Reproductive patterns in galagos (Galago zanzibaricus and Galago garnettii) in relation to climatic variability.

El Niño‐Southern Oscillation (ENSO), which occurs in the Equatorial Pacific Ocean, has been identified to have significant influence on rainfall variability throughout the world, especially in the tropics. Such variability in rainfall has implications for agrarian economies, such as that in Ghana. This study therefore sought to demonstrate the effect of ENSO‐induced variability in annual and seasonal rainfall on the development of sustainable agriculture in the Ho Municipality of Ghana. Using 61 years of monthly rainfall data (1955–2015) for the Ho Municipality and ENSO indices, this study showed that 15% of the variability in total annual rainfall is explained by the ENSO phenomena. Mean annual rainfall and rainfall in the major rainy season decreased for El Niño years, in addition to a more variable rainfall compared to that received in La Niña years. The major growing season was observed to be longer in La Niña years and shorter in El Niño years. This means that the potential for crop cultivation will be severely hampered in an El Niño year. Farmers within the municipality are therefore encouraged to harness other complementary water sources for farming activities and also employ water management strategies during El Niño years.

ABSTRACTCharacterizing population dynamics in heterogeneous environments requires comprehensive long‐term data. We monitored seven small mammals in replicated sites in a semiarid Chilean thorn‐scrub habitat over 30 years using monthly capture–mark–recapture (CMR) sampling. We applied a superpopulation CMR modeling framework to examine the following: (i) How do population sizes and demographic parameters vary seasonally and over time? and (ii) Are there commonalities in the variation of those parameters either seasonally or annually? Capture probabilities among four “core” species (Octodon degus, Phyllotis darwini, Abrothrix olivacea, and Thylamys elegans) varied strongly over time, as did apparent survival among years and rainfall seasons, with individuals generally experiencing higher survival during the wet season. Recruitment measures also showed strong annual and seasonal variation, with higher numbers in wet seasons and years. Capture probability in three “opportunistic” species (Abrocoma bennettii, Abrothrix longipilis, and Oligoryzomys longicaudatus) varied over time, as did survival and recruitment across rainfall or reproductive seasons. As predicted, annual and seasonal variation in rainfall strongly influenced the survival and recruitment of most species, and their populations increased rapidly following rainfall events. Unsurprisingly, core species shared similar overall responses to environmental drivers; opportunistic species responded differently to seasonal or annual variation in rainfall, perhaps reflecting their origins in non‐thorn‐scrub habitat. Finally, for all species, population size correlated more strongly with the number of recruits than with survival, suggesting that the former has a greater influence on the dynamics of our study populations. This study provides the first insight into the demography of the entire small mammal community at our study site, and in particular, the demography of A. bennettii, A. longipilis, and O. longicaudatus from semiarid habitat. Our results, based on the longest time series in South America, provide comprehensive demographic information on a diverse small mammal community, and offer novel insight into community‐level response to changing climate.

Rainfall variability plays an important role in many socio-economic activities such as food security, livelihood and farming in Ghana. Rainfall impact studies are thus very crucial for proper management of these key sectors of the country. This paper examines the seasonal and annual rainfall variability in the four agro-ecological zones of Ghana from the CHIRPS V2 rainfall time series spanning a period of 1981-2015. The rainfall indices were computed with the aid of the FClimDex package whereas the trends of these indices were further tested using the Mann Kendall trend test. The results show good agreement (r ≥ 0.7) between CHIRPS V2 and gauge in almost all portions of country although high biases were observed especially in DJF season over parts of the Northeastern (NE) portions of the country. The mean seasonal rainfall climatology over the country is observed to be in the range of 20 - 80 mm, 60 - 200 mm, 100 - 220 mm and 40 - 180 mm in DJF, MAM, JJA and SON seasons respectively with high intensities of rainfall dominating Southwestern portions of the country. The trend analysis revealed positive trends of consecutive dry days in the Transition, Forest and Coastal zones and negative trends in the Savannah zone of the country. Decreasing trends of consecutive wet days are observed over the Savannah, Transition and Coastal zones whereas increasing trends dominate the Forest zone. Savannah, Forest and Transition zones show weak increasing trends of the number of heavy rainfall days whilst weak decreasing trends are observed over the Coastal zone of the country. Similarly, weak increasing trends of the number of very heavy rainfall days are observed over all the agro-ecological zones except in the Transition zone. It is observed that the annual wet day rainfall total has increasing trend in the Savannah and Forest zones of the country whereas decreasing trends cover the remainder of the zones. The trends of these indices in the agro-ecological zones were all significant at a significant value of 0.05. This paper assessed the performance of the CHIRPS V2 rainfall data over the region and reports on the biases in seasonal rainfall amounts which are limited in previous studies. These findings have adverse impacts on rain-fed agricultural practices, water resource management and food security over the country.

