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This study examines the changing rainfall patterns in the Vietnamese Mekong Delta (VMD) utilizing observational data spanning from 1978 to 2022. We employ the Mann–Kendall test, the sequential Mann–Kendall test, and innovative trend analysis to investigate trends in annual, wet, and dry season rainfall, as well as daily rainfall events. Our results show significant spatial variations. Ca Mau, a coastal province, consistently showed higher mean annual and seasonal rainfall compared to the further inland stations of Can Tho and Moc Hoa. Interestingly, Ca Mau experienced a notable decrease in annual rainfall. Conversely, Can Tho, showed an overall decrease in some months of the wet season and an increase in dry season rainfall. Furthermore, Moc Hoa showed an increase in the number of rainy days, especially during the dry season. Principal component analysis (PCA) further revealed strong correlations between annual rainfall and extreme weather events, particularly for Ca Mau, emphasizing the complex interplay of geographic and climatic factors within the region. Our findings offer insights for policymakers and planners, thus aiding the development of targeted interventions to manage water resources and prepare for changing climate conditions. [ABSTRACT FROM AUTHOR]

The article provides a long-term trend analysis of the Kesinga catchment daily gridded rainfall at a (0.25° × 0.25°) high spatial resolution from 1901 to 2020 (120 years). The trend in seasonal and annual rainfall was detected using rank-based nonparametric statistical tests, namely Spearman's rho and Mann-Kendall test, which are used for detecting monotonic trends in time series data at the 5% significant level, smoothing curve, Sen's slope test, and a plot of innovative trend analysis. The results showed that statistically significant trends had a pattern with both positive (increasing) and negative (decreasing) trends, with positive and negative trends evident in the winter and negative trends shown in the monsoon, PREMON, and annual seasons. The middle of the study area revealed the highest negative trend, and the lower Kesinga catchment showed the lowest negative annual rainfall trend. In the entire Kesinga catchment, the seasonal data and annual rainfall both showed statistically significant and non-significant patterns. Consistently, the MK and SR tests were both conducted at the validated significance level. In various contexts, the statistically significant massive trend that has occurred was negative (70%). If the current pattern continues in the future, there will be a scarcity of water and more strain on the control of water resources at the given grids in corresponding temporal scales. [ABSTRACT FROM AUTHOR]

The present study analyzes trends of extreme flows for the Krishna river at the Prakasam barrage, Vijayawada of Andhra Pradesh state, India. Annual maximum and minimum flows are considered using available data from the CWC (Central Water Commission) portal from 1965–1966 to 2019–2020. The trend analysis of extreme flows is carried out using the Mann-Kendall trend test, Sen's slope, and modified Mann-Kendall test, that is, bootstrapped Mann-Kendall trend test with optional bias-corrected pre-whitening. Also, R programming is applied for the trend analysis of extreme flows. Extreme annual flows are in decreasing trend. Annual maximum flow has significant decreasing trend with Sen's slope of 105.52 cumecs per year. Annual minimum flow has insignificant decreasing trend at a magnitude of 0.0002 cumecs per year. Trend results of the present study are useful for the trend analysis of extreme river flows. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid advancement of China's urbanization, the contradiction between urban development and the ecological environment has become increasingly prominent, and the urban ecological system now faces severe challenges. In this study, we proposed an ecological index-based approach to monitor and evaluate the ecological environment using a Google Earth Engine cloud-based platform and Landsat time series. Firstly, a long-term series of Landsat images was obtained to construct and calculate the remote sensing-based ecological index (RSEI). Then, the Theil–Sen median estimation and the Mann–Kendall test were used to evaluate the trend and significance of the RSEI time series and combined with the Hurst index to predict the future development trend of the ecological environment in the study area. Finally, the coefficient of variation method was used to determine the temporal stability of the ecological environment. Taking Zhoushan Archipelago, China, as the study area, we mapped the distribution of the ecological environment using a spatial resolution of 30 m and evaluated the ecological environment from 1985 to 2020. The results show that (1) from 1985 to 2020, the average RSEI in the Zhoushan Archipelago decreased from 0.7719 to 0.5817, increasing at a rate of −24.64%. (2) The changes in the areas of each level of ecological environmental quality show that the ecological environment in the Zhoushan Archipelago generally exhibited a decreasing trend. During the study period, the proportion of the areas with excellent ecological environmental quality decreased by 38.83%, while the proportion of areas with poor and relatively poor ecological environmental quality increased by 20.03%. (3) Based on the overall change trend, the degradation in the ecological environment in the Zhoushan Archipelago was greater than the improvement, with the degradation area accounting for 84.35% of the total area, the improvement area accounting for 12.61% of the total area, and the stable area accounting for 3.05% of the total area. (4) From the perspective of the sustainability of the changes, in 86.61% of the study area, the RSEI exhibited positive sustainability, indicating that the sustainability of the RSEI was relatively strong. (5) The coefficient of variation in the RSEI was concentrated in the range of 0–0.40, having an average value of 0.1627 and a standard deviation of 0.1467, indicating that the RSEI values in the Zhoushan Archipelago during the study period were concentrated, the interannual fluctuations of the data were small, and the time series was relatively stable. The results of this study provide theoretical methods and a decision-making basis for the dynamic monitoring and regional governance of the ecological environment in island areas. [ABSTRACT FROM AUTHOR]

Climate change is a pressing issue that is affecting the lives and livelihoods of millions of people across the world. This study investigates the trend analysis and spatial-temporal variations of temperature and precipitation on a monthly, seasonal, and annual basis in Rajasthan state, India, over the past 40 years (1981-2020). The trend analysis of temperature and precipitation were analysed using the Mann-Kendall test at the confidence level of 95%. The magnitude (slope) was determined by using Theil-Sen's slope test. The results of the analysis revealed significant positive and negative trends of temperature and precipitation observed on a monthly, seasonal, and annual basis in all the 33 districts of Rajasthan state. The summer season experienced the maximum average temperature, while the winter season had the minimum. The study also found that the northern and western parts of Rajasthan experience "Mawat" rain during the winter due to cyclones happening in the Mediterranean Sea during that season. The annual average temperature and precipitation were observed to be maximum in the southern part and minimum in the northern and western parts of the state. The findings of this study provide valuable information for the future management of water resources and the likely impact of activities on the hydrologic cycle and natural resources in Rajasthan state. [ABSTRACT FROM AUTHOR]

Climate variability has significant implications for human health, necessitating a comprehensive understanding of the relationship between the two. The present study examines the trend analysis of climatic variables, and their possible impacts on health and identifies specific challenges faced by the people of Himachal Pradesh, India. The Mann-Kendall test, a non-parametric test, was used to examine temperature and rainfall trends. A survey was conducted to gauge public awareness regarding climate change and its health risks in the region. The study analysed weather patterns over 10 years, revealing fluctuating temperatures with the hottest years being 2012, 2015-17. Rainfall exhibited irregular patterns with less rainfall in 2012, 2014, 2017 and 2019. Trend analysis showed significant temperature trends in Dharamshala and Kangra. Weather-related incidents and fatalities peaked during 2012-14, while diseases, such as acute respiratory infections (ARI), asthma, cardiovascular diseases, diabetes, hypertension, and vector and water-borne diseases saw an increase over the decade. The survey results showed that the people of this region suffered mostly from respiratory problems, water-borne diseases and mental health issues. This study contributes to the existing knowledge by establishing a possible relationship between climate variability and the health of the people. Furthermore, it also provides a health database, facilitating the formulation of targeted interventions to address health-related challenges arising from the observed changes in weather patterns. [ABSTRACT FROM AUTHOR]

