Seasonal Index Research Articles

Impact of bull age, sperm processing, and microclimatic conditions on the viability and DNA integrity of cryopreserved bovine sperm.

Context Seasonal microclimatic fluctuations can cause changes in sperm quality even in dairy bulls bred under temperate climate. These changes can vary between sires of different age and affect sperm freezability. Aims We aimed to evaluate the modulating effect of bull age and equilibration time before freezing on the seasonal pattern of sperm viability and DNA integrity post-thaw. Methods In the frame of systematic sperm quality control, we assessed the integrity of sperm plasma membrane and acrosome (PMAI) in 15,496 cryopreserved bovine batches, and the percentage of sperm with high DNA fragmentation index (%DFI) after 0h and 3h incubation at 38°C post-thaw (3h) in 3422 batches. Semen was equilibrated for 24h before freezing if collected on Monday or Wednesday and 72h if produced on Friday. We investigated the effect of season, bull age, equilibration, and temperature-humidity index (THI) on the day of semen collection on sperm traits using mixed-effects linear models. Key results PMAI and %DFI (0h and 3h) deteriorated with increasing THI. The effect of THI on %DFI was detected with a 30-day time lag. Seasonal fluctuations of sperm quality were similar between young, mature, and older sires. Prolonged equilibration did not affect PMAI but was linked to elevated %DFI (3h) in summer. Conclusions Extending equilibration from 24 to 72h is compatible with commercial standards of bovine sperm quality post-thaw; however, it could interfere with the seasonal pattern of the latter. Implications Systematic monitoring of bovine sperm quality enables the prompt detection of stress factors related to microclimate and semen processing.

Multi-Method Comparative Analysis of Hydroclimatic Trends and Variability in Dry Creek Catchment, South Australia

AbstractThe trend and variability of hydroclimatic variables over time are apparent in seasonal creeks, especially those located in urbanized areas. Understanding hydro-climatic trends in urban areas is crucial for the sustainable management of water resources and the environment. This study aimed to explore the spatiotemporal variability and trends of hydroclimate variables as well as the potential connection between rainfall and streamflow in Dry Creek catchment, South Australia. The trend-free pre-whitening Mann–Kendall (TFPW-MK) test and Innovative Trend Analysis (ITA) were utilized to examine the monotonic and nonmonotonic trends, respectively, and multiple statistical tests were employed to examine the change points in the hydroclimatic time series. Sen’s slope, Simple Linear Regression (SLR), and ITA were used as alternative approaches to assess the magnitudes of change and overcome the limitations in the underlying assumptions of the various methodologies. The variability in the hydroclimate time series was estimated using several indices, such as the coefficient of variation, seasonality indices, flashiness index, and mean zero flow index. The analyses revealed important findings, notably the high variability of rainfall and streamflow during dry periods. Streamflow displayed greater variability compared to rainfall, with high CV values recorded both seasonally and annually. Furthermore, there was a significant upward trend in seasonal rainfall during winter. Additionally, the maximum and mean temperatures demonstrated a statistically significant increase, which can be attributed to global warming and significant urbanization in the catchment area. Comparative analysis has confirmed that the ITA has superior detection capabilities for nonmonotonic trends, outperforming other methods. It excels at presenting graphical representations that accurately depict trends, effectively differentiating between low, medium, and high values. The strong relationship between rainfall and streamflow demonstrated by the Tanh curve suggests that rainfall is the most reliable predictor of streamflow. The outcomes of this investigation are expected to support local governmental organizations and decision-makers in comprehending the spatial and temporal features of rainfall, as well as its correlation with streamflow. This information will further assist in developing flood and drought mitigation strategies backed by empirical evidence. Graphical Abstract

Temporal Characterization of Fire Accidents in China

Based on the fire accident statistics, this study uses the seasonal index method to comprehensively analyze the temporal pattern of fire occurrence in China from 2010 to 2015. The study scientifically reflects that the number of fire occurrences has an extremely close relationship with time, and summarizes the temporal pattern and characteristics of fire occurrences. This study concludes that the high incidence of large fire accidents in China occurs in the time period of 3:00 a.m. to 5:00 a.m. The probability distribution model of the average total number of accidents in each time period was established and the Kolmogorov test was carried out, which shows that the time characteristics of fire accidents in China can be understood with the probability distribution model as a whole. The results of the analysis of the time characteristics of the fire accidents can provide decision-making information for combating fire accidents.

