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The main purpose of this paper was to investigate the monthly, seasonal, and annual rainfall variability in the Mae Klong River Basin in Thailand using the Mann–Kendall (MK) test, Sen's slope method, Spearman's Rho (SR) test, and the innovative trend analysis (ITA) method. The monthly rainfall data of eight stations for the period 1971–2015 were used for trend analysis. Datasets with significant serial correlation were corrected by the trend-free pre-whitening (TFPW) approach for statistical methods. The MK test showed increasing rainfall trends for five out of eight stations in the dry season while 50% of stations indicated increasing trends in the wet season. On an annual scale, 75% of the stations exhibited increasing rainfall trends. The results of the SR test were in line with the MK test for seasonal and annual rainfall. The ITA method showed comparable findings with those of the statistical methods. For the entire basin, trend analysis found increasing rainfall on both seasonal and annual scales by all the tests. The findings of this study could benefit water supply and management, drought monitoring, agricultural production activities, and socioeconomic development in the Mae Klong River Basin in the future.

The Upper Indus basin (UIB) is characterized by contrasting hydrometeorological behaviors; therefore, it has become pertinent to understand hydrometeorological trends at the sub-watershed level. Many studies have investigated the snow cover and hydrometeorological modeling at basin level but none have reported the spatial variability of trends and their magnitude at a sub-basin level. This study was conducted to analyze the trends in the contrasting hydrological regimes of the snow and glacier-fed river catchments of the Hunza and Astore sub-basins of the UIB. Mann-Kendall and Sen’s slope methods were used to study the main trends and their magnitude using MODIS snow cover information (2001–2015) and hydrometeorological data. The results showed that in the Hunza basin, the river discharge and temperature were significantly (p ≤ 0.05) decreased with a Sen’s slope value of −2.541 m3·s−1·year−1 and −0.034 °C·year−1, respectively, while precipitation data showed a non-significant (p ≥ 0.05) increasing trend with a Sen’s slope value of 0.023 mm·year−1. In the Astore basin, the river discharge and precipitation are increasing significantly (p ≤ 0.05) with a Sen’s slope value of 1.039 m3·s−1·year−1 and 0.192 mm·year−1, respectively. The snow cover analysis results suggest that the Western Himalayas (the Astore basin) had a stable trend with a Sen’s slope of 0.07% year−1 and the Central Karakoram region (the Hunza River basin) shows a slightly increasing trend with a Sen’s slope of 0.394% year−1. Based on the results of this study it can be concluded that since both sub-basins are influenced by different climatological systems (monsoon and westerly), the results of those studies that treat the Upper Indus basin as one unit in hydrometeorological modeling should be used with caution. Furthermore, it is suggested that similar studies at the sub-basin level of the UIB will help in a better understanding of the Karakoram anomaly.

We examined trends in several hydro-meteorological variables in the upper Indus River basin (UIRB) in Pakistan. To represent the diversity of hydro-meteorological conditions in the basin, mean monthly data from 20 meteorological and 8 hydrometric stations were analyzed for detection of trends using the non-parametric Mann-Kendall test in combination with the trend-free pre-whitening approach for correcting time series data sets for serial correlation. Sen's slope method, a non-parametric alternative for estimating a slope for a univariate time series, was used to determine the magnitude of trends. The meteorological variables we considered were: minimum temperature, maximum temperature, and precipitation, whereas the hydrological variable considered was streamflow. For several of the variables, many more trends were identified than can be expected to occur by chance. Analysis of winter maximum temperature revealed an increasing trend with the trend in slopes of 1.79, 1.66, and 1.20°C per 39 yr for the upper, middle, and lower regions, respectively. Precipitation trends were inconsistent and showed no definite pattern. Trends in streamflow were found to be related to increasing trends in mean maximum temperature, particularly in winter and spring seasons. Increased winter temperatures are likely to increase streamflow in winter and spring. During summer months streamflow will decrease and reduce the availability of water in the Tarbela Dam, thereby requiring changes in the reservoir operating policy towards more efficient management of available water.