Hydrological conditions are strongly regulated by monsoon systems in the Mekong River Basin (MRB), while relevant studies investigating the intensity of the rainy season are still insufficient. This study employed the Mann-Kendall (M-K) test, Sen's slope estimator, and innovation trend analysis to detect the variation of summer discharge in the MRB. Wavelet analysis is used to investigate the correlation between discharge and two South Asian summer monsoon subsystems (SAMI1 and SAMI2). Results show that the summer discharge in the MRB generally shows significant downward trends during 1970–2016 with a Z value range of −3.59–−1.63, while the high discharge at Vientiane, Mukdahan, and Pakse increases after 1970. The mutation years of the summer discharge series are around 2010 for Chiang Sean and Vientiane, and in 2015 for Luang Prabang, which resulted from the newly built large dams, Xiaowan and Nuozhadu. The wavelet analysis shows that the SAMI1 can be used to predict the summer discharge at Chiang Sean at a ~8-year timescale, while the SAMI2 correlates with the summer discharge well at a 1–8-year scale, especially at Mukdahan and Kratie during 1980–2016. [ABSTRACT FROM AUTHOR]

Based on GIMMS NDVI3g data from 1982 to 2015, by trend analysis, Mann-Kendall test and R/ S analysis, the temporal and spatial variation and future trend research of vegetation cover in the southwest karst area of China was studied. The results could provide a basis for ecology in this area. (1)In term of temporal variation of NDVI, annual and seasonal NDVI increased in fluctuant trends from 1982 to 2015. The annual and seasonal NDVI showed an overall trend of fluctuation and increase from 1982 to 2015, and the seasonal NDVI significantly increased. (2)In terms of the spatial distribution of NDVI, the vegetation cover was relatively better during the study period. The vegetation cover showed obvious spatial heterogeneity on the seasonal scale. The areas of NDVI≥0. 8 changed with the seasons in a trend of transferring from the western mountains of Hubei to Lincang of Yunnan. (3)The annual NDVI showed an increasing trend during the study period, up to 80. 31% and mainly in the vicinity of Yunnan-Guizhou Plateau. The variation of NDVI had significantly heterogeneity on the seasonal scale. The degradation of vegetation area changed along the Hengduan Mountains-Sichuan Basin, Guizhou-Hunan and Guangdong-Yunnan-Guizhou plateau. (4)In the future, the vegetation cover will show a continuous change. Vegetation area improvement will be 73. 30% on the annual and seasonal scales. [ABSTRACT FROM AUTHOR]

Mann–Kendall trend test and Sen's slope estimator method were used to find out significant changes in extreme climate indices for daily temperature and precipitation in Cherrapunji over the period from 1979 to 2020. In this study, 21 precipitation and temperature-based extreme climate indices were calculated using RClimDex v 1.9-3, among which six were derived from the number of days above nn mm rainfall (Rnn) according to India Meteorological Department (IMD) convention and the rest 15, in accordance with the Expert Team on Climate Change Detection and Indices (ETCCDI). It was observed that, among all the indices, consecutive dry days (CDD), summer days (SU25) and very light rainfall (VLR) days increased significantly with 0.54, 1.58 and 0.14 days/yr, respectively, while only consecutive wet days (CWD) decreased significantly with 0.36 days/yr. A slight negative trend was observed in the case of tropical nights (TR20) and among the other precipitation indices as well. Again, the indices associated with daily maximum temperature increased significantly with 0.06–0.07°C/yr. For the indices associated with daily minimum temperature, almost no change or a slight negative change was observed, except a significant positive trend in February and significant negative trend in November for monthly minimum of daily minimum temperature (TNN) only. The analysis revealed that some of the extreme climate indices which explain the climatic conditions of Cherrapunji have shown statistically significant change over the period of 42 years and if this trend continues, then Cherrapunji will be under threat when it comes to climate change. [ABSTRACT FROM AUTHOR]

The most significant effects of climate change are related to temperature and precipitation. Changes in precipitation and temperature patterns affect water resource capacity, agricultural activities, global biodiversity and emergency management. In this study, using two statistical models (Mann-Kendall and Pettit’s test), climate data for the territory of Belgrade in the time interval 1961-2020 were processed. Trends were analyzed: average annual temperatures, average monthly temperatures, annual and monthly precipitation amounts. The aim of the research is to determine the statistical significance of the increasing/decreasing temperature and precipitation trends on a monthly and annual basis. Based on the obtained results, it was determined that the changing point of the annual temperature is in 1997, while a statistically significant increasing trend was observed in all months except November. The increasing trend recorded by the precipitation amount is 0.345 mm/year, but without statistical significance. The most significant results corresponding to July and October. [ABSTRACT FROM AUTHOR]

Rainfall characteristics are changing due to several reasons and change/trend detection is required. Literature survey reveals many relevant studies whose outcomes are divergent, possibly because different data series and different methodologies have been applied. This paper presents a critical appraisal of past studies and methodologies for trend analysis. Results of trend analysis of Indian rainfall data are presented. Data for all of India and for five homogenous regions (North-West, Central North-East, North-East, West Central, and Peninsular India) for 1871-2016 were used. The Pettitt change point test, regression, Mann-Kendall (MK), and Wavelet Decomposition were used to study different aspects of changes. Results of the change point test showed that most rainfall series had change points around 1957-65, possibly due to large-scale land use, cultivation, irrigation, and industrial changes in this period. Generally, rainfall for most homogenous regions and sub-divisions show falling trends; some are statistically significant. Series was also decomposed by the wavelet method. Approximate and detailed components of some decomposed series showed a significant declining trend. This work has focused on the magnitude of rainfalls; trends in rainfall intensities are also important. It is necessary to establish denser observation networks to collect short-term data and analyze. [ABSTRACT FROM AUTHOR]

Precipitation is a particularly important part of the Earth's hydrological cycle and, therefore, is a necessary variable for maintaining natural balance. This study investigated past, present, and future changes in precipitation in the Marmara region, and examined the effects of global warming on this variable. The study period was from 1960 to 2020, and the climate data of 15 synoptic stations in the Marmara region were used for this purpose. To achieve the objectives of the study, linear and 6th order polynomial regression, ombrothermic and hythergraph diagrams, geostatistical models, Mann-Kendall test, Pearson correlation, standard Z-scores, and multi-layer perceptron artificial neural network models (MLP-ANN) were used to model and predict precipitation. The results of the linear regression analysis showed that of the 15 stations, 6 stations had an increasing trend, 6 stations had a trendless pattern, and 3 stations had a decreasing trend. In terms of periodic analysis, the main downward trend started in 1964 and continued until 1992, while the main periodic upward trend started in 1992 and continued until 2016. The synoptic stations in the Marmara region showed a lack of precipitation over six to seven months of the year, and the precipitation changes in the region were stronger than the temperature changes. In addition, the highest precipitation was observed on the southeast coast of the Black Sea, and the lowest precipitation was observed in the eastern parts of the region. Moreover, except for the Bilecik and Kocaeli stations, the changes in the long-term trend of precipitation at the other stations were significant. Among the 15 stations, only the Kocaeli and Sarıyer stations showed a positive correlation with global temperature during the annual period. In addition, the developed ANN model was accurate in simulating and predicting precipitation and showed an upward trend over the next seven years. [ABSTRACT FROM AUTHOR]

