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The Mann–Kendall test associated with the Sen's slope is a very widely used non-parametric method for trend analysis. It requires serially uncorrelated time series, yet most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have therefore been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction of the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman lidar water vapor mixing ratio, as well as tropopause and zero-degree temperature levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study evaluates the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the number of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series. [ABSTRACT FROM AUTHOR]

The most widely used non-parametric method for trend analysis is the Mann-Kendall test associated with the Sen's slope. The Mann-Kendall test requires serially uncorrelated time series, whereas most of the atmospheric processes exhibit positive autocorrelation. Several prewhitening methods have been designed to overcome the presence of lag-1 autocorrelation. These include a prewhitening, a detrending and/or a correction for the detrended slope and the original variance of the time series. The choice of which prewhitening method and temporal segmentation to apply has consequences for the statistical significance, the value of the slope and of the confidence limits. Here, the effects of various prewhitening methods are analyzed for seven time series comprising in-situ aerosol measurements (scattering coefficient, absorption coefficient, number concentration and aerosol optical depth), Raman Lidar water vapor mixing ratio and the tropopause and zero degree levels measured by radio-sounding. These time series are characterized by a broad variety of distributions, ranges and lag-1 autocorrelation values and vary in length between 10 and 60 years. A common way to work around the autocorrelation problem is to decrease it by averaging the data over longer time intervals than in the original time series. Thus, the second focus of this study is evaluation of the effect of time granularity on long-term trend analysis. Finally, a new algorithm involving three prewhitening methods is proposed in order to maximize the power of the test, to minimize the amount of erroneous detected trends in the absence of a real trend and to ensure the best slope estimate for the considered length of the time series. [ABSTRACT FROM AUTHOR]

This article deals with the spatio-statistical analysis of temperature trend using Mann-Kendall trend model (MKTM) and Sen's slope estimator (SSE) in the eastern Hindu Kush, north Pakistan. The climate change has a strong relationship with the trend in temperature and resultant changes in rainfall pattern and river discharge. In the present study, temperature is selected as a meteorological parameter for trend analysis and slope magnitude. In order to achieve objectives of the study, temperature data was collected from Pakistan Meteorological Department for all the seven meteorological stations that falls in the eastern Hindu Kush region. The temperature data were analysed and simulated using MKTM, whereas for the determination of temperature trend and slope magnitude SSE method have been applied to exhibit the type of fluctuations. The analysis reveals that a positive (increasing) trend in mean maximum temperature has been detected for Chitral, Dir and Saidu Sharif met stations, whereas, negative (decreasing) trend in mean minimum temperature has been recorded for met station Saidu Sharif and Timergara. The analysis further reveals that the concern variation in temperature trend and slope magnitude is attributed to climate change phenomenon in the region. [ABSTRACT FROM AUTHOR]

Abstract: The annual and seasonal trends of seven meteorological variables were analyzed for twelve weather stations in Serbia during 1980–2010. The non-parametric Mann-Kendall and Sen''s methods were used to determine whether there was a positive or negative trend in weather data with their statistical significance. The occurrence of abrupt changes was detected using cumulative sum charts and bootstrapping. In the present study, the increasing trends were indicated in both annual and seasonal minimum and maximum air temperatures'' series. The relative humidity decreased significantly in summer and autumn, while the vapor pressure had a significant increasing trend in spring, summer and autumn. Besides, no significant trends were detected in summer and winter precipitation series. In general, the results of using the Mann-Kendall and Sen''s tests demonstrated the good agreement of performance in detection of the trend for meteorological variables. [Copyright &y& Elsevier]

The wind has considerable effects on the ecosystem and evaporation as an essential parameter of the hydrological cycle. Therefore, determining historical changes in the wind will help to specify these effect levels. Although there are studies on the determination of wind speed trends by several researchers in Turkey, it is necessary to investigate the changes in the trend structure with recent data. For this purpose, the trends of monthly surface wind speed data from 1970 to 2021 belonging to 199 meteorology observation stations in Turkey are determined in the present study. The nonparametric Mann–Kendall test and Sen's slope method are used in the trend analysis accounting for serial correlation effects. The trend analysis results of wind speed data are evaluated temporally and spatially for seven geographical regions within Turkey. As a result of this study, a prominent part of stations in Turkey shows a decrease or significant decrease trend. In addition, as a result of comparisons made with previous studies, it is determined that the trend structure of the wind speed in the country has changed. In the annual and monthly wind speeds, it is observed that the number of stations has a "significant trend" decreased considerably. [ABSTRACT FROM AUTHOR]

The harmful effects of factors such as human-induced effects, greenhouse gases, fossil fuel consumption, urbanization and destruction of agricultural lands on the environment and climate change are increasing day by day. All these changes can create undesirable and permanent effects on human health, living species and the environment. The analysis of climate parameters on a regional basis is very important in understanding the effects of climate change and taking the necessary predictable steps to reduce its harmful effects. This study analyzes changes in various climate parameters of Ardahan province, such as temperature, precipitation amount, wind speed and sunshine duration. For this purpose, the changes in the three-year averages of the climate parameters measured by Ardahan Meteorological Station between 2001-2021 are analyzed. In addition, non-parametric Mann Kendall and Sen slope methods are applied to climate parameters, and monthly, seasonal and annual values of each parameter are evaluated according to the statistical significance level of 5%. As a result, increasing trends in the temperature values of the region and decreasing trends in the average precipitation, relative humidity and average wind speed values are determined. Thus, this study aims to understand the impacts of climate change and manage risks, as well as to provide the necessary climate data for thermal energy projects planned to be carried out in the region. [ABSTRACT FROM AUTHOR]

In order to grasp the evolutionary trend of drought and vegetation in various regions of North China, and to analyze the degree of response of vegetation to different drought types, it is of guiding significance for drought mitigation work. In the paper, data from 90 meteorological stations in North China were used to construct the standardized precipitation evapotranspiration index (SPEI), and the normalized vegetation index (NDVI) was selected to quantify the vegetation coverage. Then, the Sen's slope estimator, Mann-Kandall test, correlation analysis, and Copula-Bayesian conditional probability formula were used to analyze the drought sensitivity of vegetation in North China. The results show that: (1) the SPEI value in most parts of North China showed a extremely significant increase, the SPEI value in some parts of North China showed a extremely significant decrease, the NDVI value in the western and northern regions of North China showed an extremely significant increase, and the rest of the regions showed an extremely significant decline. (2) Drought and vegetation in North China were dominated by insignificant spatial clustering, with high and low values of drought and vegetation clustering in small areas. (3) In North China, the correlation between annual scale SPEI and NDVI was good, the response rate of vegetation to drought was slower and the sensitivity was low, the correlation between month-scale SPEI and NDVI was better in some parts of Henan, and the vegetation was more sensitive to drought, and the joint distribution function of NDVI and SPEI was the most consistent with the Clayton Copula function. (4) The likelihood of lowest vegetation coverage occurring in North China was decreasing with decreasing drought, the likelihood of lower vegetation coverage occurring was increasing from extreme to moderate drought and decreasing from moderate to light drought, and the likelihood of medium, higher and highest vegetation coverage all increase with decreasing drought. [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]

Based on data from 1966 to 2018, the present study analyses precipitation trends in the Zohreh–Jirahi basin in Iran. The homogeneity of data was estimated based on the Kolmogorov–Smirnov method. Missing data were reconstructed using inverse distance weighted (IDW) and ordinary-linear-kriging methods. The results were evaluated by the root mean square error (RMSE), mean absolute error (MAE), and coefficient of determination (R2). A multidimensional raster was generated containing precipitation values related to the statistical period years, and time-series data were produced in array format per unit area. Based on the period range, 34,267 points underwent the time-series analysis, and the trend change was assessed based on the Mann–Kendall method. The Sen’s slope was evaluated using yearly and monthly scales in these points, followed by producing raster maps. The IDW was selected with an R2 value of 0.95 as the optimal method of missing data reconstruction in this time period based on the model evaluation. The results showed no significant trends on a yearly scale, but on the monthly scale, December–April–November–August–September showed the highest ascending trends, respectively, February–December–March–May exhibited the highest descending trend, respectively, and June–July–September showed no trends. The maximum and minimum mean Sen’s Slope were estimated for December (0.213) and February (–0.68), respectively. Water resources management is inevitable, particularly in the agricultural sector as the main consumer with macro socioeconomic dimensions. Given the notable impact of water supply time to optimise and increase productivity, the current study can contribute to revising the cultivation patterns and time in the region. The recharge of aquifers, the storage process, and the consumption process should be compatible with forthcoming changes. [ABSTRACT FROM AUTHOR]

