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In recent years, the escalating effects of climate change on surface water bodies have underscored the critical importance of analyzing streamflow trends for effective water resource planning and management. This study conducts a comprehensive regional investigation into the streamflow rate trends of 18 rivers across the United Kingdom (UK). An enhanced Mann–Kendall (MK) test was employed to meticulously analyze both rainfall and streamflow trends on monthly and annual scales. Additionally, the Innovative Trend Analysis (ITA) method was applied to elucidate the variability of streamflow rates, providing a more nuanced understanding of hydrological changes in response to climatic shifts. MK test reveals statistically significant positive trends in streamflow rates, particularly for rivers in south-central Scotland and northern England. Specifically, in January, rivers such as the Tay at Ballathie, Tweed at Peebles, and Teviot at Ormiston showed Z-scores above 2. Annually, similar positive trends were observed, with the Tay at Ballathie (Z = 3.42) and Nith at Friars Carse (Z = 3.35) exhibiting the highest increases in streamflow rates. The ITA method showed no relevant trends for the lowest values of streamflow, except for the Thames at Kingston, while considerable variability was observed for the highest streamflow rates, with several rivers showing positive trends and, however, some England rivers, like Bure at Ingworth, Test at Broadlands, and Trent at Colwick, showing negative trends. From this perspective, a more in-depth analysis of the extreme streamflow trends was carried out. In particular, the flood frequency of the maximum annual streamflow was assessed, based on the fitting of the Generalized Extreme Value (GEV) distribution on the annual maxima. Increasing location parameter (μ) and return period trends were observed for several rivers across the UK. In particular, the Tay at Ballathie (Scotland) showed the most marked increase, with μ that ranged from about 730 m3/s to more than 900 m3/s. At the same time, slight decreasing trends were observed for the Trent River (μ from 378 m3/s to 341 m3/s). The critical comparison of the MK test, ITA, and GEV distribution fitting revealed both agreements and discrepancies among the methods. While the analyses generally aligned in detecting significant trends in streamflow rates, notable discrepancies were observed, particularly in rivers with negligible trends. These inconsistencies highlight the complexity of hydrological responses and the limitations of individual methods. Overall, the study provides a comprehensive view of how streamflow dynamics are evolving in UK rivers, highlighting regional variations in the impact of climate change. This understanding can improve water resource management strategies by integrating diverse analytical approaches. [ABSTRACT FROM AUTHOR]

To properly manage the terrestrial ecosystem, it is essential to understand the vegetation sensitivity to climate variations and human actions. The main target of this survey was to evaluate the spatiotemporal variation in vegetation cover, and its relationship with climate variations and to calculate the contributions of driving factors in Namak Lake basin, Iran, during 2001–2019. To this end, Vegetation Health Index (VHI) and Standardized Precipitation Evapotranspiration Index (SPEI) in 3, 6, 9, and 12-month time scales were used to assess vegetation dynamics and its reactions to climate variations based on coefficient of determination (R2) and Linear Regression (LR). The results presented that vegetation cover had an improving trend in 87.78% and a decreasing trend in 12.19% of the basin, while it was stable in 0.03% of areas. The correlation between VHI and different time scales of SPEI indicated that coverage was mainly affected by 3-month SPEI in more than half of the basin (53.74%). High correlations between VHI and SPEI were found in upland areas in the northeast and some areas in the east of the basin. These areas also had the highest slope of VHI changes in relation to climate factors. Climate variability affected about four-fifths (79.22%) of coverage, while 16.36% was influenced by human actions, and 4.42% by both factors. Moreover, more than 99% of the significant improvements and degradations in coverage were related to climate variations and mankind's actions, respectively. The outcomes can serve as a foundation for initiating vegetation growth and protection in the Namak Lake basin. [ABSTRACT FROM AUTHOR]

Climate change and anthropogenic activities (changes in rainfall and temperature pattern) activities have significantly affected agricultural activity and crop production. So, studying cropland greenness and crop yield trends is essential to understand their impacts and to ensure food security. The present study attempted to depict the cropland greenness and wheat yield trends in Uttar Pradesh (UP) state, India, during 2001–2019. The Moderate Resolution Imaging Spectroradiometer (MODIS) based Normalised Difference Vegetation Index (NDVI) dataset was used to decipher the cropland greenness trends through the Mann-Kendall (MK) test. Furthermore, the wheat yield dataset from Baghel and Sharma (2022) was used to depict the wheat yield trends using the Thiel-Sen slope test from 2001 to 2019. The study's results showed that ~ 37.3% (82,041 km2) area of the total agricultural land was with positive cropland greenness (NDVI) trend, and 1.67% (3673 km2) area was under a negative cropland greenness trend. On the other hand, ~ 8463 km2 (at 99% significance level) and 160,557 km2 (90% significance level) area corresponding to the ~ 4% and 73%, respectively, of the total agricultural land has shown positive (increasing) wheat yield trends during 2001–2019. In conclusion, the findings of this study emphasize the need for ongoing monitoring and a comprehensive understanding of cropland greenness and wheat yield trends over different agroclimatic zones. By gaining insights into these trends, policymakers and stakeholders can develop and implement effective mitigation and adaptation strategies to minimize the adverse effects of climate change and anthropogenic activities on agricultural systems. [ABSTRACT FROM AUTHOR]

The prediction of land use and land cover (LULC) patterns is essential for land use planning, natural resources management, and climate change mitigation. In this study, the artificial neural network (ANN) technique and QGIS software have been proposed to project 5- and 10-year future LULC for the Mahanadi River basin in Chhattisgarh, India. The results reveal that slope, aspect, and hillshade maps have significant effects on LULC. The projection of the future LULC for the years 2027 and 2032 reveals that the grassland and cropland areas would significantly decline and increase, respectively, in the study region. The results from trend analysis show an increasing trend for forests, permanent wetlands, cropland, urban built-up land, waterbodies, and cropland/natural vegetation, while decreasing trends for the remaining classes. The overall finding from this study suggests that remote sensing enabled with the ANN technique has the potential to project the future LULC with high accuracy. [ABSTRACT FROM AUTHOR]

This study aimed to derive major pollutants for standard watersheds with relatively high pollution levels and identify their trends. Hence, the water quality index (WQI) and multivariate statistical techniques were used to analyze the water quality evaluation of standard watershed during the total maximum daily load (TMDL) policy period implemented since 2004. The WQI was calculated for 41 standard watersheds, divided into 14 main streams and 27 tributaries. Consequently, in the main stream, WQI decreased as it moved downstream after the MS5 site. In the tributary, WQI values were found to be low at TS11, TS15, TS16, and TS17 sites. However, the Mann–Kendall test results indicated that the overall WQI value was increasing, suggesting that the water quality of the study basin was improving as the TMDL policy phase progressed. Principal component analysis of the group with relatively low WQI among the standard watersheds revealed that water temperature, dissolved oxygen, total phosphorus, chemical oxygen demand, biochemical oxygen demand, and electrical conductivity were the main variables in the main stream cluster. In the tributary stream cluster, total phosphorus, dissolved oxygen, total suspended solids, total nitrogen, and electrical conductivity showed high loading values. This study proposed a series of methods to provide the basic data required for watershed management through detailed water quality assessment methods, statistical techniques for deriving major pollutants, and trend analysis of water quality evaluation. [ABSTRACT FROM AUTHOR]