In this study, trend analyses of six climatic variables (mean, minimum, and maximum temperature, relative humidity, wind speed, and precipitation) for 1966-2020 are conducted for the Southeastern Anatolia Region, which is the main focus of the integrated development project in Turkey (Turkish acronym GAP). The trends for seasonal and annual periods are determined using the Mann-Kendall (MK) test, and Sen's slope method and regression analyses are used to find the trends' slopes. Moreover, Innovative Trend Analysis (ITA) is also used to find the time series changes for low, medium, and high values. As a result of the analyses, the mean, minimum, and maximum temperatures in the GAP region show increasing trends according to both methods. Significant trends are obtained at a limited number of stations for the precipitation, relative humidity, and wind speed with the MK test, while consistent decreasing trends are found at most stations with the ITA method. [ABSTRACT FROM AUTHOR]

This study examines the variations in groundwater levels from the perspectives of the dynamic layers soil moisture (SM), normalized difference vegetation index (VI), temperature (TE), and rainfall (RA), along with static layers lithology and geomorphology. Using a Bayesian Ensemble Algorithm, the trend changes are examined at 385 sites in Kerala for the years 1996 to 2016 and for the months January, April, August, and November. An inference in terms of area under the probability curve for positive, zero, and negative trend was used to deduce the changes. Positive or negative changes were noticed at 19, 32, 26, and 18 locations, in that order. These well sites will be the subject of additional dynamic and static layer investigation. According to the study, additional similar trends were seen in SM during January and April, in TE during August, and in TE and VI during November. According to the monthly order, the matching percentages were 63.2%, 59.4%, 76.9%, and 66.7%. An innovative index named SMVITERA that uses dynamic layers has been created using the aforementioned variables. The average proportion of groundwater levels that follow index trends is greater. The findings of the study can assist agronomists, hydrologists, environmentalists, and industrialists in decision making for groundwater resources. [ABSTRACT FROM AUTHOR]

There is high confidence that climate change has increased the probability of concurrent temperature‐precipitation extremes, changed their spatial‐temporal variations and affected the relationships between drivers of such natural hazards. However, the extent of such changes has been less investigated in Australia. Daily data spanning the period 1889‐2019 (131 years) were extracted from SILO gridded dataset at 700 grid cells (1◦ × 1◦) across Australia to calculate annual and seasonal mean daily maximum temperature (MMT) and total precipitation (TPR). A nonparametric multivariate copula framework was adopted to estimate the return period of compound hot‐dry (CHD) events based on an 'And' hazard scenario (hotter than a threshold 'And' drier than a threshold). CHD extremes were defined as years with joint return periods of longer than 25 years calculated over the period 1889‐2019. Mann‐Kendall nonparametric tests were used to analyse trends in MMT and TPR as well as in the frequency of univariate and CHD extremes. Results showed a general cooling‐wetting trend over 1889‐1989. Significant increasing trends were detected over 1990‐2019 in the frequency and severity of hot extremes across the country while trends in dry extremes were mostly insignificant (and decreasing). A significant increase in the association between temperature and precipitation was identified at various temporal scales. While the frequency of CHD extremes was mostly stable over 1889‐1989, it significantly increased between 1990 and 2019 at 44% of studied grid cells, mostly located in the north, south‐east and southwest. [ABSTRACT FROM AUTHOR]

Analysis of rainfall distribution and its changing pattern plays a vital role in managing water resources in a region. This work examined the spatio-temporal variability and trend of rainfall on yearly and season-wise scales during 1971–2013 in the Wainganga river basin situated in middle India. The Mann–Kendall (MK) test was implemented for identifying the temporal variation in rainfall trends. The magnitude of this changing trend was estimated by applying Sen's slope (SS) method. A paired sample t-test was also employed to appraise the statistical significance of changes in rainfall data. The results of MK and SS tests show both upward and downward trends. The results show positive and negative trends in the south-eastern and northwestern parts of the study area for annual rainfall. The monsoon rainfall also shows very close proximity with annual rainfall data trends. The t-test states that the observed changes in precipitation are statistically significant. After trend and pattern analysis, state-space models (SSMs) were used to predict rainfall for future scenarios. Four SSMs (single, double, and triple exponential, autoregressive integrated moving average (ARIMA)) were employed for rainfall prediction. The analysis shows that the ARIMA is best for rainfall prediction with architecture of (0,0,0) (2,1,0) and can be used in Wainganga River basin. [ABSTRACT FROM AUTHOR]

The current study focuses on the altering historical rainfall data analysis and its variability in Kolkata (Kolkata Municipal Corporation), a metropolitan city in India. The research area experiences detrimental urban floods (pluvial floods) at near-annual regularity during the monsoon, and during the pre-monsoon seasons it commonly experiences water shortage problems. Analysing trends and temporal variability of rainfall over 120 years from 1901 to 2020 is the main objective of this study. The original Mann-Kendall (M-K) test has been applied to the rainfall dataset in conjunction with Sen's Slope Estimator using Python 3.10, after the Durbin-Watson (DW) statistic initially suggested that there is no serial correlation effect. The M-K test, with a Kendall's tau of 0.17058 (significant at a 5% level), shows an upward trend in annual rainfall between 1901 and 2020. The Sen's slope, which measures the rate of change annually, has a value of 2.48152. Regression analysis and other dispersion measures are also used in this study to investigate the monthly rainfall trend and its variability. The phase-wise (30-year) analysis of annual rainfall variability reveals a considerable variation over 120 years. While fitting the linear regression line month by month over the entire period, mostly negative trends were found in the pre-monsoon and positive trends in the monsoon and post-monsoon seasons. The findings of this analysis could be useful to urban planners for water supply and management in the study area. The primary concern of planners for effectively managing rainwater and the accompanying issues should be the growing variability of annual precipitation. [ABSTRACT FROM AUTHOR]

Since historical times, the climate of the earth has been changing. In terms of diversity and population, India is a vast country. Any significant change in its climatic conditions can have an impact on the country's entire economic and cultural structure. Temperature is the most important variable when focussing on climate change study. This study aims to recognise the changing trend of temperature in India. For this purpose, temperature data from 1901 to 2020 has been analysed. To find out the variability in temperature, various statistical tools were applied. The Mann-Kendall method has been used along with Sen's slope estimator for determining the trend and slope magnitude. ARIMA modelling is applied to the data series to forecast the temperature trend. The study has shown that variability in temperature is increasing in recent years and the temperature trend is increasing in all the months, especially in the cold months of November, December and January. The average annual temperature of India in the year has increased significantly in the past 120 years. The trend analysis of temperature is tested significantly statistically at a 95% confidence level. [ABSTRACT FROM AUTHOR]

