Mann-Kendall Test Research Articles

Hypoxia cycle in shallow lakes during winter (ice-covered to melting period): Stable and decay, hypoxia, and recovery phases

This study investigates hypoxic processes beneath lake ice and explores the interactions between water environmental factors and dissolved oxygen (DO) during winter, aiming to systematically assess the influence of winter climate changes on lake ecosystems through environmental variables (physical, chemical, and biological characteristics of water). A complete hypoxic cycle in a high-altitude shallow lake was recorded using high-frequency monitoring technology, and based on a predetermined hypoxic threshold (DO<2 mg/L), it was divided into three phases: stable and decay phase, hypoxic phase, and recovery phase. Significant trend changes of environmental variables under different levels of DO were analyzed for each phase using the Mann-Kendall non-parametric test. On the basis of evaluating the overall trend of the time series, a GAM multi-factor optimization model integrating interactive effects was constructed to explore the response mechanism of environmental variables to hypoxic processes. The optimized model showed excellent explanatory performance with R2 values of 0.833 for the stable and decay phase and 0.932 for the recovery phase, with AIC values of 1554.72 and 721.03, respectively. The trend test combined with model analysis indicates that: (1) the decay of DO under the ice is primarily affected by the increase in EC, BGA, and EC*Temp, (2) the phytoplankton biomass in shallow lakes during winter has a definite contribution to the occurrence of oxygen deficiency, and (3) the water body experiences rapid reoxygenation after the ice layer breaks, but the DO level is restricted by the increase in temperature. This study highlights the ecosystem characteristics of shallow ice-covered lakes in cold arid regions, advancing our understanding of the response relationship between hypoxic aquatic environments under ice and environmental factors.

Assessing rural revitalization potential through rural transformation degree and sustainability: A quantitative study of 460 case villages in Lingbao, China

Extensive international experience shows that only villages with sustainable transformation achieve rural revitalization, while those with intense but unsustainable or stagnant transformation struggle to revitalize. However, this cognition lacks robust quantitative substantiation. Therefore, we established a rural revitalization potential assessment framework based on the rural transformation degree and sustainability, employing the Penalty for a Bottleneck (PFB) method and Entropy Function to measure the rural transformation degree and sustainability of 460 villages in Lingbao City, Henan Province, China, from 2000 to 2021, thus evaluating rural revitalization potential. The Mann-Kendall (MK) test was conducted to detect rural transformation thresholds, further quantifying the spatial relationship between rural transformation degree and sustainability through bivariate spatial autocorrelation analysis (BSA). Our findings resonate with the central place theory that villages near those at advanced stages of development struggle to transform sustainably. Additionally, with the optimization of rural population density, and land use structure, these villages demonstrated a discernible upward trajectory in transformation sustainability. Villages with the highest rural revitalization potential were predominantly distributed in the northern of the Yellow River terrace, while those in the southwestern mountainous areas lacked revitalization potential due to low transformation degree. Generally, villages at higher altitudes and steeper slopes exhibited lower rural transformation degree, sustainability, and revitalization potential. With 30 km as the critical point, rural revitalization potential decreased initially and then increased with distance from the county government seat. This study provides essential references for the rational allocation of rural transformation and revitalization resources and for optimizing rural development planning.

Caffeine intake from foods and beverages and trends among Chinese children and adolescents: 2004–2018

BackgroundThe popularity of caffeinated foods and beverages poses risks of high caffeine exposure among Chinese children and adolescents. Nevertheless, there is a lack of national assessments on their caffeine consumption. ObjectiveTo estimate daily caffeine intake and analyze time trends among Chinese children and adolescents. MethodsThe study subjects were participants of the China Health and Nutrition Survey in 2004, 2006, 2009 and 2011, and the National Food and Beverage Consumption Survey in 2014 and 2018. Caffeine content was determined using chromatographic instrument. The Monte Carlo simulation was utilized to estimate daily caffeine intake and the Kruskal-Wallis test was used to analyze differences between population characteristics. To examine yearly trends in caffeine intake from 2004 to 2018, the partial Mann-Kendall test was applied. ResultsThe median daily caffeine intake of Chinese children and adolescents was 0.17 (95%CI: 0.15–0.20) mg/kg BW/day. Main contributors were tea (55.52%), sodas (19.52%) and tea beverages (10.37%). Approximately 4.68% of individuals consumed caffeine exceeding 2.5 mg/kg BW/day. Higher caffeine intake was observed in adolescents aged 12–17 years, males, and consumers residing in northeastern China. While no significant overall yearly trends in caffeine intake were detected from 2004 to 2018, there was an increase in intake driven by beverage consumption between 2006 and 2014. ConclusionThis study provided a national assessment of caffeine consumption among Chinese children and adolescents. Caffeinated beverages like tea, soda, and tea beverages emerged as major contributors to caffeine intake. These findings could contribute to the regulation of caffeine consumption and the promotion of healthy habits among children and adolescents.

