Seasonal Trend Analysis Research Articles

Interplay Among Recent Trends in Climate Extremes, Vegetation Phenology, and Crop Production in the Southern Mediterranean Region

ABSTRACTThe southern Mediterranean region is among the most vulnerable areas to climate change globally. However, in this region, there is a need to further understand the complex interactions between climate, vegetation, and crops to fully assess the combined impacts of extreme climate events on the agricultural sector. Using daily Normalised Difference Vegetation Index (NDVI) data, we evaluated trends across 15 vegetation phenology indicators between 1982 and 2019 and analysed their links to land‐use land‐cover changes. We found significant increases in the maximum value of NDVI (MaxV), length of growing seasons (LengthGS), and duration from crop emergence to anthesis (BMaxT), particularly within croplands. These changes positively correlated with regional crop production, especially in coastal and interior plains where croplands and forests are expanding. Conversely, southern areas bordering the Sahara showed declining MaxV and an expansion of sparsely vegetated areas. We then conducted a comprehensive seasonal trend analysis of climatic stresses and discussed how they align with recent trends in key phenological indicators. Coastal and interior plains experienced wetter conditions throughout the year, ensuring sufficient water during the growing season. Meanwhile, areas bordering the Sahara had wetter autumns and winters but drier springs and summers. Additionally, the region experienced warmer conditions from spring to autumn, with fewer cold wave events. Analysing the frequency and duration of compound extreme events, we observed a trend toward more light to moderate dry/hot days in spring and autumn and light to extreme wet/hot days from summer to autumn. These conditions are significantly correlated with increased MaxV, improved crop productivity, and extended LengthGS and BMaxT. These findings may serve as early indicators of how future climate changes could impact crop production, highlighting regional risks and opportunities to guide informed decision‐making and development of adaptive strategies in the southern Mediterranean region.

Seasonal trend analysis and influence of meteorological factors on the concentration of atmospheric pollutants in an urban area

Introduction: Air pollution is a growing concern in Greater Noida, Uttar Pradesh, where meteorological factors play a major role. As urbanization accelerates, the region has not yet thoroughly examined the impact of factors like temperature, humidity, wind direction, and speed on pollutants like Particulate Matters (PM2.5, PM10), Nitrogen dioxide (NO2), and Sulfur dioxide (SO2) Materials and methods: To explore this issue, a 12-month study (January to December 2023) was conducted to analyze the relationships between these meteorological factors and pollutant levels. In the present study, pollutant movement was tracked over time. Pearson correlation was used to correlate air pollution concentrations with weather conditions. Results: The study revealed that the average concentrations of PM , PM , 10 2.5 and NO2 were 912.54 µg/m³, 257.42 µg/m³, and 65.98 µg/m³, respectively. In contrast, SO2 had an average concentration of 26.85 µg/m³. Conclusion: The study indicates that particulate matter and temperature are strongly negatively correlated, while wind speed shows a weak positive correlation with particulate matter. Pollution levels and relative humidity also display a negative correlation, as do particulate matter concentrations and humidity. However, gaseous pollutants exhibit little to no significant correlation with these weather conditions

Trends in Vegetation Seasonality in the Iberian Peninsula: Spatiotemporal Analysis Using AVHRR-NDVI Data (1982–2023)

Vegetation seasonality is a critical indicator of ecological responses to global climate change, especially in the Iberian Peninsula, where the intersection of human activity and climate variability amplifies these effects. Understanding these changes is vital for adopting ecogeographical sustainability and developing effective climate adaptation strategies. This study examines trends in vegetation seasonality in the Iberian Peninsula from 1982 to 2023, based on weekly AVHRR NDVI data (2184 images). By integrating Seasonal Trend Analysis (STA) with Robust Trend Analysis (RTA)—including the Theil–Sen (TS) slope estimator, the Contextual Mann–Kendall (CMK) test (α = 0.05), and false discovery rate (FDR) control—we identified significant phenological shifts and widespread vegetation greening. The results reveal a regional response to global patterns of climate change, with 94.2% of the study area exhibiting significant trends, particularly in the Mediterranean ecoregion, where earlier growing seasons are becoming increasingly common. These shifts highlight the urgent need for sustainable land and resource management in the face of accelerating global change. Our findings provide critical insights into the ecological dynamics of the Iberian Peninsula, offering a robust foundation for formulating policies that promote environmental sustainability and enhance resilience to climate change.