Negative correlation between soil salinity and soil organic carbon variability

Soil organic carbon (SOC) is vital for terrestrial ecosystems, affecting biogeochemical processes, and soil health. It is known that soil salinity impacts SOC content, yet the specific direction and magnitude of SOC variability in relation to soil salinity remain poorly understood. Analyzing 43,459 mineral soil samples (SOC < 150 g kg-1) collected across different land covers since 1992, we approximate a soil salinity increase from 1 to 5 dS m-1 in croplands would be associated with a decline in mineral soils SOC from 0.14 g kg-1 above the mean predicted SOC ([Formula: see text] = 18.47 g kg-1) to 0.46 g kg-1 below [Formula: see text] (~-430%), while for noncroplands, such decline is sharper, from 0.96 above [Formula: see text] = 35.96 g kg-1 to 4.99 below [Formula: see text] (~-620%). Although salinity's significance in explaining SOC variability is minor (<6%), we estimate a one SD increase in salinity of topsoil samples (0 to 7 cm) correlates with respective [Formula: see text] declines of ~4.4% and ~9.26%, relative to [Formula: see text] and [Formula: see text]. The [Formula: see text] decline in croplands is greatest in vegetation/cropland mosaics while lands covered with evergreen needle-leaved trees are estimated with the highest [Formula: see text] decline in noncroplands. We identify soil nitrogen, land cover, and precipitation Seasonality Index as the most significant parameters in explaining the SOC's variability. The findings provide insights into SOC dynamics under increased soil salinity, improving understanding of SOC stock responses to land degradation and climate warming.

Epidemiological characterization and risk assessment of rabbit haemorrhagic disease virus 2 (RHDV2/b/GI.2) in the world

A novel variant of rabbit haemorrhagic disease virus, designated RHDV2/b/GI.2, was first discovered in France in 2010. Subsequently, RHDV2 rapidly spread to Africa, North America, Australia, and Asia. RHDV2 outbreaks have resulted in significant economic losses in the global rabbit industry and disrupted the balance of natural ecosystems. Our study investigated the seasonal characteristics of RHDV2 outbreaks using seasonal indices. RHDV2 is prone to causing significant outbreaks within domestic and wild rabbit populations during the spring season and is more likely to induce outbreaks within wild rabbit populations during late autumn in the Southern Hemisphere. Furthermore, based on outbreak data for domestic and wild rabbits and environmental variables, our study established two MaxEnt models to explore the relationship between RHDV2 outbreaks and the environmental factors and conducted outbreak risk predictions for RHDV2 in global domestic and wild rabbit populations. Both models demonstrated good predictive performance, with AUC values of 0.960 and 0.974, respectively. Road density, isothermality, and population density were identified as important variables in the outbreak of RHDV2 in domestic rabbits, while road density, normalized difference vegetation index, and mean annual solar radiation were considered key variables in the outbreak of RHDV2 in wild rabbits. The environmental factors associated with RHDV2 outbreaks identified in our study and the outbreak risk prediction maps generated in our study will aid in the formulation of appropriate RHDV2 control measures to reduce the risk of morbidity in domestic and wild rabbits.

Spatiotemporal characteristics and influencing factors of airport service quality in China

