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This study presents an analysis of long-term rainfall variability and trends in the summer monsoon region of Southeast Asia, encompassing Lao People's Democratic Republic (Lao PDR), Thailand, Vietnam, Cambodia, and Myanmar, as well as their respective river basins. Utilizing Climate Hazards Group InfraRed Precipitation with Station Data (CHIRPS) having a spatial resolution of 5 km spanning from 1981 to 2021, rainfall variability and trends were examined. Data preprocessing and geospatial analysis were conducted using R-Studio and ArcGIS software. The Mann–Kendall (MK) test and Sen's slope estimator were employed for annual and seasonal rainfall trend analysis. Myanmar exhibited the highest average annual rainfall of 2137 mm during the study period, while Thailand had the lowest (1641 mm). Over the past four decades, the Peninsula Malaysian Basin experienced the highest average annual rainfall (2691 mm), whereas the Chao Praya Basin recorded the lowest (1311 mm). Increasing trends in rainfall were observed across all five countries and nine major river basins. Vietnam displayed the highest annual rainfall trend of 5.63 mm/year, while Lao PDR exhibited the lowest trend (3.16 mm/year). Among the river basins, the Chao Phraya Basin demonstrated the maximum annual rainfall trend (11.21 mm/year), while the Peninsula Malaysia Basin had the minimum trend (1.21 mm/year). These findings could significantly contribute to climate change monitoring in the region and can aid policymakers in sectors such as agriculture, urban planning, and disaster management. [ABSTRACT FROM AUTHOR]

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

Understanding hydroclimatic variability and trend for the past four decades in the Upper Tekeze River basin is significant for future sustainable water resource management as it indicates regime shifts in hydrology. Despite its importance for improved and sustainable water allocation for water supply-demand and food security, varying patterns of streamflow and their association with climate change are not well understood in the basin. The main objective of this study was to characterize, quantify, and validate the variability and trends of hydroclimatic variables in the Upper Tekeze River basin at Ghba subbasin using graphical and statistical methods for homogeneous stations for the time period from 1953 to 2017, not uniform at all stations. The rainfall, temperature, and streamflow trends and their relationships were evaluated using the regression method, Mann–Kendall (MK) test, Spearman's rho (SR) test, Sen's slope, and correlation analysis. The analysis focused on rainfall, temperature, and streamflow collected from 11 climate and six hydrostations. For simplicity to discuss the interannual and temporal variability the stations were categorized into two clusters according to their record length, category 1 (1983–2017) and category 2 (1953–2017). About 73% and 27% of the rainfall stations exhibited normal to moderate annual rainfall variability. The MK and SR test showed that most of the significant trends in annual rainfall were no change except in one station decreasing and the test also showed no significant change in temperature except in three stations showed an increasing trend. Overall, streamflow trends and change point timings were found to be consistent among the stations and all have shown a decreasing trend. Changes in streamflow without significant change in rainfall suggest factors other than rainfall drive the change. Most likely the observed changes in streamflow regimes could be due to changes in catchment characteristics of the subbasin. These research results offer critical signals on the characteristics, variability and trend of rainfall, temperature, and streamflow necessary to design improved and sustainable water allocation strategies. [ABSTRACT FROM AUTHOR]

Climate change is an environmental challenge for rural communities that rely heavily on rainwater-based agriculture. The main goal of this study is to investigate spatiotemporal variability and trends in rainfall and temperature in southern Ethiopia. Extreme temperature and rainfall indices were computed using the ClimPACT2 software. The detection and quantification of trends in rainfall and temperature extremes were analyzed using a nonparametric modified Mann–Kendall (MMK) test and Sen's slope estimator. Results indicated that the mean annual rainfall has a declining trend at Boditi School and Mayokote stations with a statistically significant amount at magnitudes of 0.02 mm and 0.04 mm, respectively. The highest average monthly rainfall in the catchment was observed in the months of April, May, June, July, and August up to maximum rainfall of 117.50 mm, 177.43 mm, and 228.84 mm in Bilate Tena, Boditi, and Mayakote stations, respectively. On a seasonal scale, rainfall in Bilate Tena station was highly variable in all months, ranging from 49.54% to 126.92%, and three seasons except spring which showed moderate variation at 40.65%. In addition, the three locations over the catchment exhibited varied drought signs such as severe (1.28 < SRA < 1.65) and extreme drought (SRA > 1.65). The temperature indices, on the other hand, exhibited a warming trend over the catchment which was observed through an increased annual number of warm days (TX90p) and warm nights (TN90p) ranges from 0.274 to 6.03 and 0.274 to 3.16, respectively. The annual maximum value of the daily maximum temperature (TXx) ranges from 30.10 to 33.76°C in the three agroecological zones and showed low, medium, and high values in Dega, Woyna Dega, and Kola agroecologies, while the annual maximum value of the daily minimum temperature (TNx) ranged between 17 and 17.44°C at Dega and Kola, respectively. Therefore, based on trends in rainfall variability and persistent temperature rise, appropriate adaptation strategies should be adopted. [ABSTRACT FROM AUTHOR]

