Trend Analysis Of Time Series Research Articles

Irregularity and time series trend analysis of rainfall in Johor, Malaysia

Due to its effect on weather and its propensity to cause catastrophic incidents, climate change has garnered considerable global attention. Depending on the area, the effects of climate change may vary. Rainfall is among the most significant meteorological factors associated with climate change. In Malaysia, changes in rainfall distribution pattern have led to many floods and droughts events which lead to La Nina and El Nino where Johor is one of the states in southern part that usually affected. Thus, rainfall trend analysis is important to identify changes in rainfall pattern as it gives an initial overview for future analysis. This research aims to evaluate historical rainfall data of Johor between 1991 and 2020. Normality and homogeneity tests were used to ensure the quality of data followed by Mann-Kendall and Sen's slope analysis to determine rainfall trend as the rainfall data is not normally distributed (p > 0.05). Standardized precipitation anomaly, coefficient of variation, precipitation concentration index and rainfall anomaly index were used to identify rainfall variability and intensity while standard precipitation index was used to evaluate drought severity. The lowest annual rainfall recorded was 1725.07 mm in 2016 and the highest was 2993.19 mm in 2007. Annual rainfall and seasonal rainfall showed a declining trend although it is not statistically significant (p > 0.05). Results reveal that Johor experienced extreme wet and dry years, leading to drought and flood incidents. Major floods arose in 2006, 2007, 2008, 2010 and 2011 while driest years occurred in 1997, 1998 and 2016 which led to El Nino phenomenon. March and April were identified as the driest months among all. Thus, the findings from this study would assist researchers and decision-makers in the development of applicable adaptation and mitigation strategies to reduce climate change impact. It is recommended that more data analysis from more stations should be done in the future research study to obtain a clearer view and more comprehensive results.

Variability and time series trend analysis of streamflow in response to climatic and anthropogenic factors over Narmada River basin, India

Variability and time series trend analysis of streamflow in response to climatic and anthropogenic factors over Narmada River basin, India

Spatiotemporal distribution of carbon monoxide dynamics and probabilistic health risk assessment: A comprehensive sensitivity analysis across India's state capitals

Carbon monoxide (CO) is a harmful trace gas produced from the incomplete combustion of fossil fuels. Carbon monoxide (CO) poses a substantial threat to human health. The objectives of this study were to investigate carbon monoxide (CO) annual and seasonal variations with meteorological factors, study the marginal effects of weather factors on CO, analyze the impact of independent variables (meteorological factors) importance on CO distribution, time series trend analysis (Mann-Kendall and Sen's slope, additive decomposition model, and linear trend model), study the spatial distribution of CO in the capital cities and non-carcinogenic health risk in India from 2019 to 2022. The study showed that CO concentrations varied, notably across different years (from 2019 to 2022), months, and seasons. The maximum annual CO (0.91 ± 0.17 mg/m3) concentration was recorded in 2019, while the lowest was (0.67 ± 0.12 mg/m3) in 2022. Seasonally, the winter season had the highest load of CO (ranging from 0.17 to 2.17 mg/m3) in the ambient atmosphere compared to the monsoon (0.08 to 1.38 mg/m3) and summer (0.10 to 1.50 mg/m3) seasons. The analysis of the importance of the independent variables (meteorological factors) on CO indicated that temperature had the most substantial impact on CO variation, followed by wind direction, relative humidity, and wind speed. The trend analysis using different models showed a declining trend in CO concentrations over the study period. The non-carcinogenic health risk HQ values were less than 1, suggesting no notable risk. The Monte Carlo simulation revealed that the 5th and 95th percentile range of HQ risk was 0.02 to 0.07 and the sensitivity analysis of non-carcinogenic risk at the state capital level was conducted, and revealed that Delhi was at a higher risk than Chandigarh, Patna, Gandhinagar, Bengaluru, Mumbai, and others, respectively. The results of this study on CO emissions will be beneficial in creating focused plans and policies to reduce CO pollution in seasonal influences and its effects on ambient air quality and public health in the capital cities of Indian states.

Trend analysis of MODIS NDVI time series and its relationship to temperature and precipitation in Northeastern of Iran.

