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Rainfall is the most important fundamental physical parameter among the climate, as this parameter determines the environmental condition of the particular region, which affects the agricultural productivity. Global warming or climate change, is one of the most important worldwide issues discussed among scientists and researchers. One of the consequences of climate change is the alteration of rainfall patterns and an increase in temperature. The drastic changes in rainfall pattern showed a significant impact on society, and therefore its up-to-date information is needed to estimate the spatial distribution and variability at all points of the territory. In this paper, a study on trend analysis of rainfall data observed at Anasi, Haliyal, Kadra, and Supa rain gauge stations in Karnataka, India, was carried out. For this purpose, the annual 1-day maximum rainfall (AMR) and annual total rainfall (ATR) series were generated from the daily rainfall data and used in trend analysis. A non-parametric Mann-Kendall (MK) test was applied to evaluate the presence of significant trend in AMR and ATR, while the rate of significant trend was computed by Sen's slope estimator (SSE). The MK test results indicated that there is a decreasing trend in AMR series of Anasi, Haliyal, Kadra, and Supa. The study showed that the rate of decreasing trend in the AMR series of Anasi, Haliyal, Kadra, and Supa is computed as 1.4 mm/year, 0.1 mm/year, 0.6 mm/year, and 0.2 mm/year, respectively. For the ATR series, the rate of decreasing trend for Anasi was computed as 23.8 mm/year, whereas 11.2 mm/year for Supa, whereas the rate of increasing trend was 4.3 mm/year for Haliyal and 2.2 mm/year for Supa. This paper illustrated the application of the MK test and SSE for analyzing the trend in AMR and ATR of Anasi, Haliyal, Kadra, with Supa and the results obtained from the study. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal dynamics of climatic conditions within a region is paramount for informed rural planning and decision-making processes, particularly in light of the prevailing challenges posed by climate change and variability. This study undertook an assessment of the spatial and temporal patterns of rainfall trends across various agro-ecological zones (AEZs) within Wolaita, utilizing data collected from ten strategically positioned rain gauge stations. The detection of trends and their magnitudes was facilitated through the application of the Mann–Kendall (MKs) test in conjunction with Sen's slope estimator. Spatial variability and temporal trends of rainfall were further analyzed utilizing ArcGIS10.8 environment and XLSTAT with R programming tools. The outcomes derived from ordinary kriging analyses unveiled notable disparities in the coefficient of variability (CV) for mean annual rainfall across distinct AEZs. Specifically, observations indicated that lowland regions exhibit relatively warmer climates and lower precipitation levels compared to their highland counterparts. Within the lowland AEZs, the majority of stations showcased statistically non-significant positive trends (p > 0.05) in annual rainfall, whereas approximately two-thirds of midland AEZ stations depicted statistically non-significant negative trends. Conversely, over half of the stations situated within highland AEZs displayed statistically non-significant positive trends in annual rainfall. During the rainy season, highland AEZs experienced higher precipitation levels, while the south-central midland areas received a moderate amount of rainfall. In contrast, the northeast and southeast lowland AEZs consistently received diminished rainfall across all seasons compared to other regions. This study underscores the necessity for the climate resilient development and implementation of spatiotemporally informed interventions through implementing region-specific adaptation strategies, such as water conservation measures and crop diversification, to mitigate the potential impact of changing rainfall patterns on agricultural productivity in Wolaita.

Central Punjab includes over 40% of the state of Punjab. In Central Punjab, groundwater levels are dropping by more than 1 m every year. The primary cause of groundwater depletion in Central Punjab between 1998 and 2019 is presented in this paper. In order to detect any sudden changes in the area under paddy and the depth of groundwater, Pettitt's test was used. Based on change points and trends, the entire duration was split into two time periods: 1998–2008 (T1) and 2009–2019 (T2); rainfall, potential evapotranspiration, and paddy area were assessed in both periods with 1998–2019 (T3). The slope methodology developed by Mann–Kendall and Sen demonstrated the trend analysis. The findings showed that changes in climate were caused by humans during the 1998–2008 era, changes in meteorological variables were caused by humans during the 2008–2019 period, and changes in climate were caused by humans during the T3 period. The major growing trend of groundwater depth loss is thought to be caused by the combined effects of rainfall variations, an increase in the maximum temperature, cropping intensity, and the number of pumping units. HIGHLIGHTS Paddy area has been increasing in Punjab for the last 20 years, and this increase is causing a huge decrease in groundwater levels.; Pettitt's test was applied to see in which year an abrupt change happened.; Mann–Kendall and Sen's slope were applied to analyze the trend.; Rainfall was not enough for groundwater recharge as compared to declination due to the paddy area.; Central Punjab must evolve decision-making tools for arresting groundwater (GW) decline.;

Water resource management is highly impacted by variations in rainfall, maximum and minimum temperature, and potential evapotranspiration. The rice area is also a key aspect for groundwater declination due to high-water consuming crop. Groundwater in Central Punjab has declined at an alarming rate over the last two decades. The decisions regarding water resource management need accurate information for the groundwater level. Therefore, to explore the main reason for the depletion of groundwater, it is essential that the most influential factors responsible for groundwater depletion should be addressed. A study was conducted in Central Punjab by using artificial neural network (ANN) and multiple linear regression (MLR) models during 1998–2018 to forecast the groundwater depth. ANN performed better than MLR. The sensitivity analysis showed that tubewell density, rice area, and rainfall are highly responsible for groundwater fluctuation. HIGHLIGHTS In the present study, both climatic and human-induced factors were taken for groundwater modeling.; Artificial neural network, a complex phenomenon was used to forecast groundwater depth.; Python was used for groundwater modeling.; ANN was found to be more accurate than MLR.;

Severe alterations in rainfall patterns have led to a shortage of irrigation water in rice paddies, resulting in income decline and spontaneous migration to urban centers. Recognizing the crucial significance of irrigation water for rice cultivation in the Plain of Reeds (PORs), this study aims to assess the impacts of rainfall characteristics alteration on rice yield in the concern of climate variation. The study focuses on variations in rainfall characteristics and their changing trends across the PORs. Non-parametric statistical approaches such as Mann-Kendall and Sen’s slope estimator and Standardized Precipitation Index (SPI) are employed for data analysis. The study utilizes daily rainfall data spanning 38 years (1985-2022) from the National Center for Hydrometeorological Forecasting (NCHF). The results demonstrate significant changes in rainfall characteristics such as rainfall in rainy season (RRS), date of the rainy season onset (DRSO) and date of the rainy season cessation (DRSC). Rice productivity exhibits substantial changes, with increases during La-Nina years and significant decreases during strong El-Niño events across the study area. The findings provide valuable insights for discussing and formulating policies on adaptive agricultural production methods in response to changing climate conditions, ensuring sustainable crop production in the region. [ABSTRACT FROM AUTHOR]