The variability, intensity, and distribution of rainfall have drawn a lot of interest globally and especially in nations where rainfed agriculture is the norm. This article uses rainfall data from the Rwanda Meteorology Agency for the years 1981 to 2021 to delineate and analyze rainfall variability and trends in the Kayonza District. The time series were grouped using the K-means clustering technique based on computed Euclidean distance, the total within-cluster sum of squares, and the elbow plot technique to determine the optimal number of clusters. The coefficient of variation measures was employed to analyze rainfall variability, while Sen's slope and the Mann–Kendall (MK) test were used, respectively, to find trends and changes in magnitude. The results indicated four near homogeneous zones named region one to four. The dry seasons indicated higher variability compared to rainy seasons and annual rainfall total with a variability of 128–142% over the southeastern part during June to August (JJA) season, while a variability of 16–48% was observed over most of the district during both annual and rainy seasons. It was further noted that the areas in the central part of the Kayonza District indicated a significant increasing trend at a significance level of 95% and above during January to February (JF), September to December (SOND), and on annual basis, while March to May (MAM) and JJA season exhibited no significant trend. The findings of this study are essential for creating adequate mitigation strategies to lessen climate change's effects on agriculture as well as other socioeconomic sectors. [ABSTRACT FROM AUTHOR]

Variability in rainfall and temperature results in different impacts on agricultural practices. Assessesment of variability and trend of rainfall and temperature for the district of Musanze in Rwanda was conducted using six meteorological stations for a period of 37 years, ranging from 1981 to 2018, and data were obtained from Rwanda Meteorology Agency. Musanze district is located in highland areas of Rwanda, understanding the variability and trend in rainfall and temperature is paramount to increase the uptake of climate information and support strategic orientation. The Mann–Kendall nonparametric test and modified Mann–Kendall were used to assess the trend in rainfall and temperature, whereas Sen's slope estimator was used to assess the magnitude of change. The results from both methods showed much similarity and consistency. The assessment of variability and trend in rainfall and temperature in Musanza district indicated that increasing temperature and decreasing rainfall trends gave an indication of changes in variability and trend in rainfall and temperature. The annual pattern revealed a substantial downward tendency of −25.7% for Nyange, the only station with constant decreasing trend over all seasons, DJF, −61.4%, SON, −12.2%, JJA, −40.3%, and MAM, −4.35. Temperature analysis for both maximum and minimum indicated increasing trend which was signal for constant warming up in the area. The results from coefficient of variation indicated a high disparity in rainfall variation from June to August which ranged between 51 and 74%, and other seasons changes were moderate. [ABSTRACT FROM AUTHOR]

This study was employed to investigate the temporal variability and trend analysis of areal rainfall in the Muger subwatershed, Upper Blue Nile, Ethiopia. The study was run over the following procedures to handle the main objective: (1) determining the areal rainfall from gauged point rainfall using the Thiessen polygon method, (2) grouping the months in the season according to the study area, (3) evaluating the temporal dynamics of annual and seasonal areal rainfall using the coefficient of variation (CV), standard anomaly index (SAI), and precipitation concentration index (PCI), and (4) analyzing the trend of annual and seasonal areal rainfall using modified Mann–Kendall's (modifiedmk) test in RStudio. Based on the temporal variability analysis, CV results depict that annual and summer areal rainfall had low variability with values of 13.43% and 13.7%, respectively. Winter and spring areal rainfall shows high variation with a CV value of 50.5% and 36%, respectively. According to the SAI output, around 70% of the considered year was in the normal condition of wetness. On the other hand, the seasonal (winter, spring, and summer) rainfall distribution of the study area shows strong irregularity distribution throughout the considered years as a result of PCI with a value of 57.5%. The trend of the areal rainfall was shown to be both increasing and decreasing. However, the trend was insignificant with a 10% confidence level. [ABSTRACT FROM AUTHOR]