Understanding the characteristics of extreme rainfall events is necessary for proper planning and management of urban flooding impacts. In this paper, daily rainfall data from four key weather stations for the period 1967-2017 were used to investigate temporal variability in annual, seasonal, and extreme rainfall in the urban catchments of Dar es Salaam, Tanzania. The MannKendall trend analysis and Sen’s slope estimator were used to quantify the magnitudes and significance of long-term trends in rainfall. The frequencies of extreme rainfall events were modelled using the Generalized Pareto model. Results of trend analysis provided evidence of a decrease in total annual rainfall, with the highest decrement being 6.59mm per year. The statistical significance of the decrease in total annual rainfall was inconclusive. Observations of increase in both annual and seasonal maximum rainfall were also made; with the highest increments being 1.01mm and 0.79mm per event, for annual and seasonal maximum rainfall, respectively. The statistical significance of the increase in annual maximum rainfall was certain at 3 out of 4 stations. Frequencies of extreme rainfall events investigated using the R6 threshold provided reasonable results based on actual experience in the study area. Results indicated that most of the pluvial and fluvial flooding are from rainfall events with a 2 to 10-year return period. This is indicative of issues with the drainage systems in the area; either in their designed capacity or the reduction of their water carrying capacity due to anthropogenic factors.

Characterization of the seasonal and inter-annual spatial and temporal variability of rainfall in a changing climate is vital to assess climate-induced changes and suggest adequate future water resources management strategies. Trends in annual, seasonal and maximum 30-day extreme rainfall over Ethiopia are investigated using 0.5° latitude × 0.5° longitude gridded monthly precipitation data. The spatial coherence of annual rainfall among contiguous rainfall grid points is also assessed for possible spatial similarity across the country. The correlation between temporally coinciding North Atlantic Multidecadal Oscillation (AMO) index and annual rainfall variability is examined to understand the underlying coherence. In total 381 precipitation grid points covering the whole of Ethiopia with five decades (1951–2000) of precipitation data are analysed using the Mann-Kendall test and Moran spatial autocorrelation method. Summer (July–September) seasonal and annual rainfall data exhibit significant decreasing trends in northern, northwestern and western parts of the country, whereas a few grid points in eastern areas show increasing annual rainfall trends. Most other parts of the country exhibit statistically insignificant trends. Regions with high annual and seasonal rainfall distribution exhibit high temporal and spatial correlation indices. Finally, the country is sub-divided into four zones based on annual rainfall similarity. The association of the AMO index with annual rainfall is modestly good for northern and northeastern parts of the country; however, it is weak over the southern region. Editor Z.W. Kundzewicz; Associate editor S. Uhlenbrook Citation Wagesho, N., Goel, N.K., and Jain, M.K. 2013. Temporal and spatial variability of annual and seasonal rainfall over Ethiopia. Hydrological Sciences Journal, 58 (2), 354–373.

This paper discusses the variability in rainfall and trend analysis of annual and seasonal rainfall time series of Shillong and Agartala stations located in the north-east region of India. Commonly used non-parametric statistical methods namely Mann-Kendall and Sen’s slope estimator was used to analyse the seasonal and annual rainfall time series. Statistical analysis showed less variation in annual and south-west monsoon rainfall for both Shillong and Agartala stations. In the total annual rainfall, the share of south-west (SW) monsoon, north-east (NE) monsoon, winter season and summer season rainfall was observed 64.60%, 13.22%, 1.40% and 20.80%, respectively for Shillong station of Meghalaya state. However, the contribution of SW monsoon, NE monsoon, winter season and summer season rainfall in the total annual rainfall was 59.59%, 9.55%, 1.14% and 29.72%, respectively for Agartala station of Tripura state. Non-significant increasing trends of rainfall was observed by 4.54 mm/year, 2.80 mm/year and 2.54 mm/year for annual, SW monsoon, and summer season, whereas, non-significant decreasing trends in rainfall for NE monsoon and winter season was observed with a magnitude of 1.83 mm/year and 1.63 mm/year for Shillong, Meghalaya during 1992 to 2017. Results also revealed that rainfall increased by 1.07 mm/year and 0.18 mm/year in SW monsoon and winter season whereas, rainfall decreased by 7.64 mm/year, 2.58 mm/year and 1.29 mm/year during annual, NE monsoon and summer season non-significantly during 1995 to 2019 in case of Agartala. The findings of present study will be useful for water management and crop planning in hill agriculture of Meghalaya and Tripura state of India.