The study incorporates a first-time investigation of annual trends in precipitation, maximum and minimum temperature, and related extreme events across a set of 21 study sites of differing altitudes across North-East India and its contiguous areas employing reanalysis datasets of highest spatial and temporal resolution (0.12º, 1-hr) available for Indian subcontinent called Indian Monsoon Data Assimilation and Analysis (IMDAA). A total of ten precipitation and eleven temperature extremes, following the Expert Team on Climate Change Detection and Indices (ETCCDI), have been explored for trend identification at 5% and 1% significance levels combined with change point analysis using the Mann-Kendall (MK)/modified MK (mMK) tests and cumulative sum (CUSUM) charts, respectively. Noteworthy findings were revealed, where most stations exhibited significant annual increases in minimum temperatures (T min ), along with a less pronounced trend in maximum temperatures (T max ), while some stations showed decreasing annual precipitation. Results revealed predominant significant increasing trends in temperature indices across most of the locations with indices related to warmer nights showing greater significance. However, precipitation indices showed declining trends for most of the sites except for one location. Change points corresponding to minimum and maximum temperatures characterized by negative to positive shifts outnumbered those to precipitation, which showed rather opposite shifts across most of the locations. Predominant negative to positive shifts were observed in summer days (SU25), tropical nights (TR20), annual maximum of daily T min (TNx), warm days (TX90p), warm nights (TN90p), annual minimum of daily T max (TXn) and T min (TNn), whereas positive to negative in annual maximum of daily T max (TXx), cool days (TX10p), cool nights (TN10p) and diurnal temperature range (DTR) indicating a greater rise in warming scenario rather than cooling scenario. Precipitation indices generally shifted from positive to negative values, except for consecutive wet days (CWD), which indicates shifting precipitation patterns. The rise in extreme events poses a serious threat to the region and the continuously evolving pattern of climatic variables may have significant implications on human and natural ecosystems over this area of Indian subcontinent.

Hydrological climate variables such as precipitation, temperature, humidity, evapotranspiration, infiltration, and streamflow provide useful information for the effective planning, design, and management of water resources. In the present study, monthly precipitation and streamflow data were analyzed to assess the variability in trends of precipitation over the Upper Narmada Basin (UNB), Middle Narmada Basin (MNB), and Lower Narmada Basin (LNB) for 41 years (1981–2021) using the widely accepted Mann–Kendall Test (MKT), Innovative Trend Analysis (ITA), and magnitude by Sen's Slope estimator, thereby extending further by a decade, the last study by Thomas and others in 2015. The inclusion of more factors affecting the streamflow in response to the rainfall often makes the model very complex and unreliable. In the present study, to overcome this limitation, a simple yet most definite correlation was established between rainfall and streamflow, which can serve as a meaningful indicator of basin behavior. Poor correlation was observed for the UNB and the MNB compared to the LNB, which may be attributed to the presence of tributaries. The result of this study will be helpful to policymakers and water resource managers to plan and adopt suitable schemes of water conservation and management in the Narmada basin.

Pakistan, located in an arid region characterized by low rainfall and high temperatures, faces significant vulnerability to climate change. The country’s diverse meteorological conditions pose significant challenges for effective climate modeling. This study focuses on analyzing long-term meteorological time series data (1981–2020) from various regions across Pakistan to examine regional climate variability and detect emerging weather trends. Seventeen climate indices were calculated to assess weather patterns, followed by trend analysis utilizing both parametric and non-parametric methods. The parametric approach employed ordinary least squares (OLS) regression, while the non-parametric methods included the Mann–Kendall (MK) test and Sen’s Slope (SS) estimator. Over the 40-year period, the analysis revealed significant trends, such as increases in hot days, cold nights, warm nights, and extreme precipitation events. These findings emphasize the distinct and complex regional impacts of climate change in Pakistan. By identifying these trends through robust statistical techniques like OLS, MK, and SS, the study provides critical evidence of climate shifts, emphasizing the urgent need for tailored, region-specific strategies to strengthen resilience against the adverse effects of climate change.

