit possible long-term variations of the orbital period, including 18 candidates with decreasing orbital periods and eight candidates with increasing orbital periods. Among them, 12 candidates failed our leave-one-out cross validation test and thus should be considered to be marginal candidates. In addition to tidal interaction, alternative mechanisms such as apsidal precession, the Rømer effect, and the Applegate effect could also contribute to the variations during the observed period. The ephemerides derived in this work are useful for scheduling follow-up observations for these hot Jupiters in the future. The Python code (PdotQuest, https://github.com/AeoN400/PdotQuest) used to generate the ephemerides is made available online.

Long-term temperature variability and death among stroke patients: A cohort study in central Shandong province, China

Our current understanding of the repercussions stemming from long-term temperature variability (TV) within the broader context of global climate change remains considerably limited. This study aims to investigate the influence of long-term temperature variability on the probability of death and mortality among stroke patients. For this study, a sample of stroke patients aged 40 years and above between 2016 and 2019 was taken from five districts/counties in central Shandong Province. We calculated the standard deviation (SD) of the annual mean daily temperature and the annual mean diurnal temperature range (DTR) obtained from the baseline survey to express the long-term TV. To assess the link between TV exposure and mortality among stroke patients, we utilized the Cox proportional hazards regression method. This statistical method allowed us to estimate the adjusted hazard ratio (HR) and 95 % confidence interval (CI) with regard to the risk of mortality. For stroke patients, there was a correlation between a 1 °C rise in the SD of temperature and an elevated risk of mortality (HR: 1.18, 95 % CI: 1.08–1.29). Additionally, each 1 °C increase in the DTR was associated with a further increase in the risk of death (HR: 1.69, 95 % CI: 1.59–1.79). In patients diagnosed with ischemic and hemorrhagic stroke, DTR was found to be positively associated with the risk of death. Temperature SD was found to be positively associated with the risk of death only in patients with ischemic stroke. Persistent exposure to TV was strongly linked to mortality risk among individuals with stroke. By devoting increased attention to TV, these organizations can develop strategies to mitigate its adverse effects on public health.

Long-term variations and synoptic features of heat waves in Turkiye

Long-term variations and synoptic features of heat waves in Turkiye

Trajectories of Remnant Cholesterol are Associated with Diabetic Foot Ulcer in Adult Patients with Type 2 Diabetes: A Retrospective Cohort Study.

This study aimed to investigate the potential association between long-term variations in remnant cholesterol (RC) levels and the development of diabetic foot ulcers (DFU) in participants with type 2 diabetes (T2D). This was a retrospective cohort study. Variation in RC was assessed by the following metrics: mean, standard deviation (SD), coefficient of variation (CV) and trajectories pattern of RC. To identify RC trajectories, we employed the latent class mixture model. The primary endpoint was the development of DFU, and the time-to-event data were analyzed using Cox regression. A total of 1874 patients with T2D were included, with a median follow-up duration of 4.7 years. Among them, 129 individuals (6.9%) developed DFU. The proportion of DFU was significantly higher in the U-shaped group compared to the median group (P for trend < 0.001). Upon adjustment for confounding variables, the U-shaped trajectory correlated with a higher risk of DFU, demonstrating a hazard ratio (HR) of 2.57 (95% CI, 1.54-4.27). Subgroup analysis showed the U-shaped trajectory had a higher DFU risk regardless of gender (HR=2.40 and 2.81, respectively), glycemic control (HR=1.89 and 7.41, respectively), smoking (HR=2.36 and 2.93, respectively), or hypertension (HR=2.30 and 2.97, respectively). No association was found between mean, SD and CV of RC and DFU. A U-shape trajectory of RC was independently associated with an elevated risk of DFU among patients with T2D.

Unravelling long-term impact of water abstraction and climate change on endorheic lakes: A case study of Shortandy Lake in Central Asia.

Endorheic lakes, lacking river outflows, are highly sensitive to environmental changes and human interventions. Central Asia (CA) has over 6000 lakes that have experienced substantial water level variability in the past century, yet causes of recent changes in many lakes remain unexplored. Modelling hydrological processes for CA lakes poses challenges in separating climatic change impacts from human management impacts due to limited data and long-term variability in hydrological regimes. This study developed a spatially lumped empirical model to investigate the effects of climate change and human water abstraction, using Shortandy Lake in Burabay National Nature Park (BNNP) as a case study. Modelling results show a significant water volume decline from 231.7x106m3 in 1986 to 172.5x106m3 in 2016, primarily driven by anthropogenic water abstraction, accounting for 92% of the total volume deficit. The highest rates of water abstraction (greater than 25% of annual outflow) occurred from 1989 to 1993, coinciding with the driest period. Since 2013, the water volume has increased due to increased precipitation and, more importantly, reduced water abstraction. Despite limited observational data with which to calibrate the model, it performs well. Our analysis underscores the challenges in modelling lakes in data-sparse regions such as CA, and highlights the importance and benefits of developing lake water balance models for the region.