Vegetation plays a crucial role in providing organic matter and regulating energy exchange on the Earth's surface. This study investigates the changes in vegetation cover, temperature, and precipitation in northeastern Iran during 2001-2020. MODIS-NDVI time series data and climatic data from 11 synoptic stations were utilized. The nonparametric Mann-Kendall method was employed to detect trends in vegetation cover and climatic variables. Additionally, the correlation between climatic parameters and vegetation was examined. Trend analysis revealed significant increases/decreases in vegetation cover in 32%/26% of the region, respectively. The increasing trend in vegetation cover was predominantly observed in highlands, suggesting that a warmer climate has enhanced the living conditions for plants in these regions. The vegetation trend map indicates an expansion of vegetation cover in the northern and central parts of Iran during the past 20years, whereas the southern and eastern portions experienced declines. The relationship between vegetation and elevation revealed that vegetation increased above 1,850m and decreased below 850m. Trend analysis showed no significant trend in precipitation data since the beginning of the twenty-first century, but an increasing trend in temperature was observed in 82% of the region's area, excluding the western strip. Correlation coefficients between temperature, precipitation, and vegetation indicated that declining temperatures are the limiting factors for vegetation in the highlands, while in lowland areas, the decrease in precipitation significantly diminishes vegetation growth.

Creation and environmental applications of 15-year daily inundation and vegetation maps for Siberia by integrating satellite and meteorological datasets

As a result of climate change, the pan-Arctic region has seen greater temperature increases than other geographical regions on the Earth’s surface. This has led to substantial changes in terrestrial ecosystems and the hydrological cycle, which have affected the distribution of vegetation and the patterns of water flow and accumulation. Various remote sensing techniques, including optical and microwave satellite observations, are useful for monitoring these terrestrial water and vegetation dynamics. In the present study, satellite and reanalysis datasets were used to produce water and vegetation maps with a high temporal resolution (daily) and moderate spatial resolution (500 m) at a continental scale over Siberia in the period 2003–2017. The multiple data sources were integrated by pixel-based machine learning (random forest), which generated a normalized difference water index (NDWI), normalized difference vegetation index (NDVI), and water fraction without any gaps, even for areas where optical data were missing (e.g., cloud cover). For the convenience of users handling the data, an aggregated product is provided, formatted using a 0.1° grid in latitude/longitude projection. When validated using the original optical images, the NDWI and NDVI images showed small systematic biases, with a root mean squared error of approximately 0.1 over the study area. The product was used for both time-series trend analysis of the indices from 2003 to 2017 and phenological feature extraction based on seasonal NDVI patterns. The former analysis was used to identify areas where the NDVI is decreasing and the NDWI is increasing, and hotspots where the NDWI at lakesides and coastal regions is decreasing. The latter analysis, which employed double-sigmoid fitting to assess changes in five phenological parameters (i.e., start and end of spring and fall, and peak NDVI values) at two larch forest sites, highlighted a tendency for recent lengthening of the growing period. Further applications, including model integration and contribution to land cover mapping, will be developed in the future.

Long-Term Time Series Trend Analysis of Climatic Variable for Jaisalmer District in Thar Desert, Western Rajasthan, India, Using Statistical Approaches

Long-Term Time Series Trend Analysis of Climatic Variable for Jaisalmer District in Thar Desert, Western Rajasthan, India, Using Statistical Approaches

Trend Analysis, Modeling And Interpolation of Reference Evapotranspiration Time Series in The Semi Arid Region of Duhok Governorate

Trend Analysis, Modeling And Interpolation of Reference Evapotranspiration Time Series in The Semi Arid Region of Duhok Governorate

Trend Analysis and Change Points in Time Series of Water Level of Shallow and Deep Wells in Gorganrood Watershed

Trend Analysis and Change Points in Time Series of Water Level of Shallow and Deep Wells in Gorganrood Watershed

Study of Ecosystem Degradation Dynamics in the Peruvian Highlands: Landsat Time-Series Trend Analysis (1985–2022) with ARVI for Different Vegetation Cover Types