The present study assesses trends in rainfall in Madhya Pradesh, Central India, over the long (1871–2016) as well as short (1987–2016) temporal scales. The region is home to disadvantageous groups of the population including vulnerable tribals who depend on rainfall-based agriculture and forest products. Therefore, any disruption in rainfall trends due to climate change needs to be documented for better planning. This study focuses on two meteorological subdivisions, East and West Madhya Pradesh, which receive a major portion of rainfall through the Bay of Bengal branch and the Arabian Sea branch of the Indian Summer Monsoon. The study is based on the rainfall series provided by the Indian Institute of Tropical Meteorology, Pune, India. This study used the Mann–Kendall test to detect a trend and Sen's Slope to estimate the magnitude of change in rainfall. Statistically significant negative trends were detected in total annual (− 1.125 mm y − 1) and monsoon (− 1.242 mm y − 1) rainfall over long-term observation in East Madhya Pradesh only. However, during the last 30 years, no trends have been recorded in East Madhya Pradesh. In West Madhya Pradesh, rainfall has not recorded any trend for long- as well as short-term observation periods. The non-availability of a trend in rainfall during May and October at both meteorological subdivisions shows that there is no shift in the duration of the monsoon season over long as well as short temporal observations. The decreasing rainfall trends in Eastern Madhya Pradesh over the long-term observation seem to be caused by changing patterns of tropical cyclone frequency in the Bay of Bengal. [ABSTRACT FROM AUTHOR]

Climate change is already a fact, where the intensity and frequency of changes in climate extremes and their impacts on various physical and biological systems contribute to global warming. The United Nations Global Sustainable Development Goal 13 calls for urgent action to address climate change and its impacts. The goals call for strengthening resilience and adaptive capacity at the national and local levels. Goal 13- Climate Action, is one of the nine goals for which India has shown a positive push. Tamil Nadu ranked second with a composite score of 74 and is listed as a Front Runner under the Performance towards SDG's goals in 2020-21. The National Action Plan on Climate Change Assessment declares that the climate parameter is important at the regional and local scale to estimate its impacts on various sectors. Hence in the present study, an attempt is made to analyze the trend and prediction of rainfall and temperature using statistical techniques like-Homogeneity test, Box and Whisker Plot test, Coefficient of Variability, Cluster Dendrogram, Mann-Kendall, Sen's slope, and ARIMA model in Dindigul district, Tamil Nadu, India. The rainfall and temperature data are collected from 15 rainfall stations spread across the district for 39 years, from 1981 to 2020. The results can help prioritize new strategies for managing available water resources to sustain the agriculture sector's dependence in the study area. The ongoing Action Plans of the Government of Tamil Nadu to achieve climate change mitigation measures are listed. These recommendations could be utilized by agriculturalists, agro-climatologists, and decision-makers to scientifically support development in the study area. [ABSTRACT FROM AUTHOR]

Globally, changes in rainfall frequency can cause adverse impacts on many aspects of life, especially in the agriculture sector. The increased tendencies of extreme weather events (EWEs) have considerably affected the rice cultivation regions (RCRs) in Quang Nam Province due to the collapse and inundation, resulting in large damage to the crop yield. Understanding the impacts of the adverse weather phenomena on the RCRs can actively support mitigating the potential risks as well as loss in income due to a decrease in crop productivity. The study investigates trends in rainfall across Thang Binh District, a rice cultivation region of Quang Nam Province in the period 2000 - 2019. The Mann-Kendall test and Sen's slope estimate with a 95% confidence level was used to detect maximum rainfall trends over time. The results indicated that terms of annual rainfall and maximum rainfall in the typhoon season months were mostly recorded as the changing trend. Contrary, the analysis also pointed out that a significant downtrend in the number of maximum rainy days and maximum rainfall in October and annual rainfall pattern can be considered a positive signal contributing to reducing the potential risks of crop loss caused by EWEs across the study area in the background of climate variability. [ABSTRACT FROM AUTHOR]

Regional weather variability depends on various meteorological variables such as temperature and rainfall. The current research focuses on the variability and trends in annual aerosol optical depth (AOD), temperature (T), and rainfall (RF) in 11 Vidarbha districts. The annual trend analysis of AOD, T, and R is determined using the non-parametric Sen slope and Mann–Kendall (MK) test at a 5% significant level from 1980 to 2019. Annual T and AOD indicate a substantial increase in this study, whereas rainfall shows a non-significant trend (MK, test) over the study period. According to Sen's slope trends, the relatively high rainfall area (Chandrapur = 1.273 and Garchiroli = 4.06) got positive trends, but Gondia and Bhandara districts have negative (Sen's slope = − 2.79 and − 2.56) trends. The moderate rainfall areas are showing a less negative Sen slope (Wardha = − 0.21, Washim = − 1.13 and Yavatmal = − 2.75), whereas Nagpur districts' Sen's slope shows a positive value (Sens's slope = 0.72). The assured rainfall area districts show Sen's slope trends are positive (Akola = 0.45, Amravati = 1.17 and Buldana = 0.42). Sen's slope trend indicates rising rainfall, whereas negative trends indicate decreasing rainfall in the time series. This study has also looked at the effect of RF, AOD, and T on the last two decades' cash crop production (2000–2019) for Vidarbha districts. The relationship between rainfall departure (DRF) and cash crop yield has also been highlighted. Five cash crops, such as cotton (Ct), total cereals (TCrl), total oilseeds (TOsd), total pulses (TPS), and sugarcane (Sc), are selected for the present study. [ABSTRACT FROM AUTHOR]

The hydroclimatic extremes such as floods and droughts have been causing damage and losses with rising frequency than ever before. The human-induced and internal climate variability create extreme events and local hydrometeorological changes influencing climate-sensitive sectors. This research is aimed at analyzing the recent changes in the hydrometeorological extremes using indices over the Bilate basin in Ethiopia. Mann-Kendall and Sen's slope estimator were used to examine changes in hydrometeorological extreme indices. The rainy days' rate of change falls between +10.64 mm in the downstream to −10.67 mm in the upstream north. The wet day rainfall and heavy rainfall day indices were stronger in the basin's southwest, implying more likely flood events. The consecutive dry days show a rising tendency with more variability, while the consecutive wet days show no trend with less variability. The change point analysis revealed inconsistencies for the majority of the extreme indices. The stations' average warmest nights and days significantly increased at a rate of 0.0358°C and 0.0320°C per annum, respectively. The coldest nights in most of the stations show a significant and negligible rise in the basin while on the coldest days more than half of the stations declining. The peak flow in the annual and seasonal time series shows a rising trend and a dominant rise in most low flow indices, which possibly flashes downstream flooding. The global and local climate anomalies revealed a weak correlation, but with overlap of wet and drought years. Basin water resource plans may benefit from identified overlap cross of threshold years for improved flood control and drought monitoring. [ABSTRACT FROM AUTHOR]

This study presents an interface, ‘Climate Change Trend Analysis (CCTA)’, developed in MATLAB® environment to analyze the trends using non-parametric statistical methods, Mann-Kendall (MK) test and modified Mann-Kendall (MMK) test with Sen’s slope estimator. The interface was used to determine trend in annual and seasonal (kharif) rainfall depths in Pune district acquired from 13 observatories. The developed interface automates the trend analysis process, which can further use for detecting variability and trends in the meteorological as well as other hydrological and agricultural parameters. The observed rainfall trends during monsoon would play a significant role for rainfed agriculture in Pune district. [ABSTRACT FROM AUTHOR]