Temporal changes in the frequency of flood types and their impact on flood statistics

Standard flood frequency analysis assumes stationarity of flood conditions, i.e., no change of the distribution over time. However, long-term variability in climate and anthropogenic impacts question this assumption. Consequently, more and more non-stationary models are considered in flood frequency analyses. Yet, most of them only consider a change-point or trend in the magnitude of flood peaks while ignoring changes in the underlying flood geneses. Recent climate reports suggest such a change in frequency of certain flood-generating factors, e.g., the increase of frequency of heavy-rainfall events. In this study, flood types are applied to detect changes in the meteorological drivers of flood regimes. By application of a robust change-point test for the variance based on Gini’s Mean Difference, significant changes in the frequency of occurrence of certain flood types are detected. A clear tendency to more frequent heavy-rainfall floods and less snowmelt-induced floods is observed for many catchments in Central Europe. A special focus is laid on the shifts in winter floods, which occur less often and are replaced by rainfall-driven floods. The impacts of such changes on flood statistics are demonstrated by several approaches. Though the magnitude of flood peaks does not (necessarily) change, the changing frequency of floods leads to changing flood quantiles. Quantile estimations from traditional statistical analyses of annual series are compared to results of type-based flood statistics. It is shown how standard models are more affected by these changes because they are not able to compensate for changes in the frequency of individual flood types.

Spatiotemporal analysis of seasonal trends in land surface temperature within the distribution range of Moringa peregrina (Forssk.) in Southern and Southeastern Iran.

Temperature fluctuations and related factors are among the main causes of climate change. Understanding the temporal and spatial variations in temperature can shed light on how species respond to climate change. Plants generally persist in suitable microclimates in response to environmental change; however, examining long-term temperature variations within a species' range can be challenging using field observations. Thermal remote sensing, on the other hand, provides multi-scale time-series data with good coverage and regularity to overcome the challenges associated with field observations in environmental monitoring. Although changes in land surface temperature (LST) affect climate, hydrological processes, land-atmosphere interactions, and ecological activities, this metric has not received much research attention. This study aimed to analyze changes in habitat suitability and microclimatic conditions for Moringa peregrina. Seasonal changes in LST within the distribution range of the species were also investigated. To this aim, mean seasonal LST was computed in Google Earth Engine using the daily MODIS/006/MYD13A2 product from 2003 to 2023. Subsequently, a binary habitat suitability map was created based on the true skill statistic (TSS). The Mann-Kendall test was used to analyze seasonal LST trends. Major trends in LST were quantified based on the z-score, and compatibility with habitat suitability was evaluated via GAP analysis and the Kappa index. Seasonal temperature trends were evaluated by comparing each season with the following season using binary comparison. Landforms at presence points were regarded as microclimates and the sensitivity of microclimates to LST was evaluated using two methods: Principal component analysis (PCA) was used to quantify seasonal LST heterogeneity and the random forest (RF) approach was used to evaluate the effect of environmental parameters on habitat suitability within microclimates. The Kappa index revealed a weak overlapping between suitable / unsuitable habitat and the surfaces affected by the trend of changes. Moreover, the suitable habitat of Moringa peregrina in spring, autumn and winter is spatially overlapped by areas that have shown an increasing LST trend, and the presence points have not experienced an increasing temperature trend only in the summer. The findings show that the analysis of seasonal trends in LST provides insights into the effect of LST on habitat suitability and the condition of vegetation. The current study clearly shows that seasonal changes have had a significant impact on the distribution and habitat suitability of M. peregrina, particularly during summer and winter. Improved habitat suitability and range expansion were observed throughout the year. The study also highlights the role of landforms in regulating temperature. Landforms such as local ridges with minimal temperature fluctuations and regions near the Oman Sea were identified as potential future habitats due to favorable humidity conditions.

Long-term spatiotemporal variation in water area of the Xin'anjiang Reservoir (Lake Qiandao) from 1987 to 2022 and its relationships with water level and water storage

Long-term spatiotemporal variation in water area of the Xin'anjiang Reservoir (Lake Qiandao) from 1987 to 2022 and its relationships with water level and water storage

Social media insights into spatio-temporal emotional responses to COVID-19 crisis