This study addresses the long-term rainfall trends, their temporal variability and uncertainty over mainland Portugal, a small country on the most western European coast. The study was based on monthly, seasonal and annual rainfall series spanning for a period of 106 years, between October 1913 and September 2019 (herein after referred to as global period), at 532 rain gauges evenly distributed over the country (c.a. 6 rain gauges per 1000 km2). To understand the rainfall behavior over time, an initial sub-period with 55 years and a final sub-period with 51 years were also analyzed along with the global period. The trends identification and the assessment of their magnitude were derived using the nonparametric Mann-Kendall (MK) test coupled with the Sen’s slope estimator method. The results showed that after the initial sub-period with prevailing increasing rainfall, the trends were almost exclusively decreasing. They were also so pronounced that they counterbalanced the initial rainfall increase and resulted in equally decreasing trends for the global period. The study also shows that approximately from the late 1960s on, the rainy season pattern has changed, with the last months prior to the dry season showing a sustained decrease of their relative contributions to the annual rainfalls. Overall, the results support the hypothesis of less uncertainty on the pronounced decrease of rainfall over mainland Portugal in recent years, which is expected to continue. They also show that the asymmetry between a less wet North, yet still wet, and an arid South is becoming much more marked.

Bangladesh is a significant contributor to the global food basket but is also one of the most vulnerable countries to climate change. Understanding regional climatic variability helps reduce climate risks and ensure food security. This research examined monthly, seasonal, and yearly temperature, rainfall, and relative humidity in Bangladesh using data from 35 meteorological stations from 1970–2020. The study utilized the Mann–Kendall method to evaluate trends and employed Sen’s slope to quantify their magnitude. Additionally, quantile regression was applied to analyze the impact of climatic variables on rice yield. The findings revealed that maximum ( T max ) and minimum ( T min ) temperatures were steadily increasing, with the southwest experiencing a more rapid rise compared to other regions in Bangladesh. Seasonal T max and T min rose in most parts of Bangladesh, particularly during the monsoon. In most areas, there was a significant ( p < 0.05) increase in T max from June to September and T min in December. The variability of minimum temperature was considerably affected by warming throughout the country. The eastern area had the most significant ( p < 0.05) annual rainfall increase rate, while the northern region had the lowest. There was evidence of inter-seasonal rainfall shipment, with post-monsoon rainfall rising compared to monsoon season. The quantile regression showed that the rice yield of Aus (summer) and Aman (wet) seasons were influenced by maximum temperature and relative humidity, whereas the rice yield of the Boro (dry) season was affected by maximum temperature, rainfall, and relative humidity. Across geographical segmentation, the regression analysis indicated a high level of variability in the northern climate. Overall, the intensity of mean temperature increased throughout the country. So, there is a need to develop heat or drought-resistant rice varieties and modernized irrigation facilities to mitigate these climate risks. However, farmers should employ automated weather-based advisory services for sustaining rice productivity and food security.

This study identified the trends in monthly temperature, precipitation, & evaporation for Astore, Darosh, Gilgit, Gupis and Skardu and trends in inflow, outflow and reservoir level for Tarbela reservoir. Two non-parametric tests i.e. Man-Kendall and Sen’s Slope and Innovative Trend Analysis (ITA) were used to determine the trend. The first Mann-Kendall test with a significance level 5% was applied to 33-year data of five selected areas from 1990-2022. The results showed an increasing trend in temperature in March for all selected areas. No significant trend was observed in precipitation except negative trends for Darosh in March, May and December and positive trend for Gilgit in January and September. It has been observed that the trend direction given by ITA and Mann-Kendall is similar. Inflows to reservoir were found directly related to the temperature because of glacier melt in rising temperature, thus increasing the inflow, although the precipitation was found decreasing with increase in temperature. Altered snowmelt patterns can influence weather systems, potentially contributing to more extreme weather events.

Mean monthly streamflow records of ten gauging stations in the Mae Klong River Basin, Thailand, were analyzed for trends using non-parametric approaches for the period 2000–2015. The statistical significance of trends was tested on monthly, seasonal, and annual scales using three non-parametric tests; Mann–Kendall (MK), Spearman's rho (SR), and innovative trend analysis (ITA) on a 5% significance level. The streamflow stations were grouped into two categories: naturalized streamflow (four stations) and regulated streamflow (six stations). In both the dry and wet seasons, decreasing trends were observed in the naturalized inflows to the Srinagarind and Vajiralongkorn Dams. On an annual basis, all the naturalized streamflow stations showed decreasing trends. For the regulated streamflow stations, in the dry season, four out of six stations showed increasing trends, while in the wet season, five out of six stations indicated decreasing trends. On an annual basis, five stations showed decreasing trends by both the MK and SR tests, while the ITA method indicated decreasing trends for four regulated streamflow stations. The results of this study can help water resources managers for better assessment and planning of water resources in the basin.