The Seasonality of Childhood Bone and Joint Infection with Focus on Kingella kingae

Background: Seasonal trends in hospitalization for childhood bone and joint infection (BJI) are reported inconsistently. True seasonal variation would suggest an element of disease risk from environmental factors. This review evaluates all reported seasonal variations in childhood BJI, with additional analysis of seasonal trends for diseases secondary to Kingella kingae. Methods: A systematic review of the literature was undertaken from January 1, 1980, to August 1, 2024. Data were extracted on the hospitalization rate by season and/or month. Pathogen-specific studies for BJI secondary to K. kingae were examined separately. Results: Twenty studies met inclusion criteria encompassing 35,279 cases of childhood BJI. Most studies reported seasonal variation (n = 15, 75%). Eight studies specifically considered disease secondary to K. kingae, and all reported more frequent hospitalization in autumn and/or winter. This is in keeping with the role of respiratory pathogens and seasonal viruses in disease etiology for K. kingae BJI. Findings from other studies on the seasonality of childhood BJI were inconsistent. There were reported seasonal peaks in autumn/winter (4 studies), summer/spring (5 studies), or no variation (5 studies). Where microbiologic data were available, Staphylococcus aureus was the primary pathogen. The quality assessment demonstrated confounding and heterogeneous inclusion criteria affecting the seasonal analysis. Conclusion: For childhood BJI caused by K. kingae, there appears to be a higher risk of hospitalization in autumn and/or winter months. This may relate to the seasonal circulation of respiratory viruses. There is currently insufficient evidence to support other forms of seasonal variation. Reported findings are likely affected by regional disease and pathogen characteristics. Level of Evidence: Prognostic Level III. See Instructions for Authors for a complete description of levels of evidence.

Analysis of Pre-monsoon and Post-monsoon Seasonal Rainfall Trends by Mann-Kendall and Sen’s Slope Estimator Test in Himachal Pradesh, India

The purpose of the current study is to determine the trends and patterns of precipitation occurring in the periods before and after the monsoon season at 12 meteorological stations located in the twelve districts of the Himachal Pradesh region. The timeframe of the analysis is from 1991 to 2020. For this study, we used the Mann-Kendall test and estimator Sen's slope estimator test. Precipitation is an essential climatic factor that can act as a signal of climate change. In order to understand how climate change may affect water resource planning and management, it is crucial to understand rainfall pattern. The results indicate that there is a growing tendency in the pre-monsoon season at some sites, while some stations are seeing a declining trend. Keylong has the most significant falling trend, with a Sen slope of -4.154mm whereas Manali had the highest increasing trend at Sen's Slope (1.471mm). In contrast, the analysis of post-monsoon season data reveals an upward tendency in all studied regions, except for Dharamshala, where Sen's slope (-0.042mm) shows a slight downward trend whereas Manali had the highest increasing trend with the value of 1.771mm. According to the research, agricultural and horticultural sectors in Himachal Pradesh are at risk from heavy rainfall and landslides during the periods before and after the monsoon season, respectively, if the patterns continue to rise. Conversely, a decrease in these patterns might result in water scarcity. To tackle adverse climate changes, the general public must embrace adaptation measures based on long-term series data.