This study aimed to analyze the trend in land surface temperature (LST) over time using the entire archive of the available cloud-free Landsat images from 1986 to 2022 for Jordan and its nine local climate zones (LCZs). Two primary datasets were used (i) Landsat-5; -8 imagery, and (ii) map of LCZs of Jordan. All LST images were clipped, preprocessed, and checked for cloud contamination and bad pixels using the quality control bands. Then, time-series of monthly LST images were generated through compositing and mosaicking processes using cloud computing functions and Java scripts in Google Earth Engine (GEE). The Mann–Kendall (MK) test and Sen's slope estimator (SSE) were used to detect and quantify the magnitude of LST trends. Results showed a warming trend in the maximum LST values for all LCZs while there was annual fluctuation in the trend line of the minimum LST values in the nine zones. The monthly average LST values showed a consistent upward trajectory, indicating a warming condition, but with variations in the magnitude. The annual rate of change in LST for the LCZs showed that the three Saharan zones are experiencing the highest rate of increase at 0.0184 K/year for Saharan Mediterranean Warm (SMW), 0.0185 K/year for Saharan Mediterranean Cool (SMC), and 0.0169 K/year for Saharan Mediterranean very Warm (SMvW), indicating rapid warming in these regions. The three arid zones came in the middle, with values of 0.0156 K/year for Arid Mediterranean Warm (AMW), 0.0151 for Arid Mediterranean very Warm (AMvW), and 0.0139 for Arid Mediterranean Cool (AMC), suggesting a slower warming trend. The two semi-arid zones and the sub-humid zone showed lower values at 0.0138, 0.0127, and 0.0117 K/year for the Semi-arid Mediterranean Cool (SaMC), Semi-arid Mediterranean Warm (SaMW) zones, and Semi-humid Mediterranean (ShM) zones, respectively, suggesting the lowest rate of change compared to other zones. These findings would provide an overall understanding of LST change and its impact in Jordan's LCZs for sustainable development and water resources demand and management. [ABSTRACT FROM AUTHOR]

Several indices based on both single and multiple hydro-climatic factors have been established to monitor and evaluate droughts. However, no single drought indicator can accurately describe the density of the overall drought due to constraints caused by a number of factors. Therefore, combining many drought indices more properly into one instrument to better depict and manage droughts would be beneficial. This study differentiated and established two merging methods through Triple Collocation (TC) and Multi-scalar Weighted Amalgamated Drought Index (MWADI). Therefore, three individual standardized drought indices (SDIs), including the Standardized Precipitation Index (SPI), Standardized Precipitation Temperature Index (SPTI), and Standardized Precipitation Evaporation Index (SPEI), were used to fabricate a composite Merged Drought Index (MDI) based on TC and MWADI across six meteorological stations of Pakistan from 1971 to 2017. Their performance was analyzed through correlation, Taylor diagram, Kling Gupta Efficiency Criteria (KGE), Root Mean Square Error (RMSE), and Sen's slope. In addition, the performance of TC is also evaluated through Correlation Coefficient (CC's) and Fixed Residual Mean Square Error (FRMSE) through Extended Triple Collocation (ETC). The exploratory analysis reveals that individual indices SPI and SPTI are closely related to both MDI relative to SPEI across the study area. However, TC-based MDI has a stronger correlation with the individual indices than MWADI. Sen's slope indicates a similar trend with a slight divergence in magnitude from both approaches. The estimated values of KGE suggest TC exhibits better efficiency than MWADI. RMSE shows a minute difference under both approaches. Overall, TC executes well with individual series in contrast with MWADI. Furthermore, the performance of TC is also evaluated through Correlation Coefficients (CC's) and Fixed RMSE (FRMSE), where it shows high sensitivity. However, implicit presumptions place restrictions on the accuracy of TC comparison. MDI has the potential to be a useful tool for collaborating and making decisions concerning multiple kinds of droughts. [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]

Introduction Groundwater is an essential natural resource that is widely used to meet domestic, industrial, and agricultural needs. In recent years, the amount of withdrawal from groundwater has been more than the amount of its recharging leading to going out of balance. Since groundwater is in the ground and it is not possible to observe directly, identifying its properties is time-consuming and expensive. On the other hand, problems such as inconsistent and incomplete input information, heterogeneous aquifers, etc., have made groundwater study difficult. In many watersheds, groundwater resources are the strategic and primary source of water supply for different users. However, groundwater extraction has exceeded the amount rate of recharge in many regions around the world, resulting in harmful ecological and environmental problems, such as water level decline, water quality degradation, drying up of wells, increased pumping costs, and land subsidence. Assessing groundwater resources for their available water volume and obtaining an accurate prediction of groundwater levels (GWL) is central to sustainable management (i.e., balancing between demand and supply) of groundwater and surface water resources in a watershed. Therefore, tools such as groundwater modeling are used to solve this problem. Simulation of groundwater flow with mathematical models is an indirect approach to solving problems with lower costs than direct methods. In fact, the use of mathematical modeling is to simulate the natural conditions of the water surface with mathematical relationships. Groundwater modeling is done using differential equations, and one of the most widely used methods in solving groundwater problems is the use of finite differences and finite elements. Accordingly, the groundwater modeling system (GMS) model and the MODFLOW code were used in this research to study the Mahabad aquifer. Next, the trend of changes in the groundwater level of the range was analyzed by non-parametric tests. Accordingly, the groundwater modeling system (GMS) model and the MODFLOW code were used in this research to study the Mahabad aquifer. Materials and Methods The study area of Mahabad is located in West Azarbaijan province. GMS software and MODFLOW code were used for groundwater simulation. Using the information of 22 observation wells, exploitation wells information, river information, recharge, and withdrawal from groundwater, the desired model was built. . The model was run in September 2015 for the steady-state and October 2010- September 2011 for the transient state with a monthly time step. The values for hydraulic conductivity and storage coefficient were calibrated for the steady and transient states, respectively. Aquifer thickness varied from 60 to 200 m, and the cell size was considered 200 × 200 m. Rainfall infiltration, return flow, and input flows feed the aquifer. Seventeen percent of the monthly rainfall was considered rainfall infiltration that fed the aquifer. Moreover, based on the wells' primary use, return water from the wells was considered about70, 75, and 20% for drinking water, industrial and agricultural wells, respectively. The GWL is higher in the South part of the aquifer compared to other parts and, as we move from the South part of the aquifer towards its central and southern regions, the GWL declines. In conclusion, the groundwater flows from the upper South part of the aquifer towards its lower part. More exploitation wells are in the aquifer's central section, and most of their extracted water is used for urban and agricultural purposes. It was then implemented in two stable and unstable modes and its performance was evaluated with root mean square (RMSE), mean absolute error (MAE), and coefficient of determination (R²) criteria. Various statistical methods have been provided to analyze the trend of time series. Among them, non-parametric methods are more useful in the time series of hydrological variables. These methods are suitable for time series that have elongation or skewness and are independent of the statistical distribution of the time series. In the following, the Mann- Kendall method and Sen’s slope were used to determine the trend of the groundwater level at significant levels of 90, 95, 99, and 99.9%. Results and Discussion The simulation results showed that there is a very good agreement between the simulation and observational data. The model evaluation criteria including RMSE, MAE, and R² for two stable and unstable modes were calculated as 0.84, 0.63, and 0.99, as well as 0.88, 0.72, and 0.98 m, respectively. These values showed the appropriate efficiency of the model. Based on the results, the highest level of groundwater was in the south of the Mahabad aquifer and the lowest level was in the north of the aquifer. The optimized values of hydraulic conductivity, special yield, aquifer thicknesses, values of exploitation wells, and aquifer transmissivity were determined from the groundwater simulation results. The results of the Mann-Kendall test showed that Haji Khosh, Gapis, and Gorg Tapeh stations had the highest downward trend. So, in these stations, the downward trend was more significant at the level of 0.99%. The Mann-Kendall Z-parameter values were positive for the Qom Qala station, which indicated the rising trend of the underground water level in this area. The results of Sen’s slope test also confirmed the results of the Mann-Kendall test. It was so that the Sen’s slope test showed that the downward slope of the three stations Haji Khosh, Gapis, and Gorg Tapeh occurs more strongly. Conclusion The results of this research showed that GMS and MODFLOW codes are suitable tools for simulating groundwater and the condition of the aquifer with proper accuracy. Also, the results of Mann-Kendall and Sen’s slope tests showed that out of 19 wells, almost 18 had a downward trend, which shows that the Mahabad aquifer is not in a favorable condition and with the increase in harvesting and decrease in rainfall, especially in recent years, its situation will worsen. The Mann-Kendall test showed that the Mahabad aquifer is in poor condition so out of the 19 investigated wells, approximately 18 wells had a downward trend in the groundwater level. The age slope estimator method also confirmed the Mann-Kendall results. Examining the obtained results exhibits that the use of new approaches for simulation provides the opportunity to manage and balance the allocation of groundwater resources effectively. Further, the use of new tools can be considered for implementing balancing scenarios related to groundwater resources. [ABSTRACT FROM AUTHOR]