The state of Bihar, located in eastern India, experiences diverse weather patterns throughout the year, including a monsoon season that significantly influences the region's rainfall. This abstract provides an overview of the annual rainfall and groundwater level patterns in Bihar, highlighting key characteristics, trends and variations observed in the region. In this study, Mapping and Trend Analysis of Rainfall and Groundwater level for Bihar using the GIS technique was evaluated. The monthly rainfall data for 33 years (1990-2022) and groundwater level data for 25 years (1996-2020) for 38 districts of Bihar were used for study purposes. ArcGIS 10.8.2 is used in the mapping of rainfall and groundwater. The interpolation method was used for mapping in ArcGIS 10.8.2. The major conclusions were observed that the rainfall in south Bihar was less as compared to rainfall in north Bihar in different decades and the groundwater levels decreased in all districts of Bihar over the decades. The trend analysis for monthly rainfall showed that there are no trends observed for all districts except Bhagalpur, which has a decreasing trend. There were decreasing trends in groundwater levels in all districts of Bihar. The study suggested that the groundwater is extensively exploited and there is a need for more groundwater recharge in Bihar. [ABSTRACT FROM AUTHOR]

Daily and monthly rainfall data provided by surface rain gauges in the Amazon Basin are sparse and defective, making it difficult to monitor rainfall patterns for certain portions of its territory, in this sense, estimations of precipitation from remote sensing calibrated with rain gauge data are key to overcome this problem. This paper presents a spatiotemporal analysis of the precipitation distribution for Rondônia State, in southwestern Amazonia. Data from Climate Hazards Group InfraRed Precipitation and Station (CHIRPS) were analyzed, using a pooled time analysis of a forty-year period (1981–2020). Data obtained from remote sensing were validated by rain gauges distributed over the study region. Pixel-by-pixel trend analyzes were developed by applying the Mann-Kendall test and Sen's slope test to study the magnitude of the trend. The analysis revealed that CHIRPS presents a tendency to underestimate precipitation values in most cases. Among the metrics, mean values between very good (<±15%) and good (±15–±35%) were observed using PBIAS; mean RMSE values range from 57.8 mm to 107.9 mm; an average agreement level of 0.9 and an average SES of 0.5; and good fit for the linear regression model (average R2 > 0.70) for about 64.7% of the stations. Sen' slope spatialization results show a reduction of approximately −15 mm year−1, with decrease mainly in the Northern Region of Rondônia, which has extensive areas where the native forest has been replaced by pasture. [ABSTRACT FROM AUTHOR]

Floods are one of the most destructive natural hazards in Pakistan, causing significant damage. During monsoons, when westerly winds and concentrated rainfall occur in rivers' catchments, floods become unmanageable. Given the limited resources of Pakistan, there has been minimal effort to quantify the amount of rainfall and runoff generated by ungauged catchments. In this study, ten hill torrents in Koh e Suleiman (District Rajanpur and DG Khan), an area affected by flash flooding in 2022 due to extreme precipitation events, were investigated. The Hydrologic Engineering Centre's Hydrologic Modeling System (HEC-HMS), a semi-distributed event-based hydrological model, was used to delineate streams and quantify runoff. Statistical analysis of the rainfall trends was performed using the non-parametric Gumbel extreme value analysis type I distribution, the Mann–Kendall test, and Sen's slope. The results of the study show that the total inflow to the river Indus is 0.5, 0.6, 0.7, and 0.8 MAF for 25, 50, 100, and 200 years of return period rainfall, respectively. This study presents appropriate storage options with a retention potential of 0.14, 1.14, and 1.13 MAF based on an analysis of the hydrology of these hill torrents to enhance the spate irrigation potential as flood control in the future. [ABSTRACT FROM AUTHOR]

This study examined and compared the new innovative trend analysis (ITA) of monthly, seasonal and annual rainfall with traditional trend analysis methods in relation to soybean productivity in western Maharashtra. Spearman's rank correlation, Mann-Kendall and its 6 different modifications were used to analyze the trends of rainfall, whereas Spearman's rho, simple linear regression and Sen's slope with two different modifications were employed to quantify the magnitude of trends at 1%, 5% and 10% level of significance. Autocorrelation coefficient was calculated at lag-1 and tested at 5% level of significance. Rainfall variability of the region is very high (CV>30) in all the months and seasons with positively skewed rainfall distribution. Our results revealed that out of 34-time series data analyzed, ITA was able to ide ntify all the significant trends (11-time series) that can be detected by traditional methods. Meanwhile, ITA also identified trends in 17-time series which cannot be detected by any of the traditional methods. The study revealed significant increase in monsoon and annual rainfall values, which is helpful in sustaining soybean productivity in the western parts of the Maharashtra. [ABSTRACT FROM AUTHOR]

The spatiotemporal variability of rainfall, particularly in the context of climate change, has been imperative for examining the cropping patterns, farming sustainable crop production, and food security in rainfed areas. To that end, trend analysis was done to study the change in rainfall patterns in the mid-hills of Himachal Pradesh. The study investigated the historical rainfall data from 1971 to 2020 on a monthly, annual, seasonal, and decadal basis by using the variability analysis methods, viz., standard deviation (SD), coefficient of variance (CV), and transformed annual precipitation departure (Z). The trend analysis was also done by Mann–Kendall (MK) and Sen's slope estimator (SSE) test and linear regression model. The annual rainfall in the region was 1115.1 mm, which showed a decreasing trend (Z = − 0.79 mm/year). Based on the linear regression model, the decrease in annual rainfall was about − 2.28 mm/year. The monthly and seasonal variability of rainfall exhibited a sensitivity to change. The months of January, April, July, and September showed an increasing trend, whereas the rest of the other months showed a decreasing trend. The seasonal rainfall (summer, monsoon, and post-monsoon) showed a decreasing trend, whereas the winter season depicted an increasing trend. During the entire study period, 1988 recorded as the wettest year, with highest annual rainfall of about 2205.0 mm and monsoon rainfall of about 1653.0 mm. The highest annual (2205.0 mm) and monsoon (1653.0 mm) rainfall was recorded in the year 1988. The decadal analysis of the rainfall on an annual basis revealed a decrease in rainfall during the periods 1971–1980, 2001–2010, and 2011–2020 as compared to 1981–1990 and 1991–2000. The rainfall over the study region confirms the strength of the change in trend. Thus, the erratic rainfall pattern makes the cropping calendar shorter and affects the agricultural productivity. [ABSTRACT FROM AUTHOR]