The Coronavirus pandemic has presented multifaceted challenges in urban emotional well-being and mental health management. Our study presents a spatio-temporal sentiment mining (STSM) framework to address these challenges, focusing on the space-time geography and environmental psychology. This framework analyzes the distribution and trends of 6 categories of public sentiments in Shanghai during the COVID-19 crisis, considering the potential urban spatial influencing factors. The research specifically draws on social media data temporally coinciding with the spread of COVID-19 and the pre-trained language model RoBERTa-wwm-ext to classify public sentiment, in order to characterize the distribution and trends of dominant urban sentiment under the influence of epidemic at different phases. The interactions between urban geospatial features and sentiments are further modelled and explained using LightGBM algorithm and SHapley Additive exPlanations (SHAP) technique. The experimental findings reveal the subtle yet dynamic impact of the urban environment on the long-term spatial variation and trends of public sentiment under the epidemic, with green spaces and socio-economic status emerging as significant factors. Regions with higher permanent population consumption demonstrated more positive sentiments, underscoring the significance of socio-economic factors in urban planning and public health policy. This research offers the most extensive analysis to date on the influence of urban characteristics on public sentiment during Shanghai's epidemic life cycle also lays the groundwork for applying the STSM framework in future crises beyond COVID-19.

Long-term blood pressure variability: an emerging cardiovascular risk factor

Long-term blood pressure variability: an emerging cardiovascular risk factor

Spectroscopic variability of massive pre-main-sequence stars in M17

Context. It is a challenge to study the formation process of massive stars: their formation time is short, there are only few of them, they are often deeply embedded, and they lie at relatively large distances. Our strategy is to study the outcome of the star formation process and to search for signatures that remain of the formation. We have access to a unique sample of (massive) pre-main-sequence (PMS) stars in the giant H II region M17. These PMS stars can be placed on PMS tracks in the Hertzsprung–Russell diagram (HRD) as we can detect their photospheric spectrum, and they exhibit spectral features indicative of the presence of a circumstellar disk. These stars are most likely in the final stage of formation. Aims. The aim is to use spectroscopic variability as a diagnostic tool to learn about the physical nature of these massive PMS stars. More specifically, we wish to determine the variability properties of the hot gaseous disks to understand the physical origin of the emission lines, identify dominant physical processes in these disks, and to find out about the presence of an accretion flow and/or jet. Methods. We obtained multiple-epoch (four to five epochs) VLT/X-shooter spectra of six young stars in M17 covering about a decade. Four of these stars are intermediate to massive PMS stars with gaseous disks. Using stacked spectra, we updated the spectral classification and searched for the presence of circumstellar features. With the temporal variance method (TVS), we determined the extent and amplitude of the spectral line variations in velocity space. The double-peaked emission lines in the PMS stars with gaseous disks were used to determine peak-to-peak velocities, V/R ratios, and the radial velocity of the systems. Simultaneous photometric variations were studied using VLT acquisition images. Results. From detailed line identification in the PMS stars with gaseous disks, we identify many (double-peaked) disk features, including a new detection of CO bandhead and CI emission. In three of these stars, we detect significant spectral variability, mainly in lines originating in the circumstellar disk, in a velocity range up to 320 km s−1, which exceeds the rotational velocity of the central sources. The shortest variability timescale is about one day. We also detect long-term (months, years) variability. The ratio of the blue and red peaks in two PMS stars shows a correlation with the peak-to-peak velocity, which might be explained by a spiral-arm structure in the disk. Conclusions. The variable PMS stars lie at similar positions in the HRD, but show significant differences in disk lines and variability. The extent and timescale of the variability differs for each star and line (sets), showing the complexity of the region where the lines are formed. We find indications for an accretion flow, slow disk winds, and/or disk structures in the hot gaseous inner disks. We find no evidence for close companions or strong accretion bursts as the cause of the variability in these PMS stars.

Novel methods facilitating the mechanistic interpretation of multiscale optical remote sensing measurements

Plant physiology concentrates on the study of plant internal processes, such as growth, nutrient uptake and photosynthesis. The quantification of photosynthesis regulation is significant in understanding how plants react to the changing climate. Spectral remote sensing methods, using both reflected light in the visible and near infrared wavelengths, as well as chlorophyll fluorescence, are used to gather information about plant physiological variables. These methods have developed rapidly, prompted by the advances in remote sensing platforms and sensors. However, interpretation of remote sensing signals can be challenging. Due to canopy heterogeneity, the signal is affected by various elements, such as scattering, soil background and canopy structural effects. Additionally, open questions remain linked to the underlying mechanistic processes in the leaf modulating the optical signal, such as nutrient contents and leaf photochemistry, and how these processes and the optical signals diverge in response to temporal variation. Through multi-scale measurements, this thesis aims to advance the interpretation of optical remote sensing signals as they are affected by spatial and temporal variation, while promoting the use of novel methods and devices. Results indicate that diurnal and long-term variation of solar induced fluorescence (SIF) is driven by photosynthetic and structural factors, causing possible misinterpretations in SIF data. Additionally, depending on the scale of observation, results show that the capacity of remote sensing to detect changes in foliar nutrients depends on the covariation of nutrients, pigments and canopy structure, underlining the need for both leaf and canopy level measurements. Finally, we advocate for the implementation of a novel miniaturized fluorometer, demonstrating the ability to track the seasonal regulation of photosynthesis using integrated measurements of chlorophyll fluorescence and gas exchange. The results from this thesis underline the need for simultaneous multi-scale measurements of leaf and canopy physiological factors to further our understanding of photosynthesis regulation.