The present study highlights the long-term variations in temperature and precipitation using time series datasets of CRU and ERA5-Land across the Himalayan region. The Mann-Kendall and Sen’s slope-based trend analysis exhibited an apparent warming trend in the region, with higher increase in Tmin (1.5°C) and comparatively lower increase in Tmax (0.8°C) during 1901–2018. The joint CCl/WCRP/JCOMM based 19 extreme indices elucidated an increasing frequency of warm days (16%) in Central Himalayan urban agglomerations (HUAs) with a declining number of cold days (-6%) and cold nights (-4%) during 2000–2019. The moderate decline in the wet days (8%) and consecutive wet days (-5 days/20 years) observed in all HUAs compared to an increasing frequency of the consecutive dry days (3 days/20 years). Study reported higher warming in Kathmandu UA, while increased precipitation in Srinagar UA, and contributes to framing climate change adaptation and mitigation strategies in the mountainous system. [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 impact of climate change on annual and monthly air precipitation and temperature has received a great deal of attention by scholars worldwide. This study focuses on detecting trends in annual precipitation and temperature for Addis Ababa methodological station located in Addis Ababa. SPSS in combination with the excel spreadsheet used for analyzing the statistics. We were also used the Mann-Kendall trend test and Sen's slope method to detect trends and the magnitude of change. According to our analysis, the mean minimum temperature ranges between 8.45°C and 12.5°C, while, the mean maximum temperature varies between 21.1°C and 25.5°C From the analysis the monthly coefficient of variation (CV%) for both maximum and minimum temperature ranging from (5.4% in April) to (14.08% in December). According to the Mann Kendal test and Sen's slope estimate, the annual maximum temperature shows a statistically significant increasing trend but no significant positive or negative trend was detected in annual perception as well minimum temperature. [ABSTRACT FROM AUTHOR]

The continuous decline in groundwater levels/groundwater storage and the projected increase in groundwater usage along the coast of a region need a scientific, systematic management plan to protect from saltwater intrusion. Accurate estimation of groundwater level or storage variation is very much essential for better groundwater resource management in coastal areas. In this present study, the trend of groundwater storage (GW) changes is studied along the coast of Kerala using terrestrial water storage (TWS) from gravity recovery and climate experiment (GRACE) and soil moisture (SM) from GLDAS during the period 2002–2020. The trend in groundwater level is also studied using in-situ observations. The data from GRACE mission and GLDAS is interpolated to the in-situ observation well locations. The trend is analysed using Mann–Kendall (MK) test, Sen’s innovative trend analysis (ITA), Sen’s slope (SS), and wavelet analysis. The findings reveal a falling trend in 13 wells during the pre-monsoon season and high salinity was observed in a few wells. From Sen’s slope, it was found that the average decline in groundwater level is 1 cm/yr and groundwater storage change is 0.1 cm/yr. A negative trend in groundwater should be seen with caution and proper management measures should be taken to arrest further decline in groundwater levels. Research highlights: The trend analysis using MK, SS and ITA showed that there is significant falling trend of groundwater storage change and groundwater level along the coastal aquifer of Kerala. A negative trend of groundwater should be seen with caution and proper measures should be taken in these areas to arrest further decline in groundwater levels. Soil moisture has a significant role in variability of Terrestrial Water Storage. [ABSTRACT FROM AUTHOR]

Fractional vegetation cover (FVC) has been one of the most widely utilized indicators to assess the vegetation growth status for the better ecological environment. The spatiotemporal evolution of FVC and its relationship with the climate change can also be crucial to promote the ecological civilization and the sustainable development in agriculture and animal husbandry. However, the large-scale and long-term FVC research is often required to deal with the massive image data, leading to the significant challenges on research advancement. The Google Earth Engine (GEE) platform can be expected to offer the new and effective solutions. In this study, a perfect tradeoff was gained between the rich data sources and powerful processing using GEE platform. A dimidiate pixel model (DPM) was employed to produce the FVC under the different types of land cover in Inner Mongolia, northern China. The Sen's slope estimator and Pearson correlation coefficient were then used to identify the temporal and spatial change of FVC and its correlation with the climate factors. Initially, the FVC annual time series were collected from 1982 to 2021 using the NOAA AVHRR NDVI data within GEE. Subsequently, Sen's slope estimation and the Mann-Kendall test were conducted to analyze the significant trends of FVC over the four decades. Finally, Pearson correlation coefficients were calculated between FVC and climate factors. The significance tests were carried out to reveal the relationship between FVC and climate change. Additionally, the spatial distribution maps were obtained to depict the spatial pattern of the correlations in the significant correlated regions. The results showed that: 1) There was the significant band pattern in the spatial distribution of the average FVC, with the gradual increase from the west to the northeast. A large difference of FVC was also found in different types of land cover. Generally, the FVC was >0.1-0.2 in 18.7% of the region. The proportions of FVC >0.5-0.6 and >0.7-0.8 were both close to 15%. There were the relatively small proportions of 0-0.1 and >0.8-0.9, particularly without the proportions of >0.9-1.0. The rest intervals were about 10%. 2) The FVC decreased significantly in the desert area of western Inner Mongolia, accounting for 13.1%. There was no significant trend of FVC in the mountains and grasslands of the middle, where the area was accounted for 38.2%. A very significant upward trend was found in the FVC of croplands, forests and part of grasslands in the south, east and northeast, accounting for 38.3% of the total. 3) There were the significantly different correlations between FVC and meteorological factors in the different types of land cover. The FVC of grassland was significantly or extremely significantly positive correlated with the annual precipitation, while the FVC of croplands was significantly or extremely significantly positive correlated with annual average temperature, and the FVC in some areas of forests was significantly negative correlated with annual precipitation, as well as the FVC in some barren was significantly negative correlated with annual average temperature. A 40-year-long time series of annual FVC were successfully constructed using NOAA AVHRR NDVI data on the GEE platform. The robust processing and analysis capabilities of platform were better balanced to identify the trends of FVC in the various land cover types, together with the correlation with climate change. These findings can provide some significant implications to guide the ecological environment protection for the sustainable development in Inner Mongolia of China. [ABSTRACT FROM AUTHOR]