Climate change has a substantial influence on a range of meteorological factors and the global hydrological cycle. This paper presents an analysis of the annual rainfall (P), temperature (including maximum temperature: T max , minimum temperature: T min , and average temperature: T avg), stream flow (Q) data from 6 gauging sites, and groundwater level (GL) data from 7 groundwater stations in and around the Barak River Basin in India from 1986 to 2020. To identify patterns in the time series, the simple linear regression (SLR) approach, the Mann-Kendall (MK) test, the Hamid Mann-Kendall (HMK) test and the Spearman's Rho (SRO) tests have been employed. Pettit test has been used to identify the change points in the time series. The results indicated an increase in the T max and the T min across the study area, while no clear patterns were found in the T avg time series. Except for the 'Hailakhandi' station, the annual total rainfall series showed an increasing trend. T max and T min showed a directional change point between 1999 and 2005, and between 2001 and 2007, respectively, according to change point analysis. Except for two stations, overall declining trends in the annual stream flow were found. Moreover, a total of five out of seven groundwater stations indicated a rise in groundwater levels. The findings presented here offer valuable insights into the current condition of the Barak basin and emphasize the importance of implementing sustainable water management practices. • Significant impact of climate change on hydro-meteorological parameters in the study area. • A noteworthy increase in both maximum and minimum temperatures. • Overall declining trends in annual stream flow across most stations in the basin. • Groundwater stations demonstrate rising groundwater levels. [ABSTRACT FROM AUTHOR]

In this study, the trends in river discharge were investigated on different time scales for the period 1951/52–2009/10 over the northern part of the Korean peninsula based on the reliable flow datasets measured at the stations with near-natural states, and no or less anthropogenic activities. The annual mean discharge decreased by 5–11%/decade and its abrupt years appeared around 1980. On the seasonal scale, the trends in the rainy season were the most definitely observed with the decreasing rate of 6–12%/decade and could be seen to begin earlier than the annual trends. Unlike the annual and seasonal trends, the monthly trends exhibited very fluctuant variations depending on the individual months and locations. Overall, considering the detected significant trends, there is strong evidence that the river discharge remarkably decreased with the enhanced variability and the decreasing trends were a predominant part of the changes in the river discharge in the study period. With regard to the hydrological regions, the result suggested that the river discharge in the coastal hydrological regions influenced by the oceanic climate could be more sensitive to climate change than in the northern inland area with the continental climate. [ABSTRACT FROM AUTHOR]

Spatial and temporal rainfall variability analysis at a basin scale is essential to understand water availability for sustainable water resource management. This study investigated the spatial distribution and temporal rainfall trend in the Tekeze river basin using daily rainfall data collected from 43 meteorological stations with records from 1960 to 2019. Coefficient of variation, seasonality index (SI) and trend index were used to evaluate monthly, seasonal and annual rainfall variability in the basin. The non‐parametric Mann‐Kendall's test was used to understand any rainfall trends. Results showed that there is moderate annual rainfall variability in the basin. Rainfall variability is higher in the Tsedey, Bega and Belg than kiremt season. SI results showed that majority of the Tekeze river basin is categorized as seasonal, most of the rain occurring in less than 3 months. Overall, Mann‐Kendall's test result revealed that there is no significant annual rainfall trend in the basin except for four stations. The seasonal rainfall characteristics and short duration of rain in the basin indicate that there is a need to implement water harvesting measures during the wet season for use in the dry periods. [ABSTRACT FROM AUTHOR]

Background: Reliable rainfall forecast could be helpful to farmers as major decisions regarding selection of crops and sowing time are based on the rainfall. The univariate time series ARIMA model requires only past data for model formulation and to simulate stochastic processes. The current study was aimed to obtain the probability distribution of monthly rainfall using the method of moments and to forecast rainfall using the ARIMA model. Methods: The method of moments was used to determine the parameters of distributions and the chi-square test was used as a goodness of fit test to obtain the best fit distribution for monthly rainfall of Navsari, Gujarat utilizing 36 years of rainfall data. Auto regressive moving average (ARIMA) model, popular owing to its simplicity and ability to simulate various stochastic processes was used in the study. Result: It was revealed that the Weibull distribution was the best fit distribution for June and September, whereas Gumbel was the best fit distribution for July. For simulating monthly rainfall, the seasonal ARIMA model (0,0,1) (0,1,1)12 was found to be the appropriate model based on its performance. The model had the least root mean square value and also the residuals were found to have no correlation. [ABSTRACT FROM AUTHOR]

The issue of detection of hydrometeorological trends remains relevant because of the importance of climate change in design, operation, and management studies related to water resources. This study examines the effects of changes in climate and land use on monthly flows (1962–2018) in the Eastern Black Sea basin, Turkey, using innovative trend analysis methods. In this context, innovative polygon trend analysis (IPTA) and innovative trend significance test (ITST) were used to detect the trends and compared with Mann-Kendall test. Only stations with homogeneous data that did not experience non-climatic changes are used in the analysis. IPTA and ITST approaches are much more sensitive than Mann-Kendall in detecting trends. Although the innovative methods are mostly compatible with each other (90%), IPTA presents additional information about trend transitions between successive parts of time series. Results indicate significant decreasing trends in summer months, likely due to diminishing precipitation and effective evaporation. [ABSTRACT FROM AUTHOR]

Accurate and in-depth rainfall studies are crucial for understanding and assessing precipitation events’ patterns, intensities, and impacts, enabling effective planning and management of water resources, agriculture, and disaster preparedness. Despite many rainfall studies in Bangladesh at the national and regional scales, study on the spatiotemporal rainfall variability is still rare at the local scale. The current study aims to apply Mann–Kendall (MK), Modified Mann–Kendall (MMK), and Innovative Trend Analysis (ITA) techniques to assess the long-term annual and seasonal rainfall trends and variability over the southeast region of Bangladesh. Monthly rainfall data from ten Bangladesh Meteorological Department climate stations between 1981 and 2022 was used for the analysis on annual and four seasonal scales. The precipitation concentration index results showed significant variations in annual rainfall across the study area, whereas seasonal PCIs were consistent with moderate rainfall. According to standardized rainfall anomaly findings, each station experienced at least one severe to extremely severe drought episode during the 42-year study period. Homogeneity tests revealed significant breakpoints in some rainfall datasets, while 78% were declared homogeneous. MK, MMK, and ITA techniques revealed similar increasing and decreasing trend patterns throughout the study area. Annual rainfall showed an upward trend in the coastal part and a downward trend in the northern part of the study area, with monsoon rainfall exhibiting a similar trend pattern. The ITA technique outperformed the MK and MMK techniques in detecting trends, identifying significant increasing and decreasing trends in 76% (38 out of 50) of the observations, while the MK and MMK techniques detected trends in only 8% and 44% of the total observations, respectively. The outcome of the current study is expected to be helpful for the sustainable planning and management of water resources in the southeast region of Bangladesh. [ABSTRACT FROM AUTHOR]

In this study, using the long-term (1930-2020) observation data from Erzincan, the 90-year climatic course of the Erzincan Plain and its immediate surroundings was taken as a basis, and the general climatic characteristics were determined by evaluating the climatic elements. In addition, trend analyzes were performed with the Mann-Kendall and Sen method based on annual average temperature and precipitation values. Erzincan has different characteristics compared to its surroundings in terms of climatic characteristics in Turkey and the Eastern Anatolia Region. With the effect of orography and aspect factor, local climate conditions are experienced in Erzincan compared to its surroundings. In Erzincan, the long-term average annual temperature is 10.9°C and the annual average precipitation is 367.2 mm. The lowest temperatures are measured in January with -5.9ºC in some years, and the highest temperature is measured in July with 31.6ºC. The annual average number of sunny days is 105.4 and the number of cloudy days is around 197.9. In Erzincan, the annual average pressure value has been recorded as 878.1 mb for many years and the prevailing wind direction is ESE with a blow number of 46052. In Erzincan, where there is a lack of water, the soil is generally dry and therefore the vegetation consists of steppe formations. Considering these features, it is seen that continental climate conditions are experienced in Erzincan. According to the results of Mann-Kendall and Sen analysis, there is an increasing trend in temperature values and a decreasing trend in precipitation values in the long term. [ABSTRACT FROM AUTHOR]

