National Meteorological Agency Research Articles

Performance investigation of solar ejector cooling system: Case study area-Addis Ababa, Ethiopia

This study investigates the performance of a solar ejector cooling system under both on-design and off-design operating conditions, focusing on the case study area of Addis Ababa, Ethiopia. Data crucial to the analysis were gathered from the Ethiopian National Meteorology Agency (ENMA) over six consecutive years (2013-2018). The system's performance was evaluated through hourly and monthly analyses under on-design conditions, yielding promising results. Under on-design conditions, the system demonstrated favorable performance metrics. Specifically, at noontime in March, when the design points were set at Tg = 90°C, Tc = 30°C, and Te= 12°C, the system achieved a total hourly maximum coefficient of performance (COP) of 0.2049 and a cooling capacity of 186.9 W/m². Furthermore, the study determined that a collector area of 22.3 m² per ton of cooling capacity is required during noontime for all cooling seasons in Addis Ababa, Ethiopia. Additionally, an off-design operating condition map was developed for the system. The findings indicate that the solar ejector cooling system can effectively provide cooling during office hours (8:00-15:00) in Addis Ababa, Ethiopia.

Grid-based climate variability analysis of Addis Ababa, Ethiopia

Climate change is an intricate global environmental concern. However, its impact is more pervasive in developing nations such as Ethiopia. Hence, this manuscript examines temperature variability and the magnitude of change over 38 years in the specific case of Addis Ababa, Ethiopia. Gridded meteorological data consisting of minimum and maximum temperatures on a monthly time scale ranging from 1981 to 2018 was obtained from the National Meteorological Agency of Ethiopia. The coefficient of variation (CV) and standardized anomaly index (SAI) were used to examine the rate and extent of temperature anomalies. Geostatistical models, particularly ordinary kriging, are presented as a means of spatially interpolating temperature data. Modified Mann-Kendall test (MMK), Sen's Slope (SS) estimator, principal component analysis (PCA), and T-test were employed to determine the monthly, annual, and seasonal trends using Geospatial technologies, “R” programming, and statistical software. The findings revealed substantial spatial and temporal variation in Addis Ababa’s annual and seasonal maximum and minimum temperatures. The long-term mean annual maximum and minimum temperatures were 25.8 °C and 12.6 °C, respectively. The monthly, annual, and seasonal temperatures accrued significantly except in the months of January and September. It is noteworthy that the decadal maximum temperature has risen by 2.7 °C, while minimum temperatures have displayed comparatively minor fluctuations. Moreover, the findings also exhibited that the average maximum and minimum temperatures increased by 1.88 °C and 1.72 °C, correspondingly and the highest temperature occurred during the spring (Belg) season. The first two PCAs (Annual and Kiremt Tmax) account for 90% of the temperature variation. In conclusion, the findings underscore the pressing need for the implementation of climate adaptation strategies and policy measures, which can strengthen the city’s resilience to imminent climate change-induced hazards. The mounting temperature presents substantial challenges across various sectors within the city, emphasizing the urgency of preemptive actions to mitigate potential repercussions.

Climate, water, hydropower, wind speed and wind energy potential resources assessments using weather time series data, downscaled regional circulation models: A case study for Mono River Basin in the Gulf of Guinea region

Hydropower and wind energy are important renewable energies source in Gulf Guinea region, but they are sensitive to climate change, because the changing climate may alter atmospheric and hydrological conditions. In this study, firstly we analyzed the historical hydro-climate data in connection with changes in Mono river of Gulf Guinea region discharge and their associated impacts on hydropower generation and assessed the future hydropower availability in different climate scenarios. Secondly, statistical methods are used to examine past and future wind speed (WS) and wind energy potential. We used historical data (1981–2010) obtained from the National Meteorology Agency in Togo and Benin and a multi-model ensemble for future projections data (2021–2050) of eight selected Regional Climate Models (RCMs) under Representative Concentration Pathway (RCP) under scenarios 4.5 and 8.5. We found in the recent past years that the temperature and evaporation increase during dry season, while flows and hydropower generation decreased. The multi-model ensemble used have announced that in general, the decreasing trend of the rainfall and increasing trend of temperature is projected to be higher in RCP 8.5 than RCP 4.5, in the near future (2021–2050). Overall, the annual hydropower potential the hydropower will decrease between 8 and 19.3% under the RCP4.5 and 9 and 29% under the RCP8.5 while the monthly hydropower potential is projected to decrease by 4–41% under the RCP4.5 and by 6–47% under the RCP8.5 in the same period. Concerning the wind speed and the potential wind power, the observed results show that both the Weibull and Rayleigh approximations provided a better fit for the wind speed data. The projected of wind speed and wind power density (2021–2050) at an annual timescale is similar to the monthly timescale of wind speed and wind power density at 90 m height. The multi-model ensemble indicate that projected wind speeds (WS) are between 5 and 10 m/s under the RCP4.5 and, between 3 and 12 m/s under the RCP8.5 while wind power density is projected by 15–250 w/m2, and by 13–357 w/m2 under the RCP4.5 and under the RCP8.5, respectively. South and North of Mono basin, have higher WPD and are promising for installing wind turbines for small-scale power generation.