Rainfall forecasting models developed using the seasonal autoregressive integrated moving average (SARIMA) technique for spatially distributed rain gauge stations in the state of Kerala are presented in this paper. Monthly rainfall data for 113 years were considered to build models for 29 meteorological stations. As the time-series data span 11 decades, there is a high chance of non-stationarity, owing to the presence of trend and seasonality. The non-stationarities in the time-series datasets are assessed by applying the seasonal and trend decomposition using the Loess technique (STL) and Kwiatkowski–Phillips–Schmidt–Shin (KPSS) test. Prior to applying this procedure, preliminary analyses on the datasets are carried out to detect and fill the missing values, examining the properties of the time-series datasets, including the tests for detecting trend and seasonality. Out of 29 stations, the results of the Mann–Kendall trend test indicated that marginal trends were present only in the rainfall datasets belonging to two stations. The partial autocorrelation function (PACF) and autocorrelation function (ACF) plots of most of the stations showed strong seasonal autocorrelation. The developed SARIMA models also indicated that the influence of the seasonal components is dominant compared to non-seasonal components for most of the stations. Research Highlights: The percentage of missing values in the precipitation time-series datasets ranged between 0.52% (at Thiruvananthapuram (O)) and 14.75% (at Irikkur) and were filled by applying the expectation–maximisation algorithm. Shapiro–Wilk test and Kolmogorov–Smirnov test were applied to check for normality of the time series. It was found that all the stations were non-normally distributed. Classical Mann–Kendall and Sen’s slope were applied to determine the magnitude and direction of the trend. STL technique and KPSS test are applied to evaluate the stationarity behaviour in the meteorological time-series datasets. [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]

The consensus on climate warming is well‐established, and extreme values inherently encapsulate more information than averages. Against the backdrop of frequent extreme climate events, studying extreme values holds profound significance. This study aims to reveal the characteristics of extreme climate events and their role in triggering extreme hydrological events in the typical North China basin, that is, the Luanhe River Basin. Trends of 25 extreme climate indices during 1960–2018 are analysed using the Sen's slope and MK significance test to study the changing characteristics of extreme climate. Characteristics of extreme flood and dry events are examined, encompassing trend analyses at different time scales (seasonal, interannual, decadal) and concentration analysis. Finally, and most significantly, correlation analysis is conducted on extreme climate indices and features of extreme hydrological events, followed by principal component analysis of extreme climate indices, to precisely quantify the impact of extreme climate on the occurrence of extreme hydrological events. The results indicate a warming trend in extreme temperature indices, with a more significant rise in minimum temperatures compared to maximum temperatures. There is a significant decrease in precipitation, but precipitation at higher magnitudes is less affected by the overall reduction in total precipitation. Extreme dry events have markedly increased, particularly concentrated in winter with delayed occurrences, primarily induced by extreme temperature events, that is, warming effects. Conversely, extreme flood events have significantly decreased, mainly concentrated in summer and early autumn, predominantly caused by extreme precipitation and extreme high‐temperature events. The climate and hydrological conditions in the study area have become more extreme and complex. Severe river droughts may occur more frequently in winter, while extreme flooding may happen in summer. Therefore, it is necessary to pay more attention to these developments. [ABSTRACT FROM AUTHOR]

Understanding local-scale climate change is vital to developing adaptive strategies in the face of the century-old river of global warming posing a threat to humanity. This study focuses on assessing temperature and rainfall trends in Beni City, using monthly and yearly (1990–2020) weather station data. Climate variability was analysed using the standardised variable index, and rainfall concentration patterns were highlighted using the precipitation concentration index (PCI). The climate trends were analysed by using the Mann–Kendall test and Sen's slope estimator. The findings indicated that the Tmin is 18.82±0.62°C, and Tmax is 28.22±0.75°C, resulting in a mean temperature of 23.52±0.57°C. The annual and seasonal temperature trend analysis indicated that a significant warming trend was observed in both Tmin and Tmax. Beni City's precipitation trends also showed a mean annual rainfall of 1988.38±416.59 mm, with significant year-to-year variations. Annual rainfall analysis exhibited a slight upward trend; meanwhile, the seasonal trend analysis revealed an increase in rainfall during Mar–Apr–May (MAM) and Aug–Sep–Oct–Nov (ASON) seasons with roughly no discernible trend during Dec–Jan–Feb (DJF), and Jun–Jul (JJ) seasons. Overall, annual and seasonal analyses of specific temperature and rainfall patterns have shown pronounced warming and increased rainfall in the study area. Research highlights: The study reveals significant trends in temperature and rainfall in Beni city, Democratic Republic of Congo, over a 31-year period (1990–2020). Both minimum and maximum temperatures show significant warming trends, with the most recent decade witnessing substantial increases in maximum temperatures. Rainfall patterns exhibit variations, with a slight upward trend in annual rainfall, although the 1990s experienced a notable decrease in precipitation. Monthly analyses highlight specific temperature and rainfall patterns with some months experiencing pronounced warming and increased rainfall. [ABSTRACT FROM AUTHOR]

Guinea-Bissau has experienced the tangible effects of climate change in recent years. An examination of the annual and monthly precipitation patterns from 1960-2020 indicates an overall decreasing trend in climatological series. In contrast, the monthly average maximum and minimum temperatures exhibit significant increasing trends during the same period. Analysing daily rainfall and maximum and minimum temperature leads us to conclude that rainfall and temperature patterns have evolved in different ways. The non-parametric Mann–Kendall test was used to assess trends in climate indices, and Sen’s slope estimator was used to detect the magnitude of trends. Despite the significant decrease in wet days, there has been an upward trend in rainfall over the 1981-2020 climatological series. Days with maximum temperatures of at least 30 and 35ºC have increased in all seasons with a very significant upward trend for 30ºC. On the other hand, absolute annual maximum temperatures have tended to decrease in Bolama and Bafatá by -0.25 and -0.17ºC per decade respectively. There has also been a decrease of 0.30ºC and 0.39ºC per decade in the minimum temperatures in Bolama and Bafata, i.e. the nights have been getting cooler, which somewhat contradicts the global trends generally reported for this same parameter. [ABSTRACT FROM AUTHOR]

Heat waves significantly impact people's lives and livelihoods and are becoming very alarming and recognized as hot topics worldwide, including in Bangladesh. However, much less is understood regarding recent hotspots, the frequency of heat waves over time, and their underlying causes in Bangladesh. The objective of the study is to explore the current scenario and frequency of heat waves and their possible causes across Bangladesh. The Mann-Kendall and Sen's slope techniques were used to determine seasonal and annual temperature trend patterns of heat wave frequencies. Daily maximum temperature datasets collected from the Bangladesh Meteorological Department (BMD) during 1991–2021 are applied. The frequency of days with Tmax≥ 36°C as the threshold was used to compute different types of heat waves based on the BMD's operational definition. The results show that the mild heat wave (MHW) days followed the subsequent hotspot order: Rajshahi (103) > Chuadanga (79), Ishurdi (60), and Jessore (58), respectively. The frequency of days with Tmax≥36°C was persistence for many days in 2014, especially in the western part of Bangladesh compared to other parts. Similarly, the heat waves condition shown its deadliest event by increasing more days in 2021. The highest increasing trend was identified at the Patuakhali site, with a rate of 0.516 days/year, while the highest decreasing trend was noticed at the Chuadanga site, with a rate of -0.588 days/year. The frequency of days (Tmax≥36°C) is an increasing trend in the south-western part of Bangladesh. The synoptic condition in and around Bangladesh demonstrates that the entrance of heat waves in Bangladesh is due to the advection of higher temperatures from the south/southwest of the Bay of Bengal. The outcomes will guide the national appraisal of heatwave effects, shedding light on the primary causes of definite heatwave phenomena, which are crucial for developing practical adaptation tools. [ABSTRACT FROM AUTHOR]