One of the most relevant and debated topics related to the effects of the climate change is whether intense rainfall events have become more frequent over the last decades. It is a crucial aspect, since an increase in the magnitude and frequency of occurrence of heavy rainfall events could result in a dramatic growth of floods and, in turn, human lives losses and economic damages. Because of its central position in the Mediterranean area, Sicily has been often screened with the aim to capture some trends in precipitation, potentially related to climate change. While Mann‐Kendall test has been largely used for the rainfall trend detection, in this work a different procedure is considered. Precipitation trends are here investigated by processing the whole rainfall time‐series, provided by the regional agency SIAS at a 10‐min resolution, through the quantile regression method by aggregating precipitation across a wide spectrum of durations and considering different quantiles. Results show that many rain gauges are characterized by an increasing trend in sub‐hourly precipitation intensity, especially at the highest quantiles, thus suggesting that, from 2002 to 2019, sub‐hourly events have become more intense in most of the island. Moreover, by analysing some spatial patterns, it has been revealed that the south and the east of Sicily are more interested in significant increasing rainfall trends, especially at the 10‐min duration. Finally, the comparison between the two procedures revealed a stronger reliability of the quantile regression in the trend analysis detection, mainly due to the possibility of investigating the temporal variation of the tails of precipitation distribution. [ABSTRACT FROM AUTHOR]

This paper examines monthly, seasonal, and annual trends in temperature and precipitation time series in Osijek during the period between 1900 and 2018. Two new methods, innovative trend analysis (ITA) and successive average methodology (SAM), together with the classic Mann–Kendall (M–K) and Sen's slope methods, have been applied to determine potential trends in the variables at different time scales. Moreover, time series decomposition using locally estimated scatterplot smoothing (STL) was conducted to determine trend, seasonality, and the relationship between the components of each variable. Regarding the air temperature, ITA showed a monotonic positive trend at relatively low (T ≤ 10 °C) and high (T ≥ 13 °C) temperature ranges in all seasons, excluding spring. A positive trend was also found in the medium temperature range in this season, which agrees with the results of M–K test. The highest Sen's slope was obtained in January, with the second highest in April. According to the results acquired for the observed precipitation time series, it was discovered that Osijek has experienced a decreasing trend in spring precipitation. However, there is no trend in annual precipitation at a 5% significance level. Differing from the M–K results, the ITA shows a decreasing trend in both spring and autumn seasons. Summer precipitation increases with a significant change in the high precipitating levels (p ≥ 100 mm). Comparing successive pairs of partial trends in both historical temperature and precipitation, our results show that trends in peak and trough change-points are very close to each other, indicating a slight positive trend in temperature and a negative change in precipitation over the past century. [ABSTRACT FROM AUTHOR]

The aim of this study is the detection of trends of precipitation from (1986-2020) over Târgu Mureș city. Precipitation data for 35 years were processed with MS Excel spreadsheets to find monthly, seasonal and annual variability of rainfall. The Mann-Kendall test was used for trend analysis of precipitation and the Sen's slope estimator was used for the magnitude of variation. The calculations of the two methods were performed using the MAKESENS program. The standard deviation and the coefficient of variation were used to highlight the dispersion. Results show that all three scales (annual, seasonal and monthly) show a tendency to increase rainfall. The highest monthly average of precipitation is 227.70 mm (August, 2005), and the lowest monthly average of precipitation is 0.80 mm (November, 2011). The maximum value of annual precipitation is 852.60 mm and was registered in 2005, and the minimum value was 408.70 mm registered in 2000. [ABSTRACT FROM AUTHOR]

In recent years, gridded precipitation products have been widely used in hydrology studies and other fields of water sciences. This study evaluated the potential of several gridded precipitation products, including GPCC, TRMM, CRU, ERA-Interim, and ERA5, in trend analysis of precipitation depth and the number of rainy days in various regions of Iran. Moreover, the observational precipitation data of the daily time series were collected from 68 Iranian synoptic stations. The Mann–Kendall test was conducted to determine gridded and observed precipitation trends in the period of 1997 to 2017. The probability of detection (POD) and false alarm ratio (FAR) indices were utilized to compare gridded and observed precipitation trends. Results showed that the best consistency (POD: 52% ~ 80%, FAR: 60% ~ 88%) was observed between the observed trends of the number of rainy days and those obtained by TRMM product over different regions of Iran. Moreover, ERA-Interim offered a better performance (POD: 50% ~ 100%, FAR: 58% ~ 72%) in the trend analysis of precipitation depth in Iran. The consistency between observational and gridded precipitation trends has never been analyzed in Iran at this level; therefore, this is considered a unique analysis. Besides, the generated maps of precipitation products' performance provide a comprehensive view of better water resources management over different regions of Iran. [ABSTRACT FROM AUTHOR]

Horizontal visibility is of utmost importance in climate and air quality studies as it can provide key information related to changes in meteorological conditions, air quality, and their impact on human health. In this study, multiple trend analysis methods were applied to investigate the annual and seasonal horizontal visibility trends based on 1985 to 2018 at 23 locations in Saudi Arabia. Different forms of the Mann-Kendall (MK) test were conducted for the trend analysis, which includes the MK test with (without) autocorrelation corrections. In addition to that, Sen's slope estimator and Kendall's Tau were used to examine horizontal visibility trends and trend magnitudes. The sequential Mann-Kendall test was used to locate trend fluctuations over time and to detect abrupt changes in the horizontal visibility. It was found that 64.4% of the time series have significant decreasing trends (visibility decreasing with time). The average trend magnitude for Saudi Arabia was − 0.024 km/year over the evaluated period. Stations in the North-East quadrant (NE-Region) had a higher visibility degradation in spring and lower visibility degradation in summer. Stations in the Western area (W-Region) have higher visibility degradation in winter and lower visibility degradation in autumn. Northeastern part of the NE-Region showed the highest visibility deterioration. The sequential MK test detected increasing visibility trends in the early 1990s to the early 2000s, while during the mid-2000s to 2018, most of the data showed decreasing trends. The time series of annual horizontal variability in most of the stations located in the NE-Region showed more significant decreasing abrupt changes; however, few stations located in these two regions revealed insignificant increasing abrupt changes. [ABSTRACT FROM AUTHOR]