The non-stationary behavior of climatic variables has been increasingly recognized as a challenge that disrupts the equilibrium of human-defined climate-based stationary processes, including hydrological and agricultural practices, and irrigation systems. This study aims to investigate long-term trends and non-stationarity in climatic variables across 23 stations of the Krishna River basin, India. Prominent trends in rainfall, temperature, and their extreme indices were identified using the Modified Mann-Kendall (MMK), Bootstrapped Mann-Kendall (BMK), and Sen's Slope Estimator tests, while the Innovative Trend Analysis (ITA) test uncovered hidden trends and potential shifts in climatic patterns. This study addresses a critical research gap by exploring both significant and hidden trends in climatic variables, providing a better understanding of future dynamics. Traditional methods like MMK and Sen's Slope were insufficient to reveal these hidden trends, but ITA offered a more comprehensive analysis. The findings revealed an increase in total annual rainfall for almost 50% of the basin, which aligns with rising maximum temperatures, suggesting enhanced evaporation rates and subsequent fluctuations in rainfall patterns. Seasonal analysis indicated a shift towards decreased rainfall during winter and pre-monsoon seasons, contrasted by increased precipitation during the monsoon and post-monsoon periods, highlighting a clear alteration in rainfall distribution. The Simple Daily Intensity Index (SDII) and other indices suggest intensified rainfall events despite a decrease in the number of rainy days, indicating fewer but more intense events. Temperature analysis showed an overall increase in maximum temperatures, with the Diurnal Temperature Range (DTR) significantly increasing across all stations, implying greater daily temperature variations and potential for intensified water cycles and extreme climatic events. Furthermore, the study simplifies these trends by classifying them into two attributes: intensity and frequency, aiding policymakers in site-specific management of water resources and planning for future climatic scenarios. The presence of non-stationarity in extreme rainfall was confirmed by the Augmented Dickey-Fuller (ADF), Phillips-Perron (PP), and Kwiatkowski-Phillips-Schmidt-Shin (KPSS) tests. These findings are significant as they conclude how climate change is altering hydrological patterns at each station. The study emphasizes the necessity for adaptive management strategies to mitigate the adverse impacts on agriculture, infrastructure, and human safety. • Identifies trends & non-stationarity in climatic variables using advanced trend tests. • Reveals shift in seasonal rainfall patterns with higher intensity. • Utilizes Innovative trend analysis to find nature of potential hidden trend. • Reveals greater daily temperature variations and potential for extreme events. [ABSTRACT FROM AUTHOR]

Natural environmental disasters in the developing countries of West Africa are at alarming rate which necessitate the investigation of long-term trend of rainfall and temperature. Current variation, trends of temperature, and rainfall across Nigeria were investigated using parametric and non-parametric statistical tools. Meteorological data obtained from the Nigeria Meteorological Agency in Lagos, Nigeria, from 1970 to 2010 were used for this analysis. Seasonal and annual trends of maximum temperature, minimum temperature, and rainfall were carried out using Mann-Kendall and Sen’s slope methods. Long-term linear regression of these meteorological variables was analyzed across eighteen locations in the country. Spatial distribution of seasonal trends of these variables was also estimated for the four seasons in Nigeria. The result of the linear regression on temperatures and rainfall showed increasing trends in most of the locations across the country. Similarly, Mann-Kendall and Sen’s slope analysis showed a significant increasing trend in most areas across the country. Consequently, recent phenomena of environmental hazard such as an outbreak of airborne diseases and flooding leading to the collapse of buildings and various environmental disasters can be linked to the observed result. [ABSTRACT FROM AUTHOR]

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A study was under taken for identifying the trends in pre and post-monsoon groundwater levels using Mann- Kendall test and Sen's slope estimator, and for time series modelling of groundwater levels for forecasting the pre and post-monsoon water levels in Karnal district of Haryana. Results showed that the groundwater levels had significantly declined during 1974 to 2010. Average rates of water level decline were 0.228 and 0.267 m/yr during pre and postmonsoon seasons, respectively. There was rapid decline in water level between 2001 and 2010. The ARIMA (0, 1, 2) was identified as the appropriate model for time series modelling and forecasting. Results showed that the pre and postmonsoon groundwater level in 2050 would decline by 12.97 m and 12.00 m over the observed water level in 2010, and reach to a level of 29.95 m and 28.14 m below ground surface. The average rate of decline of pre and post-monsoon groundwater level in the district during this period would be 0.32 and 0.30 m/yr, respectively. [ABSTRACT FROM AUTHOR]

Rainfall is the dynamic weather parameter having a significant role in the agriculture livelihood. Growth in agriculture and other related sectors depends mainly on the adequate amount and timely availability of rainfall. Variability in precipitation exerts a huge impact on human beings and agriculture. This variation gives the necessity to study the regular pattern of rainfall. The present study aims at analysing the trends in rainfall intensity across various blocks of Tiruchirappalli district using daily rainfall data for the period 1990 to 2017. Non-parametric tests such as Mann- Kendall test and Sen's slope estimator were employed for this purpose. Results revealed that two blocks showed statistically significant increasing trends (95% confidence level) and one block showed significant decreasing trend (95% confidence level) and the remaining eleven blocks showed no significant trends. [ABSTRACT FROM AUTHOR]

Investigating the spatiotemporal variation of meteorological parameters in the context of changing climate, particularly in countries where rainfall is sparse and agriculture is predominant contributor to economy, is vital to assess climate-induced changes and plan necessary adaptation strategies. To gain insight, trend analysis has been employed to inspect and quantify the change of precipitation in the highland of Aseer region of Saudi Arabia. Forty-eight-year long historical rainfall data series (1970–2017) from 30 rain gauges scattered across the study region was used for the analysis. Considering high spatial and temporal variability of rainfall data, the temporal scale chosen for the study was annual, rainy season (Mar–Jun), and bi-monthly. The trend was investigated using Mann–Kendall (MK) test. For serially correlated rainfall data series, modified Mann–Kendall (MMK) was applied for trend detection. Two variants of modified Mann-Kendall test were used in this study, namely, modified Mann–Kendall test using variance correction approach (mk1ylag) and bootstrapped Mann–Kendall Trend (pbmk) test. For quantification of trend magnitude, Theil–Sen approach (TSA) was used for calculation of Sen's slope. Further, the abrupt change year detection in the trend was carried out using sequential Mann–Kendall (SQMK). All tests were performed using R-script. The application of abovementioned statistical test has shown statistically significant decreasing trend across majority of the stations at all temporal scale. It is observed that 10 stations located along the north-west to south-west boundary in mountainous region do not exhibit any significant trend. However, seven stations numbered as 14, 15, 38, 46, 514, 31 and 538 showed decreasing trends during most of the temporal scale. On analysing the trend magnitude of these seven stations, top three highest decreasing trend was found at station 31, 538, and 514 with trend magnitude as −9.325, −9.059, −7.516 mm/year, respectively. These three stations are located on north-west upper part of the study region. To understand the decade during which most of the stations experienced abrupt change, the period of 1970–2017 was broken down into four decades, 1980–1990; 1990–2000; 2000–2010; 2010–2017. Using SQMK, it was inferred that the decade of 2000–2010 witnessed drastic shift. Particularly for rainy season time series data, it is observed that the shift had occurred during the initial part of the decade 2000–2010 which can be attributed to increasing population and accelerated urbanisation. This result of declining and erratic trend of rainfall should be considered to design future strategies for agricultural sector, food security policy of the country and future water resource planning and management. [ABSTRACT FROM AUTHOR]