Rainfall seasonality in Serbia is examined trough the analysis of several indices: seasonality index (SI), individual seasonality index ( SI i) , and replicability index (RI). Based on data from 14 synoptic stations that are well distributed over Serbian territory, spatial distribution of general seasonality index SI ¯ , mean individual seasonality index SI ind ¯ , and RI are analyzed in two subperiods (1961–1990 and 1991–2020). The modified Mann–Kendall test (MMK) and Sen's slope methods are used to investigate the possible trends and its magnitudes in time series of SI i . The values of SI ¯ show that precipitation regime in Serbia is very equitable or equitable with definite wetter season. For the entire Serbian territory, the values of RI are low, indicating that the month of maximum rainfall occurs over large spread of months along the studied periods. The existence of significant negative correlation between RI and longitude is found. Results show a highest influence of the North Atlantic Oscillation and East Atlantic/West Russia pattern on rainfall seasonality in Serbia. [ABSTRACT FROM AUTHOR]

Malaria is one of the greatest public health challenges in sub-Saharan Africa. Benin records malaria as the leading cause of mortality and morbidity. This study aims to analyze the climate and examine the relationship between the incidence of malaria and climatic variables in Northern Benin. The precipitation concentration index (PCI), Pettit test, Mann Kendall (MK) test, and Sen's slope method estimates were used to analyze the trends of temperature, rainfall, and rainfall intensity using monthly data from 1991 to 2021 at two meteorological stations and nine rain gauge stations in northern Benin. Pearson correlation tests, principal component analysis, and plots were computed to determine the relationship between malaria incidence and climatic variables over 2009–2021. Total precipitation and rainfall intensity are decreasing. The temperature showed a positive trend with an increase in the monthly and annual temperature. Monthly rainfall; minimal, maximal, and mean; relative humidity; and mean and maximal temperature have a significant positive correlation with malaria incidence. A range of 80–220 mm of precipitation, 25–35°C of temperature, 55–85% of relative humidity, and 1.6–2.7 m/s of wind speed is suitable for the transmission of malaria. Maximal temperature and relative humidity may have a large influence on how much malaria spreads in Northern Benin. These factors could help to develop a malaria early warning system in the study area. [ABSTRACT FROM AUTHOR]

This study explores the spatiotemporal variability and determinants of rainfall patterns during the March to May (MAM) season in Rwanda, incorporating an analysis of teleconnections with oceanic–atmospheric indices over the period 1983–2021. Utilizing the Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) dataset, the study employs a set of statistical tools including standardized anomalies, empirical orthogonal functions (EOF), Pearson correlation, the Mann–Kendall (MK) trend test, and Sen's slope estimator to dissect the intricacies of rainfall variability, trends, and their association with large-scale climatic drivers. The findings reveal a distinct southwest to northwest rainfall gradient across Rwanda, with the MK test signaling a decline in annual precipitation, particularly in the southwest. The analysis for the MAM season reveals a general downtrend in rainfall, attributed in part to teleconnections with the Indian Ocean Sea surface temperatures (SSTs). Notably, the leading EOF mode for MAM rainfall demonstrates a unimodal pattern, explaining a significant 51.19% of total variance, and underscoring the pivotal role of atmospheric dynamics and moisture conveyance in shaping seasonal rainfall. The spatial correlation analysis suggests a modest linkage between MAM rainfall and the Indian Ocean Dipole, indicating that negative (positive) phases are likely to result in anomalously wet (dry) conditions in Rwanda. This comprehensive assessment highlights the intricate interplay between local rainfall patterns and global climatic phenomena, offering valuable insights into the meteorological underpinnings of rainfall variability during Rwanda's critical MAM season. [ABSTRACT FROM AUTHOR]

Agricultural production across the African continent is subjected to various effects of climate variability. One of the main staple foods in Sub-Saharan Africa is maize. However, limited scientific research has recently focused on understanding the possible effects of hydro-climatic variability on maize production. The aim of the present work was to contribute to policy and climate adaptation, thus reducing the vulnerability of maize production to climate change over Equatorial Africa. This study firstly examined long-term trends of precipitation (PRE), soil moisture (SM), actual evapotranspiration (E), and potential evapotranspiration (Ep), as well as surface air temperatures, including the minimum (TMIN) and maximum (TMAX). Secondly, the relationship between maize production and these climate variables was quantified for 18 Equatorial African countries (EQCs) over 1980−2021. To assess the linear trends, Mann–Kendall and Sen's slope tests were used to quantify the magnitude of the hydro-climatic variable trends at the 5% significance level, and Pearson's correlation coefficient was used to evaluate the relation of these climate parameters with the maize production. The annual mean PRE declined at 0.03 mm day−110a−1. Other climate variables increased at different rates: SM at 0.02 mmday−110a−1, E at 0.03 mm day−110a−1, Ep at 0.02 mm day−1 10a−1, TMIN and TMAX at 0.01 °C day−110a−1. A regional analysis revealed heterogeneous significant wet–dry and warm–cool trends over the EQCs. While, spatially, dry and warm climates were observed in the central to eastern areas, wet and warm conditions dominated the western regions. Generally, the correlations of maize production with the E, Ep, TMAX, and TMIN were strong (r > 0.7) and positive, while moderate (r > 0.45) correlations of maize production with PRE and SM were obvious. These country-wide analyses highlight the significance of climate change policies and offer a scientific basis for designing tailored adaptation strategies in rainfed agricultural regions. [ABSTRACT FROM AUTHOR]

The study's main objective was to analyse daily wind speed data in Brazil collected on‐site from 1961 to 2020 and assess changes in two 30‐year climatological normals, denoted as P1 and P2. Data were sourced from 54 INMET‐managed meteorological stations, with missing data filled using the bootstrap expectation maximization algorithm. Imputation performance was evaluated, and results suggested that the 10% imputed level was most reliable, showing low RMSE values (below 0.5), SD values near 1, and r values exceeding 0.93. This method proved suitable for reconstructing historical wind speed data in Brazil. The analysis included the application of the Mann–Kendall test to detect linear trends and Sen's slope test to quantify trend intensity, both at a 5% significance level. Positive trends during P2 were observed in the Amazon basin and Brazil's semiarid region, while negative trends increased along the coast and in the Southeast region. Average wind speeds during P1 were higher than in P2. Comparing the two climatological normals revealed significant positive trends in some regions during P2, with an increase in average monthly variation of up to 0.85 m·s−1. The average slopes of significant trends were −1 and 1 m·s−1·decade−1 for all periods. The results indicated high wind magnitudes, with maximum power density of 778.08 W·m−2 for the NEB region, 170.90 W·m−2 for the SEB region and 401.67 W·m−2 for the SUB, making these regions promising for future wind power projects. These findings have the potential to enhance the optimization of renewable wind energy generation in Brazil. [ABSTRACT FROM AUTHOR]

This study examines the spatiotemporal variability of mean monthly temperature in the Moulouya watershed of northeastern Morocco, highlighting associated trends. To this end, statistical methods widely recommended by climate researchers were adopted. We used monthly mean temperature data for the period 1980–2020 from 9 measuring stations belonging to the Moulouya Watershed Agency (ABHM). These stations were rigorously selected, taking into account their reliability, the length of their records, and their geographical position in the basin. In addition, a quality test and homogenization of the temperature series were carried out using the Climatol tool. The results obtained show a significant upward trend in mean monthly temperature, mainly pronounced during the summer months, in the Moulouya watershed. In fact, Z values generally exceeded the 0.05 significance level at all stations during April, May, June, July, August, and October. According to the results of Sen's slope test, mean monthly temperatures show an annual increase ranging from 0 to 0.13°C. The maximum magnitude of warming is recorded in July, specifically at Oujda Station. On an overall watershed scale, May, August, and July show a rapid warming trend, with average rates of 0.093, 0.086, and 0.08°C per year, respectively. By contrast, the series for the other months show no significant trend. Significant trend change points were also identified at watershed and station scales, mainly around 2000, primarily for accelerated warming of the summer months. [ABSTRACT FROM AUTHOR]