The Himalaya represents the most fragile ecosystem in the world, signifying its sensitivity towards global climate change. In the current scenario, the Himalaya needs to assess the climatic change trends at the regional level. To investigate climatic trends, we analysed the long-term (1980–2020) climatic data (maximum temperature—TMax and minimum temperature—TMin, precipitation, and relative humidity—RH) collected by the five different meteorological stations from Shiwalik to Pir Panjal mountain range of Jammu province. The nonparametric Mann–Kendall test was used to analyse the significance of climatic change trends for temperature, precipitation, and RH data on seasonal and annual scales, whereas the nonparametric Sen's estimator of the slope was applied to quantify the magnitude of climatic change trends. The results reveal that the Jammu province region received 53% of annual precipitation from Western Disturbances and 47% from Indian Summer Monsoon. The contribution of Western Disturbances was greater towards the northern region of Jammu Province, while the southern lower region was substantially influenced by ISM. With substantial inter-station variations, our results indicated a significant increasing trend of TMax only for Banihal, while all the studied stations except Jammu exhibited a significant increasing trend of TMin. For the entire Jammu province, a significant increasing trend of TMax was observed for the spring season. On an annual scale, the increasing rate of changes was observed more for TMin (0.066 °C) than TMax (0.016 °C) of Jammu province while a nonsignificant declining rate of change was exhibited for precipitation (− 0.098 mm a−1). The shifting pattern in temperature and precipitation could have serious environmental implications that will greatly influence the biodiversity and ecological stability of the region. [ABSTRACT FROM AUTHOR]

Preserving the quality of groundwater has become Bangladesh's primary challenge in recent years. This study explores temporal trend variations in groundwater quality on a broader scale across 18 stations within the Dhaka division over 35 years. The data set encompasses an analysis of 15 distinct water quality parameters. Modified Mann-Kendal, Sens Slope and Mann-Kendal tests were performed to determine the trend's variation and slope. In addition, the spatial–temporal changes in the quality of groundwater are studied through Geographic Information System (GIS) mapping and Piper diagram was applied to identify the unique hydrochemical properties. This is the first study conducted on this area using various trends analysis and no in-depth study is available highlighting the trends analysis of groundwater quality on a larger magnitude. In contrast, the correlation matrix reveals a high association between Mg2+ and SO42−, Na+ and Cl− that affects salinity and overall hardness at the majority of sites. The Piper diagram also demonstrates that the groundwater in Madaripur Sadar has major salinity issues. The analysis reveals a distinctive dominance of bicarbonate (HCO3−) ions across all sampling stations, with (HCO3−) equivalent fractions consistently ranging from 0.70 to 0.99 which can cause a significant impact on groundwater uses. This extensive analysis of long-term groundwater quality trends in the Dhaka Division enables researchers to comprehend the overall transition of groundwater quality for hardness related complications in future. Moreover, it can be a baseline study considering the valuable implications and future steps for sustainable water resource management in this region. [ABSTRACT FROM AUTHOR]

A population is regarded as the main non-economic driver of carbon emissions, causing the climatic crisis, especially in China experiencing a dramatic demographic transition. In contrast to aging, low fertility, the most remarkable feature of the Chinese population transition, has always been ignored when evaluating carbon emissions, due to the lack of long-run data. To narrow this gap, an integrated framework combining the continuous input–output tables from 1997 to 2018 with the Mann–Kendall test and vector auto-regression was presented to clarify the fluctuating trend of household embedded carbon emissions and the driving pattern of low fertility, aging, and urbanization. Our main findings showed that changes in household embedded carbon emissions have increased sharply in the last two decades. The growth of Chinese household embedded carbon emissions began to accelerate in 2001, which lagged 1 year behind the demographic indicators. Low fertility has a positive impact on households' embedded carbon emissions. More importantly, the impact of low fertility is more significant and far-reaching than that of aging. These suggest that aggressive policies for stimulating fertility and low-carbon lifestyles should be considered by policy makers. [ABSTRACT FROM AUTHOR]

Daily maximum, TX, and minimum, TN, temperatures recorded at Fabra Observatory, for the period 1914–2020 (from June to September), have permitted to identify the daytime and night time heatwaves, HWs, at the Barcelona Metropolitan Region, BMR, along 107 years. Four heatwaves indices have been computed both for maximum and minimum temperatures: the number of events per season, HWN; their maximum duration, HWD; their frequency of occurrence, HWF; and the amplitude of the hottest day event, HWA. Trends in these indices have been obtained and their statistical significance quantified by means of the Mann-Kendall test. For the whole period (1914–2020), positive significant trends have been detected for the four indices, with a marked positive trend since year 2000 for TX, and since 1985 for TN. Nevertheless, when 31-year moving windows have been analysed, both positive and negative significant trends are obtained, being remarkable the change in trend signs around 1960s. The convenience of using the four HW indices is evaluated applying a Principal Component Analysis, PCA, obtaining two principal components. Additionally, the spatial distributions of the MODIS land surface temperatures, LST, corresponding to some extreme heatwaves, permits the detection of two hotspots in the BMR, one for TX and the other for TN. It is also worth mentioning that correlations between the Western Mediterranean Oscillation index, WeMOi, and HW episodes are detected, being notable that, at the beginning of these episodes, WEMOi values are usually lower. These correlations could improve the HWs forecasting. [ABSTRACT FROM AUTHOR]

The surface solar radiation in most parts of the world has undergone a phenomenon known as global dimming and brightening, characterized by an initial decrease followed by an increase. As a result, the sunshine duration (SD) has decreased in the past 60 years. Against the backdrop of global dimming and brightening, SD has decreased to varying degrees in many regions of China. Using the observed data of SD, cloud amount (total cloud amount and low cloud amount, abbreviated as TCA and LCA), precipitation, and relative humidity (RH) from 34 meteorological stations in Chongqing during the period of 1961–2020, along with a digital elevation model (DEM) with a resolution of 90 m, this study analyzed the spatiotemporal variations and influencing factors of SD. The analysis employed methods such as linear regression, Mann-Kendall test, wavelet transformation, and DEM-based possible SD distributed model. The results showed that the annual SD in Chongqing has significantly decreased over the last 60 years, with a decreasing interannual trend rate (ITR) of 40.4 h/10a. Except for no obvious trend in spring, SD decreased significantly in summer, autumn and winter at the ITR of 21.1 h/10a, 8.5 h/10a and 7.5 h/10a, respectively. An abrupt decrease in the annual SD was found in 1979. The difference before and after the abrupt decrease was 177.7 h. The difference before and after the abrupt decrease was 177.7 h. The annual SD possessed the oscillation period of 11a. The spatial heterogeneity of the mean annual SD during the last 60 years was obvious. The distribution of SD in Chongqing is high in the northeast and low in the southeast. In addition, about 73% of the total area in Chongqing showed a significant and very significant decreasing trend. The regions with significant changes are mainly concentrated in the regions with altitudes of 200∼1000 m. The increasing LCA was the main cause of the decrease of the annual SD in the regions with 200–400 m altitude decreased the most and changed the most. Increasing LCA is the primary cause of the reduction in annual SD, showing a strong negative correlation coefficient of −0.7292. In Chongqing, PM2.5 concentration showed a significant decrease trend in annual, spring, autumn and winter during 2000–2020, but the significant correlation between PM2.5 concentration and SD was only in autumn and reached an extremely significant level. [ABSTRACT FROM AUTHOR]

The Selenge River Basin is one of the major tributaries of Lake Baikal, one of the world's freshwater resources, and is a sensitive region to climate change, and is also an important China–Mongolia–Russia economic corridor basin. Therefore, studying the relationship between the eco-hydrological processes of this basin and its land cover changes is important for sustainability and environmental protection. In this research, trend analysis was used to determine water and climate changes, and satellite data and statistical analysis were used to calculate spatial and temporal changes in land cover. Based on the research findings, the flow of the Selenge River has exhibited fluctuations ranging from 125.2 to 576.3 m3/s over the past three decades, with a discernible downward trend observed in the basin area or at the Zuunburen hydrological station. This decline can be attributed to the combined effects of climate change and human activity. During the last 30 years of climate change in the Selenge River basin, the average annual air temperature has increased by 2.4 °C. The average total annual precipitation of the basin ranges from 200 to 400 mm, and the precipitation fluctuation is relatively small, and in recent years, less than average precipitation has fallen. In the basin, the air temperature has experienced an annual increase of 0.09 °C, attributable to natural climatic factors. Additionally, there has been a reduction in both precipitation levels and river flow within the basin, while significant alterations have occurred in the extent of water bodies and wetlands within the land cover. In this region, studies show that changes in eco-hydrological processes significantly impact changes in land cover. [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]