Maize is one of the most important cereal food crops, and it can be grown all year in various agroecological zones. However, its vegetative growth and yield are susceptible to rainfall and temperature variability. As a result, the analysis of rainfall and temperature variability and trend was urgently needed in maize-growing agroecology zones to restructure the production system. The aim of the study was to examine rainfall and temperature variability and trends for developing a climate-resilient maize farming system in major agroecology zones in northwest Ethiopia. The study was implemented in low productive agroecology zones (LPZ), medium productive agroecology zones (MPZ), and high productive agroecology zones (HPZ) of northwest Ethiopia using daily time series climate data during the period 1987–2018. The coefficient of variation (CV), precipitation concentration index (PCI), rainfall anomaly index (RAI), and standardized precipitation (SPI) were applied to examine rainfall variability. Mann–Kendall's and Sen's slope estimator trend tests were used to detecting the statistical significance of changes in rainfall and temperature. Statistically significant increasing trends for annual maximum and minimum temperatures were recorded for all maize-producing agroecology zones. The mean annual temperature has exhibited a significant warming trend of 0.12 to 0.54°C per decade. The average annual rainfall has decreased by 38 to 67 mm per decade in all maize agroecology zones. Our research also showed that droughts now happen every one to three years; even consecutive droughts were seen in 2009, 2010, and 2011. For this reason, it could be required to develop a system of climate-resilient maize farming to address the issues of both global warming and the sub-Saharan countries that make up our study area. Climate-resilient maize agronomic activities have been determined by analyzing the onset, length of the growth period (LGP), and cessation date. Accordingly, the lower and upper quartiles of the date of onset of rainfall were in a range of May 9 to June 2, respectively; the length of the growth period (LGP) during the rainy season ranges from 97 to 232 days, and the cessation date of rainfall was November 1. Therefore, the short- to long-maturing maize varieties can be planted from May 9 to June 2 and can begin to be harvested in the first week of November under the current climatic circumstances. [ABSTRACT FROM AUTHOR]

Changes in precipitation patterns significantly affect flood and drought hazard management and water resources at local to regional scales. Therefore, the main motivation behind this paper is to examine the spatial and temporal rainfall variability over Sri Lanka by Standardized Rainfall Anomaly Index (SRAI) and Precipitation Concentration Index (PCI) from 1990 to 2019. The Mann–Kendall (MK) trend test and Sen's slope (SS) were utilized to assess the trend in the precipitation concentration based on PCI. The Inverse Distance Weighting (IDW) interpolation method was incorporated to measure spatial distribution. Precipitation variability analysis showed that seasonal variations are more than those of annual variations. In addition, wet, normal, and dry years were identified over Sri Lanka using SRAI. The maximum SRAI (2.27) was observed for the year 2014 for the last 30 years (1990–2019), which shows the extremely wet year of Sri Lanka. The annual and seasonal PCI analysis showed moderate to irregular rainfall distribution except for the Jaffna and Ratnapura areas (annual scale-positive changes in Katugastota for 21.39% and Wellawaya for 17.6%; seasonal scale-Vavuniya for 33.64%, Trincomalee for 31.26%, and Batticaloa for 18.79% in SWMS). The MK test, SS-test, and percent change analyses reveal that rainfall distribution and concentration change do not show a significant positive or negative change in rainfall pattern in Sri Lanka, despite a few areas which experienced significant positive changes. Therefore, this study suggests that the rainfall in Sri Lanka follows the normal trend of precipitation with variations observed both annually and seasonally. [ABSTRACT FROM AUTHOR]