The high-Andean vegetation ecosystems of the Bombón Plateau in Peru face increasing degradation due to aggressive anthropogenic land use and the climate change scenario. The lack of historical degradation evolution information makes implementing adaptive monitoring plans in these vulnerable ecosystems difficult. Remote sensor technology emerges as a fundamental resource to fill this gap. The objective of this article was to analyze the degradation of vegetation in the Bombón Plateau over almost four decades (1985–2022), using high spatiotemporal resolution data from the Landsat 5, 7, and 8 sensors. The methodology considers: (i) the use of the atmosphere resistant vegetation index (ARVI), (ii) the implementation of non-parametric Mann–Kendall trend analysis per pixel, and (iii) the affected vegetation covers were determined by supervised classification. This article’s results show that approximately 13.4% of the total vegetation cover was degraded. According to vegetation cover types, bulrush was degraded by 21%, tall grass by 18%, cattails by 16%, wetlands by 14%, and puna grass by 13%. The Spearman correlation (p < 0.01) determined that degraded covers are replaced by puna grass and change factors linked with human activities. Finally, this article concludes that part of the vegetation degradation is related to anthropogenic activities such as agriculture, overgrazing, urbanization, and mining. However, the possibility that environmental factors have influenced these events is recognized.

Statistical forecast of the grain production development in Kemerovo region

Relevance. The growth of geopolitical tensions and climate change lead to a decrease in stability in the world grain market, which ensures the relevance of further grain production growth in our country. Such a task is also set for industrial regions that have the necessary capabilities for this, which should increase the level of self-sufficiency. Despite certain successes in recent years, significant reserves remain for further growth in grain production.Methods. In the course of the study, there were used methods of comparison, economic-statistical, trend analysis of dynamic series, calculation-constructive, abstract-logical and other methods.Results. Based on the official statistics, an assessment was made of the existing patterns and trends in the dynamics of grain production, incl. on the main crops in the Kemerovo region. The influence of the main factors on the gross harvest of grain crops is determined. A forecast was made for the yield of grain crops based on a trend analysis of time series. The reserves for the growth of sown areas and the gross harvest of grain crops until 2025 were estimated.

Wavelet-based de-noising approach for trend analysis and coherency relation in hydrological time series (case study: Ardabil plain)

Wavelet-based de-noising approach for trend analysis and coherency relation in hydrological time series (case study: Ardabil plain)

Driving Force Exploration for Flash Flood Based on Mann–Kendall Test and Geographical Detector: A Case Study of Hainan Island, China

Flash floods are among the deadliest hazards in China and have led to substantial casualties and losses, especially on Hainan Island. Therefore, it is of great significance to explore the main driving force behind them. Nevertheless, research on the driving force of flash floods is limited here. This study explores the driving force of flash floods on Hainan Island from 14 factors involving three categories: natural, social, and rainfall factors. Two quantitative methods, like the Mann–Kendall test and the geographical detector method, are applied. The Mann–Kendall test is usually used for time series trend analysis and is introduced to divide the flash flood periods into D95 (years from 1980 to 1995) and D14 (years from 1996 to 2014) through the results of reported flash flood trend analysis. The geographical detector is applied to analyze the driving force of flash floods. There are several key findings from this study that help better understand the driving force about flash floods. Firstly, the results show that the main driving forces of flash floods are natural factors like Elevation and Soil in both periods, and they are on the rise. Secondly, the influence of short-term heavy rainfall on flash floods is becoming more and more serious. Thirdly, even though the driving forces from social factors to flash floods are small, the impact of population density on that is significantly increasing.

Meteorological Drought Assessment and Trend Analysis in Puntland Region of Somalia