The Upper Indus Basin (UIB) is a major water resource for Pakistan, and recent years have seen it profoundly affected by climate change, resulting in catastrophic events. Analyzing climatic patterns in UIB and its sub-basins is vital for sustainable water resource management. This research delves into the precipitation and temperature trends within the Gilgit and Hunza River basins (sub-basins of UIB) from 1980 to 2019, utilizing an extensive dataset comprising ground station records and global climate datasets such as CRU and ERA 5. Our findings indicate a notable increase in temperatures, particularly pronounced in the Hunza basin compared to Gilgit. Conversely, precipitation trends reveal a predominant decrease. Employing validation techniques, we confirmed that ERA 5 surpassed CRU in providing accurate temperature and precipitation data. Employing methods including the Mann–Kendall, Sen's slope, and Abrupt Change Point Tests, we identified temperature increases of 0.21 °C and 0.25 °C per decade. Additionally, CRU assessments unveiled a decline in diurnal temperature. Seasonal analysis showcased significant temperature rises during summer, autumn, and spring, while winter conditions remained consistently stable. Our analysis also pointed to slight increases in summer precipitation, with winters exhibiting heightened wetness according to the CRU datasets. Continual monitoring of the Upper Indus Basin (UIB) is imperative for enhancing precision and reliability. This proactive approach, complemented by meticulous spatiotemporal analysis, will establish a robust foundation for comprehending and addressing climate changes in the region. [ABSTRACT FROM AUTHOR]

The average annual precipitation values in the Cheliff-Zahrez basin range from 80 to 600 mm/an, indicating a semi-arid climate. This work revolves around a new drought exceedance probability index (DEPI), a standardized precipitation index (SPI) and effective drought index (EDI) in different time scales (3, 6, 9, 12 and 24) derived from monthly precipitation series stretching from September 1970 to August 2015. The latter aims to analyze the performance, similarities and differences between the most used drought indices such as SPI and EDI and to compare their results with those obtained using DEPI. The results indicated that the majority of selected stations tend to a decline in annual rainfall, with a dominant break in series between 1970 and 1980. The Mann–Kendall test result showed that the monthly rainfall trend had significantly decreased in the majority of studied stations. Furthermore, the SPI and EDI series showed persistent monthly drought conditions from January 1970 to March 2010 with an extreme drought peak of − 3. However, the DEPI index showed monthly drought events (< 0.5) of different classes beginning from March 1980 to December 2010. In the study area, the coefficient of determination explained a high variation (> 80%) between SPI and EDI at the time scale of 24 months. At the 9- and 12-month scale, the coefficient of determination showed a variance greater than 50% over the entire basin. On the other hand, the coefficients of determination results between SPI and EDI for the time scale of 3 to 6 months are characterized by a gradient from the northeast to southwest or from 10 to 90%. The stations of the Coastline 2 basin have revealed that the relationship between SPI and DEPI as well as EDI and DEPI is not significant, being allowed between 10 and 30% on time scales of 3, 6, 9, 12 and 24 months. The results of this work and information on the severity and persistence of droughts will be essential to enable managers to adopt an integrated and sustainable management of scarce resources and aim at minimizing agricultural production losses in the region. [ABSTRACT FROM AUTHOR]

The objective of this study was to propose bioclimatic zoning to classify human thermal comfort and discomfort in the state of Minas Gerais, Brazil; both historical and future scenarios are considered. Thus, historical series (1961 to 2017) of the effective temperature index as a function of the wind (ETW) were obtained as a function of the monthly average values of the minimum, mean, and maximum dry-bulb air temperatures (tdb,min, tdb,mean, and tdb,max, respectively), in addition to the mean relative humidity (RH , %) and mean wind speed (V , m s −1). The data were obtained from 34 weather stations and subjected to trend analysis by using the nonparametric Mann–Kendall test, thus enabling the simulation of future scenarios (for 2028 and 2038). Then, to define the thermal ranges of the bioclimatic zoning, maps of ETWmin, ETWmean, and ETWmax were created from geostatistical analysis. Overall, the results show warming trends for the upcoming years in Minas Gerais municipalities. All climatic seasons showed an increase in the frequency of new classifications in the upper adjacent classes, which indicates climate warming. Therefore, when considering future scenarios for the autumn and winter seasons, attention should be given to changes in predicted thermal sensation, especially in the Central Minas Gerais, Belo Horizonte Metropolitan, South/Southwest Minas, Campo das Vertentes, and Zona da Mata. [ABSTRACT FROM AUTHOR]

Estimation of baseflow is a complex hydrographic task. Baseflow techniques and coefficients vary from basin to basin, stream to stream, and year to year. In this study, meta-heuristic optimization is used to automatically identify baseflow. The Particle Swarm Optimization (PSO), a meta-heuristic optimization approach, is chosen. The constraint and cost functions were determined using the PSO algorithm, Lyne and Hollick techniques, and a computer application. Over the period 1980–2015, the data were collected at the Kale station in the Yesilirmak River basin to validate the study model. The results show that the hydrographs and baseflow dividing line were separated effectively. It has also been revealed that the PSO has a high speed as well as a high level of precision. In the research, in addition to the baseflow separation, the hydrograph, baseflow, and ratio of the baseflow to the streamflow at the station No. 1402 were assessed using the Mann–Kendall test and Innovative Trend Test (ITA), and as a result, their trends have been found. By the use of both of these methods, it has been shown that all parameters have an unfavorable trend. In addition, the research came to some other significant conclusions, such as the fact that the baseflow declines in tandem with the flow values and that the baseflow rates are low in years with high peak values of the hydrograph. [ABSTRACT FROM AUTHOR]

The Sistan region in Southeastern Iran is one of the world's most sensitive areas when it comes to sandstorms and wind erosion. One of the most influential factors in interpreting sandstorms is sand drift potential (DP), which is directly related to wind speed. Accurately, monitoring this phenomenon is still being determined, considering various temporal scales. Therefore, the main aim of this research is to analyze the trend of DP on monthly and annual scales. Our results showed that monthly variations of DP reached the highest and lowest values in July (609 VU) and January (47 VU), respectively. Blowing sand predominantly moved southeast, and the directional index fluctuated from 0.88 to 0.94. The annual DP was measured equal to 2700 VU, signifying a relatively high value when compared to other arid regions worldwide. The trend analysis results obtained from the Mann–Kendall test revealed both positive trends during the period 1987–2001 and negative ones from 2002 to 2016). However, the positive trend was found statistically insignificant. Furthermore, Sen's slope test results demonstrated that a negative trend could be observed with a steeper slope during July, September, and August, while a positive trend could be observed with a steeper pitch during November, December, and June. We recommend that land managers and stakeholders involved in controlling blowing sand using biological and physical methods should consider these trends in the Sistan region. Implementing nature-based solutions or control strategies should focus on these temporal sequences. [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 consensus of the scientific community across the globe showed that the planet's annual average temperature is likely to be 2 °C above pre-industrial levels of 2050 standards. It is believed that warming the globe by 2 °C due to greenhouse effect and/or climate change will result in severe rainfall amount and more frequent and intense droughts, floods, heat waves, and other extreme weather events. Gondar City of the Amhara Region will not be exceptional from this scenario. The objective of the study was to assess UHI caused by climate change in Gondar City of the Amhara Region, Ethiopia. Thirty temperature and thirty rainfall observations were taken from the Meteorological Agency. It was analyzed using Mann–Kendall trend test and homogeneity test using XLSTAT software. Analysis of the historical climate data in Gondar City revealed that there has been an increasing trend of maximum temperature and a decreasing trend in minimum temperature and a general tendency of decreasing rainfall. The results of Mann–Kendall trend test and homogeneity test also showed that the average amount of annual rainfall and maximum temperature change were significant at p < 0.05. The result also noted that there was a constant increase in maximum temperature, while high variability in rainfall from 2010 to 2020. However, there is a constant increase in rainfall from 1990 to 2020 in the study area. The increasing in temperature and the variability and increasing rainfall are evidences for the existence of climate change. The finding of Landsat temperature measurement indicates the rising temperature in Gondar City as compared to the surrounding rural areas. In conclusion, the test result and the Landsat measurement showed the presence of urban warming an indicator of UHI in the city of Gondar. [ABSTRACT FROM AUTHOR]