Climate variability, particularly, that of the annual air temperature and precipitation, has received a great deal of attention worldwide. The magnitude of the variability of the factors changes according to the locations. The present study focuses on detecting the trends and variability in the annual temperature and rainfall for the district of Mandi in Himachal Pradesh, India. This study used annual and monsoon time series data for the time period 1981-2010 and modified the Mann-Kendall test and Sen's slope estimator in analyzing the problem. The results of the analysis indicate that the annual maximum temperature (TMX) and annual minimum temperature (TMN) for the period of 30 years have shown an increasing trend, whereas the monsoon's maximum and minimum temperatures have shown a decreasing trend, although it is statistically not significant. The amount of annual rainfall does not show any significant trend, but the monsoonal rainfall has shown an increasing trend that is also statistically not significant. The resulting Mann-Kendall test statistic (Z) and Sen's slope estimate (Q) of all the parameters studied indicate that changes are occurring in the magnitude and timing of the precipitation and temperature events at the Mandi station. [ABSTRACT FROM AUTHOR]

Abstract Understanding the trend of temperature and rainfall is of prime importance for climate change related research. This research aims to identify the trends of temperature and rainfall for the last 50 years (1966–2015) for Bangladesh. To detect the trends, the non-parametric Mann–Kendall test in combination with a trend-free pre-whitening approach for correcting the time series to eliminate the influence of serial correlation has been used whereas the Sen's slope method is used for identification of the trend value. Maximum, minimum, and mean monthly temperatures have been analyzed to identify the monthly, seasonal and annual trend. Four seasons are distinct in Bangladesh, namely, pre-monsoon, monsoon, post-monsoon and winter. In case of rainfall, mean monthly rainfall, 1-day maximum rainfall and consecutive 3-day maximum rainfall are analyzed in a similar fashion. For maximum, minimum and mean monthly temperature, the majority of the stations show an increasing trend for all three indices. Monsoon season observes highest increasing trend of 0.015 °C/yr for mean monthly temperature whereas the winter season shows the negative trend of −0.002 °C/yr. On an annual basis, the trend is increasing with a value of 0.008 °C/yr or 0.4 °C in 50 years having significance level, p <.05. On the other hand, mean rainfall shows an increasing trend for the three seasons except for winter, however, significance level in this case is p >.10. The trend of mean rainfall for annual basis shows an increasing trend of 5.675 mm/yr. The trend in one day and consecutive three-day maximum rainfall depicts that the trend is increasing in few places whereas in other places the trend is decreasing with no significant change. The research concludes that the climate of Bangladesh is getting cooler and drier for winter, on the other hand, warmer and wetter for the rest of the year. Highlights • Data did not show significant serial correlation except for post monsoon temperature and winter rainfall series • In case of rainfall, the country as a whole observed no significant trend for all the three indices analyzed. • It has been predicted that, because of climate change, there will be gradual increase of temperature and change in rainfall pattern [ABSTRACT FROM AUTHOR]

The present study aimed to carry out the spatial and temporal trend analysis of annual, seasonal and monthly rainfall and rainy days in the Konkan region of Maharashtra state. Trend analysis was performed for rainfall and rainy days observed at twelve different locations spread over the region. Mann Kendall and Sen's slope test were used to know the nature of trend and its magnitude, respectively and results were tested at 90, 95 and 99% confidence levels. Results showed that significant increasing trend in rainfall ranging from 26.23 to 38.27 mm yr-1 was observed at Suksale, Mulde and Bhatsanagar. A decreasing non-significant trend was observed at Karjat and Awalegaon while at remaining locations a non significant increasing trend in annual average rainfall was observed. In most locations the number of rainy days was found to exhibit an increasing non significant trend and only at Wakawali the number of rainy days was significantly increasing @ 0.25 days yr-1. At Karjat and Pali a non-significant decreasing trend in rainy days was observed. Results also illustrated significant trends in monthly rainfall during August, September, November and December. Monthly rainy days also exhibited significant change during May, July, August, September, October and November. Overall, the changes in annual rainfall amount and rainy days were observed in the region. These changes may have further influence on water resource availability for agriculture in the region. [ABSTRACT FROM AUTHOR]

ABSTRACT The socio-economic consequences posed by climate change in Africa are giving increasing emphasis to the need for trend analysis and detection of changes in hydro-climatic variables in data deficient areas. This study analyses rainfall data from 17 rain gauges unevenly distributed across the Logone catchment in the Lake Chad basin over a 50-year period (1951-2000). After quality control of the rainfall data using homogeneity tests, nonparametric Mann-Kendall and Spearman's rho tests were applied to detect the presence of trends. Trend magnitude was calculated using Sen's slope estimator. Results of the homogeneity test showed that rainfall was homogeneous across the catchment. Trend analysis revealed the presence of negative trends for annual rainfall at all the stations. Results of long-term trend analysis at a monthly time scale revealed the presence of statistically insignificant positive trends at 32% of the stations. Spatially, the analysis showed a clear distinction in rainfall magnitude between the semi-arid and Sudano zones. The slope of the trend lines for annual rainfall averaged over the respective zones was higher in the semi-arid zone (−4.37) compared to the Sudano zone (−4.02). However, the station with the greatest reduction in annual rainfall (−8.06 mm) was located in the Sudano zone. [ABSTRACT FROM AUTHOR]

Mountainous regions are considered highly vulnerable to the effects of climate change. The extent of change and variability of climatic parameters is still unexamined in many remote mountainous areas. This paper aims to understand the change in pattern of rainfall and temperature during 1986-2015 in the Himalayan state of Mizoram, India. Time series analysis of climatic parameters is carried out by us¬ing Coefficient of Variation (CV), Mann-Kendall (M-K)and Sen's Slope estimator. The analysis revealed that during the entire study period, the highest seasonal rainfall variation occurs during winter and the highest seasonal temperature variation occurs during spring. Mann-Kendall test showed a significant change in rainfall with November showing the highest negative trend of rainfall. The temperature trend analysis in the study also reveals drastic change of temperature over 2°C in the last 30 years. Lastly, linear regression analysis shows that changes in the climate variables have significant effect on the production of maize and ginger at 0.05 significance level with r2=0.49 and 0.24 respectively. [ABSTRACT FROM AUTHOR]

Abstract The present study assesses trends in rainfall in Madhya Pradesh, Central India, over the long (1871–2016) as well as short (1987–2016) temporal scales. The region is home to disadvantageous groups of the population including vulnerable tribals who depend on rainfall-based agriculture and forest products. Therefore, any disruption in rainfall trends due to climate change needs to be documented for better planning. This study focuses on two meteorological subdivisions, East and West Madhya Pradesh, which receive a major portion of rainfall through the Bay of Bengal branch and the Arabian Sea branch of the Indian Summer Monsoon. The study is based on the rainfall series provided by the Indian Institute of Tropical Meteorology, Pune, India. This study used the Mann–Kendall test to detect a trend and Sen’s Slope to estimate the magnitude of change in rainfall. Statistically significant negative trends were detected in total annual (− 1.125 mm y − 1) and monsoon (− 1.242 mm y − 1) rainfall over long-term observation in East Madhya Pradesh only. However, during the last 30 years, no trends have been recorded in East Madhya Pradesh. In West Madhya Pradesh, rainfall has not recorded any trend for long- as well as short-term observation periods. The non-availability of a trend in rainfall during May and October at both meteorological subdivisions shows that there is no shift in the duration of the monsoon season over long as well as short temporal observations. The decreasing rainfall trends in Eastern Madhya Pradesh over the long-term observation seem to be caused by changing patterns of tropical cyclone frequency in the Bay of Bengal.