Drought assessment and trend analysis of precipitation and temperature time series are essential in the planning and management of water resources. Long-term precipitation and temperature historical records (monthly for 41 years, from 1980 to 2020) are used to investigate annual drought characteristics and trend analysis in Somalia’s northern region. Six drought indices of the normal Standardized Precipitation Index (normal-SPI), the log normal Standardized Precipitation Index (log-SPI), the Standardized Precipitation Index using the gamma distribution (Gamma-SPI), the Percent of Normal Index (PNI), the Discrepancy Precipitation Index (DPI), and the Deciles Index (DI) are used in this study for the annual drought assessment. The log-SPI, the gamma-SPI, the PNI, and the DPI could capture historical extreme and severe droughts that occurred in the early 1980s and over the last two decades. The results indicate that Somalia has gone through extended drought periods over the past quarter century, exacerbating the existing humanitarian situation. The normal-SPI, gamma-SPI, and PNI indicate less and moderate drought conditions, whereas log-SPI, DPI, and DI accurately capture historical extreme and severe drought periods; thus, these methods are recommended as annual drought assessment tools in the studied region. Not only are the PNI and DPI less correlated to each other, but their correlation coefficient (CC) with SPI-based drought indices are not as high as SPI-based indices which are close to unity. For the purpose of the trend analysis, the Mann Kendall (MK) test, the Spearman’s rho (SR) test, and the Şen test are used. Furthermore, the Pettitt test is implemented to detect the change points and the Thiel-Sen approach is used to estimate the magnitude of trend in the precipitation and temperature time series. The results indicate that there is overall warming in the region which has experienced a significant shift in trend direction since 2000. The trend analysis of annual precipitation data time series shows that Bossaso and Garowe stations have significant positive trends, while the Qardho station has no trend. In 1997 and 1998, respectively, abrupt changes in annual precipitation are detected at Qardho and Garowe stations. Due to the civil war of more than three decades in Somalia and the non-institutionalized governance to inform historical drought conditions in the country, determining the most appropriate meteorological drought index would help to develop a drought monitoring system for states and the entire country.

Research on the Effect of Knowledge Stock on Technological Advance and Economic Growth in Republic of Korea

The Republic of Korea’s R&D investment has been rapidly increasing over the long term. This R&D investment is accumulated as knowledge stock, which means the amount of knowledge held by individual countries. However, is the rapid increase in R&D investment contributing to the advancement of domestic science and technology and economic growth? In general, technological advance will lead to a country’s sustainable economic growth. If so, how much has it contributed? This is a very important question for a neoclassical economist. This study reviewed previous studies on “knowledge stock”, “TFP estimation”, and “R&D investment, TFP and economic growth relationship”, and sequentially analyzed the relationship among knowledge stock, TFP, and economic growth through a three-step process. The analysis procedure first performs data processing and time series trend analysis, then time series characteristics analysis, and finally Orderful Least Squares (OLS). The analysis results showed that all three models had a positive (+) relationship and most of them were derived to be statistically significant. On this basis, three policy implications are presented. First, the knowledge stock is estimated to have had a positive (+) effect on economic growth, and the continuous increase in domestic R&D investment is believed to have acted as a growth engine. In particular, financial of the government by financial resources and the mid-to-high tech industry groups by industrial characteristics are statistically positively significant. Next, domestic knowledge stocks have had a positive effect on TFP but the efficiency is judged to be decreasing. It is implied that high-tech industries need to form knowledge stocks above the threshold through continuous R&D investment. Finally, TFP was analyzed to have had a high impact on economic growth, and this confirmed that innovation-led growth was important. It is still relevant that innovation-led sustainable growth is more important than factor led in the Republic of Korea.

Forecasting of local tax realization with least square method in Tanggamus Regency

Purpose: This study aims to identify the contribution of local taxes to regional revenues in Tanggamus Regency and predict the pattern of realization of regional tax revenues in the coming period. Research methodology: This research is a quantitative study with a time series trend analysis according to the type of regional tax and an overview of the target and its realization. The sample used in this study was data from seven types of regional taxes obtained from the Regional Financial Management Agency of Tanggamus Regency. The data analysis process uses the least squares method, often used to forecast, and the application uses Minitab software with fitted line plots and descriptive statistics features. Results: This study's results showed that the seven tax subjects seemed to go through changes and tended to grow in the future. Limitations: This research is limited to the specific scope of taxes selected and does not cover all other types of taxes and levies. Contributions: This study's ability to predict the future helps make plans for regional tax revenue, especially when it comes to planning for policies that might change in the near future. Novelty: This research is new because it has a new update on how to use predictive methods with Minitab and an analysis of how local taxes have changed over time.