The temporal and spatial variability of seasonal, annual, and decadal rainfall over 44 years in western Nepal was investigated using rainfall data from 36 meteorological stations in various physiographic regions. Missing data were addressed using the normal ratio method, and significant trends in annual rainfall were assessed through the Man-Kendall test. Western Nepal receives about 79.7% of annual rainfall during the monsoon, followed by 10.7% during the pre-monsoon, 3.3% during the post-monsoon, and 6.3% during the winter. The analysis revealed distinct seasonal excess and deficit episodes, with the highest monsoon rainfall recorded in 2000 and the lowest in 1979. During the study period, there is a large temporal variability of seasonal and annual rainfall. The central part of western Nepal receives heavy rainfall in monsoon seasons than other parts. In winter seasons more rainfall is received in the Northwest part and decreases towards the central and eastern parts of western Nepal. The midlands of western Nepal received higher annual rainfall than the other regions. The present study identified that the seasonal rainfall has been decreasing patterns in the Western region of Nepal for the past four decades.

Rainfall play an important role in crop management and monitoring, as it directly influences planting calendar, irrigation needs, crop growth and yield outcomes. This study was attempted to analyze seasonal rainfall variability and its impact on wheat crop production to understand association of seasonal rainfall characteristics with wheat crop production over North Showa Zone Amhara Region. Both time series station and satellite gridded rainfall data sets were obtained from Ethiopian Meteorology Institute from 1985 to 2021. Wheat crop production for main rainy season (<i>Meher</i>) was obtained from Ethiopian Statistical Service from 2010-2021, collected from household farm association level. Analysis of rainfall data sets was provided with climate data tool (CDT V8), R studio and Microsoft excel and ArcGIS 10.8. Coefficient of variation, precipitation concentration index and Standardized anomaly index (SAI) were applied to analyze long year seasonal and annual rainfall variability. Man-Kendall trend analysis methods were applied for rainfall trend analysis. Correlation coefficient on the other side has been applied to analyze rainfall variability impact on wheat crop production. The findings of this paper indicate irregular variation of spring <i>(Belg)</i> rainfall, moderate to low variation in summer and annual rainfall over the study area. Using correlation and coefficient of determination (R²) analyses, significant spatial variability was observed in the rainfall-crop relationship. Strong positive correlations were found in districts such as Ensaro (r = 0.7, R² = 0.4384) and Tarmaber (r = 0.7, R² = 0.5223), where over 40% and 50% of the variability in wheat production can be attributed to rainfall, respectively. Conversely, weaker correlations were evident in areas like Mojana Wodera (r = 0.5, R² = 0.2049), and Ankober (r = -0.3, R² = 0.097), suggesting that in these districts, rainfall plays a less dominant role in determining crop production, with other factors such as soil fertility and agricultural practices possibly having a greater influence.

Rainfall variability and change are critical drivers of food production in tropical regions. This study analyzed rainfall trends and variability in the drylands of Wolaita Zone (DLWZ), southern Ethiopia. It used observed rainfall data (1990–2022) and projections for 2026–2075 under two shared socioeconomic pathways (SSP2-4.5 and SSP5-8.5). Rainfall variability was assessed using the coefficient of variation, while trends were examined with the modified Mann–Kendall test and Sen’s slope. Results show extremely high (>30%) to high (20–30%) seasonal and inter-annual variability during 1990–2022, with decreasing rainfall trends except for winter, which showed a non-significant increase. Projections indicate medium (10–20%) to high variability in annual rainfall and high to extremely high variability in seasonal rainfall. Significant decreases are expected in spring rainfall, while summer and winter rainfall are projected to increase, particularly in 2051–2075. These findings suggest that persistent variability and declining rainfall during critical seasons may intensify risks to agricultural production in southern Ethiopia. The dataset provides a valuable basis for future studies on the impacts of rainfall change on crop and livestock systems.