Trend analysis of hydrometeorological data is vital for proper water resources planning and management. This paper examines the trends of the hydrometeorological data in Gilgel Gibe catchment and whether the trends are significant. Daily rainfall, temperature, and streamflow data of the stations in/around (nearby) the catchment (7 stations for rainfall, 4 stations for temperatures, and 6 stations for streamflow) for a period longer than 25 years were collected and then analyzed to detect the variability and the changes in trend. Prior to conducting trend tests, the missed data were filled, and their inconsistencies were also adjusted. The nonparametric Mann-Kendall test along with Sen's slope technique was employed to detect monotonic trends in the data series. The results showed that, on average, the rainfall exhibits an insignificant increasing tendency. It was also observed that there is, in general, an increasing trend in temperature (both maximum and minimum) in the study area. The analysis of the stream flows indicated that only one station (Bulbul Nr. Serbo) showed a positive slope at a 5% significance level. Two stations (Aweitu Nr. Babu and Gibe Nr. Seka) showed a slightly increasing trend, whereas the remaining 3 stations (Gibe Nr. Assendabo, Aweitu at Jimma, and Kitto Nr. Jimma) indicated an insignificant decreasing trend. The streamflow of the catchment generally shows a tiny decreasing tendency (0.007% per year) at its outlet. However, the results in general specified statistically insignificant trend changes of the hydrometrological data of the study catchment. [ABSTRACT FROM AUTHOR]

Precipitation trend detection is vital for water resources development and decision support systems. This study predicts the climate change impacts on long-term precipitation trends. It deals with the analysis of observed historical (1960–2010) and arithmetic mean method in assembling precipitation from CMIP5 Global Climate Models (GCMs) datasets for a future period (2020–2099) under four emission scenarios. Daily precipitation data of 32 weather stations in the Xijiang River Basin were provided by National Meteorological Information Centre (NMIC) of the China Meteorological Administration (CMA) and Global Climate Models (GCMs) with all four emission scenarios statistically downscaled using Bias Correction Special Disaggregation (BCSD) and applied for bias correction via Climate Change Toolkit (CCT). Nonparametric Mann–Kendall test was applied for statistical significance trend analysis while the magnitude of the trends was determined by nonparametric Sen's estimator method on a monthly scale to detect monotonic trends in annual and seasonal precipitation time series. The results showed a declined trend was observed for the past 50 years over the basin with negative values of MK test (Z) and Sen's slope Q. Historical GCMs precipitation detected decreasing trends except for NoerESM1-M which observed slightly increasing trends. The results are further validated by historical precipitation recorded by the Climate Research Unit (CRU-TS-3.1). The future scenarios will likely be positive trends for annual rainfall. Significant positive trends were observed in monsoon and winter seasons while premonsoon and postmonsoon seasons will likely be slightly downward trends. The 2040s will likely observe the lowest increase of 6.6% while the 2050s will observe the highest increase of 11.5% over the 21st century under future scenarios. However, due to the uncertainties in CMIP5, the future precipitation projections should be interpreted with caution. Thus, it could be concluded that the trend of change in precipitation around the Xijiang River Basin is on the increase under future scenarios. The results can be valuable to water resources and agriculture management policies, as well as the approach for managing floods and droughts under the perspective of global climate change. [ABSTRACT FROM AUTHOR]

The effect of climate variability on the rainfall pattern is canvassed on the Uma Oya river basin, Sri Lanka, consisting of 5 rainfall gauging stations. The Uma Oya basin (720 km2) is given utmost precedence due to environmental concerns seen in the ongoing Uma Oya multipurpose development project (529 million USD worth) which is expected to divert water to the southeast dry zone of the country while adding 231 GWh/year electricity to the national grid. The rainfall data for a period of 26 years (1992–2017) were analysed using Mann–Kendall's test and Sen's slope estimator test to identify the rainfall trends. Both of these trend analysis test results depict only one negative trend for Hilpankandura Estate for the month of June; however, the seasonal trend analysis and annual trend analysis do not support this observation. Nevertheless, Mann–Kendall's test showed potential positive trends for the 3 rainfall gauging stations Kirklees Estate, Ledgerwatte Estate, and Welimada Group only in the 1st intermediate period (March-April), and this is well supported by the monthly trend analysis. Other than these trends, the results do not show any significant negative trends in the Uma Oya catchment. Therefore, the results vividly explain that there is no threat of water scarcity to the catchment area being resistant to changing global climate for the past 26 years. [ABSTRACT FROM AUTHOR]