Paciorek and Wehner raise important questions around our use of the Mann‐Kendall nonparametric trend test on smoothed data for analyzing long‐term hydrometeorological trends in Zhang et al. (2021, https://doi.org/10.1029/2020gl092293). We thank them for initiating this important conversation and their gracious cooperation in exploring the issues addressed in their comment. In this reply we confirm the inflation of significant p‐values by our choice to smooth, illustrate the relatively minor impacts on the main conclusions of our paper, and add our voices to those of Paciorek and Wehner in highlighting the lack of methodology for hypothesis testing across multiple stations that have spatial structure (i.e., testing for regionally consistent trends). Plain Language Summary: Our colleagues Drs. Paciorek and Wehner have raised concerns about our paper (Zhang et al., 2021, https://doi.org/10.1029/2020gl092293), which showed widespread increases in the duration of drought events over the last five decades in the western United States. They point out that our decision to smooth the data using a moving average inflated the number of weather stations at which the trends toward longer droughts were deemed significant by a statistical test. We agree with them on this point, and we have recomputed all our results using unsmoothed data to determine the impacts. We find that for most stations and regions, trend magnitudes remained largely unchanged, with many stations nearby one another often suggesting similar trends. Finally, we agree with Paciorek and Wehner that there is a lack of statistical methods to test such coherent regional patterns, and we caution that over‐reliance on p‐values limits the power of regional data to identify important climate trends. Key Points: We agree that smoothing to 5‐year moving windows introduced serial correlation into time series of annual statistics of daily rainfall data, inflating the number of weather stations individually showing significant trends (p < 0.05) with the Mann‐Kendall testRecomputation with unsmoothed values produced substantially the same dry intervals trend magnitude and direction at most stations individually and had only minimal impacts on dry interval trends computed for National Ecological Observatory Network domains using the Regional Kendall testNo perfect statistical approach leverages the capacity of coherent regional patterns among spatially correlated weather stations, and an over‐reliance on p‐values as a binary (significant vs. insignificant) determinant of trends limits the power of regional data [ABSTRACT FROM AUTHOR]

Clearance of terrestrial wetland vegetation and rainfall variations affect biodiversity. The rainfall trend–NDVI (Normalized Difference Vegetation Index) relationship was examined to assess the extent to which rainfall affects vegetation productivity within Nylsvley, Ramsar site in Limpopo Province, South Africa. Daily rainfall data measured from eight rainfall stations between 1950 and 2016 were used to generate seasonal and annual rainfall data. Mann-Kendall and quantile regression were applied to assess trends in rainfall data. NDVI was derived from satellite images from between 1984 and 2003 using Zonal statistics and correlated with rainfall of the same period to assess vegetation dynamics. Mann-Kendall and Sen's slope estimator showed only one station had a significant increasing rainfall trend annually and seasonally at p < 0.05, whereas all the other stations showed insignificant trends in both rainfall seasons. Quantile regression showed 50% and 62.5% of the stations had increasing annual and seasonal rainfall, respectively. Of the stations, 37.5% were statistically significant at p < 0.05, indicating increasing and decreasing rainfall trends. These rainfall trends show that the rainfall of Nylsvley decreased between 1995 and 2003. The R2 between rainfall and NDVI of Nylsvley is 55% indicating the influence of rainfall variability on vegetation productivity. The results underscore the impact of decadal rainfall patterns on wetland ecosystem change. [ABSTRACT FROM AUTHOR]

Climate change likely to impact rainfall patterns leading to higher uncertainty and difficulties in management of both water scarcity and flood events. Temporal trends of rainfall and its variability of Karnal district, representing fresh groundwater zones of Haryana, were analysed by non-parametric Mann- Kendall (MK) test and Sen's slope approaches. Analysis of long-term rainfall data (1972-2010) indicated that Karnal receives a mean annual rainfall of 757.6 mm with a high degree of variation (CV = 34.3%). Categorization of monsoon rainfall based on long-period average (LPA) and its CV indicates that during the last decade (2001-2010) Karnal received deficit rainfall in 6 years (18-57% lower than LPA), normal rainfall in 2 years and excess rainfall (9-70% higher than LPA) also for 2 years. The rainfall and rainy days during the last decade (2000-2010) decreased by 13% and 20% respectively, over longterm (1972-2010) averages. The MK and Sen's slope approach applied to pre- and post-monsoon groundwater levels indicated significant declining trend emphasizing the need to augment groundwater by artificial groundwater recharge (AGR) system. AGR through recharge wells installed by CSSRI at village Nabiabad in Karnal districts resulted in 2.32 m and 3.16 m rise in water table during 2009 and 2010 respectively. Installation of artificial groundwater recharge in low lying areas has proven highly effective in enhancing groundwater and improve its quality. [ABSTRACT FROM AUTHOR]

Medjerda is a key transboundary watershed in the Maghreb region, crossing from the Algerian mountains through northern Tunisia. Therefore, the analysis of the rainfall regime in this basin is of paramount importance for water resources management and regional economic development, notably concerning agriculture. This study examines the rainfall trends over the Medjerda watershed on multi-temporal scales (monthly, seasonally and annually) with a database of monthly rainfall observed in 60 stations evenly spread over the watershed. After filling gaps and homogenizing data, the Mann–Kendall test for trend detection was applied to rainfall series and the Sen's slope method was adopted to estimate the trend's magnitude, interpolated over the sub-catchments, to analyze the spatial distribution of rainfall changes within the watershed. Results showed the absence of significant trends at the annual scale for the entire catchment. However, rainfall redistribution was observed throughout the year, with a notable precipitation reduction during spring and increased winter precipitation, which could impact agriculture and ecosystem functioning. This modification of the rainfall regime implies an adaptation of the management of dams and reservoirs, with a reduced filling capacity during spring in anticipation of the summer dry season. [ABSTRACT FROM AUTHOR]