B-352 Effects of COVID-19 on Breast Cancer Positivity: A Retrospective and Predictive Study

Abstract Background The COVID-19 pandemic has had a significant impact on the healthcare system, resulting in overall reduction in access to healthcare services, including the suspension of screening programs and postponement of surgeries and medical treatments. The aim of this study was to evaluate the effects of the pandemic on breast cancer positivity from cases received at a laboratory in São Paulo, Brazil. Methods A retrospective analysis of a database from a pathology laboratory in São Paulo, Brazil, was performed during the 2017-2023 period and classified as following: pre-pandemic (January 2017-February 2020), pandemic (March 2020 - April-2022), and post-pandemic (May 2022-December 2023). Cases were classified as negative or positive for malignancies. Positive anatomopathological examinations (breast biopsies and surgical specimens) considered primary, metastatic mesenchymal or endothelial malignancies. The Mann-Kendall test was used to evaluate the trend, and the PACF (Partial Autocorrelation Function) correlogram was used to analyze historical memory for definition of the autoregressive model. The augmented Dickey-Fuller hypothesis test was applied to analyze whether the series is stationary or not, and the first differences test was applied. The ARIMA model was used for the estimates (1,1,1). Statistics were performed using the gretl software and pymannkendall library in the Python language. Results A total of 74,973 samples were evaluated during the 2017-2023 period. In the three-year pre-pandemic period, 32,644 tests were recorded, with an average of 29.34% positive cases. From the analysis of the pre-pandemic time series, there was a positive trend towards an increase in cases (p &amp;lt; 0.0001, z=6.1360, Tau=0.6955). In a non-pandemic scenario, from March 2020 to February 2021, the number of new tests expected would be 11,535; however, in the pandemic period, only 8,904 were observed, a 22.81% reduction on expected cases. According to the historical average positivity rate in the pre-pandemic period, the expected and observed total were, respectively, 3,384 and 2,566 positive cases for malignancy. Conclusions This study reveals a significant decrease of approximately 22.81% in new breast anatomopathological diagnoses during the COVID-19 pandemic in a laboratory in São Paulo, Brazil. For comparison purposes, we identified a 24.17% of missed opportunities on diagnoses of new malignancy-positive cases, compared to the pre-pandemic period. These findings highlight the need for strategies to mitigate the negative effects of the pandemic on the early detection and treatment of breast cancer.

B-215 Evaluating RT-qPCR for Dengue Virus Detection: Outcomes of a Brazilian External Quality Assessment Program