Assessment of pesticide residues: a comprehensive analysis of seasonal trends and health implications

This study assessed the presence of eight pesticide residues in the Indus River, Mianwali, Pakistan, focusing on three sampling sites (S1, S2, and S3) in water, sediment, and the fish species Cyprinus carpio during both dry and wet seasons. Analysis was conducted using gas chromatography with an electron capture detector. Results indicated elevated pesticide concentrations in both seasons, with levels of 0.84 and 0.62 μg/L in water, 12.47 and 9.21 μg/g/dw in sediment, and 17.33 and 12.17 μg/g/ww in fish, with higher concentrations observed during the dry season. Cypermethrin and carbofuran were the primary pesticides detected in water, while endosulfan and cypermethrin were dominant in sediment and fish tissue, often exceeding standard safety thresholds. Principal Component Analysis (PCA) and cluster analysis revealed stronger correlations between sediment and fish muscle, with varying associations among pesticides across seasons. The Hazard Index (HI) surpassed 1 in both seasons, signaling potential health risks to humans. These findings underscore the substantial risk agricultural pesticides pose to the aquatic ecosystem and food chain, highlighting the urgent need for sustainable agricultural practices and stricter regulations to minimize pesticide use and encourage eco-friendly pest management strategies.

Optimized deep learning modelling for predicting the diffusion range and state change of filling projects

Concealment of filling constructions poses significant challenges for quality assurance in filling engineering. Direct surveillance of fill dispersal currently remains infeasible, while conventional detection techniques suffer deficiencies in efficiency. This research proposes a framework integrating elastic wave monitoring and hybrid deep learning for predictive modelling of filling state transitions and diffusion range. During the sand filling of the immersed tunnel, elastic wave data is collected via elastic wave testing, and the response energy characteristic is derived through time-domain analysis. The trends in elastic wave response energy are correlated with three filling states: free diffusion, accumulation, and filled state, using Seasonal and Trend decomposition using Loess (STL) for seasonal trend analysis. Convolutional Neural Networks (CNN) and Long Short-Term Memory Networks (LSTM) are utilized to extract spatiotemporal features from the response energy trends, facilitating accurate prediction of the trends’ development and the sand filling state over time. The performances of the proposed strategy are illustrated through an application to the case study of the sand filling construction of the Chebeilu immersed tunnel. The CNN + LSTM model with the proposed strategy gave excellent results (MAE 0.0663, MSE 0.0071, RMSE 0.0845). The model can predict fill state changes and quantify diffusion radii to optimize and guide the construction process.

Forecasting of Inland Fish Production: Case of Catla (<em>Catla catla</em>) and Gift Tilapia (<em>Oreochromis niloticus</em>) Production in Udawalawa Reservoir in Sabaragamuwa Province, Sri Lanka

This study focused on assessing and forecasting Catla (Catla catla) and Tilapia (Oreochromis niloticus) production in the Udawalawe reservoir in Sri Lanka using an 11-year time series data from 2010 to 2021. The relevant secondary data were collected from the National Aquaculture Development Authority of Sri Lanka (NAQDA). Unit root tests, Dickey-Fuller tests, and Seasonal Auto Regressive Integrated Moving Average (SARIMA) models and trend analysis were applied for the data analyses. According to the results, stationarity behaviour was observed in both Catla and Tilapia production information series. Hence, SARIMA (2,2,1) (1,0,1)12 is the appropriate empirical model for forecasting Catla production. Furthermore, the maximum average landings of Catla production were observed in the March-June time period. SARIMA (1,0,1) (1,1,1)12 was indicated as the most suitable model to forecast Tilapia production in the reservoir. The maximum average of Tilapia production was also observed in the March-June period. Both the SARIMA model and trend analysis gave approximately equal production information regarding Catla and Tilapia. Thus, these findings showed that the SARIMA model has more accuracy than trend analysis when analysing the time series information for short durations. However, when the duration becomes larger, forecasting is not suitable using the SARIMA model, and trend analysis gives a clear view up to a certain level. Both Catla and Tilapia production exhibited significant changing patterns and multiple seasonalities. Thus, trend analysis gives valuable insights than SARIMA model in long term predictions in case of this study. These findings are highly important for enhancing fish production in this reservoir, as they predict future fish catches under the current reservoir conditions.