Antibiotic prescribing trends in primary care 2014–2022

Antimicrobial resistance (AMR) is a global healthcare challenge that governments and health systems are tackling primarily through antimicrobial stewardship (AMS). This should, improve antibiotic use, avoid inappropriate prescribing, reduce prescription numbers, aligning with national/international AMS targets. In primary care in the United Kingdom (UK) antibiotics are mainly prescribed for patients with urinary and respiratory symptoms (22.7% and 46% of all antibiotic prescriptions respectively).This study aimed to capture the time-series trends (2014–2022) for commonly prescribed antibiotics for respiratory and urinary tract infections in primary care in England. Trends for Amoxicillin, Amoxicillin sodium, Trimethoprim, Clarithromycin, Erythromycin, Erythromycin ethylsuccinate, Erythromycin stearate, Doxycycline hyclate, Doxycycline monohydrate and Phenoxymethylpenicillin (Penicillin V) were determined. In doing so providing evidence regarding meeting UK antibiotic prescribing rate objectives (a 15% reduction in human antibiotic use 2019–2024).Time series trend analysis of 62,949,272 antibiotic prescriptions from 6,370 General Practices in England extracted from the National Health Service (NHS) Business Services Authority web portal were explored. With additional investigation of prescribing rate trends by quintiles of the Index of Multiple Deprivation (IMD).Overall, there is a downwards trend in antibiotic prescribing for those explored. There is an association between IMD, geographical location, and higher antibiotic prescribing levels (prescribing hot spots). England has a well-documented North-South divide of health inequalities, this is reflected in antibiotic prescribing. The corona virus pandemic (COVID-19) impacted on AMS, with a rise in doxycycline and trimethoprim prescriptions notable in higher IMD areas. Since then, prescribing appears to have returned to pre-pandemic levels in all IMDs and continued to decline.AMS efforts are being adhered to in primary care in England. This study provides further evidence of the link between locality and poorer health outcomes (reflected in higher antibiotic prescribing). Further work is required to address antibiotic use in hot spot areas.

Herd immunity in older adults from a middle-income country: A time-series trend analysis of community-acquired pneumonia mortality 2003-2017.

Community-acquired pneumonia is responsible for substantial mortality, and pneumococcus is commonly accepted as a major cause of pneumonia, regardless of laboratory confirmation. Child immunization programs have reported success in decreasing pneumonia mortality: directly in young children and indirectly (herd immunity) in unvaccinated adult populations in some countries. We assess changes in mortality trends for all-cause pneumonia in older adults associated with the introduction of pneumococcal vaccination for children in Peru. This is a secondary analysis on administrative data collected periodically by the Peruvian Ministry of Health. An observational retrospective time series analysis was conducted using longitudinal population-based data from death certificates in Peru between 2003 and 2017. The time series includes 6 years before and 9 years after the introduction of the pneumococcal-conjugated vaccines in the national child immunization program in 2009. Monthly frequencies and annual rates for all-cause pneumonia deaths in children under 5 years of age and adults over 65 years of age are presented. Linear and quadratic trends are analyzed. Deaths among older adults accounted for 75.6% of all-cause pneumonia mortality in Peru, with 94.4% of these reporting "pneumonia due to unspecified organism" as the underlying cause of death. Comparing pre- and post-child immunization program periods, annual average mortality rates from unspecified pneumonia decreased by 22.7% in young children but increased by 19.6% in older adults. A linear trend model supports this overall tendency, but a quadratic curve explains the data better. Pneumococcal-conjugated vaccines are developed using serotypes prevalent in selected countries from less common (invasive) pneumococcal disease and expected to prevent mortality worldwide from widespread (noninvasive) pneumonia. Our results do not support the presence of herd immunity from pneumococcal vaccination of children for community-acquired pneumonia in the increasingly ageing population of Peru. This should direct future research and could influence public health policy.

The impact of water legislation on groundwater sustainability in an arid region: Spatial statistical approach

Sustainable use of groundwater resources and preservation of groundwater quality are critical issues for society as these resources directly impact the health of the environmental system and the population that rely on them for consumption. Despite this, and considering the importance of legal frameworks for establishing and enforcing sustainable practices, there is a lack of research looking at the effectiveness of environmental legislation on groundwater resources which ultimately inhibits opportunities for improvement. Considering this, this study aims to investigate the impact of water legislation on groundwater usage in Jordan, a small country located in a typically arid region, using hotspot analysis (HSA) and time series trend analysis (TA). In addition, the study assesses legislation's effectiveness on groundwater quality through data collected on static water level, water extractions, total dissolved solids (TDS), chloride (Cl−), and nitrate (NO3−).The study results show that the groundwater levels declined (average of 1 m/yr) in most of the monitored wells in the observed time irrespective of the legislation implemented to monitor and reduce extraction. In fact, the HSA results showed a significant increase in the volume of water extracted after legislation was introduced. In addition, water quality also deteriorated particularly in areas of intensive agricultural activity where there are high levels of water usage and chemical run-off. In particular, TDS and Cl-levels increased during the assessed period by more than 40% over the Jordanian standards for drinking water.As such, the study concludes that existing legislation has been ineffective at preserving groundwater quantity and quality. It is therefore recommended that legislation be reformed to ensure that quality standards are upheld and extraction of groundwater resources is permitted at sustainable rates without exceeding the basin yield. This could be achieved by implementing an integrated institutional framework that better facilitates coordination between relevant ministries; introducing an advanced monitoring system; substituting existing crops for low-water use crops; introducing higher tariffs; and enforcing a stricter penalty system.