Abstract Background Dengue virus (DENV), responsible for a prevalent arthropod-borne viral illness, poses a substantial public health challenge worldwide. Clinically, DENV's manifestation often resembles that of other arboviruses, resulting in frequent healthcare visits and complicating the early diagnosis, which is critical for patient management. While virus isolation is a viable method, RT-qPCR assays yield significantly quicker results. Rapid immunochromatographic tests are available; yet, RT-qPCR not only matches their sensitivity but also provides distinct advantages in clinical laboratory settings. These include higher throughput and integrated, traceable quality control within each routine, offering less subjective and more dependable results. Maintaining high diagnostic standards for DENV is imperative. External quality assessment programs (EQAP) play a key role in enhancing laboratory diagnostic capabilities by monitoring and assessing diagnostic methods. This study evaluates the diagnostic efficacy of RT-qPCR for DENV using data from an EQAP conducted by a Brazilian provider, accredited by ABNT NBR ISO/IEC 17043:2011. Methods The quality control samples comprised lyophilized suspensions containing viable DENV particles, grown on vero cells (BCRJ 0245/ATCC CCL-81) under BSL-3 conditions. From February 2017 to November 2022, twenty-four EQAP survey rounds were carried out, each encompassing three samples, culminating in 1,153 data points. The evaluation metrics included participant numbers, adequacy percentage, sensitivity, specificity, and the incidence of false positives and negatives. The Mann-Kendall test assessed trends in these metrics over the survey rounds. The RT-qPCR kits employed by participants were classified either as in-vitro diagnostic (IVD) or as laboratory-developed tests (LDT). Kits that demonstrated high performance in terms of adequacy, sensitivity, and specificity were identified and listed. Results The number of laboratories participating has increased. Starting with four in 2017, the program expanded to 28 by 2022. Laboratory accuracy was high during the evaluation period, with a median of 99% (97.8% in 2017 and 99.2% in 2022). The Mann-Kendall test indicated a significant trend of increased accuracy (p&amp;lt;0.001). Specificity remained stable at 98.8%, while sensitivity improved, with a median of 99.4%—rising from 93.3% in 2017 to 100% in 2022 (p&amp;lt;0.001). Only three false negatives were reported over three separate rounds, and six false positives were reported across four rounds. Both LDT and IVD RT-qPCR kits demonstrated high accuracy (99.4% and 98.4%, respectively). Biomanguinhos ZDC, Viasure ZDC, and ZDC Multiplex kits all showed 100% adequacy, sensitivity, and specificity. Conclusions This study highlights the increasing adoption of RT-qPCR for DENV detection, as evidenced by the growing number of laboratories in the EQAP program and the consistently high adequacy rates from 2017 to 2022. Nonetheless, the number of participating laboratories remains relatively small for a vast tropical country like Brazil. The high sensitivity and specificity, along with the infrequency of false results, underscore its effectiveness. The superior performance of certain kits suggests possible advancements in DENV diagnosis, contributing to global public health initiatives against this arboviral disease.

B-212 Laboratory Performance in Molecular Detection of Mycobacterium tuberculosis Through Analysis of External Quality Assessment Program Results: WHO-Recommended Tests Outperform Others

Abstract Background In 2022, tuberculosis caused 11,200 deaths in Brazil and 1,300,000 globally. With 87,344 new cases, it remains a leading infectious cause of death, second only to COVID-19. Prompt detection is critical, particularly with the growing resistance to rifampicin and isoniazid, highlighting the need for molecular testing. External Quality Assessment Programs (EQAP) are essential to ensure the accuracy, reliability, and harmonization of laboratory results amid the complexities of M. tuberculosis infection. Here, we evaluate the overall performance of participating laboratories (labs) in three EQAP related to tuberculosis: M. tuberculosis molecular detection (MTB), Rifampicin resistance detection (RIF), and Isoniazid resistance detection (INH). All are organized by a Brazilian provider accredited according to the ABNT NBR ISO/IEC 17043:2011 standard. Methods The EQAP samples were suspensions containing human cells, with or without the addition of cultured, inactivated, and lyophilized M. tuberculosis. Labs received two samples quarterly for testing accuracy, sensitivity, and specificity. We computed these metrics along with participant and round numbers. The Mann-Kendall test assessed accuracy trends over time. Furthermore, kit type accuracies for in-vitro diagnostic (IVD) vs. laboratory-developed tests (LDT), World Health Organization recommended (WHO-recommended) vs. other kits (non-WHO), and methods (qPCR vs. PCR) were compared using chi-square tests and odds-ratio calculations. Results In the MTB EQAP (2018-2022), 63 labs participated over 20 rounds, with 22 (range: 6-48) labs per round on median, achieving 96.6% accuracy (853/883), 97.5% sensitivity (556/570), and 94.9% specificity (297/313). The Mann-Kendall test found no significant accuracy trends over time (τ = -0.91, p = 0.36). IVDs outperformed LDTs with 97.8% (751/768) vs. 87.9% (102/116) accuracy (OR=6.0, 95%CI 3.42-20.9, p&amp;lt;0.0001). WHO-recommended kits were more accurate than non-WHO kits, 98.2% (674/686) vs. 90.4% (179/198) (OR=5.96, 95%CI 1.05-12.7, p&amp;lt;0.0001). qPCR exceeded the accuracy of PCR, 97.2% (833/857) versus 74% (20/27) (OR=12.1, 95%CI 4.8-32.9, p&amp;lt;0.0001). For RIF EQAP (2020-2022), 49 laboratories in 9 rounds, with 25 (range: 18-42) laboratories per round on median, showed 97.4% accuracy (297/305), 97.2% sensitivity (176/181), and 97.6% specificity (121/124). In the INH EQAP (2021-2022), 7 laboratories over 7 rounds, with 4 (range: 2-5) laboratories per round on median, reached 95.2% accuracy (20/21), 100% sensitivity (4/4), and 94.1% specificity (16/17). Differences between types of kits and methods for RIF and INH, as well as trends over time, were not significant or could not be calculated due to the limited number of samples. Conclusions The MTB EQAP underscored the high participation and suitability of labs, along with excellent diagnostic performance; IVD kits were 6 times more likely to yield adequate results than LDTs. WHO-recommended kits were 5.9 times more likely to produce adequate results than non-WHO kits. qPCR was 12.6 times more likely to achieve adequate results than PCR. Despite fewer participants in RIF and INH, their performance rates were high; more data are needed for conclusive results on kit and method types. This study affirms the quality of molecular diagnostics for tuberculosis in Brazil, informs on kit and method selection, and underscores the critical role of EQAPs in combating a significant global health issue.