Highlights • Innovative trend analysis (ITA) and Mann-Kendall (MK) determine average trend. • Double-ITA, Triple-ITA, and partial MK are proposed for stability test of trends. • ITA should be used together D-ITA or T-ITA for better trend analysis. • MK should be used together partial MK for better trend analysis. • Comparisons of ITA, D-ITA, T-ITA, classical MK, and partial MK are presented. Abstract The climate change is an important event that affects hydrological, agricultural and water resources planning variables, and therefore, the hydrologists and meteorologists frequently try to identify trend possibilities especially in rainfall, runoff and temperature time series. For this purpose, the classical Mann-Kendall (MK), Spearman's rho (SR), Sen's slope, and linear regression approaches are applied frequently in the literature. Recently, innovative trend analysis (ITA) provides visual inspection and identification of categorical trends, which is one of the main concerns in this paper. On the basis of ITA methodology, several improvements namely double-ITA (D-ITA) and triple-ITA (T-ITA) procedures are suggested using with simple ITA together. These methods are attractive for the trend stability assessment by comparing partial trend components during different sub-periods of a given record series. Furthermore, partial MK test approach is proposed in this paper for the same purpose. These procedures and approach are applied to a set of annual rainfall records at many stations in different regions of Turkey. As a result, the comparison of the suggested methods based on partial sub-series of the same time series helps to improve trend detection with stability identification. [ABSTRACT FROM AUTHOR]

The southern region of Bangladesh is one of the most severely affected regions by climate change, notably in terms of crop production. This study aims to analyze the trends in climatic parameters (air temperature, rainfall, and humidity) and their impacts on the yield of four significant crops in the southern coastal district of Bangladesh. The relationship between climatic parameters was estimated using the Mann–Kendall test, the Theil-Sen slope estimator, and multiple linear regression (MLR) modeling on the regional-level datasets from 1990 to 2016. The maximum temperature of the Aus, Aman, Boro, and Wheat crop growing seasons showed a significant increasing trend (p < 0.01), and the magnitude of changes varied from 0.4 to 0.5 °C/decade during the study period. The minimum temperature of the Aus growing season also showed an increasing trend (p < 0.01) at a rate of 0.06 °C/decade. However, the relative humidity of Aus growing season (− 0.7%/decade) and the rainfall of the wheat-growing season (− 87.5 mm/decade) showed significant decreasing trends (p < 0.05). The MLR model revealed that maximum temperature, minimum temperature, and rainfall all had a positive influence on Aus paddy production. The humidity had a significant negative impact on Aus and Aman paddy production, while Boro paddy production was adversely influenced by the maximum temperature and rainfall. All explanatory variables positively influenced wheat yield production. Climatic parameters showed significant influences on important crop production in the southern Bagerhat District of Bangladesh. In order to make agriculture sustainable in the face of climate change, this study shows that all the relevant authorities need to look at the climate-resilient crop cultivation system. [ABSTRACT FROM AUTHOR]

Drought is one of the most serious natural disasters that threaten human societies and environments in nearly every region of the world; therefore, evaluating drought is becoming increasingly important and helpful in eradicating the effects of climate change. This study investigates the spatiotemporal drought pattern over the Luni basin from 1959 to 2019. This research analyzed spatio-temporal drought status using the standardized precipitation index (SPI). The drought trends were analyzed using the Mann-Kendall (MK) test or modified MK test and graphical innovative trend analysis (ITA). The SPI result shows that 39 studied stations are subject to mild to severe drought. The MK/mMK trend test results detected only a monotonic trend of drought across different time ranges and locations within the study area, where the ITA trend test revealed that 43% of the recorded rain gauge stations experienced a non-monotonic negative trend, more than 23% of stations were experienced a monotonic positive trend and 10% non-monotonic positive trend. In addition, with the exception of some small patches of western and eastern parts, the Z statistic result did not reveal any significant trend. The results showed that the ITA approach is more consistent than the MK test and can detect monotonic and non-monotonic trends that the MK method cannot detect. This research provided evidence that drought trends can be studied using the ITA approach, and the results of this analysis would be very useful for identifying droughts to develop effective management plans. [ABSTRACT FROM AUTHOR]

In this study, the authors evaluated the spatial and temporal variability of rainfall over the central Pindus mountain range. To accomplish this, long-term (1961–2016) monthly rainfall data from nine rain gauges were collected and analyzed. Seasonal and annual rainfall data were subjected to Mann–Kendall tests to assess the possible upward or downward statistically significant trends and to change-point analyses to detect whether a change in the rainfall time series mean had taken place. Additionally, Sen's slope method was used to estimate the trend magnitude, whereas multiple regression models were developed to determine the relationship between rainfall and geomorphological factors. The results showed decreasing trends in annual, winter, and spring rainfalls and increasing trends in autumn and summer rainfalls, both not statistically significant, for most stations. Rainfall non-stationarity started to occur in the middle of the 1960s for the annual, autumn, spring, and summer rainfalls and in the early 1970s for the winter rainfall in most of the stations. In addition, the average magnitude trend per decade is approximately −1.9%, −3.2%, +0.7%, +0.2%, and +2.4% for annual, winter, autumn, spring, and summer rainfalls, respectively. The multiple regression model can explain 62.2% of the spatial variability in annual rainfall, 58.9% of variability in winter, 75.9% of variability in autumn, 55.1% of variability in spring, and 32.2% of variability in summer. Moreover, rainfall spatial distribution maps were produced using the ordinary kriging method, through GIS software, representing the major rainfall range within the mountainous catchment of the study area. [ABSTRACT FROM AUTHOR]

This study aimed to examine the spatiotemporal seasonal and annual trends of rainfall indices in Perak, Malaysia, during the last 35 years, as any seasonal or spatial variability in rainfall may influence the regional hydrological cycle and water resources. Mann–Kendall and Sequential Mann–Kendall (SMK) tests were used to assess seasonal and annual trends. Precipitation concentration index was used to estimate variations in rainfall concentration, and Theil–Sen's slope estimator was used to determine the spatial variability of rainfall. It was found that most of the rainfall indices are showing decreasing trends, and it was most prominent for the southwest monsoon season with a decreasing rate of 2.20 mm/year. The long-term trends for seasonal rainfall showed that rainfall declined by 0.29 mm/year during the southwest monsoon. In contrast, the northeast and the inter-monsoon seasons showed slight increases. Rainfall decreased gradually from 1994 to 2008, and the trend became more pronounced in 2008. On a spatial basis, rainfall trends have shifted from the western regions (i.e., −19 mm/year) to the southeastern regions (i.e., 10 mm/year). Overall, slightly decreasing trends in rainfall were observed in Perak Malaysia. [ABSTRACT FROM AUTHOR]

In this work, we investigate the spatial and temporal trend of short-duration (1 to 24 h) annual maximum rainfall depths, derived from the Improved Italian—Rainfall Extreme Dataset (I2-RED). The investigation is conducted using time series of at least 30 years of data both at the national and regional level using the record-breaking analysis, the Mann-Kendall test, the Regional Kendall test and the Sen’s slope estimator. The results confirm that rainfall extremes of different durations are not increasing uniformly over Italy and that separate tendencies emerge in different sectors, even at close distances.

This study examined and compared the new innovative trend analysis (ITA) of monthly, seasonal and annual rainfall with traditional trend analysis methods in relation to soybean productivity in western Maharashtra. Spearman’s rank correlation, Mann-Kendall and its 6 different modifications were used to analyze the trends of rainfall, whereas Spearman’s rho, simple linear regression and Sen’s slope with two different modifications were employed to quantify the magnitude of trends at 1%, 5% and 10% level of significance. Autocorrelation coefficient was calculated at lag-1 and tested at 5% level of significance. Rainfall variability of the region is very high (CV>30) in all the months and seasons with positively skewed rainfall distribution. Our results revealed that out of 34-time series data analyzed, ITA was able to ide ntify all the significant trends (11 -time series) that can be detected by traditional methods. Meanwhile, ITA also identified trends in 17-time series which cannot be detected by any of the traditional methods. The study revealed significant increase in monsoon and annual rainfall values, which is helpful in sustaining soybean productivity in the western parts of the Maharashtra.