Airport service quality (ASQ) is essential for determining the quality of ground civil aviation services. In this study, ASQ was assessed using the monthly airport aviation complaint data from 2015 to 2019 of 196 airports in mainland China (except for airports in Hong Kong, Macao, and Taiwan, which are not included in the statistics). First, we constructed a seasonal index of aviation complaints to evaluate and compare the overall temporal characteristics of ASQ in China. Second, the spatial distribution pattern of ASQ in China was determined using the aviation complaint concentration index and hot spot analysis model. Finally, the major influencing factors and categories of ASQ in China were analyzed considering spatiotemporal dimensions using the correspondence analysis method. The results revealed that there were clear seasonal differences among ASQ in China, with a high–low–low–high distribution during all four seasons. The regional agglomeration trend of airport aviation complaints was obvious, and the spatial difference in ASQ was large. Northern and western China performed better than southern China. Spatiotemporal and influencing factors of ASQ were the most strongly correlated factors in each quarter and region. The predominant source of aviation complaints across all types of airports is related to fundamental services, with check-in services identified as the most impactful category affecting ASQ. This study, based on 60 months of statistical data, offers a comprehensive evaluation of ASQ throughout the entire airport network in mainland China, from the perspective of aviation complaints. Additionally, a systematic ASQ evaluation method and research system encompassing time, space, and elements were established. This framework not only stimulates the improvement and enhancement of ASQ but also provides a theoretical foundation for differentially enhancing ASQ in regional airports. Overall, our results contribute to breaking through the qualitative research thinking system in ASQ research from a theoretical perspective, paving the way for exploring broader research in enhancing the quality of ground civil aviation services.

Assessment of Variability and Seasonal Patterns in Lemon Arrivals and Prices across India

The current study examined the variability and seasonal patterns in lemon arrivals and prices across the significant lemon-producing states in India from 2013 to 2022. Six important markets (Tenali, Surat, Pune, Udipi, Attabira, and Lashkar) in the country, one from each of the selected states, were chosen based on their annual market arrivals of lemons. The variability measured through the Cuddy-Della-Valle index showed similar price behaviour in all the selected markets, with the highest price variability during the peak season. The price behaviour of lemons showed a significant positive trend in all selected markets. Seasonal indices for market arrivals were highest during the peak harvesting season of lemons, with the Tenali market demonstrating the highest seasonal variation among all the selected markets. The prices were high during the off period of arrivals.

Long-term responses of maar lakes water level to climate and groundwater variability in central Mexico

Besides water level (WL) variability, several lakes worldwide have shown water level decline trends and total desiccation. In maar lakes, there is paleoenvironmental evidence of water level responses to climate and groundwater inflow changes; however, historical studies are scarce. Understanding the relative contributions of natural variability and anthropogenic impacts to the decline of lakes' water level is fundamental to preserving these ecosystems. In this study, we developed a conceptual baseline of the response of tropical maar lakes to climate and groundwater variability at different time scales in near natural conditions. The four analyzed maar lakes are in a semiarid, endorheic basin in central Mexico and the study time frame (1959 to 1992) includes a historical period before direct human impact. We analyzed the effects of meteorological records, large-scale climate phenomena indices (ENSO, PDO, and AMO), and regional groundwater levels on WL datasets using multiple linear regression models, trend analysis and cross correlations. WL responded to meteorological seasonality and climate variability indices. All lakes lost more water to evaporation than they gained from precipitation, which partially explains long-term WL declines. Regional groundwater levels decreased, suggesting a reduction of groundwater inflow to the lakes, which contributed to WL declines. The latter may reflect a lagged effect of long-term recharge decrease, probably due to long-term climate change in the region since the formation of the lakes evidenced in previous studies. The results add to evidence of water level declines caused by groundwater-mediated natural variability, warning about how this process is being exacerbated by anthropic activities under the context of global change.

Study of the River Discharge Alteration

This article aims to analyze the alteration in water discharge due to the building of one of the largest dams in Romania. Modifications in the hydrological patterns of the studied river were emphasized by a complex technique that includes decomposition models of the series into trends, seasonal indices, and random components, as well as into Intrinsic Mode Functions (IMFs). The Mann–Kendall trend test indicates the existence of different positive slopes for the subseries S1 and S2 (before and after the inception of the Siriu dam, respectively) built from the raw series, S. The stationarity hypothesis was rejected for all series. The multifractal analysis shows two different patterns of the data series. After decomposing the subseries S1 and S2, it resulted that the seasonality indices are not the same. Moreover, the seasonal variations decreased after building the dam. Empirical Mode Decomposition (EMD) unveils different short- and long-term patterns of the series before and after building the dam, concluding that there is a significant alteration in the river discharge after the dam’s inception.