Long-Term Seasonal and Annual Rainfall Trend Analysis of Giridih District, Jharkhand, India

District Giridih is primarily rain dependent on its 95 per cent net cultivable land under summer-monsoon crops. It is classified as a mono-cropping area as it lacks irrigation facilities, essential for winter crops. In the last few years, the district experienced water stress due to the shortage of monsoon rainfall. We study long term seasonal and annual rainfall trend analyses for the Giridih district of Jharkhand in India using linear regression modelling. The last 100 years of data were taken for the study and were divided into two-time phases of 50 years each to analyse the changing pattern of rainfall trends. We found a decreasing trend in monsoon rainfall from 1 mm y-1 to 3 mm y-1 between the initial (1921-1971) and final (1972-2021) phase of the study period. Similarly, the return period of droughts (rainfall deficit years) has also reduced from 11 years to 5 years. The study has shown that the rainfall pattern has changed, with the winter months showing a decrease and the pre-monsoon months an increase in their relative contributions to the annual rainfalls. The study may help in the formulation of water resource conservation-oriented decision-making to cope-up with the rainfall uncertainties and meet the future water demand.

Pediatric respiratory pathogen dynamics in Southern Sichuan, China: a retrospective analysis of gender, age, and seasonal trends.

To address the research gap in the epidemiology of pediatric respiratory tract infections (RTIs) in Luzhou, Southern Sichuan, China, by analyzing respiratory pathogens in a large pediatric cohort from 2018 to 2021, covering the pre- and during-COVID-19 periods. This study conducted a retrospective analysis of children with RTIs in Luzhou from July 2018 to January 2021. Strict exclusion criteria were applied to ensure an accurate representation of the pediatric population. Pathogen detection included viruses, bacteria, and atypical agents. Pathogens were identified in 52.8% of 12,546 cases. Viruses accounted for 32.2% of infections, bacteria for 29.8%, and atypical agents for 29.7%, with significant findings of Staphylococcus aureus, Moraxella catarrhalis, and Mycoplasma pneumoniae. Age-related analysis indicated a higher incidence of bacterial infections in infants and viral infections in preschool-aged children, with atypical pathogens being most prevalent in 3-5-year-olds. Gender-based analysis, adjusted for age, revealed similar overall pathogen presence; however, females were more susceptible to viral infections, while males were more prone to Streptococcus pneumoniae. Notably, there was an unusual increase in pathogen cases during spring, potentially influenced by behavioral changes and public health measures related to COVID-19. Co-infections were identified as a significant risk factor for the development of pneumonia. The study provides essential insights into the epidemiology of respiratory pathogens in pediatric populations, emphasizing the need for healthcare strategies tailored to age, gender, and seasonality. The findings highlight the impact of environmental and public health factors, including COVID-19 measures, on respiratory pathogen prevalence, underscoring the importance of targeted diagnostic and treatment protocols in pediatric respiratory infections.

A Level 3 monthly gridded ice cloud dataset derived from 12 years of CALIOP measurements

Abstract. Clouds play important roles in weather, climate, and the global water cycle. The Cloud-Aerosol Lidar with Orthogonal Polarization (CALIOP) onboard the Cloud-Aerosol Lidar and Infrared Pathfinder Satellite Observation (CALIPSO) spacecraft has measured global vertical profiles of clouds and aerosols in the Earth’s atmosphere since June 2006. CALIOP provides vertically resolved information on cloud occurrence, thermodynamic phase, and properties. We describe version 1.0 of a monthly gridded ice cloud product derived from over 12 years of global, near-continuous CALIOP measurements. The primary contents are monthly vertically resolved histograms of ice cloud extinction coefficient and ice water content (IWC) retrievals. The CALIOP Level 3 Ice Cloud product is built from the CALIOP Version 4.20 Level 2 5 km Cloud Profile product that, relative to previous versions, features substantial improvements due to more accurate lidar backscatter calibration, better extinction coefficient retrievals, and a temperature-sensitive parameterization of IWC. The gridded ice cloud data are reported as histograms, which provides data users with the flexibility to compare CALIOP’s retrieved ice cloud properties with those from other instruments with different measurement sensitivities or retrieval capabilities. It is also convenient to aggregate monthly histograms for seasonal, annual, or decadal trend and climate analyses. This CALIOP gridded ice cloud product provides a unique characterization of the global and regional vertical distributions of optically thin ice clouds and deep convection cloud tops, and it should provide significant value for cloud research and model evaluation. A DOI has been issued for the product: https://doi.org/10.5067/CALIOP/CALIPSO/L3_ICE_CLOUD-STANDARD-V1-00 (Winker et al., 2018).