Understanding the dynamics of temperature trends is vital for assessing the impacts of climate change on a regional scale. In this context, the present study focuses on Madhya Pradesh state in Central Indian region to explore the spatial-temporal distribution patterns of temperature changes from 1951 to 2021. Gridded temperature data obtained from the Indian Meteorological Department (IMD) in 1° × 1° across the state are utilised to analyse long-term trends and variations in temperature. The Mann-Kendall (MK) test and Sen’s slope (SS) estimator were used to detect the trends, and Pettitt’s test was utilised for change point detection. The analysis reveals significant warming trends in Madhya Pradesh during the study period during specific time frames. The temperature variables, such as the annual mean temperature (Tmean), maximum temperature (Tmax), and minimum temperature (Tmin), consistently increase, with the most pronounced warming observed during winter. The trend analysis reveals that the rate of warming has increased in the past few years, particularly since the 1990s. However, Pettitt’s test points out significant changes in the temperature, with Tmean rising from 25.46 °C in 1951–2004 to 25.78 °C in 2005–2021 (+0.33 °C), Tmax shifting from 45.77 °C in 1951–2010 to 46.24 °C in 2011–2021 (+0.47°C), and Tmin increasing from 2.65 °C in 1951–1999 to 3.19 °C in 2000–2021 (+0.46 °C). These results, along with spatial-temporal distribution maps, shed important light on the alterations and variations in monthly Tmean, Tmax, and Tmin across the area, underlining the dynamic character of climate change and highlighting the demand for methods for adaptation and mitigation. [ABSTRACT FROM AUTHOR]

Karnataka state has the second highest rainfed agricultural land in India, where agricultural output relies heavily on rainfall. The Shimsha basin, a sub-basin of Cauvery in the state, comes under a semi-arid region and predominantly consists of rainfed agricultural land. Rainfall patterns have changed dramatically with time resulting in frequent floods and droughts. Understanding the spatiotemporal distribution of rainfall and its change patterns in the area would benefit sustainable agriculture planning and water resources management practices. The current study aims to determine the variability and trend in rainfall. The daily rainfall data of the Shimsha basin from 1989 to 2018 is collected, and the annual, seasonal, and monthly rainfall totals and the number of rainy days are derived. All the time series are subjected to statistical methods to examine rainfall variability and trend. The mean, standard deviation, coefficient of variation (CV), and Standardized Anomaly Index are used for the preliminary and variability analysis, while the coefficient of skewness and kurtosis are used to understand the rainfall distribution characteristics. The homogenous and serially independent series are identified by homogeneity and serial correlation tests. The trend in the homogenous and serially independent series is identified by Mann–Kendall and Spearman's rank correlation tests, while the magnitude of the trend is quantified using the Sen's slope technique, and the trend change point is evaluated using the sequential Mann–Kendall test. Based on the study, the average rainfall in the study area is 801.86 mm, with CV ranging from 43.3 to 22.27%. The southwest monsoon (SWM) season brings the greatest rain to the basin, followed by the post-monsoon (PM), summer, and winter seasons. In the annual time frame, except one station, all other stations have shown significant or insignificant increasing trends. The seasonal rainfall has shown insignificant rising trends during the summer and winter seasons while insignificant increasing and decreasing trends during the PM season. The SWM season has indicated significant increasing trends, insignificant increasing and decreasing trends. Overall, the study area has noticed an increased annual and seasonal rainfall except for the post-monsoon season, during which the rainfall showed a considerable decline. The findings of the study are helpful in water resource management, agricultural planning, and socioeconomic development in the study area. [ABSTRACT FROM AUTHOR]

Aridity is a climatic phenomenon characterized by shortage of water availability in a given time and space resulting in low moisture and reduced carrying capacity of ecosystems. It is represented by a numerical indicator known as Aridity Index (AI), a function of rainfall and temperature. Aridification is a slow and steady effect of climate change and assessing its spread and change is vital in context of global climatic variations. Aridity is predominantly significant for agrarian countries like India, where a slight rise in drylands area can have a significant impact on the economy and community sustenance. AI is an inclusive indicator of climatic conditions in most arid and semi-arid regions. It helps in identifying and interpreting large scale trend in temperature and precipitation; and thus, classifying region into different climatic classes. The present study assessed long-term AI based on precipitation and temperature data obtained from the India Meteorological Department at the resolution of 1 × 1 degree for years 1969–2017. AI is estimated as a ratio of mean precipitation to mean potential evapotranspiration, calculated using Thornthwaite method. The results highlight the trend of aridity over pan-India with Innovative Trend Analysis and Mann–Kendall test. The study concludes that there is a relatively slow, however steadily progressive drier conditions being established in most of the regions. A shift from 'Semi-arid' towards 'Arid' class appeared in central mainland. The north-eastern Himalaya showed decrease in humid conditions ('Humid' to 'Sub-humid'). The study implies that there is a rising aridity trend over the years due to changing climatic conditions. The shifts in aridity can have serious implications on agriculture, long-term water resource utilization and land use management plans. Our results have scope for future landscape management studies in drylands and better adaptation methods in arid regions. [ABSTRACT FROM AUTHOR]

The emission, transport, and deposition of desert dust are influenced by environmental factors evaluated mainly as a function of precipitation, air temperature, and wind. The present study estimates the desert dust balance and its relationship with environmental factors for the period 1981–2020 in the Baja California Sur region, Mexico. Monthly data on wind, air temperature, precipitation, dust emission and deposition from in situ measurements, reanalysis, satellite estimates and a numerical parameterization scheme are used. First, the in-situ time series were reconstructed with a non-linear principal component analysis based on an autoassociative neural network. We then evaluated the performance of the satellite estimates and the reanalysis data using the matching technique, after which we quantified the dust emission in three source zones. To estimate the state of the climate, we obtained the annual cycle of monthly climatologies, and yearly averages. The effect of climate on the dust budget is estimated by means of non-parametric associations between the variables of interest. Finally, we estimate climate trends using Pettitt, Modified Man-Kendall and Theil-Sen tests. The results show that the data sets used represent the mean and variability of in situ measurements. There are three seasons with transitional periods for precipitation and air temperature. The wind shows a longitudinal west–east gradient. Its maximum intensity is from April to September. Northern zone was dominated by highest emissions. Dry deposition mechanism was the most representative. Emissions and dry deposition are controlled by wind intensity and precipitation, while precipitation controls wet deposition. The Pettitt test showed that abrupt changes in the time series coincide with periods where positive sunspot anomalies converge with negative PDO and SOI anomalies. Finally, trend analyses identified a temporal broadening of the dry season, warmer winters, and summers with increases of ~ 0.02 °C, predominantly increasing trends in dust emissions and wind intensity, and a progressive decrease in dust deposition mechanisms. [ABSTRACT FROM AUTHOR]