Efficacy of Multiseason Sentinel-2 Imagery for Classifying and Mapping Grassland Condition

Assessing the condition of ecosystems is imperative for understanding their degree of degradation and managing their conservation. The increasing availability of remote sensing products offers unprecedented opportunities for mapping vegetation with high detail and accuracy. However, mapping complex ecosystems, like grasslands, remains challenging due to their heterogeneity in vegetation composition and structure. Furthermore, degraded ecosystems affected by invasive vegetation present different condition levels within vegetation classes, limiting the accuracy of classifications and condition assessment. Here, we evaluated the capacity of Sentinel-2 multispectral time series imagery as an input for classifying different levels of cover within a vegetation class to detect the subtle differences needed to assess the condition of degraded ecosystems. Our study was conducted in the iron-grasslands of South Australia, a perennial tussock grassland dominated by iron-grasses (Lomandra spp.) and severely affected by invasive annual grasses. We developed random forest models to discriminate classes defined by the cover of iron-grasses, wild oats (Avena barbata), and woodland (training points = 250). We tested the importance of data seasonality, spatial resolution, spectral bands, and vegetation indices. The combination of spatial, temporal, and spectral detail produced the best classification results. Random forest classifications performed best at 10 m resolution, suggesting that detailed resolution outweighs spectral detail for discriminating vegetation patterns in systems with high spatial heterogeneity. The model at 10 m resolution combining all periods and all variables (spectral bands and vegetation indices) produced a mean kappa coefficient of 56% and a mean overall accuracy of 67%. The dry season imagery and vegetation indices emerged as the most informative, suggesting that vegetation classes presented different phenological properties critical for their discrimination. Our study contributes to mapping complex ecosystems, facilitating the discrimination of different levels of condition in grasslands degraded by invasive species, and thus benefits the conservation of native grasslands and other ecosystems.

Reliability analysis of wind turbines considering seasonal weather effects

The failure rate of wind turbines shows obvious fluctuation due to seasonal environmental factors. However, few efforts have been devoted to modeling the seasonal failure rate. This paper develops a novel model that consists of a baseline failure rate function, seasonal indices, and a residual term to describe the monthly failure rate of wind turbines. A two-stage procedure is developed to estimate the 16 unknown parameters in the model. The first stage explores the relationship between the parameters in the baseline function and the monthly coefficients by maximum likelihood estimation and then integrates the properties into the genetic algorithm to estimate the main parameters. In the second stage, the variance of the residual term is estimated based on the analysis of the differences between the observed and predicted failure rates. The failure history of 48 months has been used to illustrate the proposed approach. The results show that the monthly failure rate function can well fit the real failure history of wind turbines, and it outperforms the traditional reliability model.

CHIRPS rainfall product application for analyzing rainfall concentration and seasonality in Johor river basin, Malaysia

It is crucial to carry out a spatiotemporal evaluation of rainfall concentration in the Johor river basin (JRB) given that erratic rainfall can lead to floods. The study utilized the CHIRPS rainfall dataset from 1983 to 2018 to determine rainfall concentration using the Concentration Index (CI). To further define the rainfall regime, the Seasonality Index (SI) and Precipitation Concentration Index (PCI) were examined. The Mann-Kendall (MK) test was utilized to examine the trend at a 95% level of significance. The results showed the better accuracy of SI than PCI in classifying the rainfall regime in the JRB, defined as “equable but with a definite wetter season” type. The CI values ranged from 0.61 to 0.72 and were typically high all over the basin, with the northwestern and western regions having the highest values. At 27 grid points on the eastern border of the upper and middle parts of the basin, CI has significantly increased as a result of an upsurge in rainfall concentration brought on by climate change. More than 67% and up to 83% of the yearly rainfall for all grid locations was accounted for by the North East monsoon (NEM), with the 25% wettest days caused by high-intensity rainfall events.

Price Dynamics of Tomato, Onion and Potato (TOP) in India

The development of society depends on several core elements, including food, livelihood, nutritional security, and healthcare. To gain insight into the dynamics and volatility of prices of top crops, a study was conducted analysing wholesale monthly price data for selected states and all of India from January 2005 to December 2021. Various methodologies such as the Compound Annual Growth Rate (CAGR), seasonality index, Cuddy Della Valle Index, rescaled range analysis, and ARCH-GARCH model were utilized to achieve the research objectives. According to the study, West Bengal had major crop price fluctuations with tomatoes being costly from July to November, onions from August to January, and potatoes from July to December. Karnataka, Rajasthan, and Punjab experienced the highest monthly price instability for tomatoes, onions, and potatoes. Additionally, the prices of TOP crops were more unpredictable than cereals and pulses. The price series of potatoes had long-term memory detection, and onions had the highest price volatility among TOP crops. According to the study, in order to tackle the price variability of TOP crops, it is necessary to enhance price stabilization measures. By implementing integrated policies, a stable pricing system can be established for top crops, which will not only safeguard the interests of consumers but also ensure food and economic security for farmers.