District wise spatiotemporal analysis of precipitation trend during 1900-2022 in Bihar state, India

Precipitation over a region plays vital role in determining availability of water resource whereas study of spatiotemporal distribution of rainfall helps in managing precipitation associated risks like drought, flood etc. The present study is carried out in the state of Bihar, India using District-wise rainfall data of Bihar for period 1900–2022. Major findings of study indicate that CV and SD of monsoon season and annual rainfall are in a moderate range except for districts Begusarai and Jahanabad; however, CVs for pre-monsoon, winter, and post-monsoon seasons were high for all the districts. Innovative trend analysis of the rainfall indicates either no trend or a negative trend except in few districts, and in recent years the negative trend is observed in almost all the districts. To understand homogeneity in rainfall, the precipitation concentration index and its trend analysis performed through Mann-Kendall test. PCI analysis indicates irregularly distributed annual and uniformly distributed monsoon rainfall. High inhomogeneity in annual rainfall is due to the post-monsoon PCI contribution. PCI trend was found positive in the southern, extreme northern, and central parts of Bihar, whereas extreme west districts of Bihar like Aurangabad, Bhojpur, Buxar, Rohtas, and extreme eastern districts like Purnia, Katihar, Araria, and Supaul had negative trend.

Spatio-temporal tendencies of urban land surface temperature on the Andean piedmont under climate change: A case study of Metropolitan Lima, Peru (1986–2024)

The increase in land surface temperature (LST) in megacities has contributed to global warming due to poor urban planning and high population concentration. This study analyzes spatio-temporal trend patterns of LST in the city of Metropolitan Lima, Peru, during the summers of 1986–2024. The Mann-Kendall tests and the Theil-Sen Slope method were used to analyze the spatiotemporal trends of LST, relating R2 and the Mann-Kendall p-value of the annual average LST in each district. A hierarchical cluster analysis was performed to group districts according to their average yearly LST. Urban heat islands were identified based on basin configuration and distance to the sea at 1 km intervals. The results reveal a significant increase in LST (p<0.001) related to El Niño-Southern Oscillation phenomena, in addition to urban growth and land cover change. The significant positive trend of LST showed a heterogeneous distribution in all districts. The districts were grouped into three clusters with statistically significant differences in LST (p<0.001), spatially configured radially from the sea to the foot of the Andes, up to 1179 m a.s.l. Urban heat islands did not correlate with significant positive trends in basins. Still, they showed a considerable increase in LST in areas of high economic activity, including dense commercial, industrial, and residential areas. This information is crucial to managing climate change adaptation and mitigation measures and contributing to sustainable urban planning focused on the population’s well-being.

A multi-docking strategy for robotic LAR and deep pelvic surgery with the Hugo RAS system: experience from a tertiary referral center.

In June 2023, our institution adopted the Medtronic Hugo RAS system for colorectal procedures. This system's independent robotic arms enable personalized docking configurations. This study presents our refined multi-docking strategy for robotic low anterior resection (LAR) and deep pelvic procedures, designed to maximize the Hugo RAS system's potential in rectal surgery, and evaluates the associated learning curve. This retrospective analysis included 31 robotic LAR procedures performed with the Hugo RAS system using our novel multi-docking strategy. Docking times were the primary outcome. The Mann-Kendall test, Spearman's correlation, and cumulative sum (CUSUM) analysis were used to assess the learning curve and efficiency gains associated with the strategy. Docking times showed a significant negative trend (p < 0.01), indicating improved efficiency with experience. CUSUM analysis confirmed a distinct learning curve, with proficiency achieved around the 15th procedure. The median docking time was 6min, comparable to other robotic platforms after proficiency. This study demonstrates the feasibility and effectiveness of a multi-docking strategy in robotic LAR using the Hugo RAS system. Our personalized approach, capitalizing on the system's unique features, resulted in efficient docking times and streamlined surgical workflow. This approach may be particularly beneficial for surgeons transitioning from laparoscopic to robotic surgery, facilitating a smoother adoption of the new technology. Further research is needed to validate the generalizability of these findings across different surgical settings and experience levels.