Managing climate change-induced impacts on livelihoods in the Himalayan belt is a challenging task. This is because livelihoods are highly vulnerable to climate change and depend on both biophysical and social processes. In this study, the vulnerability of livelihoods to climate variability was investigated in Jammu province. The variability of climate variables was studied by applying Mann–Kendall test and Sen's Slope estimator. Similarly, vulnerability index was applied to assess livelihood vulnerability. The result of the study showed that weather stations in Katra, Batote and Banihal showed negative trend in rainfall during 1987–2019, while Samba, Rajouri and Jammu showed positive trend. However, only Jammu district showed significant trend at 0.01 level of significance. Similarly, Katra, Bhaderwah and Jammu showed negative trend in average maximum temperature while Banihal and Batote showed positive trend. However, none of the weather stations showed a significant trend at the 0.01 level of significance. The livelihood vulnerability index results showed that Ramban and Kishtwar districts were the most vulnerable, while Jammu district was the least vulnerable to climate variability. The vulnerability results show that mountainous regions and remote districts are particularly vulnerable to climate variability and change. The results of this study could be useful for livelihood vulnerability mitigation and planning in Jammu province and other mountainous regions of India and the world. [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 present study investigates long-term changes in the rainfall regime over the Sabarmati River Basin, Western India, during 1981–2020 using computational and spatial analysis tools. Daily gridded rainfall data from India Meteorological Department (IMD) at 0.25 × 0.25 spatial resolution was employed to determine changes in rainfall at annual, monthly, and seasonal scales and analyze changes in rainfall characteristics using different thresholds for dry/ wet days and prolonged spells over Western India. Mann–Kendall test, Sen slope estimation, and linear regression analysis indicate that annual and monsoon rainfall over the basin has increased while the rest of the seasons have shown a declining trend. However, none of the trends obtained was found to be statistically significant. Spatial analysis of rainfall trends for each decade between 1980 and 2020 revealed that certain parts of the basin had experienced a significant declining trend during 1991–2000. Monthly rainfall analysis indicates the presence of a unimodal distribution of rainfall and a shift in rainfall towards later monsoon months (August and September). It is also inferred that days with moderate rainfall have decreased while low and extreme rainfall events have increased over the basin. It is evident from the study that the rainfall regime is highly erratic, and the study is important in understanding the changes in the rainfall regime during the last 40 years. The study has significant implications for water resource management, agricultural planning, and mitigation of water-related disasters. [ABSTRACT FROM AUTHOR]

Analysis of the spatiotemporal pattern of burned areas over time is necessary to understand how fire behavior in the Himalayan region has altered as a result of the complex climatic variables. The differenced Normalized Burnt Ratio (NBR) is calculated utilizing the cloud-based platform Google Earth Engine (GEE) to quantify the extent of burned regions. The spatial distribution of burnt areas in the Himalayan region over the last 21 years has been examined and correlated with climatic and edaphic factors in the current study. The area affected by forest fire has shown a direct correlation with the land surface temperature, but an inverse relationship with surface soil moisture, pre-fire precipitation, pre-fire Normalized Difference Vegetation Index (NDVI) and pre-fire Enhanced Vegetation Index (EVI). The p-value for 9 of the 20 regions in which the research area has been divided for the spatial analysis is less than 0.05, implying that the regression model is statistically significant. Trend analysis done using Mann–Kendall test and Theil–Sen estimator state the distinct trends of burnt area and other meteorological and edaphic parameters in the Western, Central and Eastern Himalaya. The assessment of burned areas aids forest managers in mitigating the impacts and managing the forest fires, as well as in the implementation of the restoration methods following a forest fire. [ABSTRACT FROM AUTHOR]

Since climate change is a major challenge that affects the environment, especially the hydrological flow, this research has used the Soil and Water Assessment Tool (SWAT) to study the climate change effects on the runoff in Alvand mountain basin in Iran. The SWAT model calibration was done based on the SUFI-2 algorithm and the validation results for a 20-year prediction period (2020–2040) showed that the model had high accuracy in simulating the runoffs. CMIP5 models were used to predict the basin's temperature and rainfall (the best scenario was Miroc5) and the results showed that the basin's future climate conditions increase in temperature and decrease in rainfall were appropriate. According to Miroc5 (RCP8.5), the annual runoff in the mentioned period would decrease by 8.36% compared to the past. In a seasonal scale, the largest runoff reduction would be 75% in summer (under the Miroc5 (RCP4.5)), and in a monthly scale, it would be 79% in April increasing dramatically to reach 335% in September (under Miroc5 (RCP8.5)). The trend of the future runoff was investigated using the Mann–Kendall model and the Gresson estimation method. Results showed that there would be no significant trend in the mentioned period and the trend of the future drought intensity would be increasing under all studied scenarios. [ABSTRACT FROM AUTHOR]

Land degradation is a global environmental issue, affecting human sustainable development. The trend of land cover change (LCC) is one of the core sub-indicators of land degradation assessment in Sustainable Development Goal 15.3.1, which is closely related to the quality of land resources and regional ecological environment. Sustainable management of land resources is the basis and premise of sustainable development of human society and economy. In order to more comprehensively assess the land degradation situation from LCC, optimize the land cover (LC) structure and land resource utilization. This paper combines land cover qualitative change (LQaC) (LC type change) and land cover quantitative change (LQtC) (land vegetation cover change) to comprehensively assess land degradation, and applies it to the Pakistan part of the China-Pakistan Economic Corridor. The results showed that land degradation area in Pakistan from 2001 to 2020 was 1.99% (17,550.07km2), of which 1.90% (16,756.35km2) was caused by LQaC, and 0.09% (793.72km2) was caused by LQtC. Land improvement was 10.75% (94,805.65 km2). Land degradation is mainly caused by LQaC, and it mainly in the Indus River Valley and the central and eastern plains, which is significantly affected by human activities. At the same time, we found that in Pakistan: the continuous agricultural expansion (grassland to cropland) and vegetation establishment (unused land to grassland) are accompanied by serious withdrawal of agriculture (cropland to grassland) and vegetation loss (grassland to unused land). The agricultural expansion and the intensification of human activities lead to land degradation. Our assessment suggests that the government should focus on formulating effective land use policies, while protecting the ecological environment and vegetation: improving the productivity of cropland, optimizing the use of cropland resources, and reasonably reclaiming cropland to realize "Land Degradation Neutrality" and sustainable development of land resources in Pakistan. [ABSTRACT FROM AUTHOR]

This study aims at investigating annual and seasonal precipitation trend over Macta basin (northwest of Algeria) using the Mann–Kendall (MK) test, the modified Mann–Kendall (MMK) test, and the Innovative Trend Analysis (ITA). To achieve this aim, the monthly precipitation amounts from 48 meteorological stations for the period between 1974–1975 and 2011–2012 have been used. At the annual scale, the MK and MMK tests revealed that 19 stations (40%) show significant trends, while for the ITA method, the number of stations that shows a significant trend is greater (37 stations). Findings also unveiled that there is the sub-region of decreasing trends in the northern part close to the Mediterranean Sea coastal area, particularly in the western part, whereas in the south the trends are rather positive. At seasonal scale, the MK and MMK tests show few significant trends. In autumn, precipitation increases (42 stations up and 6 down). In winter, spring, and summer, the number of positive and negative trends is almost equal. The ITA method, whose significant trends are once again greater, shows that in autumn, 60% of the series are positive while 40% are negative. For the other seasons, the number of stations with decreasing trends is slightly greater than those increasing. Thus, seasonal precipitation tends to increase during the dry season mainly in autumn. Conversely, during the rainy season, precipitation undergoes a negative trend. Findings also unveiled that the ITA approach produces more significant results than the MK and MMK tests. Similarly, study of spatiotemporal trends showed a complex behavior. [ABSTRACT FROM AUTHOR]