Investigation of winter weather crash injury severity using winter storm classification techniques

This study employs a unique methodology for analyzing seasonal traffic crashes by first identifying adverse winter-weather events using traditional traffic-crash data (from Nebraska) and correlating them to detailed meteorological data for subsequent analyses. Specifically, the novel component of this analysis utilized a state-specific winter storm and seasonal severity index metric in conjunction with classifications of storm type. Both crash and meteorological data were combined to determine if the amount of snowfall and/or storm intensity/severity could be a predictor of the number and severity of crashes. Analysis was performed to identify weather conditions causing highway safety concerns. The important takeaway of the study was that most winter weather-related vehicular crashes in Nebraska were associated with relatively minor winter weather conditions. The reported crashes typically occurred with relatively low snowfall amounts due to residual snowfall on the ground, emphasizing the importance of continuing winter maintenance operations activities and public service announcements long after a storm has passed through the region. Modeling crash injury severity revealed that icy pavements, higher duration of snowfall, work zones, and higher number of trucks and buses involved in a crash were linked to higher injury severity; higher visibility was associated with a greater chance of crashes involving visible injuries but a lower likelihood of crashes involving disabling injury/fatality. Snowfall was associated with more visible injury crashes, whereas snow-depth was linked to fewer visible injury and disabling injury/fatality crashes. The study's limitations include the spatial and temporal aggregation of weather data and a lack of detailed winter roadway maintenance/clearance activity data.

Climate variability, trend, and associated risks: Tana sub-basin, Ethiopia

Abstract The study focused on analyzing the variability and trends of climate parameters in the Tana sub-basin. Various statistical methods and indices were employed to assess precipitation and temperature patterns in the region. The findings indicated a statistically non-significant increasing trend in rainfall across the sub-basin, with values ranging from 1.64 to 5.37 mm/year. In terms of temperature, there was an increasing trend observed, but it was also not statistically significant. The seasonality index ranged between 0.87 and 1.03, indicating different rainfall distribution patterns. In 36.69% of the sub-basin, rainfall occurs in marked seasonal patterns with a long dry season, and the remaining (63.31%) is concentrated in 3 or fewer months, indicating a different rainfall distribution pattern. In addition, the study assessed the precipitation concentration and found that 57.5% of the rainfall data exhibited a strong irregular concentration, 41.5% showed an irregular concentration, and 1% exhibited a moderate concentration. The study underscores the presence of climate variability and trends in the Tana sub-basin, emphasizing the need to align agricultural and water resource management practices with the observed climate variability.

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.

Utilizing treated wastewater for pasture irrigation: Effects on productivity, plant community structure and soil properties

AbstractSurplus wastewater accumulates in winter and requires a discharging solution. In our study we examined the effects of using treated wastewater (TWW) as supplemental pasture irrigation in winter. To test the effect of TWW irrigation on the vegetation and soil, 10 grazing prevention exclosures were established at Ramat‐Hanadiv Park, Israel, with half of them irrigated with TWW. Soil and vegetation were sampled during 2 years of irrigation and 1 year after cessation of irrigation. Vegetation composition, species richness, and productivity were sampled at the peak of the growing season and various indices of herbage quality were measured. Our results show that irrigation with wastewater increased herbage production by an average of 38% and total pasture protein production by 9% relative to the control subplots. Conversely, TWW irrigation decreased plant protein content while increasing herbage fibre content and lowering its digestibility. Over a period of 2 years, species richness decreased in the irrigated subplots. Most of the soil indices examined were not affected by irrigation, but in the irrigation subplots, sodium values and the sodium adsorption‐ratio were higher than in areas without irrigation. Additional resources in winter enabled the plants to grow rapidly and increased interplant competition. Changes in vegetation composition were revealed through functional group reshuffling and species richness decline. The results of our study show that wastewater irrigation causes an increase in productivity but a decrease in forage quality. At the pastoral system level, the significant increase in productivity leads to increased availability of protein and herbage for animals.