An ensemble multi-scale framework for long-term forecasting of air quality.

The significance of accurate long-term forecasting of air quality for a long-term policy decision for controlling air pollution and for evaluating its impacts on human health has attracted greater attention recently. This paper proposes an ensemble multi-scale framework to refine the previous version with ensemble empirical mode decomposition (EMD) and nonstationary oscillation resampling (NSOR) for long-term forecasting. Within the proposed ensemble multi-scale framework, we on one hand apply modified EMD to produce more regular and stable EMD components, allowing the long-range oscillation characteristics of the original time series to be better captured. On the other hand, we provide an ensemble mechanism to alleviate the error propagation problem in forecasts caused by iterative implementation of NSOR at all lead times and name it improved NSOR. Application of the proposed multi-scale framework to long-term forecasting of the daily PM2.5 at 14 monitoring stations in Hong Kong demonstrates that it can effectively capture the long-term variation in air pollution processes and significantly increase the forecasting performance. Specifically, the framework can, respectively, reduce the average root-mean-square error and the mean absolute error over all 14 stations by 8.4% and 9.2% for a lead time of 100 days, compared to previous studies. Additionally, better robustness can be obtained by the proposed ensemble framework for 180-day and 365-day long-term forecasting scenarios. It should be emphasized that the proposed ensemble multi-scale framework is a feasible framework, which is applicable for long-term time series forecasting in general.

Long-term variation of Arctic Sudden Stratospheric Warmings (SSW) and potential causes

Long-term variation of Arctic Sudden Stratospheric Warmings (SSW) and potential causes

Downward Mediterranean Cloudiness Beyond Little Ice Age Background Variability

Cloudiness–sea interaction substantially influences Earth’s climate by shaping precipitation patterns and radiative budgets. However, our understanding of long-term variations in land-based cloud cover, spanning over decades, centuries, and more, remains limited. This study presents the most extensive reconstruction to date of the annual total cloud cover fraction across the Mediterranean, spanning 1500–2022 CE. A notable shift occurred after 1818, marked by a surge in multidecadal variability and a sustained decline in cloud cover. This change has been attributed to several factors, including the eruption of Mount Tambora in Indonesia in 1815, intensified solar forcing, and a positive phase of the Atlantic Multidecadal Oscillation. These factors collectively reshaped cloudiness beyond typical background variability. By shedding light on the complex interactions with the climate system, our findings suggest that temperature changes may drive increased cloudiness in the Mediterranean when influenced by cold air masses through advection. This emphasizes the need for a deeper exploration of the impact of clouds on regional atmospheric circulation and climate changes. Ultimately, our enhanced understanding of the relationship between cloudiness and climate change provides valuable insights for climate models. While these models consistently project a decrease in cloudiness over the Mediterranean, our findings can also be helpful in understanding cloud variability in other parts of the globe.

ЧАСОВА ДИНАМІКА, РОЗПОДІЛ ТА СУЧАСНІ ЗМІНИ ПАРАМЕТРІВ ТА ХАРАКТЕРИСТИК ВЕСНЯНОГО ВОДОПІЛЛЯ НА Р. ДЕСНА – М. ЧЕРНІГІВ

The article analyzes the long-term variability of the main parameters and characteristics of spring irrigation on the Desna River – the city of Chernihiv. The main methods used are the methods of mathematical statistics, systematization, generalization and analysis of initial data and obtained results. To carry out the research, a bank of average daily water consumption of the Desna River – Chernihiv city for the observation period 1895–2020 (126 years) was created. The analysis of the initial information was carried out and the following series of random variables were formed over a multi-year period: a series of dates of the start of spring irrigation (in the number of days from January 1); range of maximum water consumption of spring irrigation (in m3/c); a number of dates of maxima (in the number of days from January 1); a number of dates of the end of spring watering (in the number of days from January 1); range of duration of spring irrigation (in number of days). The method of comparison is used to identify modern changes. To do this, all the above-mentioned series of parameters and characteristics of spring irrigation are divided into two periods equal in number of years – 1895-1957 (63 years) and 1958-2020 (63 years). The multi-year average values (norms), coefficients of variation and asymmetry of spring irrigation start dates, maximum water flows during spring irrigation and their onset dates, end dates and duration of spring irrigation on the Desna River – Chernihiv city were determined for all studied periods. Constructed histograms of relative frequencies in the form of partial intervals of all studied indicators and characteristics of the spring irrigation of the Desna River – the city of Chernihiv demonstrated the peculiarities of their distribution during the observation period 1895–2020.