Precipitation studies have a crucial role in deciphering climate change and monitoring natural disasters such as droughts. Such studies lead to better assessment of rainfall amounts and spatial variabilities; and have a vital role in impact assessment, mitigation, and prediction of occurrence. Thus, this study has been undertaken in the Subarnarekha River basin using Climate Hazards group Infrared Precipitation with Stations (CHIRPS) dataset. Precipitation datasets helped in deriving hydrometeorological indices such as the Rainfall Anomaly Index (RAI) and Standardized Precipitation Index (SPI) for the identification of drought occurrences. The core objective was to infer spatio-temporal drought scenarios and their trend characterization covering four decades over the years 1981 to 2020. Quantitative drought assessment was done using run theory for identifying the Drought Duration (DD), Drought Severity (DS), Drought Intensity (DI), and Drought Frequency (DF). Mann–Kendall (MK) test was performed to understand the precipitation and drought trends at annual and seasonal scales. Eight severe drought events were identified in the Subarnarekha River basin for the past 40 years and the average DI value of 0.8 was recorded. MK test results for the precipitation showed a significant positive trend (95% confidence level) for pre-monsoon periods. However, for SPI, a significant positive trend was observed over the intervals of 3 (SPI3), 6 (SPI6), and 12 (SPI12) months respectively at an annual timescale, suggesting wetter conditions within the study area. Moreover, there had been insignificant negative trends for SPI1 and SPI3 during winter. It indicates that during the short-term SPI scale, i.e., 1 month (SPI1) and 3 months (SPI3), the instances of negative SPI values inferred were high, which point to the increasing incidences of meteorological drought possibly due to deficient soil moisture. Thus, the results indicated that the CHIRPS precipitation product-derived hydrometeorological indices could act as a valuable tool for assessing the past spatio-temporal drought conditions of the Subarnarekha River basin. This may further be helpful in planning for sustainable water resource management of such river basins. [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]

Understanding the climate change impacts on drought occurrence is of great importance for comprehensive water resources management. Reconnaissance drought index (RDI) considers, in addition to precipitation, potential evapotranspiration (PET), which is a vital parameter to detect droughts under climate change. RDI is first applied to global land precipitation and PET data during 1959–2018. The annual drought trend analysis reveals while around 79% of the world's lands present no significant drought trend, approximately 13 and 8% are getting drier and wetter, respectively. Therefore, the area of drying zone is larger than that of wetting zone. Seasonal drought trend analysis presents contradictory tendencies. Spring (MAM) and summer (JJA) indicate a drying trend. Conversely, fall (SON) and winter (DJF) present a wetting tendency. Climate projections of 16 global climate models (GCMs) from the fifth phase of the Coupled Model Intercomparison Project (CMIP5) under RCP8.5 scenario are used to assess future drought during 2006–2055. PET is estimated using Penman–Monteith model. The results obtained from all the applied GCMs suggest that the areal extent of RDI downward trend (shifting to more arid) is larger than that of more wetness tendency. Average of the area percentages presented by the 16 GCMs indicates while the area of drying zone is almost the same with that of the past period with 13%, the wetting zone in the future with 5% is smaller than that of past with 8%. Therefore, the observed drying trend in the past period will continue during the future period. [ABSTRACT FROM AUTHOR]

The meteorological drought dynamics and its impacts on rice productivity has been evaluated for the Indian Summer Monsoon Rainfall (ISMR) season using the standardized precipitation index (SPI) over the middle Gangetic plains (MGP) of Bihar. The meteorological drought over the ISMR period was found to be a recurring phenomenon coinciding with the rice growing season over Bihar. The rice crop has an intensive water requirement; therefore, it is significantly impacted by the meteorological droughts. In the present study, spatiotemporal characteristics viz. intensity, frequency, and probability of meteorological drought has been assessed along with an investigation for significant trends and detection of regime shift points to identify the impact of drought on rice production. For the purpose, SPI-4 derived from high resolution gridded daily rainfall data (0.25° × 0.25°) from India Meteorological Department (IMD) has been considered to analyse the meteorological drought episodes over agro-climatic zones of Bihar from 1961 to 2019. The regime shifts were determined using the Rodionov test for the drought dynamics and production of rice in Bihar. A moderate to severe drought-prone zone was found over the zone BRZ3B; while zone BRZ2 and BRZ3A showed comparatively a greater number of mild drought events persisting with more than 70% probability of occurrence. An inkling of increasing dependency on groundwater is found, which is in turn governing the rice production regime. The present study shows there is a substantial need for climate resilience and food security policies incorporating the subtle linkage between SPI variability and crop production, especially over rice producing regions of the globe. [ABSTRACT FROM AUTHOR]

In recent years, the effects and consequences of climate change have shown themselves by creating irregularities and trends in the essential climatic variables. In most cases, the trend of climatic variables is associated with periodicity. In this study, the trends and periodicity of these data (precipitation, temperature, evapotranspiration, and net available water (NWA) have been investigated in a period of 60 years in Iran. The Mann–Kendall trend test and Sen’s slope estimator are applied for analyzing the trend and its magnitude. Wavelet transform is used to detect the periodicity of time series and to determine the correlation between NWA and temperature, precipitation, and evapotranspiration in common periodicity. The results show that the stations located in eastern and western Iran have more significant increasing/decreasing trends. Evapotranspiration shows the highest increasing trend in most stations, followed by temperature, while NWA and precipitation have trends at lower significance levels and decreasing direction. The examination of periodicity in time series showed that, among all the studied stations, evapotranspiration has the most extended periodicity with an average length of 8.3 years, followed by NWA, temperature, and precipitation with 7.3 years, 5.8 years, and 5.5 years. The results of the correlations investigation showed that in about 80% of the stations, there is a high correlation between precipitation and NWA in the short-term periodicity and at the end of the studied period. The evapotranspiration variable in most stations has a high correlation in different periodicities with the amount of NWA. [ABSTRACT FROM AUTHOR]

This research examines the temporal sequence of trends for rainfall data, calculated for 11 grid stations. LOWESS curves showed the correlation between the predicted and observed time series. The temporal rainfall trend was evaluated using the non-parametric MK and Sen's slope method which was applied to notice the rainfall tendency and slope with a 5% significance level. In the study period, non-parametric tests reveal a significant positive trend at 7 meteorological grid stations for June, but the remaining 4 stations exhibit a non-significant positive trend in June and the significant negative trend was noticed in October only for the Sidlaghatta station and a non-significant negative trend appeared in October and December with 6 and 4 stations, respectively. The output of the study indicates that yearly precipitation is rising showing the highest in Bangarapete with a change of 53.22%, and the lowest in Sidlaghatta with the value of –3.56%. [ABSTRACT FROM AUTHOR]

As has been noted on numerous occasions, between 2012 and 2021, the pace of publications by Russian researchers rapidly increased; however, this growth was uneven in both quantitative and qualitative terms. Moreover, some research organizations had zero or even negative publication activity growth. To achieve a full understanding of the real development processes of the research sector requires analysis of the dynamics of dozens of indicators for hundreds of organizations. However, the question remains: how can these numerous data series be studied? To address this problem, this article proposes a method of classifying trends in time series based on a combination of three statistical tests of randomness against trend: the Cox-Stuart test, the inversion test of randomness, and the Mann–Kendall test. The method is used to classify a series of scientometric parameters that characterize various aspects of the publication activity of 701 leading Russian research organizations and universities, including a comprehensive publication score (an analogue of the official Composite Score for Publication Performance (CSPP)), the average quality level of publications, and the share of articles in conference proceedings. The differences trends of organizations of different types and performance categories are identified. This information is used to formulate new questions for further research into the causes of the observed trends in the development of Russian science. [ABSTRACT FROM AUTHOR]

Long-term assessment of basic meteorological field variability is an important factor that influences the Indian summer monsoon and consequently affects the socio-economic aspects of India. In this study, the spatial and temporal variation of meteorological parameters during summer monsoon season using NCEP/NCAR reanalysis datasets for the period of 70 years (1948–2017) has been analyzed in climatology, early-late phase and multidecadal epochs over India and its regions. Statistical techniques such as the standardized anomaly index of surface temperature, rainfall and zonal and meridional wind (at 850 and 200 hPa) and temporal analysis of Mann–Kendall trend test over six selected regions, viz., North India (NI), Central India (CI), Southern India (SI), Arabian Sea (AS), Bay of Bengal (BoB) and Equatorial Indian Ocean (EIO) reveal higher variability during summer monsoon season from 1948 to 2017. The significant spatial changes in the value of standard deviation and coefficient of variation confirm the early-late phase and multidecadal modulation of the seasonal variability of selected climatic parameters. The results indicate that the escalation in the surface temperature multidecadal variability and trend has dominating characteristics over NI, CI and SI regions at an alarming range (0.5–1.0°C). The major hotspots of increasing early-late phase and multidecadal variability and average precipitation have been found over BoB, EIO and SI (~1–3.5 mm/day). The decreasing changes in the mean rainfall pattern and associated variability is strongly linked with increasing surface warming and significant reduction in the strength of surface zonal wind over BoB, IO, SI and CI region which cause the weakening of important atmospheric circulations such as the role of Somali jet and strong low-level jet (LLJ) during Indian summer monsoon season. Also, the meridional wind at the surface and upper level has shown significant enhancement over AS and EIO. The recent decadal anomaly (2008–2017) is really a matter of concern as precipitation and wind circulation anomaly at 850 and 200 hPa have shown decreasing trends over all the regions. In recent years, the variation in meteorological parameters and distribution are asymmetrical during summer monsoon season in changing climate. [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]