Secular trends and determinants of ad libitum energy intake measured in a research setting from 1999-2020.

Historically, secular and seasonal trend analyses have been examined using self-report measures of intake. Rarely are objective measures and known determinants of dietary intake used in these analyses. Our objective was to quantify the seasonal and secular differences in an objective ad libitum intake paradigm while considering the contribution of determinants, such as fat-free mass (FFM) index and spontaneous physical activity (SPA) limited to the restricted space of a whole-room calorimeter. For this study, recruitment of N = 292 healthy, diabetes free, adults occurred from 1999 to 2020. Assessment during their 10-day stay included body composition (by DXA), SPA (by an approximately 24-h stay in whole-room calorimetry), and ad libitum intake (by a vending machine for 3 days). This secondary analysis used general linear models (GLM) to investigate secular and seasonal differences while adjusting for sex, age, FFM index, FM (fat mass) index, SPA, and race/ethnicity. FFM index and SPA were positively associated with all intake measures (p < 0.05). In all adjusted seasonal models, season did not affect intake. Adjusted secular trends models (kcals/year) demonstrated a decrease in total kcals (β = -55), intake as percent weight maintaining energy needs (β = -2), protein kcals (β = -10), fat kcals (β = -27), and carbohydrates kcals (β = -22) (all p < 0.05). After further adjustment for SPA, significance remained in all intake measures (p < 0.05). Secular trends in body composition revealed no changes in weight, BMI, and percent body fat (all p > 0.20). Our results indicate that over time, ad libitum intake decreased in this controlled research setting and remained significant even after accounting for positive determinants of intake. A significant ad libitum decrease, coupled with no change in body composition, may highlight a participant bias toward calorie restriction in a controlled setting over time and deserves further investigation.

Seasonal and Time-Series Analysis of Road Traffic Accidents

Road Traffic Accidents (RTAs) contribute a significant and escalating rate in harm, hospitalization, and mortality. This study assesses the seasonal trend and time series analysis of RTAs cases brought to casualty department of Liaquat University Hospital, Hyderabad, Pakistan. Objective: To conduct seasonal and time series analysis of RTA cases at Casualty Department of Liaquat University Hospital Hyderabad. Methods: Nine hundred and sixty-six road traffic accidents-related cases were examined through prospective study at casualty department, Liaquat University Hospital, Hyderabad, Pakistan from January 2020 to December 2022. The seasonal trend was demonstrated by drawing seasonal index plot and time series plot. Results: The largest occurrences of RTA-related cases were reported in the month of December, having 103 total cases with a mean of 34.33 ± 18.92. July had the second highest frequency of RTAs-related cases, with a total of 94 cases having a mean of 31 ± 5.43 cases. The second-highest number of cases was observed in July and November with approximately equal mean number of RTAs. The time series analysis demonstrated an increasing trend of RTAs-related cases in the last three months of the year. Conclusions: The number of cases connected to RTAs increased during seasons of extreme weather, such as the summer and winter, and it also showed an increasing tendency in the last quarter of 2022.

A metabolomics analysis of interspecies and seasonal trends in ruminant milk: The molecular difference between bovine, caprine, and ovine milk