Investigating spatial-temporal trend of snow cover over the three provinces of Northeast China based on a cloud-free MODIS snow cover product

Snow serves as a pivotal water resource in the three provinces of Northeast China (TPNC), the investigation of spatial and temporal distribution changes of snow cover is essential for the region’s efficient water resource management. The Moderate Resolution Spectroradiometer (MODIS) snow cover product has proven effective in providing detailed spatial–temporal distribution and identification of snow cover. However, its accuracy and reliability are significantly hampered by cloud obscuration. To address this issue, we have developed a novel four-step cloud removal approach, which includes Terra and Aqua combination, 5-day temporal combination, snowline method (IMS) as well as 8-pixel and 24-pixel adjacent pixel method. Results indicate that the proposed approach successfully eliminates all cloud cover, yielding a highly accurate cloud-free snow cover product with validation accuracies of 92% and 97% when using in situ snow depth measurements and cloud masking method, respectively. We investigated the spatial and temporal variations of snow cover for the TPNC during 2003–2018 with snow coverage and snow cover days (SCD) and analyzed the correlation between snow cover and climate factors. The results showed that the annual basin-averaged snow coverage increased at a rate of 1.27% yr−1 before 2009 and decreased at a rate of 1.80% yr−1 after 2009. The Mann-Kendall tests indicated that about 72% of the total area showed an increasing trend of annual SCD during 2003–2018. Conversely, 94% of the total area exhibited a decreasing trend of annual SCD for the period of 2009–2018 due to the increasing trend of temperature and decreasing trend of precipitation. Our approach shows a promising application of generating accurate cloud-free products and its potential to examine spatial–temporal variations of snow cover under climate change for other snow-covered regions globally.

Spatiotemporal variation and GeoDetector analysis of NDVI at the northern foothills of the Yinshan Mountains in Inner Mongolia over the past 40 years

The study of spatiotemporal variation and driving forces of the normalized difference vegetation index (NDVI) is conducive to regional ecosystem protection and natural resource management. Based on the 1982–2022 GIMMS NDVI data and 26 influencing variables, by using the Theil-Sen median slope analysis, Mann-Kendall (M-K) test method and GeoDetector model, we analyzed the spatial and temporal characteristics of vegetation cover and the driving factors of its spatial differentiation in the northern foothills of the Yinshan Mountains in Inner Mongolia. The NDVI showed a significantly increasing trend during 1982–2022, with a growth rate of 0.0091 per decade. It is further predicted that future change in NDVI will continue the 1982–2022 trend, and sustainable improvement will dominate in the future; however, 17.69% of vegetation will degrade, that is, NDVI will degrade instead of improvement.. The spatial distribution of the NDVI in the northern foothills of the study area was generally characterized by high in the east and low in the west. Annual precipitation (Pre), evapotranspiration (Evp), relative humidity (Rhu) and sunshine hours (Ssd) had > 70% explanatory power (73.5, 79.9, 79.0, and 74.9%, respectively). The explanatory power of edaphic factors was > 30%, whereas anthropogenic and topographic factors had little influence on the spatial variation of NDVI, with an explanatory power of < 30%. Thus, climatic factors were the dominant factors influencing the spatial variability of NDVI in the study area. The results of the interaction detector analysis showed nonlinear strengthening for any two factors, and the interaction between Rhu and barometric pressure had the highest explanatory power. There were optimal ranges or characteristics of each factor that promoted vegetation growth. This study investigated the differences in the explanatory power of different factors on the NDVI and the optimal range of individual factors to promote vegetation growth, which can provide a basis for the development of vegetation resource management programs.