This study investigates rainfall and drought characteristics in southeastern Australia (New South Wales and Victoria) using data from 45 rainfall stations. Four homogeneity tests are adopted to determine inhomogeneities in the annual total rainfall (ATR) and monthly rainfall data, namely The Pettitt test, the SNHT, the Buishand range test and the Von Neumann ratio test at significance levels of 1%, 5%, and 10%. Temporal trends in rainfall (ATR, monthly, and seasonal) and droughts are examined using autocorrelated Mann–Kendall (A-MK) trend test at 1%, 5%, and 10% significance levels. We also assess meteorological droughts by using multiple drought indices (3-, 6-, 9-, 12-, 24-, and 36-month Standardized Precipitation Index (SPI) and Effective Drought Index (EDI)). Furthermore, spatial variability of temporal trends in rainfall and drought are investigated through interpolation of Sen's slope estimator. The results represent an increasing trend in ATR between 1920 and 2019. However, southeast Australia is highly dominated by a significant negative trend in the medium term between 1970 and 2019. Winter is found to be dominated by a significantly negative trend, whereas summer and spring seasons are dominated by a positive trend. April is detected as the driest month according to magnitude of Sen's slope and the A-MK test result. Positive trends on droughts are observed at inner parts of the study area, whereas a negative trend is detected in the south, southeast, and northeast of the study area based on SPIs and EDI. The findings of this study help to understand changes in rainfall and droughts in southeastern Australia. [ABSTRACT FROM AUTHOR]

Globally, forest habitats are threatened by human-induced deforestation and degradation. This study was aimed to assessing changes in land-use patterns in Awi Zone, Ethiopia, during the last 34 years and its relationship with land surface temperature (LST) and rainfall using digital change detection approaches. Multi-temporal Landsat imagery (1985, 2000, 2009, and 2019), MODIS, and CHIRPS were used in combination with Google Earth and field data for this study. Support vector machine classification technique was used to produce LULC maps. Mann–Kendall test for LST (2000‒2019) and rainfall (1981‒2019) was used to compute non-parametric trend analysis. Results revealed that 47,255.5 ha of forest and 24,674.9 ha of other land-cover types had increased from 1985 to 2019. In contrast, agriculture and bare land had decreased by 16,819.9 ha and 55,110.5 ha, respectively. In Dega agro-climatic zone, mean and minimum LST trend analysis has revealed a significant decrease. Sen's slope shows mean, minimum, and maximum LST decreasing trend at 0.13 °C/yr., 0.12 °C/yr. and 0.10 °C/yr., respectively. In Woyna Dega and Kolla, LST trend analysis showed decreasing and increasing trends from 2000 to 2019. Coefficient of determination (r2) of forest cover and mean LST for Dega, WoynaDega, and Kolla zones revealed 0.98, 0.81, and 0.20, respectively. Kolla is highly influenced by anthropogenic factors and rapid urbanization that caused depletion of forest cover and conversion of farmlands into human settlements. In future, community-based land-use and land-cover planning and sustainable forest management system are recommended to protect, conserve and rehabilitate the remaining natural environment in the study area. [ABSTRACT FROM AUTHOR]

Climate change has received worldwide attention as it affects all sectors of life. The present study examined the trends in the climatic variables, rainfall and temperature over the area covered by three humid tropical basins, Meenachil, Manimala and Achencoil, Kerala, India. The distribution of rainfall was assessed with precipitation concentration indices (PCIs). The trend analysis of the climatic variables was evaluated using Mann-Kendall test and Sen's slope at annual and seasonal time scales. The historical rainfall showed the predominance of an insignificant declining trend in annual, winter, pre-monsoon and monsoon time steps. The post-monsoon rainfall showed a positive trend in the area. The maximum and minimum temperature showed prominent rising trend in annual and seasonal time steps. The nature of extreme events was evaluated with extreme climate indices. The trend was exhibited by eight precipitation indices: maximum daily rainfall (Rx1day), maximum 5-day rainfall (Rx5day), number of heavy rainfall days (R10), number of very heavy rainfall days (R20), consecutive wet days (CWD), consecutive dry days (CDD), annual wet day rainfall total (PRCPTOT), simple daily intensity index (SDII) and 4 temperature indices: warmest day (TXx), coldest day (TXn), warmest night (TNx) and coldest night (TNn) over the area was determined. The Rx1day and Rx5day showed an increasing trend in the area, while dominance of negative trend was observed for R10 and R20. The CDD showed a mixed pattern of rising and declining trends, whereas CWD showed significant decreasing trend. The dominance of declining trend was noticed for PRCPTOT and increasing trend for SDII. The results revealed the possibility of rainfall getting concentrated in fewer days. The extreme temperature indices showed rising trend with significant rise for TXx and TXn, which can be a signal to the climate warming in the region. The results of the study can benefit in planning and management of water resources of the area. [ABSTRACT FROM AUTHOR]

Changes in the streamflow pattern raise a plethora of implications on the morphological, economic, social, and cultural aspects of an entire river basin. Trend analysis of hydrological time series is the most common method to identify these changes. Several methods have been proposed to identify the trends in time series. In the present study, Mann–Kendall (MK), innovative trend analysis (ITA), and revised innovative trend analysis (RITA) were used to identify the trend in discharge and sediment load at two gauging sites of the lower Drava River Basin, during 1988–2017 with an objective to compare and understand the limitations of these methods. Innovative trend analysis provided greater visualization of the trend of different magnitude of discharges and sediment load as compared to Mann–Kendall test. However, it was observed that due to certain assumptions, ITA produced significant trends when the trends were insignificant. The significance of trends obtained from RITA was in close agreement with the Mann–Kendall test. In this regard, the method of determining the significance of trend in ITA needs to be rechecked and revised. [ABSTRACT FROM AUTHOR]

The study assessed the radiological impact of the Kaiga Generating Station (KGS) on the surrounding environment and the safety of the nearby population. Over a period of 15 years (2005–2020), the radiological monitoring detected only trace levels of 3H and 137Cs (attributed to fallout) in the surrounding environment. The study utilized the non-parametric Mann–Kendall test to analyze the long-term trends, identifying either decreasing or no trend in the radioactivity levels across various environmental matrices such as air, water, biota, and dietary items. These findings indicate no significant accumulation of radioactivity, confirming the minimal environmental impact of the KGS operations. [ABSTRACT FROM AUTHOR]

Predicting the effect of climate change on rainfall data is essential to the economic headway of any country. This study investigated changes in rainfall data series due to climate change across ten stations in the Kurdistan Region, Iraq. The Mann–Kendall (MK), modified MK, and innovative trend analysis methods were used to identify trends in the rainfall data series. In addition, the homogeneity of rainfall data has been investigated by the Pettit, Standard Normal Homogeneity, Buishand Range, and Von Neumann Rate tests. Over the 30-year study period, it was found that just three stations out of ten exhibited significant serial correlations in rainfall data series. Annual rainfall has shown an increasing trend at most of the stations, with only four stations reporting statistically significant trends. Seasonally, a noticeable decrease in rainfall values has been observed in the northern part of the region during winter and spring compared with fall season trends. Based on the Pettitt change-point test, it was found that annual rainfall data in the study area is dominated by a positive shift. Moreover, it was found that the study area is dominated by humid rainfall intensity and has the highest concentration of irregular to strong distribution levels in annual rainfall data covering the north-eastern part of the study area. In addition, it was found that most of the annual rainfall data series is dominated by homogeneous trends. [ABSTRACT FROM AUTHOR]