The Horn of Africa has sensitive, arid ecosystems, with its vegetation commonly distressed by factors such as climate change, population increase, unstable water resources, and rarely enforced land use management practices. These factors make countries such as Djibouti highly variable locations for the growth of vegetation and agricultural products, and these countries are becoming more vulnerable to food insecurity as the climate warms. The rapid growth of satellite and digital image processing technology over the last five decades has improved our ability to track long-term agricultural and vegetation changes. Data cubes are a newer approach to managing satellite imagery and studying temporal patterns. Here, we use the cloud-based Digital Earth Africa, Open Data Cube to analyze 30 years of Landsat imagery and orthomosaics. We analyze long-term trends in vegetation dynamics by comparing annual fractional cover metrics (photosynthetic vegetation, non-photosynthetic vegetation, and bare ground) to the Normalized Difference Vegetation Index. Investigating Djibouti-wide and regional vegetation trends, we provide a comparison of trends between districts and highlight a primary agricultural region in the southeast as a detailed example of vegetation change. The results of the Sen's slope and Mann–Kendall regression analyses of the data cube suggest a significant decline in vegetation (p = 0.00002), equating to a loss of ~0.09 km2 of arable land per year (roughly 2.7 km2 over the 30-year period). Overall, decreases in photosynthetic vegetation and increases in both non-photosynthetic vegetation and bare soil areas indicate that the region is becoming more arid and that land cover is responding to this trend. [ABSTRACT FROM AUTHOR]

In this study, we evaluated the spatiotemporal variability and trends of hydro-climatic time series over the Modjo watershed, central Ethiopia using long-term records of precipitation, temperature, and river flow. In the study, the modified Mann–Kendall trend test was used to identify temporal trends, and Sen's slope estimator was employed to determine trend magnitude at multiple scales. Results of the study show that the spatial variability of annual and seasonal rainfall has decreased from north to south and west to east, whereas this was increased for the evapotranspiration (ETo) over the study watershed. Based on the trend tests, the annual and main rainy season flow exhibit significantly decreasing trends, with magnitudes of −19.2 mm/yr and −18.82 mm/yr, respectively, at a 95% confidence level. Whereas, the rainfall and ETo experienced monotonic trends during most of the wet season months and the annual total. The study highlights that the drop in river flow may be better explained by human activities, as seen by the weak association with climatic indicators. Water resources and irrigated agricultural production systems may be at risk in the area due to the unpredictability and declining trend in the river flow. Therefore, the study suggests appropriate watershed management, enhanced alternative water management techniques, and efficient use of the available water resources in the future. [ABSTRACT FROM AUTHOR]

The present research aims to assess the historical change in rainfall patterns with the changing climate in the Ahmedabad-Gandhinagar district in the state of Gujarat in India. The Mann-Kendall (MK) test along with Sen's slope estimator have been used for detecting the trend of rainfall data series. The trend of annual rainfall is carried out for – (1) six rain gauge stations established by the State Water Data Center (SWDC) and (2) 11 grid data available from the National Center for Environmental Prediction-Climate Forecast System Reanalysis (NCEP-CFSR) for 35 years starting from 1979 to 2013. Results obtained from these two data sets for the trend detection were found consistent. Furthermore, the analyses of annual and monthly rainfall using MK test and Sen's slope estimator at six rain gauge stations are carried out in three time periods i.e. 1974-1987, 1988-2001 and 2002-2016. The inverse distance weighted (IDW) method of interpolation is used for the results obtained from the spatial distribution of the temporal rainfall trend for interpolating the station value over the study area. Annual rainfall for data length of 1979 to 2013 shows an increasing trend. The trend of annual and monthly rainfall for July and September shows a positive trend for the span 2002-2016. This study would be useful to the water resource department and policymakers for climate change adaptation in the study area. [ABSTRACT FROM AUTHOR]

The radiometric calibration network (RadCalNet) comprises four pseudo-invariant calibration sites (PICS): Gobabeb, Baotou, Railroad Valley Playa, and La Crau. Due to its site stability characteristics, it is widely used for sensor performance monitoring and radiometric calibration, which require high spatiotemporal stability. However, some studies have found that PICS are not invariable. Previous studies used top-of-atmosphere (TOA) data without verifying site data, which could affect the accuracy of their results. In this study, we analyzed the short- and long-term radiometric trends of RadCalNet sites using bottom-of-atmosphere (BOA) data, and verified the trends revealed by the TOA data from Landsat 7, 8, and Sentinel-2. Besides the commonly used methods (e.g., nonparametric Mann–Kendall and sequential Mann–Kendall tests), a more robust Sen's slope method was used to estimate the magnitude of the change. We found that (1) the trends based on TOA reflectance contrasted with those based on BOA reflectance in certain cases, e.g., the reflectance trends in the red band of BOA data for La Crau in summer and autumn and Baotou were not significant, while the TOA data showed a significant downward trend; (2) the temporal trends showed statistically significant and abrupt changes in all PICS, e.g., the SWIR2 band of La Crau in winter and spring changed by 1.803% per year, and the SWIR1 band of Railroad Valley Playa changed by >0.282% per year, indicating that the real changes in sensor performance are hard to detect using these sites; (3) spatial homogeneity was verified using the coefficient of variation (CV) and Getis statistic (Gi*) for each PICS (CV < 3% and Gi* > 0). Overall, the RadCalNet remains a highly reliable tool for vicarious calibration; however, the temporal stability should be noted for radiometric performance monitoring of sensors. [ABSTRACT FROM AUTHOR]

In this study, a spatial and temporal analysis was conducted for natural disasters that occurred in Ağrı province, located in eastern Turkey. Natural disasters often occur in the province due to geological, geomorphological, climatic, and hydrographic characteristics. According to data from the Disaster and Emergency Management Presidency, the province of Ağrı is one of the most vulnerable in our country in terms of natural disasters. Consequently, the disaster statistics from 1955 to 2017 were analyzed in this study to reveal the unsafe condition of this province. A total of 955 natural disasters have occurred in the province of Ağrı according to the disaster data report. Among these disasters, torrents and floods take the first place, followed by earthquakes, landslides, rockfalls, and avalanches. The Mann–Kendall test, Sen’s slope, Spearman’s Rho, and Kernel density estimation methods were used to determine the future trends of natural disasters in the province. According to the findings of the study, there has been a significant increase in the number of disasters in the province. In the trend analysis findings, the increase in natural disasters between 2010 and 2017 has drawn attention. Geographic Information System programs were used to create thematic maps of disasters and to reveal the average annual disaster frequency, total number of people affected, and spatial distribution of disasters. The distribution maps show that the intensity of natural disasters increases in the areas where the rivers open to the plain, on the slopes, and in fault zones. [ABSTRACT FROM AUTHOR]

This study aims (1) to study the trend and characteristics of average annual air temperature (Tann), annual precipitation (Prann), and annual evapotranspiration (PETann) in Thailand over the present period (1987–2021) and (2) to extract the climate pattern in form of a map using the New Thornthwaite Climate Classification method in Thailand considering the present period. The data were prepared by the Thai Meteorological Department. Data variability, the mean of the data calculation in time series, the homogeneity test of data, and abrupt changes were examined. The trends of each variable were calculated using the Mann–Kendal and Sen's slope test. The results indicated that the high Tann found in Bangkok gradually decreased in the next area. Tann data were heterogeneous with the abrupt change period, and increasing trends were found. Prann values were high in the west side of the southern area and the bottom area of the eastern area; in addition, low rainfall was found in the inner area of the land. Prann data were homogenous with no abrupt change period and slight changes in trends. PETann and %CV spatial distribution were determined for the same pattern of Tann. PETann data were heterogeneous with abrupt change periods and rising trends. The torrid thermal index determined based on the New Thornthwaite Climate Classification results indicated an overall torrid-type climate. A semi-arid climate pattern was found in the small area of the middle of Thailand, and then it shifted toward a moist-type pattern in the next area with an in precipitation. The most climate variability was found to be extreme with the power of temperature changes. [ABSTRACT FROM AUTHOR]