Fentanyl, Heroin, Methamphetamine, and Cocaine Analyte Concentrations in Urine Drug Testing Specimens

The US is experiencing a protracted drug overdose crisis primarily associated with exposure to illicitly manufactured fentanyl (IMF), methamphetamine, and cocaine. Overdose risk and treatment responses may be directly affected by absolute drug exposure concentrations and drug use prevalence. To quantify changes in absolute drug exposure concentrations from 2013 to 2023. This cross-sectional study analyzed urine drug testing (UDT) results from urine specimens collected between January 1, 2013, and August 22, 2023, in 49 states and the District of Columbia. Urine specimens were obtained from patients aged 18 years or older who presented to substance use disorder treatment clinics. The UDT was ordered by clinicians based on medical necessity. Urine specimens were analyzed for the following drugs or metabolites (analytes tested in parentheses): fentanyl (fentanyl), heroin (6-monoacetylmorphine), cocaine (benzoylecgonine), and methamphetamine (methamphetamine) using liquid chromatography with tandem mass spectrometry. Relative concentrations of fentanyl, heroin, cocaine, and methamphetamine. Creatinine-normalized drug concentration values were log-transformed prior to visualization and statistical analyses. The Mann-Kendall trend test was performed to examine trends over time. To estimate the geospatial and temporal patterns of drug concentration, a second series of models (1 for each drug) with an interaction effect for clinic location and collection year were fit. A total of 921 931 unique UDT samples were collected from patients (549 042 males [59.6%]; median [IQR] age, 34 [27-44] years). The adjusted fentanyl concentration in urine specimens was 38.23 (95% CI, 35.93-40.67) ng/mg creatinine in 2023 and 4.61 (95% CI, 3.59-5.91) ng/mg creatinine in 2013. The adjusted methamphetamine concentration was 3461.59 (95% CI, 3271.88-3662.30) ng/mg creatinine in 2023 and 665.27 (95% CI, 608.51-727.32) ng/mg creatinine in 2013. The adjusted cocaine concentration was 1122.23 (95% CI, 1032.41-1219.87) ng/mg creatinine in 2023 and 559.71 (95% CI, 524.69-597.06) ng/mg creatinine in 2013. The adjusted heroin concentration was 58.36 (95% CI, 48.26-70.58) ng/mg creatinine in 2023 and 146.59 (95% CI, 136.06-157.92) ng/mg creatinine in 2013. Drug concentrations varied across US Census divisions. This cross-sectional study found that absolute concentrations of fentanyl, methamphetamine, and cocaine in urine specimens increased from 2013 to 2023, with a decrease in heroin concentration during that period. The findings suggest that exposure to these substances, as well as the illicit drug supply, has fundamentally changed in many parts of the US, highlighting the need to reinforce surveillance initiatives and accelerate efforts to treat individuals with IMF and/or stimulant exposure.

Assessment of rainfall trend over Periyar Vaigai Command area of Tamil Nadu

Rainfall trend analysis is critical for studying the effects of climate change. The most crucial aspect of understanding climate change at the basin level is, how it will affect water usage, planning, and development. The present study has been attempted to analyze the trend of annual and seasonal rainfall data over Periyar Vaigai Command area from 1982 – 2021 (40 years) employing different trend analysis methods i.e., Mann-Kendall test for trend significance, Sequential Mann-Kendall test for assessment of start and end of trend, Sen’s slope test for trend magnitude, Innovative Trend Analysis for combination of trends and linear regression analysis for trend detection. Statistically positive trend was detected in summer season using all methods whereas, annual and SWM rainfall showed a statistically significant decreasing trend. Winter and NEM rainfall resulted with no significant trend. The results from this study helps to adopt various water management practices and useful for policy makers to prepare appropriate mitigation strategies.

Water Budget Input Linked to Atmospheric Rivers in British Columbia's Nechako River Basin

ABSTRACTThis study explores the contribution of atmospheric rivers (ARs) to the water budget input of the Nechako River Basin (NRB) in British Columbia (BC), western Canada. The study quantifies the fraction of precipitation, rainfall, snowfall, and snow water equivalent (SWE) associated with ARs at multiple scales and tests for trends using the Mann–Kendall (MK) test. AR‐related totals for 1950–2021 were created by linking AR events to water budget input variables of the ERA5‐Land reanalysis product on a daily scale. Associations with different phases of the El Niño‐Southern Oscillation (ENSO) climate pattern and AR‐related contributions to the NRB are also investigated. Results indicate an increasing fractional contribution of rain in ARs landfalling in the NRB in the last two decades (2000–2019). Moreover, 21% of the total annual precipitation in the NRB is associated with ARs, with decreasing contributions from west to east. October has higher AR‐related total precipitation than other months, while March, May and June are the least affected. ARs contribute disproportionately more to mid‐ and high‐intensity daily precipitation totals, and provide up to 45% and 24% of the seasonal rainfall and snowfall, respectively. AR‐related SWE is relatively higher in autumn due to the increased frequency and intensity of ARs, resulting in a greater fractional contribution of ARs to the snowpack compared to winter. ARs influence snowpack accumulation during fall (18%) and winter (13%) but also increase the risk of natural hazards. The MK test for AR‐related water budget variables on the annual scale identified no significant trends. However, AR‐related snowfall shows decreasing trends in the NRB, more specifically in the Upper Nechako, Lower Nechako and Stellako sub‐basins during the summer. Over the study period, ARs consistently contribute up to one‐fifth of the annual input to the NRB's water budget. This study provides the first quantitative assessment and trend analyses of AR contributions to the water budget input of a reservoir‐regulated watershed in north‐central BC, yielding valuable information for hydropower production, ecological flows, irrigation, domestic and industrial water use.