Computation of comfort indices for livestock in central Punjab

Climate change imposes detrimental heat stress, which disrupts the thermo-regulatory balance of cattle and buffaloes. Quantifying heat stress through bioclimatic indices is a vital step for identifying suitable mitigation/adaptation strategies. So, the trend of different comfort indices for cattle was computed (2000-2019) and used for estimating milk production as these indices provide a holistic view of the bovine’s thermoneutral status. The trend analysis of seasonal comfort index (CI) through box plot analysis indicated that Black Globe Humidity Index (BGHI) had shifted from ‘Low Impact’ to ‘High Impact’, Temperature Humidity Index (THI) had shifted from ‘Normal’ to ‘Danger’ and Comprehensive Climate Index (CCI) had shifted from ‘No stress’ to ‘Mild stress’ from winter to summer season indicating the impact of heat stress during the latter period. The milk production in April had a significant correlation with BGHI, Heat Load Index (HLI), Respiration Rate (RR), THI, CCI, and Equivalent Temperature Index (ETI). Milk production in May and June had a significant relationship with ETI and THI. Lactation-wise milk production analysis indicated that sixth lactation is related to ETI and HLI. Fourth and second lactation had a significant relation with all indices estimated and the first stage of lactation with BGHI, ETI, and RR. The CI with the highest correlation coefficients were used to develop a regression model for a respective month and lactation stage.

Spatial and remote sensing monitoring shows the end of the bark beetle outbreak on Belgian and north-eastern France Norway spruce (Picea abies) stands.

In 2022, Europe emerged from eight of the hottest years on record, leading to significant spruce mortality across Europe. The particularly dry weather conditions of 2018 triggered an outbreak of bark beetles (Ips typographus), causing the loss of thousands of hectares of Norway spruce stands, including in Wallonia and North-eastern France. A methodology for detecting the health status of spruce was developed based on a dense time series of satellite imagery (Sentinel-2). The time series of satellite images allowed the modelling of the spectral response of healthy spruce forests over the seasons: a decrease in photosynthetic activity of the forest canopy causes deviations from this normal seasonal vegetation index trajectory. These anomalies are caused by a bark beetle attack and are detected automatically. The method leads in the production of an annual spruce health map of Wallonia and Grand-Est. The goal of this paper is to assess the damage caused by bark beetle using the resulting spruce health maps. A second objective was to compare the influence of basic variables on the mortality of spruce trees in these two regions. Lasted 6years (2017-2022), bark beetle has destroyed 12.2% (23,674ha) of the spruce area in Wallonia and Grand-Est of France. This study area is composed of three bioclimatic areas: Plains, Ardennes and Vosges, which have not been equally affected by bark beetle attacks. The plains were the most affected, with 50% of spruce forests destroyed, followed by the Ardennes, which lost 11.3% of its spruce stands. The Vosges was the least affected bioclimatic area, with 5.6% of spruce stands lost. For the most problematic sites, Norway spruce forestry should no longer be considered.

Analysis of the seasonality of rain in Brazil from 1990 to 2022

Water resources are subject to hydroclimatic and spatio-temporal variations. This article aimed to analyze the seasonality of rainfall in Brazil. Precipitation data from 1306 rain gauge stations with data from 1990 to 2022. The precipitation seasonality index (PSI) was determined to evaluate the spatial and temporal variation in seasonality. The spatialization of ISP values ​​was carried out using the geostatistical method of ordinary kriging. The ISP values ​​found throughout the Brazilian territory vary from 0.273 to 1.176. This indicates that Brazil has humid regions with precipitation well distributed throughout the months throughout the year, to arid regions, with few months with rain. The lowest seasonal variability was found in the three states in the South region, on the coast of the states in the Southeast region and in the North region near the Amazon forest. The greatest seasonal variability occurs markedly in the Northeast region, close to the semi-arid Northeast, with long periods of drought