Ruminant milk composition can be affected by many factors, primarily interspecies differences, but also environmental factors (e.g., season, feeding system, and feed composition). Pasture-based feeding systems are known to be influenced by seasonal effects on grass composition. Spring pasture is rich in protein and low in fiber compared with late-season pasture, potentially inducing variability in the composition of some milk metabolites across the season. This study aimed to investigate interspecies and seasonal differences in the milk metabolome across the 3 major commercial ruminant milk species from factories in New Zealand: bovine, caprine, and ovine milk. Samples of bovine (n = 41) and caprine (n = 44) raw milk were collected monthly for a period of 9 mo (August 2016-April 2017), and ovine milk samples (n = 20) were collected for a period of 5 mo (August 2016-January 2017). Milk samples were subjected to biphasic extraction, and untargeted metabolite profiling was performed using 2 separate liquid chromatography high-resolution mass spectrometry analytical methods (polar metabolites and lipids). Major differences in the milk metabolome were observed between the 3 ruminant species, with 414 of 587 (71%) polar metabolite features and 210 of 233 (87%) lipid features being significantly different between species. Significant seasonal trends were observed in the polar metabolite fraction for bovine, caprine, and ovine milk (17, 24, and 32 metabolites, respectively), suggesting that the polar metabolite relative intensities of ovine and caprine milk were more susceptible to changes within seasons than bovine milk. We found no significant seasonal difference for the triglycerides (TG) species measured in bovine milk, whereas 3 and 52 TG species changed in caprine and ovine milk, respectively, across the seasons. In addition, 4 phosphatidylcholines and 2 phosphatidylethanolamines varied in caprine milk within the season, and 8 diglycerides varied in ovine milk. The interspecies and seasonal metabolite differences reported here provide a knowledge base of components potentially linked to milk physiochemical properties, and potential health benefits of New Zealand pasture-fed dairy ingredients.

Evaluation of 22 CMIP6 model-derived global soil moisture products of different shared socioeconomic pathways

The Sixth Phase of the Coupled Model Intercomparison Project (CMIP6) provides ample soil moisture (SM) products during 2015–2100, which are widely used in investigating future hydrological pattern. However, there remains limited understanding regarding the accuracy of multi-model CMIP6 SM future simulations following different Shared Socioeconomic Pathways (SSP). This study assesses the spatial–temporal accuracy of 22 CMIP6 model-derived SM products using 670 ground observation stations globally during 2015–2023. Each CMIP6 model encompasses SM estimations from SSP1-2.6 (The sustainable pathway), SSP2-4.5 (The medium pathway), and SSP5-8.5 (Fossil-fueled development). The latest Soil Moisture Active Passive (SMAP) and European Centre for Medium-Range Weather Forecast Reanalysis v5-Land (ERA5-Land) SM products serve as reference benchmarks. The SM among three SSPs within the same model exhibits similar performance in in situ validation, the extended triple collocation assessment, and the classical seasonal decomposition trend analysis. This similarity may suggest a subtle variation in the influence of different SSPs on SM in the early stages of the future scenario. The majority of CMIP6 model-derived SM could reasonably capture the spatial distribution pattern and temporal evolution trend of the truth value. Meanwhile, noticeable discrepancies in accuracy, particularly regarding goodness-of-fit, are evident when compared to SMAP and ERA5-Land SM products. The CMIP6 models could provide better SM estimation in temperate and tropical zones than cold zones. In terms of differences between the land cover types, CMIP6 model-derived SM products perform well over various vegetated regions except for barren, where there are sequential overestimation and relatively low fitting degree. Extensive accuracy discrepancies exist among the 22 CMIP6 model-derived SM products due to variations in model parameterization methods and atmospheric variables.

Exploring the Trend of Aerosol Optical Depth and its Implication on Urban Air Quality Using Multi-spectral Satellite Data During the Period from 2009 to 2020 over Dire Dawa, Ethiopia

This study focuses on atmospheric aerosols, especially aerosol optical depth (AOD), over Dire Dawa, Ethiopia, from 2009 to 2020. At first, a correlation between the four satellite sensors and AERONET was made for validation purposes and to determine the sensor that best represents Dire Dawa. Intercomparisons were also made among the four satellite sensors. After all statistical tests, annual, seasonal, and decadal trend analyses were made. The validation results indicated that the AOD of MODIS-terra showed the best correlation with AERONET with R2 (0.78), RMSE (0.03), and MBE of 0.02 and represented the area better than the rest. The inter-comparison of AOD retrieved from multi-spectral satellite sensors showed a positive and satisfactory correlation between MODIS-Terra and OMI. Only MODIS-Aqua showed a linearly increasing mean annual AOD with R2 = 0.43. In three seasons (summer, autumn, and spring), AOD showed linear increments over the 12 years, with R2 ranging between 0.3 and 0.5. The three seasons also had nearly identical AODs of 0.23-0.28. However, winter had the lowest value of 0.2. MODIS-terra, out of the four sensors, exhibited increasing decadal tendency over the 2009-2020 period. Monthly analysis revealed that August had the highest AOD (0.265), and January had the lowest (0.14). The value of AOD obtained from this study over Dire Dawa shows a higher value during all seasons except during winter. Thus, this study gives a glimpse into the use of multi-spectral satellite sensors to monitor air quality over a semi-arid urban region.