The impacts of climate change and climate variability on human life have led the scientific community to monitor the behavior of weather and climate variables at different spatial and temporal scales. This paper explores seasonal and annual trends of rainfall in the Lake Tana basin (LTB) and their teleconnections with global sea surface temperatures (SSTs) over the period between 1979 and 2015. The nonparametric Mann–Kendall test and Sen’s slope estimate are applied to the rainfall data collected from the National Meteorology Agency (NMA) of Ethiopia for detecting and estimating rainfall trends. Additionally, Pearson’s correlation coefficient method is used to determine the effect of SST variations on rainfall. The assessment of rainfall trends indicates that the amount of annual rainfall in the Lake Tana basin is increasing, but the rate of increase is not statistically significant. Seasonal analysis reveals that the smallest amount of rainfall occurs in the Bega season, and this season is getting drier with time. However, the analysis indicates that the other two seasons (Belg and Kiremt) are becoming wetter. The rainfall in Kiremt is increasing significantly (significant at the p=0.05 level) in Debre Tabor station with a rate of 10.20 mm/year. Besides, 78.1% of the total annual rainfall in the basin occurs during this rainy (Kiremt) season, whereas Bega and Belg contribute some 9.4% and 12.5%, respectively. Furthermore, the correlation analysis of rainfall and SSTs indicates that rainfall of the LTB is highly affected by the variations of SSTs. [ABSTRACT FROM AUTHOR]

Identifying rainfall trends in highly urbanized area is extremely important for various planning and implementation activities, including designing, maintaining and controlling of water distribution networks and sewer networks and mitigating flood damages. However, different available methods in trend analysis may produce comparable and contrasting results. Therefore, this paper presents an attempt in comparing some of the trend analysis methods using one of the highly urbanized areas in Sri Lanka, Colombo. Recorded rainfall data for 10 gauging stations for 30 years were tested using the MannKendall test, Sen's slope estimator, Spearman's rho test, and innovative graphical method. Results showcased comparable findings among three trend identification methods. Even though the graphical method is easier, it is advised to use it with a proper statistical method due to its identification difficulties when the data scatter has some outliers. Nevertheless, it was found herein that Colombo is under a downward rainfall trend in the month of July where the area receives its major rainfall events. In addition, the area has several upward rainfall trends over the minor seasons and in the annual scale. Therefore, the water management activities in the area have to be revisited for a sustainable use of water resources. [ABSTRACT FROM AUTHOR]

Vegetation dynamics have been visibly influenced by climate variability. The Normalized Difference Vegetation Index (NDVI) has been the most commonly used index in vegetation dynamics. The study was conducted to examine the effects of climatic variability (rainfall) on NDVI for the periods 1982–2015 in the Gojeb River Catchment (GRC), Omo-Gibe Basin, Ethiopia. The spatiotemporal trend in NDVI and rainfall time series was assessed using a Theil–Sen (Sen) slope and Mann–Kendall (MK) statistical significance test at a 95% confidence interval. Moreover, the residual trend analysis (RESTREND) method was used to investigate the effect of rainfall and human induction on vegetation degradation. The Sen's slope trend analysis and MK significant test indicated that the magnitude of annual NDVI and rainfall showed significant decrement and/or increment in various portions of the GRC. The concurrent decrement and/or increment of annual NDVI and rainfall distributions both spatially and temporarily could be attributed to the significant positive correlation of the monthly (RNDVI-RF = 0.189, P ≤ 0.001) and annual (RNDVI-RF = 0.637, P ≤ 0.001) NDVI with rainfall in almost all portions of the catchment. In the GRC, a strongly negative decrement and strong positive increment of NDVI could be derived by human-induced and rainfall variability, respectively. Accordingly, the significant NDVI decrement in the downstream portion and significant increment in the northern portion of the catchment could be attributed to human-induced vegetation degradation and the variability of rainfall, respectively. The dominance of a decreasing trend in the residuals at the pixel level for the NDVI from 1982, 1984, 2000, 2008 to 2012 indicates vegetation degradation. The strong upward trend in the residuals evident from 1983, 1991, 1998 to 2007 was indicative of vegetation improvements. In the GRC, the residuals may be derived from climatic variations (mainly rainfall) and human activities. The time lag between NDVI and climate factors (rainfall) varied mainly from two to three months. In the study catchment, since vegetation degradations are mainly caused by human induction and rainfall variability, integrated and sustainable landscape management and climate-smart agricultural practices could have paramount importance in reversing the degradation processes. [ABSTRACT FROM AUTHOR]