Extreme cold events ("cold waves") have disastrous impacts on ecosystem and human health. Evidence shows that these events will still occur under current increasing mean temperatures. Little research has been done on extreme cold events, especially in developing countries such as South Africa. These events pose a significant threat due to the low adaptive capacity, urgent development needs and relatively inadequate infrastructure in South Africa. This study presents annual and seasonal, spatial and temporal trend analyses of extreme cold temperature events for the period 1960–2016. We apply the World Meteorological Organisation Commission for Climatology and Indices Expert Team on Sector‐Specific Climate Indices (ET‐SCI) to South Africa for the first time, with comparison to the World Meteorological Organisation Expert Team on Climate Change Detection (ETCCDI) indices previously used in South Africa. The extreme cold indices are calculated using the RClimDex and ClimPACT, respectively. Trends were calculated using the non‐parametric Mann‐Kendall test, Spearman Rank Correlation Coefficient and Sen's slope estimates. A decreasing trend is found for annual cold spell duration and cold wave frequency, at rates of 0.10 days.day−1 and 0.02 events.day−1, respectively. Seasonally, coldest day temperatures increased in autumn, with increases of 0.02°C.day−1 for the period 1960–2016. Regionally, increasing trends in annual cold spell duration days were evident in stations located in the Western Cape, Eastern Cape, North‐West Province, at a rate of 0.03 days.day−1. Increasing trends in cold waves were observed for stations in Northern Cape, Gauteng, KwaZulu‐Natal and the Eastern Cape Province, at a rate of 0.01 events.day−1. These results contribute to the awareness and recognition of the incidence and duration of cold extreme events in South Africa, seeing that studies suggest that anomalously cold events may persist in a warming world. [ABSTRACT FROM AUTHOR]

Rainfall is the key climatic variable that governs the regional hydrologic cycle and availability of water resources. Rainfall trend analysis in a localized watershed can improve many aspects of water resource management not only to the catchment itself but also to some of the related other catchments. The trend analysis of monthly rainfall data over Cherrapunji of Meghalaya in India for the period 1872-2007 has been carried out in this work. While the magnitude of the trend in the time series has been determined using Sen's estimator, the significance of the trend in monthly rainfall series has been tested using Mann-Kendall test. During the time span 1872-2007, an increasing trend has been found in the monthly rainfall for the months July, October and November, and a decreasing trend has been found in the monthly rainfall for the months February to June, August and September. On the other hand, it was found that none of Mann-Kendall Z values was significant at 5% level of significance. Therefore, from Mann-Kendall Z test, it can be concluded that there is no trend in any month in monthly rainfall for the station Cherrapunji. For the better assessment of the temporal variation in monthly rainfall trend, whole period was divided into two halves, 1872-1939 and 1940-2007. Then, trend magnitude through Sen's estimator and Mann-Kendall Z for test of significance were determined for these two time periods separately. The analysis of trends of monthly rainfall in these two halves showed large variability in the magnitude and direction of the trend in various months from one half to another. Accurate prediction of trends in monthly rainfall is an important aspect of climate research and we believe that present study could provide a scope to correlate between current rainfall trend and climate change scenario of the study area. [ABSTRACT FROM AUTHOR]

Due to increasing concern on developing measures for predictive adaptation to climate change impacts on hydrology, several studies have tended to be conducted on trends in climatic data. Conventionally, trend analysis comprises testing the null hypothesis H0 (no trend) by applying the Mann-Kendall or Spearman's rho test to the entire time series. This leads to lack of information about hidden short-durational increasing or decreasing trends (hereinafter called sub-trends) in the data. Furthermore, common trend tests are purely statistical in nature and their results can be meaningless sometimes, especially when not supported by graphical exploration of changes in the data. This paper presents a graphical-statistical methodology to identify and separately analyze sub-trends for supporting attribution of hydrological changes. The method is based on cumulative sum of differences between exceedance and non-exceedance counts of data points. Through the method, it is possible to appreciate that climate variability comprises large-scale random fluctuations in terms of rising and falling hydro-climatic sub-trends which can be associated with certain attributes. Illustration on how to apply the introduced methodology was made using data over the White Nile region in Africa. Links for downloading a tool called CSD-VAT to implement the presented methodology were provided. [ABSTRACT FROM AUTHOR]

Global warming is a biggest issue around the world. In this research paper, the temporal and spatial trend analysis of seasonal and annual rainfall on Bhogavo River watersheds in Sabarmati lower basin of Gujarat state of India has been analysed using the data of 11 rain gauge stations installed in Bhogavo watershed. Linear regression, Mann–Kendall Test, Sen's slope test and innovative trend analysis methods are used to carry out monthly and annual rainfall trend analysis. In addition to the rainfall analysis, a number of rainy days change in magnitude as a percentage of mean rainfall have also been analysed using linear regression and Sen's slope method, respectively. The IDW method is used to develop a spatial distribution of annual and seasonal rainfall trend over the study area. From the results, it is concluded that annual rainfall shown increasing (positive) trend at nine stations out of 11 stations. The highest value of change in magnitude of trend as a percentage of mean monthly rainfall has been obtained in the month of July, attributing increasing trend at Sayla station and lowest value magnitude of trend as a percentage of mean rainfall in the monthly rainfall has been obtained in the month of August, attributing decreasing trend at Bavla station. [ABSTRACT FROM AUTHOR]

Estimation of evapotranspiration, representing a highly sensitive variable to global warming, is often required in water resources and agricultural studies. This study explores the possible effects of climate change on evapotranspiration (ETo) across Iran. The ETo was computed using the Penman‐Monteith Temperature (PMT) method that requires only minimum and maximum temperature (Tmin and Tmax), provided by the NASA Earth Exchange Global Daily Downscaled Projections dataset (NEX‐GDDP) with a 0.25° × 0.25° spatial resolution. Accuracy of the NEX‐GDDP Tmin and Tmax were evaluated against ground truth observations at 41 synoptic weather stations distributed across the country using a set of statistical measures, including the coefficient of determination (R2) and Nash‐Sutcliffe Efficiency (NSE). Similarly, the PMT estimated evapotranspiration based on the NEX‐GDDP Tmin and Tmax (ETo‐GDDP) were statistically evaluated against the ETo computed with observed variables at selected stations (ETo‐obs). Then, at each grid cell, the 45‐years ETo‐GDDP time series was partitioned into three 15‐years subperiods such that the differences between three subperiods were computed and inter‐compared. Furthermore, annual, seasonal, and monthly ETo time series trends of each cell were evaluated using the Mann–Kendall trend test (MK) while the corresponding change rates were estimated using Theil–Sen's slope estimator (TSSE). The results demonstrated a good agreement between ETo‐GDDP and ETo‐obs with R2 greater than 0.8 and NSE greater than 0.7 in 90.2% and 65.8% of the stations, respectively. The maps of differences between the three subperiods showed negligible changes in ETo between 1975–1990 and 1950–1975 subperiods. The spatial patterns of annual and seasonal MK statistics showed a significant increasing trend in most parts of Iran. On seasonal scale, the highest and lowest ETo changes were observed in spring (0.00035–0.0045) and in autumn (0.0005–0.0015), respectively. [ABSTRACT FROM AUTHOR]

In the last 50 years, large changes in the runoff processes of the Luanhe River Basin have exacerbated the scarcity and spatiotemporal variability of water resources. It is both theoretically and practically important to study the characteristics, trends and periods of runoff changes in the Luanhe River Basin. In this study, the moving average method, the Mann-Kendall test and wavelet analysis are applied to analyze the evolution of the natural runoff of the Luanhe River using runoff data for 1959-2018 at the representative stations in the Luanhe River Basin, namely, the Goutaizi, Sandaohezi and Luan County stations. The results show that the runoff volumes at the three stations decreased for 1959-2018, including abrupt decreases at the Goutaizi station in 1969 and at the Sandaohezi station in 1976. The Luan County station exhibited a significant increase in runoff after 1997. The significant periods of variation in the runoff for the upper, middle and lower reaches of the Luanhe River are 28 years, 27 years and 30 years, respectively. [ABSTRACT FROM AUTHOR]