Seasonal Trend Analysis of Major Air Pollutant (PM2:5 and PM10) Concentration in Visakhapatnam During 2020 – 2022: A Case Study

Seasonal Trend Analysis of Major Air Pollutant (PM2:5 and PM10) Concentration in Visakhapatnam During 2020 – 2022: A Case Study

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.

Implications of rainfall variability on groundwater recharge and sustainable management in South Asian capitals: An in-depth analysis using Mann Kendall tests, continuous wavelet coherence, and innovative trend analysis

Groundwater resources, a critical component of hydrogeological systems, are under increasing pressure due to climate change and anthropogenic activities. South Asia, a region highly vulnerable to climate shocks, has seen over half of its population affected by climate-related disasters in the past two decades. These climate changes, characterized by intensifying heat waves, cyclones, droughts, and flooding, pose significant risks to groundwater resources management. This study aimed to address the recent variability, intensity, and depth of rainfall patterns in the capitals of south Asian countries on a daily, monthly, and annual basis. Mann-Kendal tests (MKT), Innovative Trend Analysis (ITA), and Continuous Wavelet Coherence were conducted to identify trend patterns. The Gumbel distribution of Intensity Depth Frequency (IDF) analysis was performed to analyze the depth and intensity of rainfall patterns for rainfall durations of 5, 10, 15, 30 min, 1–hr, 2–hr, 12–hr and 24–hr for 2, 5, 10, 25, 50, 75 and 100 years return periods. Results of ITA and MKT showed similar trend results for all cities and time scales. Findings suggest that Kotte receives the highest annual rainfall of 1745.28 mm and Kabul the lowest (380.76 mm). MKT showed both positive and negative monthly trends in most of the months, while annual rainfall exhibited positive trends in all cities with a change rate between +0.56 and + 2.34 mm/year. Seasonal trend analysis confirms positive trends for all seasons, except in Delhi, Malé, and Kathmandu in winter. IDF analysis reveals that current rainfall trends will slightly affect the depth and intensity of rainfall events for the various return periods. The study provides valuable insights into the water-energy-food nexus and contributes to the broader discourse on water policy and social aspects of groundwater in the face of climate change.

Revisiting regional and seasonal variations in decadal carbon monoxide variability: Global reversal of growth rate

Carbon monoxide (CO) is one of the important trace gases in the atmosphere capturing the evolution of chemical properties of the troposphere. Here we analyze the growth rates of CO during the period of 1991–2020 using in situ measurements from the World Meteorological Organization's (WMO) Global Atmospheric Watch (GAW) program. The analysis of trends has been done on different spatial and temporal scales. Our analysis supports the decline in the overall CO mixing ratios over the globe but inter-decadal and regional trend analysis has shown heterogeneous changes in the given period of study. On average, there has been a decrease of −16.22 ± 1.92 ppb and −4.5 ± 0.64 ppb observed at the sites in the northern hemisphere (NH) and southern hemisphere (SH), respectively. This decline occurred at rates of −0.80 ± 0.12 ppb yr−1 in the NH and − 0.12 ± 0.03 ppb yr−1 in the SH. Bifurcating the annual trends for seasonal analysis reveals the impact of emissions, chemistry and atmospheric transport on CO variation over different regional clusters of stations. Seasonal trend analysis provides further evidence regarding heterogeneous patterns in the South-East Asia region. Our study highlights a slowdown in CO decline during the 2011–2020 decade when compared to the rate of decrease observed in 2001–2010. This is inferred from the variability and much slower decline of CO emissions across different regions, contributing to a weakening in CO trends.