Deltas are geographically and socio-ecologically distinct systems, with a unique climate and contextually high vulnerabilities to climate dynamics. Hence, they require specific climate change adaptation and policy responses, informed by delta-scale analysis. However, available climate knowledge on deltas is based mainly on broad-scale analysis that masks information unique to deltas. This applies to the Volta Delta system of Ghana. This study presents annual and intra-annual climate variability and trend analysis carried out across the Volta Delta, using the coefficient of variation (CV), anomaly, Mann-Kendall and Pettit statistics. There were time and space differences in climate change and variability. Statistically significant (P < 0.05) positive trends were observed for the major wet season and the mean annual rainfall for Ada and highly significant (P < 0.01) positive trends for Akatsi. These contrasted with the observations in Adidome, which experienced a statistically highly significant (P < 0.01) decreasing trend in rainfall. There were significant (P< 0.05) increases in annual minimum, maximum, and mean temperatures over time in both coastal and inland delta stations. The annual rate of change of mean temperature ranged from 0.03 to 0.05. Ada, the more coastal location, has experienced a narrow range of temperature change, most probably due to the buffering capacity of the ocean. Point changes were observed in the climate data series in four (4) localities. We recommend that adaption and policy actions should include, the provision of small-scale irrigation, encouraging adoption of drought-resistant crop varieties and crop diversification, and also be made responsive to the existing spatiotemporal climate variability and change within the Volta Delta. [ABSTRACT FROM AUTHOR]

Medjerda is a key transboundary watershed in the Maghreb region, crossing from the Algerian mountains through northern Tunisia. Therefore, the analysis of the rainfall regime in this basin is of paramount importance for water resources management and regional economic development, notably concerning agriculture. This study examines the rainfall trends over the Medjerda watershed on multi-temporal scales (monthly, seasonally and annually) with a database of monthly rainfall observed in 60 stations evenly spread over the watershed. After filling gaps and homogenizing data, the Mann–Kendall test for trend detection was applied to rainfall series and the Sen’s slope method was adopted to estimate the trend’s magnitude, interpolated over the sub-catchments, to analyze the spatial distribution of rainfall changes within the watershed. Results showed the absence of significant trends at the annual scale for the entire catchment. However, rainfall redistribution was observed throughout the year, with a notable precipitation reduction during spring and increased winter precipitation, which could impact agriculture and ecosystem functioning. This modification of the rainfall regime implies an adaptation of the management of dams and reservoirs, with a reduced filling capacity during spring in anticipation of the summer dry season.

Analyzing spatiotemporal dynamics of meteorological variables in evolving climate, especially in regions where rain-fed agribusiness is predominant is paramount. In this regard, this study examines the spatiotemporal variability, trend, and magnitude of observed rainfall from 33 synoptic stations during 1979–2017 over Kenya. Non-parametric techniques like Mann–Kendall test, Sequential Mann-Kendall (SMK), and Sen's slope estimator (SSE) were deployed in assessing trend, change points, and magnitude. Results indicate that significant net change of monthly total rainfall mean is observed in May and December. Boreal spring and annual rainfall are insignificantly diminishing at a rate of −1.58 and −0.93 mm/year respectively. During boreal autumn, rainfall is insignificantly increasing at a rate of 1.48 mm/year. High interannual variability was observed, but no abrupt change was recorded during the study period. Spatial distribution of trend reveals dynamic variations from station to station. Amidst boreal spring (autumn), 27 (29) of stations considered had negative (positive) trends wherein 4 (8) stations recorded significant trend at α ≤ 0.01, respectively. At the annual scale, 10 (23) of the stations reported positive (negative) with 1 (5) significant trends at α ≤ 0.01, respectively. At all-time scales, stations at lower eastern and northeastern parts of the country reported a negative trend. Spatial decadal analysis portrays anomalous dry event over the study domain with the first decade being the wettest during boreal spring. To date, anomalously wet conditions exist throughout the country except lower eastern parts during boreal autumn. Comprehensive knowledge about variability, trend, and change point in precipitation is of unprecedented interest to scientists since they are prerequisites for developing effective contingency strategies to curb the impacts of climate change and future preparedness. • Trend, change points and abrupt changes in seasonal and annual rainfall from 33 synoptic stations in Kenya from 1979 to 2017 are assessed. • Rainfall is decreasing (increasing) insignificantly during boreal springs (autumn) and decreasing at annual scale. • Spatial distribution of seasonal and annual trend revealed that trend is dynamic and varied from station to station. • At all-time scales, stations at lower eastern and northeastern parts of the country reported negative trend. • Drying decadal trend in rainfall is observed during boreal springs while current decades are becoming wetter during boreal autumn. [ABSTRACT FROM AUTHOR]

Annual and seasonal rainfall trends in the Limbang River Basin (LRB), located in the equatorial tropics of Malaysian Borneo, have been characterised through Mann-Kendall and Spearman's Rho non-parametric tests. Rainfall from 13 rain gauge stations in the LRB for the period 1948–2016 was examined in the present study. Basic statistical analysis of rainfall in the region indicates normal distribution, low missing percentage and homogenous characteristics of precipitation. Annual and seasonal rainfall in the LRB shows spatial variation while considering different rain gauge stations. In annual rainfall, eight stations showed a decreasing trend and five stations showed an increasing trend. Rain gauge stations which showed a statistically significant increase in annual rainfall were Limbang DID (2.77 and 2.85 mm/year) and Long Napir (3.65 and 3.77 mm/year). In recognising the two annual monsoon seasons in this area, a significant increase in rainfall was noticed in Long Napir (2.79 and 2.88 mm/year) during the Southwest monsoon (SWM) period. During the Northeast monsoon (NEM), along with Long Napir (3.90 and 3.95 mm/year), Limbang DID (2.86 and 3.02 mm/year), Pandaruan (1.82 and 1.87 mm/year) and Medamit Nanga (1.93 and 2.00 mm/year) also showed a significant increase in rainfall. At the same time, a distinct trend was noticed in rainfall amounts during the inter-monsoon (IM) periods. During the first inter-monsoon month (April), seven rain gauge stations showed an increasing trend in rainfall, whereas in the second inter-monsoon month (October), nine stations showed a decreasing rainfall trend. In April, Long Napir (1.86 and 1.95 mm/year) indicated a significant increasing trend, whereas in the month of October, the rain gauge station at Ukong showed a significant decreasing trend (− 2.45 and − 2.37 mm/year). Though minor spatial changes in trend characteristics were observed among the rain gauge stations, the LRB as a whole showed a consistent increasing (significant and non-significant) trend in annual and seasonal rainfall. [ABSTRACT FROM AUTHOR]