In this study, it was aimed to determine the meteorological drought trends in the Euphrates Basin by using rainfall-based Standardised Precipitation Index (SPI), Statistical Z Score Index (ZSI), Rainfall Anomaly Index (RAI) and rainfall and temperature-based Standardised Precipitation Evapotranspiration Index (SPEI) and Reconnaissance Drought Index (RDI). Mann-Kendall (MK) and Modified Mann-Kendall (MMK) tests were applied during the monthly and annual periods to determine drought trends. The trend analysis results of meteorological drought indices are mapped in 90% (z > 1.65) and 95% (z > 1.96) confidence intervals. According to the analyses applied, it was determined that the trend analysis results of SPI, ZSI and RDI indicate almost the same results. In addition, SPEI was found to be more sensitive in detecting drought trends compared to other indices. Moreover, it has been concluded that the Euphrates Basin is at great risk of meteorological drought due to the dominance of decreasing drought trends and trends in the basin. Making a drought management plan and going into alarm in endangered areas is recommended to prevent existing risks. [ABSTRACT FROM AUTHOR]

Background: Understanding the climate variability at local scale could help suggest local adaptation responses to manage climate associated risks. This paper analyzed the variability and trend of climate in semi-arid Borana zone of southern Ethiopia over the period 1981–2018 using Mann–Kendall (MK) test, Sen's Slope Estimator (SEE) and inverse distance weighted (IDW) interpolation technique. Gridded (4 km * 4 km) climate data (daily precipitation, daily maximum temperature (Tmax) and minimum temperature (Tmin)) were collected from National Meteorology Agency (NMA) of Ethiopia. Results: The results revealed the study area received a mean monthly precipitation of 39.19 mm and a monthly mean Tmax and Tmin of 29.66 °C and 16.31 °C were observed respectively. Rainfall shows a significant increasing trend during August, October and November and extremely variable during December, January and February where CV > 100%. Tmax shows a significant warming trend during August but January, February, August and October exhibited similar trend for Tmin. Rainfall and Tmin shows a significant trend during Meher and no trend for the rest of the seasons. Mean annual rainfall shows a significant increase where no trend observed for both Tmax and Tmin at this timescale. Decadal rainfall and Tmin exhibited no trend but Tmax show significant warming trend during 2001–2010 decade. Better rainfall and cooler temperature were observed in the north central, northeastern and northwestern whereas the southeastern and southwestern regions were drier and warmer. Conclusion: Rainfall is highly variable than temperature both at temporal and spatial scales in Borana. The intensity of rainfall decreases from the northeastern and northwestern parts towards the southwest while temperature increased from the north central parts towards the southwest for Tmax and the southeast for Tmin. The effect of topography is substantial for the local scale variability observed in the study area. Internal variability is observed at temporal and spatial scales and therefore any adaptation responses to local climate variability should consider the microscale climate. [ABSTRACT FROM AUTHOR]

The hydrometeorological characteristics of the Mahaweli Basin are influenced by rainfall distribution. For that reason, it is significant to identify spatiotemporal rainfall fluctuations and trends over the Mahaweli Basin. Accordingly, rainfall data from 1990 to 2019 available for the 15 raingauge stations were analyzed for rainfall variability and trends. Serial autocorrelation was checked before applying rainfall time series data to Mann–Kendall (MK) test. The result exhibited no serial autocorrelation in the data. The MK test, Sen's slope estimator (SSE), and innovative trend analysis (ITA) were applied to recognize rainfall trends. The inverse distance weighting (IDW) interpolation method was applied to show the spatial pattern of rainfall characteristics with the support of ArcGIS 10.1. Some fluctuations were observed in the rainfall over the 30 years with decreasing and increasing trends. Nevertheless, significant trends in the annual rainfall were noted for Bandarawela (+ 15.7 mm), Ledgerwatta (+ 40.3 mm), Duckwari (− 36.3 mm), and Bakamuna (24.3 mm). At the basin scale, no significant trends were noted in rainfall of the Mahaweli Basin. The rainfall trend analysis results obtained by ITA have validated the results of the nonparametric test. Therefore, the analysis showed that despite the seasonal variations in rainfall over the Mahaweli Basin, rainfall is regular, and results acquired by MK test, SSE, and ITA methods are reliable. [ABSTRACT FROM AUTHOR]

Climate variability analysis is essential for predicting the behavior of various extreme weather events and making communities resilient. Notwithstanding the profound concerns, climate variability assessment faces numerous challenges due to inadequate and sometimes unavailability of data at spatiotemporal scales. This study makes an attempt to analyse climate variability in the Bhagirathi Sub-basin of India. Six meteorological variables were analysed from fourteen weather stations located in the Sub-basin during 1968–2017. Modified Mann–Kendall test was employed to ascertain the trends in meteorological variables. One-way ANOVA was used to assess the relationship between and within the variables. A total of 432 households were selected for reaffirming climate variability and impact on landscape. Significant trends were detected in highest maximum, mean maximum (Mmax) and mean minimum (Mmin) temperatures, relative humidity (Rh), rainfall and vapour pressure (Vp) at annual and seasonal scales. Stations located in eastern and deltaic Sub-basins registered varying trends in these meteorological variables due to anthropogenic activities-induced land use changes. ANOVA revealed a robust relation among rainfall, Vp, Mmin and Mmax. Perceptions of the sampled households revealed that climate variability has considerably affected flood intensity, vegetation, soil, water resources and agricultural pattern. We find modified Mann–Kendall method effective in analysing climate variability in the Sub-basin. Thus, this method can be utilized for effective analysis of climate variability at spatial scales in geographical regions. [ABSTRACT FROM AUTHOR]

Climate change, which is one of the main determinants of agricultural production, has started affecting the pattern of crop growth, productivity, and quality of produce from the last few couple of decades in various agro-climatic zones globally. Any change in climatic factors such as temperature, evapotranspiration (ET), and rainfall is bound to have a significant impact on agricultural production. Thus, climate monitoring, trend analysis, and model-based prediction are highly significant to mitigate the climate change impacts on crop growth patterns, production, and quality traits. A study was thus undertaken at Punjab Agricultural University, Ludhiana, India, to (1) estimate reference evapotranspiration (ETo) using FAO-ETo calculator; (2) study and detect trend in long-term (1970–2019) recorded temperature (Tmin and Tmax), rainfall and ETo using Mann–Kendall's test, Sen's slope test, standard normal homogeneity test (SNHT), and Pettitt's test in XLSTAT software; (3) study correlation of ETo with Tmin, Tmax, and rainfall; and (4) develop regression models for estimating ETo on seasonal and annual basis. All the tests indicated a significant trend in Tmin (increasing) and ETo (decreasing) during all seasons (spring, summer, autumn, and winter), as well as on annual basis at 5% level of significance, whereas no trend was recorded in Tmax and rainfall data. The SNHT and Pettitt's test confirmed the existence of a change-point in both ETo and Tmin data for all seasons as well as on annual basis. Both Mann–Kendall's and homogeneity tests indicated no trend or change point in Tmax (except a change-point during spring) and rainfall data. The positive correlation of ETo with Tmax, wind speed (vw), and sunshine hours (SSH) formed an increasing trend in ETo with increase in these variables and vice-versa. The negative correlation of ETo with relative humidity (RHmin and RHmax), rainfall, and Tmin indicated a decreasing trend in ETo. The study offers a basis to predict the futuristic climate scenarios in the region for planning crops and manage irrigation to mitigate the climate change impacts on agricultural production. The statistical comparison indicated that the developed models were sufficiently accurate and would be useful in simplified estimation of ETo on seasonal and annual basis for the study region. [ABSTRACT FROM AUTHOR]