Droughts significantly decrease production of crops. Therefore, three drought indices, SPI and SPEI (meteorological drought) and SSI (agricultural drought) have been evaluated for 82 rain gage stations in the Bundelkhand region, India. The non-parametric trend test (the Mann–Kendall test) and the Sen's slope estimator have been used to analyze the monthly, seasonal, and annual trends for both categories of drought. Monthly and annual trends of rainfall and temperature (both minimum and maximum) have also been analyzed for the study area. The analysis revealed the negative trend of SPI mainly for the northern part of Bundelkhand and especially for monsoon season and for August–September. The trend for precipitation amount is also negative for these months. The minimum temperature has increased (0.03 to 0.07°C/year) more as compared to the maximum temperature (< 0.01°C/year with an insignificant trend). PET was evaluated using the Penman–Monteith method for the further calculation of SPEI. The inverse distance weightage (IDW) method of interpolation has been used to represent the trends on ArcGIS. [ABSTRACT FROM AUTHOR]

This paper examined seasonal and annual rainfall trends of the Mae Klong River Basin in Thailand. Monthly rainfall data of eight stations were used for the period 1971--2015. The non-parametric Mann-Kendall test was used to detect trends whereas the slope of the trends was determined by Sen's slope method. Datasets with significant serial correlation were corrected by the pre-whitening approach. For the dry season, 5 out of 8 stations showed increasing rainfall trends while for the wet season, 50% of the stations showed increasing trends. On an annual scale, 75% of the stations exhibited increasing rainfall trends. The upper region of the basin which contributes inflows to the Srinagarind and Vajiralongkorn Dams had increasing annual rainfall trends. For the entire basin, the trend analysis found increasing rainfall on both seasonal and annual scales. The results of this study can help water resource managers prepare better assessments and planning of water resources in the basin. [ABSTRACT FROM AUTHOR]

Rainfall trend analysis provides useful information for effective planning, and management of water resources, and gives an insight into climate change of a region. This study investigates trends in annual and seasonal rainfall over Sri Lanka using an Innovative Trend Analysis (ITA) and Mann–Kendall (MK) test with Sen's slope estimator. The MK test showed increasing trends in annual rainfall at 24 stations (65%) with five stations showing significant increasing trend. Annual rainfall at 13 locations (35%) showed decreasing trend, but none were significant (p <.05). ITA results for annual rainfall showed increasing trend at 67% stations while 33% stations showed decreasing trend. MK test results for seasonal rainfall indicated increasing trend at 76, 51, 32, and 86% of stations during First Inter‐Monsoon (FIM), Second Inter‐Monsoon (SIM), South West Monsoon (SWM), and North East Monsoon (NEM) seasons, respectively. Seasonal analysis of rainfall trend using ITA method showed increasing trend in 81, 70, 32, and 65% stations during FIM, SIM, SWM, and NEM, respectively. ITA and MK tests exhibited similar trend results for 80% of the stations. Moreover, Spearman's rho correlation coefficient between ITA and MK test trends showed significant (p <.05) positive correlation. In general, eastern, south eastern, north and north central regions of the country showed increasing rainfall trend over the last 31 years (1987–2017) while western, part of north western and central part of the country indicated a decreasing rainfall trend during the same period. [ABSTRACT FROM AUTHOR]

Eight rainfall indicators were used to analyze the 5 min. × 5 min. grid-based rainfall characteristics in the central plain region of Thailand. The non-parametric statistics called Mann-Kendall test was applied to 32-year (1980-2011) time series data, statically significant (p-value ≤ 0.05) and Kendall’s tau correlation maps were considered for informative examination. Annual rainfall showed increasing trend in the upper basin and statically increasing by 35.8% of the whole basin. A number of rainy days (RD) and number of consecutive wet days (CWD) showed increasing by 70.2% and 57.9% of the basin for RD and CWD, respectively, while a number of consecutive dry days (CDD) exhibited decreasing trend over 85.0% of the study area even only 13.8% satisfied at 95% statically significant. The simple daily rainfall intensity (SDRI), heavy rainy days (HRD), max. 1-day, and max. 3-day demonstrated upward trends. The findings suggest that the region is shifting towards wetter condition. The explicit results of rainfall characteristics maps are expected to facilitate and allow various users to make better water resources management and overcome reluctant interpretation rainfall changes/trends for areas lacking of measured data as well as baseline information for climate change study. [ABSTRACT FROM AUTHOR]

Global warming has substantial impacts on the hydrologic cycle and precipitation characteristics. This study aimed to explore variability in the form of precipitation by analysis of trends in the snowfall-day (SD)/total precipitation-day (PD) ratio across Iran. To this end, the PD and SD records were acquired from 33 synoptic stations during 1965–2014. The Mann-Kendall (MK) test was the main method used for trend detection. Analyzing data exposed that the annual SD/PD ratio was less than 25% in a plethora of the stations. The MK test findings indicated downward tendencies in the SD/PD ratio over most regions of the country; although in many cases, the observed trends were not statistically significant. The changes in the annual SD/PD ratio were estimated down to − 2% per decade over ~ 70% of Iran's total area. Further studies are required to ascertain the linkages between the large-scale climate oscillations and the form of precipitation across this region. Also, using remotely sensed data (e.g., obtained from the MODIS) and fine-resolution gridded data (e.g., global and regional reanalysis data) in future research could improve the findings of such studies and dwindle the uncertainties caused by the scanty site-based observations. [ABSTRACT FROM AUTHOR]

In this study, decadal trend analysis of atmospheric temperature, water vapour, ozone, methane and carbon-monoxide has been presented over few major cities of India using Aqua-AIRS products from 2003 to 2012. The atmospheric column is studied in few atmospheric layers, viz., surface-850, 850–500, 500–100, 100–50 and 50–1 hPa for temperature, water vapour and ozone. However, CH4 and CO results are presented in total column amounts. Non-parametric Mann–Kendall test has been applied to investigate the trends of annual means of parameters and Sen's slope estimate has been used to find the rate of the change, if there is a trend. The layer average temperature (LAT) has been found to be increasing in lower troposphere (surface-850 hPa) and decreasing in lower stratosphere (100–50 hPa). The warming trend over Chennai is found to be not limited in lower tropospheric region, but extended in 850–500 hPa layer also. However, LAT (850–500 hPa) has decreasing trend over Thiruvananthapuram. LAT in 500–100 hPa has significant decreasing trend only over Ahmedabad. The decreasing LAT trend in 100–50 hPa is quite prominent with significant decreasing trends over Mumbai, Ahmedabad, Kolkata and Hyderabad. The layer integrated water vapour (LIWV) is found be increasing mainly in surface-850 hPa and 850–500 hPa layers. The decreasing trend of LIWV has been observed only over Ahmedabad in 500–100 hPa layer. For total column water vapour, the trends are mostly increasing, however, it is statistically significant only over Hyderabad. The layer integrated ozone has been found to be increasing in troposphere and decreasing in lower stratosphere. The increasing trend of ozone in troposphere is most prominent in lower-mid tropospheric region (850–500 hPa layer). No significant trend has been observed for total column ozone. Total column methane has shown significant increasing trend over all cities with very good significance level. However, for total column carbon-monoxide, the trends are decreasing and the decreasing trends are significant over Delhi and Mumbai. [ABSTRACT FROM AUTHOR]