Assessing Precipitation Impact on Vegetation Using Satellite Data and Remote Sensing Techniques: A Case Study of Yakawalang-Afghanistan

Water is essential for all living creatures, including humans. Precipitation directly impacts plant growth; adequate water supply promotes sufficient growth, while drought conditions lead to inadequate growth and plant desiccation. This research investigates the effects of precipitation on vegetation in the Yakawalang District. The study utilizes NDVI vegetation data from the MODIS Terra satellite and precipitation data from the Global Precipitation Measurement (GPM) system. This study employed the non-parametric Mann-Kendall test to identify changes in the obtained data. The research covers the period from 2010 to 2020. Our findings indicate an increasing trend in precipitation and vegetation in the Yakawalang District during the study period. Interestingly, 2010 recorded the lowest precipitation (277 mm) but the highest vegetation index (0.100). Conversely, 2015 saw high precipitation (510 mm) but the lowest vegetation index (0.079). Further analysis of temperature data for 2015 suggests that low temperatures may have inhibited vegetation growth despite increased precipitation. This research concludes that precipitation alone does not guarantee increased vegetation. Other factors, such as appropriate temperature, are crucial in vegetation growth. These findings underscore the complex interplay of environmental factors in plant development and highlight the need for comprehensive approaches to understanding and managing vegetation dynamics.

Trend detection in the temperature of Quebec (Canada) rivers

ABSTRACT Trend detection in environmental data subjected to natural fluctuations can prove difficult. With river temperature, the difficulty also lies in the typically limited lengths of the time series. In this work, three different river temperature data sources (continuous and spot instream measurements, and satellite thermal images) were combined to build 30- to 44-year-long time series at 116 locations, distributed throughout most of the Quebec Province territory. Mann-Kendall tests and a non-linear model were used to detect and quantify trends. Uncertainty and biases due to the different data sources and uneven samplings were evaluated and included in the assessment of the trends’ quantification and reliability. Overall, most trends detected in the temperature of Quebec rivers are positive, with higher warming occurring in summer. Annual maxima are found to have increased by 0.023°C/year (median value) between 1979 and 2022, and this maximum appears to occur 2.6 days (median value) later.

Temporal dynamics of leaf area index and land surface temperature correlation using Sentinel-2 and Landsat OLI data

BackgroundUnderstanding the complex relationship between vegetation dynamics and land surface temperature (LST) is crucial for comprehending ecosystem functioning, climate change impacts, and sustainable land management. Hence, this study conducts a temporal analysis of leaf area index (LAI) and LST data derived from Sentinel-2 and Landsat Operational Land Imagery (OLI) in the Mille River Basin, a tropical region in Ethiopia. LAI data were generated using Sentinel-2 imagery processed with the Sentinel Application Platform (SNAP) toolbox, an open-access earth observation analysis tool, while Landsat OLI collection 2 level 2 data were utilized for precise LST retrieval. The Mann–Kendall test was used to detect trends in the time series data.ResultsThe trends in the mean LAI were statistically significant at P values of 0.05 and 0.10 for the annual and seasonal trends, respectively. The mean LST trends were insignificant throughout the study period except for the summer season, for which the P value was 0.07. The correlation between the LAI and LST was weak (R2 = 0.36) during the crop-growing seasons (summer and spring) but moderate in winter (R2 = 0.46) and autumn (R2 = 0.41).ConclusionThe findings of this research clarify the complex relationships between variations in surface temperature and vegetation growth patterns, providing insight into the environmental mechanisms driving the dynamics of localized ecosystems. The study underscores the implications of these findings for informed decision-making in sustainable land management, biodiversity conservation, and climate change mitigation strategies.