A study was carried out to analyse rainfall and temperature trends and variability in selected agro-ecological zones in Kitui County. Climate Hazards Group Infrared Precipitation with Stations (CHIRPS) rainfall dataset was used while temperature data was obtained from the Climatic Research Unit gridded Time Series (CRU TS) dataset. The results indicated that there was a non-significant decreasing trend (p<0.05) in average annual rainfall in all the four agro-ecological zones (p<0.05) for a 30-years period (1988-2018) in the study area. A decreasing trend in March-April-May (MAM) seasonal rainfall trend was reported in the arid and semi-arid agro-ecological zones while an increasing trend was recorded in the transitional and semi-humid zones. For the October-November-December (OND) seasonal rainfall, a non-significant decreasing trend was reported in all the four agro-ecological zones. Additionally, a higher annual rainfall variability was recorded in the drier (arid) and wetter (semi-humid) zones compared to that in semi-arid and transitional zones. Moreover, the study established that there was a spatial variation in both MAM and OND seasonal rainfall variability and that rainfall variability was higher in the OND seasonal rainfall than that of the MAM seasonal rainfall in all the agro-ecological zones. With reference to temperature trends, a statistically significant increasing trend in annual and OND seasonal average maximum and minimum temperatures was reported in all the four agro-ecological zones. Further, the study noted a non-significant increasing trend in maximum and minimum temperatures for the MAM season in all the agro-ecological zones. In regard to temperature variability, the study deduced that there was low temperature variability compared to rainfall variability in all the four agro-ecological zones. The study recommends that location-specific rainfall and temperature analysis should guide planning and implementation of adaptation strategies for effective response to climate variability. Keywords: Agro-ecological Zones, Mann-Kendall Test, Coefficient of Variation, CHIRPS, CRU TS DOI: 10.7176/JEES/12-12-05 Publication date: December 31 st 2022

Location-specific data on rainfall and temperature trends at different temporal scale is crucial for developing agricultural strategies and managing water resources in drought prone areas like Purulia, West Bengal. Therefore, the present study assessed the spatio-temporal variability of rainfall and temperature in Purulia by utilizing gridded data for 40 (1984–2023) years from India meteorological department (IMD). We employed the Mann-Kendall (MK), modified Mann-Kendall (mMK) test and Sen’s slope estimator (β) to identify the trend and magnitude of change. Furthermore, innovative trend analysis (ITA) is used to detect sub-trends of the data series. The results of MK/mMK and Sen’s slope showed a declining trend of annual rainfall (Z= -1.990, β =-7.015) and increasing trend of annual maximum (Z = 2.200, (β) = 2.021) and minimum (Z = 2.060, β = 0.015) temperature. However, variability of annual rainfall (16.03%) is higher than variability in annual temperature (maximum temperature = 2.29%, minimum temperature = 1.81%). On seasonal scale, negative trends have been observed for winter and monsoonal rainfall and positive trends for summer and post-monsoonal rainfall. While maximum and minimum temperature of the study area is dominated by increasing trends. The result of ITA is more or less similar to the MK/mMK and Sen’s slope. Additionally, Spatial mapping of Z statistics and ITA slope showed that agriculturally productive subdivisions Purulia Sadar and Manbazar are significantly vulnerable to climate variability. The findings of this study offer a suitable framework for the development of future projects since it accurately explains the variability of rainfall and temperature.