Drought is one of the most significant hazards in Sri Lanka. Status of drought in Sri Lanka was assessed using Standardized Precipitation Index (SPI) at 3, 6, and 12 months' time scales using monthly rainfall (1970 to 2017) data of 54 weather stations. The frequency of drought events was evaluated using SPI, and trend of SPI was also detected using the Mann–Kendall (MK) test and Sen's slope estimator. The result based on SPI identified hydrological years 1975-76, 1982-83, 1986-87, 1988-89, 2000-01, 2001-02, 2013-14, and 2016-17 as drought years for 52, 32, 35, 33, 33, 31, 31, and 31% of tested stations (54), respectively, at annual time scale. Comparison of the SPI at different time scales revealed that more drought events (SPI ≤ −1) occurred during Yala season than Maha cropping season. Considering the Thiessen polygon average rainfall, more frequent drought events occurred in the dry zone (57%) than the wet (49%) and intermediate zone (47%) at the annual time scale. SPI trend results showed greater increase in drought (59% of stations) during Yala seasons as compared to the Maha cropping season (15% of stations) in Sri Lanka. [ABSTRACT FROM AUTHOR]

This study evaluates the spatiotemporal rainfall variability over the semimountainous Soan River Basin (SRB) of sub-Himalayan Pothwar region, Pakistan. The temporal rainfall trend analysis of sixteen rain gauges was performed on annual basis with long-term (1981–2016) data. The results depicted that there is substantial year-to-year and season-to-season variability in rainfall patterns, and rainfall patterns are generally erratic in nature. The results highlight that most of the highland rainfall stations showed decreasing trends on annual basis. The central and lowland stations of the study area recorded an increasing trend of rainfall except for Talagang station. The average annual rainfall of the study area ranges between 492 mm and 1710 mm in lowland and high-altitude areas, respectively. Of the whole year's rainfall, about 70 to 75% fall during the monsoon season. The rainfall spatial distribution maps obtained using the inverse distance weighting (IDW) method, through the GIS software, revealed the major rainfall range within the study area. There is a lack of water during postmonsoon months (November–February) and great differences in rainfall amounts between the mountainous areas and the lowlands. There is a need for the rational management of mountainous areas using mini and check dams to increase water production and stream regulation for lowland areas water availability. The spatiotemporal rainfall variability is crucial for better water resource management schemes in the study area of Pothwar region, Pakistan. [ABSTRACT FROM AUTHOR]

The Didesa catchment, which is the second largest subbasin of the Abay basin, significantly contributes to the Blue Nile's outflow. Understanding the dynamics of water availability under the changing climate in such a basin assists in the proper planning of land use and other development activities. This study presents changes in climatic elements such as rainfall, temperature, and evapotranspiration using observation data and regional climate models (RCMs) under two representative concentration pathways (RCPs) for three future periods. We use a calibrated hydrological model to further assess climate change's effects on streamflow. We select three RCMs and their ensemble's mean by evaluating their performance with respect to observations. We apply the modified Mann–Kendall test to detect trends in each dataset. The result shows that annual mean maximum and minimum temperatures increase in the catchment for the 2021–2040, 2041–2070, and 2071–2100 periods as compared to baseline (1989–2018) under both RCP4.5 and RCP8.5 scenarios. Annual mean maximum temperature and potential evapotranspiration experienced a significant decreasing trend during the year from 1989 to 2018. Furthermore, there was an increasing trend in annual rainfall from 1989 to 2018, which could be related to the cooling of sea surface temperature over the equatorial Pacific. We detect an increasing trend in temperature in both scenarios and all periods; however, no clear trend pattern is found in rainfall. The result from hydrological model simulations reveals that the mean monthly streamflow slightly increases in the winter season while it decreases during the main rainy season. Further study of detailed weather systems, which affect the subbasin's climate, is recommended. [ABSTRACT FROM AUTHOR]