This study presents a statistical analysis of maximum precipitation amounts at different time intervals in Dobrudja - a historical region in Romania, with semi-arid mid-latitude climate, spanning over the entire historical period of measurements available for each weather station taken into consideration. The data provided by the National Meteorological Administration have different time spans for each weather station, starting with 1948 (at Constanţa weather station). Two methodologies were used for data recording: the pluviographs being used during the last century, and the automatic weather sensors, which have been introduced into general use starting with 2008. In this study, a sub-hourly temporal scale was used to estimate extreme rainfalls: 5, 10, 15, 20, 25, 30 minutes. Then, the corresponding evolution trends were calculated over the entire period of reference (1948-present day), with Mann-Kendall and Sen's slope tests being applied (cases with p-level < 0.05 have been considered statistically significant). The probability for extremely high rainfall amounts to occur in very short periods of time and consequently, the resulting rainfall intensities have also been computed by means of Gumbel distribution. Our findings show that, despite rather scarce rainfall amounts, mostly due to the prevalent mid-latitude semi-arid conditions (the annual rainfall amounts in Dobrudja hardly exceed 400 mm/year), when occurring, they turn into downpours, mounting up to 21 mm in 5 minutes (two events exceed 20 mm) and sometimes 10 mm in one minute (one event). [ABSTRACT FROM AUTHOR]

The impact of precipitation variability on food production is very significant. For food-insecure rural areas, understanding the nature of precipitation variability and its teleconnection has paramount importance in guiding regional and local-level decisions. In this study, we analyzed the monthly, seasonal, and annual precipitation variability and the strength of its teleconnection with the global sea-surface temperature (SST) and El Niño/La Niña Southern Oscillation (ENSO) indices in the food-insecure rural areas of Tigray region, Ethiopia. The precipitation, SST, and ENSO indices data for the study were used from 1979 to 2019. A summary of descriptive statistics and Mann-Kendall test methods were applied to detect the existence of trends, and Sen's slope and coefficient of variation are used to analyze the magnitude of the trend and degree of variation in the precipitation pattern. Further, Pearson's correlation is used to determine the effect of ENSO, and SST variations on the precipitation using the canonical correlation analysis. The results revealed that the precipitation over the three districts is characterized by a distinctive bimodal pattern with limited rains in March–May preceding the main rainy season June–September. The limited amount of precipitation, exacerbated by a higher degree of variability, makes the food production in the three districts more uncertain. Besides, there was a very significant decline in the trend of March–May average precipitation and a significant decline in the trend of the annual average precipitation in Hintalo area. The SST of central and eastern equatorial Pacific Ocean, and northeast and northwest equatorial Atlantic Ocean was strongly correlated with the April average precipitation of the three districts. Further, SST of the south, west, and southwest of the equatorial Indian Ocean, and west equatorial Pacific Ocean was associated with July–September average precipitation with greater variation in strength among of the three districts. Moreover, July's average precipitation of the three districts, April's average precipitation of Atsbi and Eirop, and May's precipitation of Hintalo are found significantly associated with the ENSO indices of JFM, FMA, MJJ, and MAM. Therefore, the task of achieving food security in the three districts should incorporate the design of informed food production strategies that can adapt to the limited and variable precipitation based on these SST and ENSO indices. [ABSTRACT FROM AUTHOR]

Karst aquifers are crucial sources of municipal water in Iran. This study aims to assess the impact of anthropogenic activities and climate change on the Sarbalesh aquifer in Fars province, which has experienced an intensive depletion in the last three decades. To achieve this, trends in rainfall and temperature series as climate variables, and groundwater level time series as a hydrologic variable, are detected using the Mann–Kendall (MK) and modified MK tests. The magnitude and rate of the trends were supported by Sen's slope and Kendall-τ coefficient. The dominant periodicities contributing to the observed trends in the original series were identified by a combination of discrete wavelet transform (DWT), MK, and sequential MK analyses, using three error criteria and three border extension modes. The monthly rainfall, temperature, and groundwater series exhibited prominent periodicities of 8 months, 8 months, and 64 months, respectively. Similarly, the annual rainfall, temperature, and groundwater series displayed significant periodicities of 4 years, 2 years, and 8 years, respectively. Using the Pettitt-Mann–Whitney (PMW) and cumulative sum approaches, we detected abrupt shifts (change points) in the studied time series and identified their causes. The coinciding change year points in the rainfall series and Southern Oscillation Index (SOI) series, along with the negative correlation between rainfall and SOI and El Niño cycles, indicate that climate change and the La Niña phenomenon have increased SOI after the change year, resulting in decreased precipitation from November to April in the study area. Our multi-statistical approach demonstrates that the over 30-m decline in the groundwater level of the Sarbalesh karstic aquifer is due to continuous over-exploitation of water storage from this aquifer over the past 32 years. Additionally, decreasing rainfall and increasing temperature, as indicators of climate change, have further contributed to this declining trend. The result was further justified by the cross wavelet transform coherence (WTC) analysis. Our analysis provides an elaborate view of future hydro-climatic conditions, it can be used as a foundation for the management of many other karst aquifers that experience the same fate as the Sarbalesh aquifer in Iran and elsewhere. [ABSTRACT FROM AUTHOR]

One of the direct and unavoidable consequences of global warming-induced rising temperatures is the more recurrent and severe heatwaves. In recent years, even countries like Malaysia seldom had some mild to severe heatwaves. As the Earth's average temperature continues to rise, heatwaves in Malaysia will undoubtedly worsen in the future. It is crucial to characterize and monitor heat events across time to effectively prepare for and implement preventative actions to lessen heatwave's social and economic effects. This study proposes heatwave-related indices that take into account both daily maximum (Tmax) and daily lowest (Tmin) temperatures to evaluate shifts in heatwave features in Peninsular Malaysia (PM). Daily ERA5 temperature dataset with a geographical resolution of 0.25° for the period 1950–2022 was used to analyze the changes in the frequency and severity of heat waves across PM, while the LandScan gridded population data from 2000 to 2020 was used to calculate the affected population to the heatwaves. This study also utilized Sen's slope for trend analysis of heatwave characteristics, which separates multi-decadal oscillatory fluctuations from secular trends. The findings demonstrated that the geographical pattern of heatwaves in PM could be reconstructed if daily Tmax is more than the 95th percentile for 3 or more days. The data indicated that the southwest was more prone to severe heatwaves. The PM experienced more heatwaves after 2000 than before. Overall, the heatwave-affected area in PM has increased by 8.98 km2/decade and its duration by 1.54 days/decade. The highest population affected was located in the central south region of PM. These findings provide valuable insights into the heatwaves pattern and impact. [ABSTRACT FROM AUTHOR]

In addition to serving as North China's water supply and ecological barrier, the mountainous area of the Haihe River basin (MHRB) is a crucial location for the application of ecological engineering. Vegetation is an important component in the ecological conservation and eco-hydrological progress of the MHRB. A better understanding of regional vegetation growth can be achieved by a thorough investigation of vegetation indicators. In this research, the leaf area index (LAI) and gross primary productivity (GPP) were chosen as vegetation indicators. The characteristics and driving forces of the spatiotemporal variations of LAI and GPP in the MHRB were explored through Sen's slope, the Mann–Kendall test, the optimal parameter-based geographical detector model, and correlation analysis. From 2001 to 2018, the annual LAI and GPP increased significantly on the regional scale. The areas with significantly increased vegetation accounted for more than 81% of the MHRB. Land use was the most influential element for the spatial heterogeneity of LAI and GPP, and the humidity index was the most crucial one among climate indicators. Non-linear enhancement or bivariate enhancement was discovered between any two factors, and the strongest interaction was from land use and humidity index. The lowest vegetation cover was found in dry regions with annual precipitation below 407 mm and the humidity index under 0.41; while in both forests and large undulating mountains, higher LAI and GPP were observed. About 87% of the significantly increased vegetation was found in areas with unaltered land use. The increase in vegetation in the MHRB from 2001 to 2018 was promoted by the increased precipitation and humidity index and the reduced vapor pressure deficit. The sensitivity of GPP to climate change was stronger than that of LAI. These findings can serve as a theoretical guide for the application of ecological engineering and ecological preservation in the MHRB. [ABSTRACT FROM AUTHOR]