Influence of precipitation and temperature on burned areas in the agricultural and cattle ranching frontier of the Brazilian Amazon.

Fire occurrence, intensity, and spread are highly influenced by climatic variables. This study investigates the correlation between burned area, precipitation, and temperature in Rondônia, an agricultural frontier in the southwestern Brazilian Legal Amazon, from 2001 to 2022. The analysis utilized climatological data from the Climate Hazards Group InfraRed Precipitation with Station (CHIRPS) and MODIS product MOD11A1.061 for temperature, along with MODIS product MCD64A1 for burned area. The study was conducted on a monthly scale, employing the cross-correlation function to determine the lagged effects of temperature and precipitation on burned areas. Trend analysis was performed using the Mann-Kendall test, with the magnitude of trends estimated by Sen's Slope. Results indicated a significant negative correlation between burned areas and precipitation, with a 2-month lag and an R2 of - 0.51. In contrast, temperature exhibited a significant positive correlation with burned areas, showing a 1-month lag and an R2 of 0.55. Trend analysis revealed a decrease in precipitation by - 0.0542mm.month-1, temperature increased by 0.006°C.month-1, while burned areas decreased by - 111.13 km2.month-1. These findings underscore the intricate relationship between climate variables and fire occurrences, highlighting the urgent need for policies addressing climate change and environmental degradation in the Amazon.

Enhancing sustainable development through Spatiotemporal analysis of Ramsar wetland sites in South Asia

The ecological significance of wetlands makes it imperative to study changes in their inundation extent and propose necessary conservation measures. Monitoring wetland dynamics and implementing strategies to protect these essential ecosystems is crucial for maintaining the balance of natural systems. This study used pre-processed Landsat imagery (1991–2020) to generate yearly composites and produce inundation maps based on an automated Short-Wave Infrared thresholding technique within the Google Earth Engine platform. The analysis was executed on individual wetlands to describe their typical condition owing to regional climatic and geographical circumstances. The Mann-Kendall test was used to understand the trends in the change of inundation extent. The thresholding method achieved an overall accuracy of 89.0 %, with average dry and wet Producer's accuracies of 90.6 % and 86.6 %, respectively. The accuracy was higher for open water lakes compared to wetlands with complex vegetation dynamics. The trend analysis revealed that 46 sites follow an increasing trend, while the remaining 43 sites were found to be decreasing. Among these 43, 12 sites were found to be significantly decreasing, with the Upper Ganga River showing a maximum decrease of about 59 % in the inundation extent. Factors such as elevation, precipitation, temperature, and climate type were found to influence the trends in wetland inundation. Wetlands at high altitudes (>4000 m) and those receiving less than 500 mm of annual precipitation were more likely to exhibit decreasing trends. Coastal wetlands showed varying trends, with five increasing and three significantly increasing. The findings of this study provide valuable insights into the relationship between sustainable development and wetland conservation, supporting the Ramsar Convention's goals and the UN's Sustainable Development Goals. The individualized analysis of Ramsar sites enables the development of localized management strategies, climate change adaptation, and informed policy-making, ultimately contributing to the sustainable use of these critical ecosystems in South Asia.

Assessing the Response of the Net Primary Productivity to Snow Phenology Changes in the Tibetan Plateau: Trends and Environmental Drivers

Understanding the relationship between climate, snow cover, and vegetation Net Primary Productivity (NPP) in the Tibetan Plateau (TP) is crucial. However, the role of snow cover in influencing the NPP remains unclear. This study investigates the connection between the NPP and snow phenology (SP) across the TP from 2011 to 2020. Interannual trends were assessed using the Theil–Sen non-parametric regression approach combined with the Mann–Kendall test. Additionally, the pathways through which snow cover affects the NPP, considering various environmental factors, were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). Approximately 10.72% of the TP showed a significant decrease in the NPP, accompanied by advancing trends in the Snow Onset Date (SOD) and Snow End Date (SED), as well as a gradual decrease in the Snow Cover Duration (SCD). The PLS-SEM results reveal that precipitation and soil temperature significantly influenced the NPP, with total effects of 0.309 and 0.206 in the SCD structural equation. Temperature had a relatively strong indirect effect on the NPP through its influence on the SOD and SCD, contributing 16% and 10% to the total effect, respectively. Neglecting the mediating effect of SP underestimates the environmental impact on the NPP. This study highlights how environmental factors influence the NPP through snow cover changes as the biomass increases, thereby enhancing our understanding of SP’s impact on the TP.