Central Vietnam is characterized by severe flooding associated with heavy rainfall events caused by interactions between multiscale atmospheric circulations and the complex local terrain. Previous studies believed rainfall in central Vietnam is closely related to the cold surge; however, it fails to explain the cause of the early rainfall occurrence in August in the subregion. For the first time, this study investigates the detailed atmospheric mechanisms associated with rainfall variations in central Vietnam using the empirical orthogonal function (EOF) applied to the recently developed high-resolution Vietnam gridded precipitation (VnGP) dataset. Reanalysis data NCEP/NCAR is used to associate the rainfall changes with respective atmospheric mechanisms. EOF analysis detected two dominant rainfall modes. The primary mode explains the rainfall variation from October to November over the central and is directly related to the interaction of cold surges and tropical disturbances. The second mode accounts for rainfall occurring in north central from September to mid-October, which is attributed to the westerly summer monsoon activities. Also, we revealed that, while the first mode exhibits a significant correlation with El Niño-southern oscillation, the second depends highly on the contrast of sea surface temperature in the northern and southern Hemispheres. This different oceanic forcing and the local topological effect of Truong Son mountain range reasonably explain the opposite rainfall pattern in central Vietnam in early fall. [ABSTRACT FROM AUTHOR]

Climate change is a major threat to agricultural production and undermines the efforts to achieve sustainable development goals in poor countries such as Ethiopia that have climate-sensitive economies. The objective of this study was to assess characterization of local climate and its impact on productivity faba bean (Vicia faba L.) varieties (Gora and Tumsa) productivity in Welmera watershed area, central Ethiopia. Historical climate (1988–2017) and eight years of crop yield data were obtained from National Meteorological Agency of Ethiopia and Holeta Agricultural Research Center. Trend, variability, correlation, and regression analyses were carried out to characterize the climate of the area and establish association between faba bean productivity and climate change. The area received mean annual rainfall of 970 mm with SD of 145.6 and coefficient of variation (CV %) of 15%. The earliest and latest onset of rainfall were April 1 (92 DOY) and July 5 (187 DOY), whereas, the end date of rainy season was on September 2 (246 DOY) and October 31 (305 DOY), respectively. The average length of the growing period was 119 days, with a CV% of 35.2%. The probability of dry spell less than 7 days was high (>80%) until the last decade of May (151 DOY); however, the probability sharply declined and reached 0% on the first decade of July (192 DOY). Kiremt (long rainy season that occurs from June to September) and belg (short rainy season that falls from February to April/May) rainfall had increasing trends at a rate of 4.7 mm and 2.32 mm/year, respectively. The annual maximum temperature showed increasing trend at a rate of 0.06°C per year and by a factor of 0.34°C, which is not statistically significant. The year 2014 was exceptionally drought year while 1988 was wettest year. Kiremt (JJAS) start of rain and rainy day had strong correlation and negative impact on Gora yield with (r = −0.407 and −0.369), respectively. The findings suggests large variation in rainfall and temperature in the study area which constraints faba bean production. Investment on agricultural sector to enhance farmer's adaptation capacity is essential to reduce the adverse impacts of climate change and variability on faba bean yield. More research that combines household panel data with long-term climate data is necessary to better understand climate and its impact on faba bean yield. [ABSTRACT FROM AUTHOR]

The monitoring of rainfall variability over recent decades has become a necessity due to its devastating effects such as floods and droughts, which render humans vulnerable across different parts of the West African region. The current study seeks to provide a good understanding of variability within the minor rainfall season over southern Ghana by employing statistical tools to quantify variability in rainfall. Daily rainfall data from 1981 to 2018 for seventeen (17) synoptic weather stations across southern Ghana are used for this analysis. We perform trend and descriptive statistics of rainfall amount and extreme indices intending to identify the areas with the greatest variability in rainfall. Further, for five recent years (2014–2018), we do an interpolation of the ground station rainfall data and compute anomalies. We find increasing trends of rainfall in the minor rainy season for 16 out of the 17 stations, with rainfall increasing between 0.10 mm and 4.30 mm each season. For extreme rainfall indices, the 17 stations show nonsignificant trends of very wet and extremely wet days. We also find that the middle parts of Ghana have the highest rainfall amounts (262.7 mm/season–400.2 mm/season), while the East Coast has the lowest (125.2 mm/season–181.8 mm/season). Over the whole of southern Ghana, we find high variability in rainfall amount with the coefficient of variations (CV) between 25.3% and 70.8% and moderate to high variability in rainfall frequency (CV = 14.0%–48.8%). The results of rainfall anomalies show that the middle parts had an above-normal rainfall amount. In the same period, the transition areas experienced below-normal rainfall. Our finding of high variability in the minor rainfall season has implications for agricultural productivity in Ghana and countries in the West African region, which rely heavily on rain-fed agriculture. Hence, this study recommends more research to understand the causes of variability in the West African monsoon and how this will change in the region. [ABSTRACT FROM AUTHOR]