Climate warming influences highly on snow cover regime in the midlatitudes. Snow cover conditions, in turn, affect human activity very much. The aim of this study was (a) to analyse spatial and temporal variability of snow cover duration, (b) to analyse spatial and temporal variability of the start and end dates of the period with the permanent snow cover, (c) to describe spatial, temporal and seasonal variability of median and maximum snow depth in Estonia and (d) to determine the presence of long-term changes and trends in these parameters during the period 1950/51–2015/16. Time series of daily snow depth at 22 stations for that period were processed in order to obtain reliable estimates of changes in the snow regime. Snow cover data are non-normally distributed, therefore, median and quartile range were used to describe the mean state and variability of snow cover. Only these dates were included into the analysis when snow cover was observed at least on 50% of days in the time series. Trend analysis was made using the Mann-Kendall test and trend values were found using the Theil-Sen's method. A large spatio-temporal variability of snow cover duration was found. The median number of days with snow cover at the 22 stations was 112, varying between 61 and 130 days. In the coastal regions of Estonia and especially on the western coast of Saaremaa Island snow cover duration has been much lower than in the continental part. The longest snow cover period is observed on uplands in south-eastern and north-eastern Estonia. It was found that, in the average, the period with the permanent snow cover in the continental Estonia begins on 19 December and ends on 18 March. There was a negative trend in snow cover duration due to the earlier snow melting in spring at the majority of stations. The end date of the permanent snow cover has shifted earlier by 10–30 days in 66 years and its duration has decreased accordingly. The maximum snow depth has been recorded on uplands of south-eastern Estonia with the median value 38 cm. There was a decreasing multiannual trend in snow depth from January to the end of March. Changes in snow depth were largest in the end of February and in March when the trend was statistically significant. In the average, snow depth has decreased by 0.5–1.5 cm per decade, i.e. by 2–9 cm throughout the whole study period. [ABSTRACT FROM AUTHOR]

Rainfall is a key factor of hydrological variability in a changing climate. Based on daily precipitation dataset of 50 station records, spatial and temporal variations of annual and seasonal precipitation from 1962 to 2011 in Qinghai Province, China are investigated using four statistical methods. Moreover, rainfall is analyzed for different rainfall intensities using the innovative trend analysis (ITA) method and the double ratio statistic (DRS) method. The results indicate that the reduction of precipitation in the densely populated eastern and southeastern regions is closely related to human activities. And human activities primarily affect extreme precipitation. The decrease in summer and autumn precipitation plays a major role in the decrease in the annual total precipitation. Spring and winter rainfall exhibit significant increasing trends. The inhibition of autumn precipitation caused by human activities is the most obvious, and this inhibitory effect can cause drought. It is hoped that this study can provide support for water resources planning and for dealing with droughts and floods. • The influence of human activities on the spatiotemporal variability of precipitation was revealed. • Variations of annual and seasonal precipitation for different rainfall intensities were detected. • Results varied with regions and seasons. • The findings provide support for water resources planning and for responding to droughts and floods. [ABSTRACT FROM AUTHOR]

The impact of climate change on sea level has re]a great deal of attention by scientists worldwide. In this context, the problem of sea levels on global and regional scales have been analyzed in a number of studies based on tide gauges observations and satellite altimetry measurements. This study focuses on trend estimates from 18 high-quality tide gauge stations along the Mediterranean Sea coast. The seasonal Mann-Kendall test was run at a 5% significance level for each of the 18 stations for the period of 1993–2015 (satellite altimetry era). The results of this test indicate that the trends for 17 stations were statistically significant and showed an increase (no significant trend was observed only at one station). The rates of sea level change for the 17 stations that exhibit significant trends, estimated using seasonal Sen's approach, range after correction for Vertical Land Motion (VLM) from 1.48 to 8.72 mm/a for the period 1993–2015. Furthermore, the magnitude of change at the location of each tide gauge station was estimated using the satellite altimetry measurements. Thus, the results obtained agree with those from the tide-gauge data analysis. [ABSTRACT FROM AUTHOR]

A basic statistical data analysis and trend analysis were performed for the flows recorded at the Tisne Stine 1 gauging station on the Cetina River. The analysis was carried out using the linear and nonlinear regression, Mann-Kendall test, and the so-called RAPS method. [ABSTRACT FROM AUTHOR]

A basic statistical data analysis and trend analysis were performed for the flows recorded at the Tisne Stine 1 gauging station on the Cetina River. The analysis was carried out using the linear and nonlinear regression, Mann-Kendall test, and the so-called RAPS method. [ABSTRACT FROM AUTHOR]

Study on rainfall pattern of a region over a number of years is very useful for crop planning and irrigations scheduling. The present study deals with the probability and trend analysis of monthly rainfall in selected rain gauge stations scattered over the entire state of Haryana. Probabilities for drought, normal and abnormal events for monthly rainfall have been worked out using monthly rainfall data for 42 years (1970- 2011), covering 27 rain gauge stations in the state of Haryana. Analysis indicated that drought months are more probable than normal months while normal months are more probable than abnormal months. The monotonic trend direction and magnitude of change in rainfall over time have been examined using the Mann-Kendall test and Sen's slope estimator tests. Using the Mann-Kendall test and Sen's slope estimator, the significant decrease in annual rainfall was noticed at Ballabgarh and Thanesar, While in monsoon rainfall, a significant decrease was noticed at Thanesar and Narnaul. But Sirsa is the only district which shows a significant increase in annual and monsoon rainfall. In probability analysis the highest per cent of normal, draughts and abnormal months was observed for Ambala, Hassanpur and Dujana respectively. [ABSTRACT FROM AUTHOR]

A comprehensive evaluation of trends in annual instantaneous maximum flows (AIMF) from 153 gauge stations located in 26 river basins in Turkey is presented. Two traditional non-parametric trend tests, the Mann-Kendall (MK) and Spearman's rho (SR), are used to quantify the significance of trends, while Sen's slope method is applied to determine the magnitude of trends. The traditional tests indicate that the AIMF records of 57 stations showed statistically decreasing trends, while those of six stations showed an increasing trend. Sen's trend method, which provides more detailed assessment of the trends in different clusters (low, medium and high), was applied to the AIMF series and the results were compared with traditional tests. Sen's trend method indicated that all flow clusters at nine stations have increasing or decreasing trends, although no significant trend was detected by the MK and SR tests. [ABSTRACT FROM AUTHOR]

Hydrological and meteorological studies indicate that hydrological processes and water resources are significantly affected by the climate change, particularly with increasing greenhouse gases and temperature. In this study, the trend analyses of the biggest precipitation intensity values in Trabzon, the most populous province of the Eastern Black Sea Basin in Turkey, have been carried out by using Mann-Kendall and Şen's Innovative Trend methods. In line with this purpose; the precipitation intensity data of standard time series from 5 minutes to 24 hours for two meteorological stations in Trabzon (Trabzon and Akçaabat) were used. Before carrying out trend analyses, the Run (Swed Eisenhart) Homogeneity Test was applied to all data and non-homogeneous data were not analysed. Before applying the Mann-Kendall Method, the internal dependency of the data was examined. When the analysis results are reviewed; in Trabzon Meteorology Station an increasing trend for intensities of all-time series has been detected, whereas in the Akçaabat Meteorology Station, a general result of the trend could not be obtained as most the data related to different time series were not homogeneous data. [ABSTRACT FROM AUTHOR]