Expected changes in climate variables, such as precipitation and temperature, can change the hydrological regime, impacting water availability in already stressed watersheds. The predictions indicate annual surface temperature increase trends in the Cerrado region. Climate models are essential tools in predicting future climate. To increase the degree of confidence in the projections of these climate models, it is necessary to understand the performance of the models and identify and correct the biases observed in the climate variables simulated by them. This study aimed to evaluate global climate models nested with regional climate models in the simulation of precipitation and average temperature in localities in the Brazilian Cerrado. A comparison of historical data from climate models (Eta-HadGEM2-ES, Eta-MIROC5, Eta-BESM, and Eta-CANESM2) was carried out with data observed at the climatological stations present in the area through statistical metrics. In general, the model with the best statistical fits for precipitation and average temperature in the Cerrado region was Eta-HadGEM2-ES. The Run, Mann-Kendall, Pettitt, and Sen's slope tests demonstrated that a reduction in precipitation and an increase in temperature are expected in the studied locations in the Cerrado region by the end of the 21st century. [ABSTRACT FROM AUTHOR]

This study focuses on modeling and predicting extreme rainfall based on data from the Southern Highlands region, the critical for rain-fed agriculture in Tanzania. Analyzing 31 years of annual maximum rainfall data spanning from 1990 to 2020, the Generalized Extreme Value (GEV) model proved to be the best for modeling extreme rainfall in all stations. Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. The Bayesian MCMC approach demonstrated superior performance by incorporating noninformative priors to ensure that the prior information had minimal influence on the analysis, allowing the observed data to play a dominant role in shaping the posterior distribution. Furthermore, return levels for various future periods were estimated, providing guidance for flood protection measures and infrastructure design. Trend analysis using p value, Kendall's tau, and Sen's slope indicated no statistically significant trends in rainfall patterns, although a weak positive trend in extreme rainfall events was observed, suggesting a gradual and modest increase over time. Overall, the study contributes valuable insights into extreme rainfall patterns and underscores the importance of L-moments in identifying the best fit distribution and Bayesian MCMC methodology for accurate parameter estimation and prediction, enabling effective measures and infrastructure planning in the region. [ABSTRACT FROM AUTHOR]

The precipitation variability and trends were investigated in the Usumacinta River Basin (URB) for the period 1959–2018, based on imputed and homogenized data records from 60 meteorological stations in Mexico and Guatemala. The homogenization process played a crucial role in enhancing the quality of the original precipitation series, reducing regional inconsistencies and improving temporal and spatial coherence. The dataset reliably captured large‐scale climate variations, revealing three regions with similar precipitation variability and trends in the URB. Notably, maximum precipitation occurred at 636 m a.s.l., while minimum precipitation was at 1531 m a.s.l., indicating an orographic effect in the region. Extreme precipitation events were linked to El Niño–Southern Oscillation. Although the Mann–Kendall test showed statistically significant negative trends in only 18% of the stations, integration of Sen's slope analysis and 30‐year normals and dry year occurrences highlighted a progressive shift towards dryer conditions throughout the study period in the URB. These drier conditions could notably affect regions with higher precipitation, requiring special attention due to possible socioeconomic impacts associated with drought events. By identifying these vulnerable regions, policymakers and stakeholders can proactively plan and execute adaptive measures to mitigate the potential impacts of droughts on communities, ecosystems, and economic activities within the basin. [ABSTRACT FROM AUTHOR]

The annual, seasonal, and monthly trends of air temperatures were analyzed for thirteen urban and five rural meteorological stations in Kazakhstan for the 1963–2020 period. The non-parametric Mann–Kendall (M–K) rank correlation and Sen's slope estimator methods and the parametric least-squares linear regression (LSLR) were used to determine whether there were positive or negative statistically significant trends in mean, average maximum, and minimum air temperature time series along with diurnal temperature ranges (DTRs), and temperature differences between five large and small cities. In addition, Kazakhstan's annual and seasonal air temperature series were analyzed in terms of autocorrelation (serial correlation) coefficients. Coefficients of variations indicated that mean annual temperature variability is the highest in northern cities. Results of the M–K trend test indicated that the highest and lowest increases in the mean air temperatures were observed in spring and autumn, respectively. The magnitudes of the significant increasing trends in annual air temperature ranged between 0.23 °C/decade at Karagandy and 0.54 °C/decade at Kyzylorda. Annual and seasonal diurnal temperature ranges (DTRs) reveal insignificantly decreasing and increasing trends at most of the stations characterized by urbanization. According to the results of both M–K and LSLR tests, annual and winter air temperature differences of some station pairs tend to significantly increase, which may mean that the differences in the calculated temperature range between large and small cities might have widened significantly. However, significance test for the calculated autocorrelation coefficients of the annual and seasonal air temperature data showed that most of the series clearly appear as a low-frequency variability on the significantly increased long-term averages. [ABSTRACT FROM AUTHOR]

Analyzing trends in precipitation data is crucial for understanding the effects of climate change and making informed decisions about water management and crop patterns. The objective of the presented study was to investigate precipitation trends, analyze temporal and spatial variations and identify potential change points in Turkey throughout the period from 1980 to 2019. Precipitation data were analyzed for both regional and 81 meteorological stations in Turkey on a monthly, seasonal, and annual basis. Spearman rank correlation and Mann–Kendall tests were utilized to detect possible trends and Sen's slope test to estimate the magnitude of change throughout the entire time series. The average precipitation amount of Turkey was determined 639.2 mm between the years 1980 and 2019. While Central Anatolian and Eastern Anatolian regions had below 639.2 mm, other regions were above. The range of seasonal precipitation values were found for winter 128.7–320.8 mm, 108.9–260.0 mm for spring, 43.9–109.3 mm for summer, and 79.7–238.4 mm for autumn. The analysis of the data revealed no significant increase or decrease in annual values on a regional basis, with the greatest change on a seasonal basis being observed in the winter. The 40-year trends of annual precipitation data belonging to 81 stations were decreasing in 23 provinces and increasing in 58 provinces, and 11 of them (14% of the total) were found to be statistically significant. Moreover, November was found to be a month of particular significance in terms of precipitation changes across the country, with a decrease observed in 80 out of 81 provinces. Spatial distribution analysis showed that the magnitude of variation in precipitation decreased as one moved from the southern to the northern regions of the country. [ABSTRACT FROM AUTHOR]

The problem of detecting hydrometeorological trends is still a concern. In this study, monthly, seasonal, and annual temperature and precipitation variations in the Van Province of Türkiye are assessed using both traditional such as Mann–Kendall (MK) and Spearman's Rho (SRHO) and graphical such as Innovative trend significance test (ITA-ST) and Innovative polygon trend analysis (IPTA) methods. In order to determine trend slope and change-point detection, Sen's Slope and Sequential Mann–Kendall tests are also used, respectively. The findings show that the MK, SRHO, and ITA-ST approaches detect a decreasing trend for annual total precipitation at the Erciş and Başkale stations. Additionally, the MAM and SON seasons at Erciş and the SON season at Gevaş stations also show a noticeably decreasing trend. All stations have an increasing trend at a 95% significant level for annual mean temperatures. Except Gevaş station, all four seasons have an increasing trend for temperature series. Results from the IPTA show that for precipitation data, the transition from May to June exhibits the highest trend length. In addition, for temperature data, the minimum trend length and slope are captured in the transition from July to August. In general, ITA-ST and IPTA are superior to MK and SRHO at identifying trends and that the outcomes of these methods are significantly more suitable to visual inspection and linguistic interpretation. Different hydro-climatological variables can be analyzed more flexibly and thoroughly utilizing innovative methodologies. [ABSTRACT FROM AUTHOR]