The Impact of COVID-19 on Access, Delivery, and Utilisation of Essential Primary Healthcare in Region 4 of Guyana

Background: The COVID-19 pandemic has posed considerable operational challenges for public health systems to deliver COVID-19 response and maintain essential health services simultaneously. Low-resource countries were significantly affected by the COVID-19 pandemic because of weak health infrastructure, reduced health systems capacity and significant socio-economic inequalities. Objectives: This study aimed to explore the impact of COVID-19 on access, delivery and utilisation of essential primary health services in Region 4 of Guyana. Methods: An explanatory sequential study design was used to collect data on health services utilisation from 41 health centres in Region 4. The statistical data informed a semi-structured questionnaire to explore community and healthcare providers' perceptions of the COVID-19 pandemic and their experiences concerning access and delivery of primary health services during the COVID-19 pandemic in Guyana. The statistical data were analysed descriptively with SPSS version 20 software and by time series trend analysis. The qualitative data were descriptively analysed with SPSS software and by thematic analysis. Results and Discussion: An analysis of the statistical data showed a stable trend of essential health services utilisation from January 2018 – May 2019. However, during the COVID-19 pandemic, there were significant decreases in utilising all essential health services across the 41 health centres of Region 4. Healthcare providers and community perspectives and experiences identified fear and anxiety among the common causes for decreased access to primary healthcare during the pandemic. Conclusions: This study confirmed that during the COVID-19 pandemic, access, delivery, and utilisation of essential health services were severely affected in the health centres of Region 4 in Guyana. COVID-19 pandemic, essential primary healthcare, health centres of Region 4 in Guyana.

Arctic sea ice melt pond fraction in 2000–2021 derived by dynamic pixel spectral unmixing of MODIS images

The Arctic has experienced extensive sea ice retreat in recent decades, as observed from satellite remote sensing and simulated by general circulation models. The significant low albedo of melt pond compared with snow and ice results in more absorption of radiation compared with sea ice without melt pond, which will then enhance sea ice melt to form more melt pond. This so-called positive feedback of melt pond is crucial for sea ice thermal and physical properties. The melt pond coverage varies significantly spatially and temporally and satellite remote sensing provides a possible solution in determining the spatial coverage of melt pond. This work aims to develop a new physically based algorithm for retrieving the cover fraction of melt ponds on sea ice using remote sensing data. In the newly developed algorithm, dynamic sea ice and melt pond reflectances are used in the pixel spectral unmixing for melt pond fraction (MPF) estimation. Radiative transfer models for sea ice and melt pond reflectance modeling are developed for MPF retrieval algorithm. The validation of retrieved MPF and its comparison with static pixel spectral unmixing results show that the dynamic algorithm proposed in this study has the potential of application in wide range of situations by considering dynamic spectral reflectances of melt pond and sea ice. The Arctic MPF and sea ice cover fraction (SIF) in 2000–2021 are generated by applying the proposed algorithm. The spatial and temporal characteristics of MPF and SIF are analyzed. Time series trend analysis of MPF in 2000–2021 reveals the significant increasing trend of relative MPF in the Arctic. The melt pond expansion in 2000–2021 is more obvious in the marginal areas, especially the marginal areas close to Russia coast. Trend analysis for typical regions such as Beaufort Sea, Greenland Sea, the Baffin Bay, Chukchi, East Siberian, Laptev, and Kara seas and center Arctic also reveals that the relative MPF and SIF show contrasting trend.