With varied implications, Ghana's temperature and rainfall are projected to rise and decline, respectively. A study exposing specific areas of concern for appropriate responses in this regard is a welcome one. This study sought to describe the temporal variations in temperature and rainfall in the Bawku Area of Ghana. A forty-year (1976–2015) daily climate data was collected on three meteorological stations from the Ghana Meteorological Agency. Normality test, homogeneity test, Standardised Precipitation Index (SPI) analysis, Mann–Kendall trend test, and One-way post hoc ANOVA were performed using XLSTAT and DrinC. Over the period under study, the mean annual rainfall pattern was generally erratic, fluctuating between 669.8 mm and 1339.4.6 mm with an annual average of 935.3 mm. The long-term (40-year period) average temperature of the three stations, on the other hand, was 28.7°C, varying between 26.9°C and 29.9°C annually. Whereas the SPI value of 2006 was ≥2.0, indicating extremely wet year with 2.3% probability of recurring once every 50 years, 1988 was the hottest year with temperature anomaly value of 1.2°C, while coolest years were 1979 (−1.8°C) and 1976 (−1.0°C). The Mann–Kendall trend test showed a rise in rainfall in Binduri, Garu-Tempane, and Manga, yet none of the rainfall changes were statistically significant (P > 0.05). Mean temperature on the other hand experienced a significant rise (P < 0.05). With an R-square of 34.7%, the rise in temperature in Manga witnessed the most significant change in annual temperature changes. There were statistically significant (P < 0.05) differences in the interdecadal temperature over the 40-year period. Generally, it can be stated that both temperature and rainfall vary in the study area with various degrees of disparities, but temperature assumes an upward trend at a faster rate. We therefore recommend that stakeholders resort to the construction of dams and boreholes to ensure regular availability of water for both domestic and agricultural uses. [ABSTRACT FROM AUTHOR]

Monsoon rainfall is the principle source of fresh water essential for agricultural practices and human sustenance in the Indian subcontinent during summer. This study is primarily designed to analyse the extent of rainfall and temperature variations in Pakistan over the northern monsoon belt by using satellite and ground-based observations. The satellite gridded data for rainfall are acquired from Tropical Rainfall Measuring Mission (TRMM) along with rainfall and temperature data from 15 ground stations of Pakistan Meteorological Department (PMD). Data were analysed to identify changes in climatic parameters and spatiotemporal shift in monsoon precipitation in Pakistan. Analysis shows that there is significant correlation between TRMM and PMD datasets. Decrease in monsoon rainfall is observed during the last two decades. A more pronounced decrease is observed in monsoon rainfall during the years 2010–2017, i.e., 17.58 mm/year accompanied by 0.18°C increase in temperature. A southward spatial shift in monsoon rainfall occurrence (rainfall ≥2.5 mm/day) is observed while an eastward shift in moderate to heavy monsoon rainfall is identified. This study may be helpful for an agricultural country like Pakistan which is heavily dependent on monsoon rainfalls for assessing the impacts of changing monsoon season and to adapt towards changing climate. [ABSTRACT FROM AUTHOR]

Time series analyses for climatic factors are important in climate predictions. Rainfall is being one of the most important climatic factors in today's concern for future predictions; thus, many researchers analyze the data series for identifying potential rainfall trends. The literature shows several methods in identifying rainfall trends. However, statistical trend analysis using Mann–Kendall equation and graphical trend analysis are the two widely used and simplest tests in trend analysis. Nevertheless, there are few studies in comparing various methods in the trend analysis to suggest the simplest methods in analyzing rainfall trends. Therefore, this paper presents a comparison analysis of statistical and graphical trend analysis techniques for two tropical catchments in Sri Lanka. Results reveal that, in general, both trend analysis techniques produce comparable results in identifying rainfall trends for different time steps including annual, seasonal, and monthly rainfalls. [ABSTRACT FROM AUTHOR]