Rainfall is one of the most important climate elements for agricultural production throughout the world. This study analyzes the seasonal and annual rainfall variability and its implications on agriculture production in the East Hararghe Zone of Oromia, Ethiopia. Rainfall is a critical factor influencing agricultural productivity and water resource management in this region, where agriculture serves as the mainstay of the economy and livelihood for millions. Utilizing historical precipitation data from meteorological stations across East Hararghe from 1981 to 2023, we employed statistical methods to analyze trends, patterns, and anomalies in both seasonal (Kiremt and Belg) and annual rainfall totals. This study used GeoCLIM version 1.2.1, ArcGIS version 10.3.1, and QGIS 3.12.1, and Python version for data analysis. The findings revealed that significant seasonal and inter-annual variability characterized by periods of extreme weather events that have profound implications for food security. There are two main rainy seasons, the Belg (February, March, April, and May; short rains) and the Kiremt (June, July, August, and September; long rains), and one dry season, the Bega (October, November, December, and January; no rain). The Kiremt season (June to September), essential for crop cultivation, exhibits distinct fluctuations linked to regional climate phenomena such as El Niño/La Niña–Southern Oscillation. Moreover, our results indicated an increasing trend of Kiremt rainfall standardized anomaly during 2007, 2014, 2019, and 2020 but also highlighted concerning patterns of erratic distribution within the Belg season. Based on the results, we underscore the necessity for adaptive agricultural practices to mitigate risks associated with climate variability. Understanding these dynamics is vital for concerned sectors, policymakers, and researchers aiming to enhance resilience among vulnerable communities dependent on rain-fed agriculture.

Identifying the spatiotemporal variability and characterization of rainfall in a given area is essential for effective water resource management, agricultural planning, and disaster preparedness. This study used ENACTS data to assess the variability of rainfall and characterize seasonal rainfall in Gojjam from 1991-2020. The results indicated that Gojjam had received mostly on <i>Bega</i>, <i>Belg</i>, <i>Kiremt</i>, and annually 120-200 mm, 200-450mm, 1000-1300 mm, and 1300-1700 mm rainfall respectively. The mean annual rainfall is 1395.1 mm. The results show that the main rainy seasonal rainfall starts on average from 24 May to 3 June and ceases averagely around 13-27 September. The mean length of the growing season was 120 days. It also indicated that high rainfall variability was observed over the study area. The result stated that most parts of the study area are under strong precipitation concentration during all seasons. Characterization and identified variability of seasonal rainfall were important especially for rain-fed agriculture and hydrological advisory at the zone level to support the community. These findings highlight the need for adaptation strategies to mitigate the potential impacts of changing rainfall patterns on agriculture and water resources in Gojjam.

This paper analyses the fire regime over 41 years (1956 to 1996) in Hluhluwe-Umfolozi Park, a mesic savanna area in South Africa. The study focuses at the landscape scale of tens of kilometres and at the medium term temporal scale of decades. Variation in fire regime was analysed in relation to variation in annual rainfall and the influence of management. The average annual area of the park that burnt was 26% and the average area of individual fires was 9.1 km2. Statistics relating to the percentage of area burnt annually, the average size of an individual burn, the mean and median fire return periods and the influence of management philosophy relative to the spatial and temporal variation in rainfall conditions are presented. Sources of ignition and implications for management are discussed.

Agriculture is the primary livelihood for most of the people in Tripura state falling under northeast, India. The type and pattern of farming in the state are significantly influenced by rainfall distribution, which varies greatly over time and across different locations. Understanding long-term seasonal and annual rainfall variability and trend patterns are essential for effective water resource management, optimizing the use of rainwater for the drinking, domestic and agricultural use. This study used non-parametric tests, notably Mann-Kendall and Sen slope estimator to determine trends in seasonal and annual rainfall data in Tripura state. Monthly meteorological data from 2000 to 2019 was used from fifteen stations across the state. The seasonal and annual rainfall time series were used for analyzing rainfall variability and detection of trend pattern. The findings revealed that the coefficients of variation for the monsoon season ranged from 15.36% to 83.95%. Kamalpur recorded the largest coefficient of variation during the northeast season (78.31%), while Kanchanpur had the lowest (42.38%). No significance trend was observed for in any of the stations during the monsoon season; however, there were indications of significance for other periods. Similarly, no significance trend was observed in any of the stations throughout the winter and summer season, except for Sabroom in summer, which showed a considerable decrease in slope. Notably, Bishalgarh experienced a significant decrease in annual rainfall (Z = -2.64, B = -84.1), while other stations did not show any significant trends. Findings of the study highlighted the need for improved rainwater harvesting, climate-resilient agricultural practices, and enhanced water storage infrastructure to mitigate risks from rainfall variability.