Towards an intelligent malaria outbreak warning model based intelligent malaria outbreak warning in the northern part of Benin, West Africa

BackgroundMalaria is one of the major vector-borne diseases most sensitive to climatic change in West Africa. The prevention and reduction of malaria are very difficult in Benin due to poverty, economic insatiability and the non control of environmental determinants. This study aims to develop an intelligent outbreak malaria early warning model driven by monthly time series climatic variables in the northern part of Benin.MethodsClimate data from nine rain gauge stations and malaria incidence data from 2009 to 2021 were extracted from the National Meteorological Agency (METEO) and the Ministry of Health of Benin, respectively. Projected relative humidity and temperature were obtained from the coordinated regional downscaling experiment (CORDEX) simulations of the Rossby Centre Regional Atmospheric regional climate model (RCA4).A structural equation model was employed to determine the effects of climatic variables on malaria incidence. We developed an intelligent malaria early warning model to predict the prevalence of malaria using machine learning by applying three machine learning algorithms, including linear regression (LiR), support vector machine (SVM), and negative binomial regression (NBiR).ResultsTwo ecological factors such as factor 1 (related to average mean relative humidity, average maximum relative humidity, and average maximal temperature) and factor 2 (related to average minimal temperature) affect the incidence of malaria. Support vector machine regression is the best-performing algorithm, predicting 82% of malaria incidence in the northern part of Benin.The projection reveals an increase in malaria incidence under RCP4.5 and RCP8.5 over the studied period.ConclusionThese results reveal that the northern part of Benin is at high risk of malaria, and specific malaria control programs are urged to reduce the risk of malaria.

Assessment of Climate Risks, Vulnerability of Urban Health Systems, and Individual Adaptation Strategies in the City of N’Djaména (Chad)

Climate variability and change are already having a negative impact on the health of tens of millions of Africans through exposure to sub-optimal temperatures and extreme weather conditions as well as increasing the range and transmission of infectious diseases. This study aims to identify climate risks and the vulnerability of health systems as well as individual coping strategies in the city of N’Djaména. To achieve this, we adopted a methodology combining both quantitative and qualitative approaches. Meteorological data on wind, temperature, and rainfall were collected at daily and monthly intervals from the National Meteorological Agency in N’Djaména. Qualitative data were collected via focus group discussions with targets of the city’s health system and quantitative data were collected from the population on the basis of oriented questionnaires. The results show that rising temperatures with heat waves, regular flooding, and strong winds are the major climate risks identified. These have numerous impacts and effects on the city’s health system due to the following vulnerability factors most recognized by city dwellers: insufficient medical equipment in health facilities (IEME), the fragile nature of people’s physiological state in the face of climatic risks (CFEP), and the failure of city sanitation strategies and policies (DSPA). This study proposes a set of recommendations for transformational adaptation of the healthcare sector, which remains vulnerable to climate risks.

Linking Climate Change Information with Crop Growing Seasons in the Northwest Ethiopian Highlands

In Ethiopia, the impacts of climate change are expected to have significant consequences for agriculture and food security. This study investigates both past (1981–2010) and future (2041–2070) climate trends and their influence on the length of the growing season (LGS) in the North-Western Ethiopian highlands. Climate observations were obtained from the National Meteorological Agency of Ethiopia, while the best performing and highest resolution models from the CMIP5 experiment and RCP6 (Coupled Models Intercomparison Project and representative concentration pathway 6) were used for the analysis. Standard statistical methods were applied to compute soil water content, evaluate climate variability and trends, and assess their impact on the length of the growing season. Maximum temperature (tasmax) and minimum temperature (tasmin) inter-annual variability anomalies show that the region has experienced cooler years than hotter years in the past. However, in the future, the coolest years are expected to decrease by −1.2 °C, while the hottest years will increase by +1.3 °C. During the major rainfall season (JJAS), the area has received an adequate amount of rainfall in the past and is very likely to receive similar rainfall in the future. On the other hand, the rainfall amount in the season February to May (FMAM) is expected to assist only with early planting. For the season October to January (ONDJ), the rainfall amount may help lengthen the growing season of JJAS if properly utilized; otherwise, the season has the potential to destroy crops before and during the harvesting time. The soil water content changes in the future remain close to those of the past period. The length of growing seasons has less variable onset and cessation dates, while in the future, the length of the growing period (LGP) of 174 to 177 days will be suitable for short- and long-cycle crops, as well as double cropping, benefiting crop production yield in the North-Western Ethiopian highlands in the future.

Trends in Extreme Precipitation Indices in Northwest Ethiopia: Comparative Analysis Using the Mann–Kendall and Innovative Trend Analysis Methods

This study analyzed long-term extreme precipitation indices using 4 × 4 km gridded data obtained from the National Meteorological Agency of Ethiopia between 1981 and 2018. The study examined trends in extreme precipitation over three districts (Lay Gayint, Tach Gayint, and Simada) in the northwestern highlands of Ethiopia. Innovative Trend Analysis (ITA) and Mann–Kendall (MK) trend tests were used to study extreme precipitation trends. Based on the ITA result, the calculated values of nine indices (90% of the analyzed indices) showed significant increasing trends (p < 0.01) in Lay Gayint. In Tach Gayint, 70% (seven indices) showed significantly increasing trends at p < 0.01. On the other hand, 60% of the extreme indices showed significant downward trends (p < 0.01) in Simada. The MK test revealed that 30% of the extreme indices had significantly increasing trends (p < 0.01) in Lay Gayint. In Tach Gayint, 30% of the extreme indices showed significant increasing trends at p < 0.05, while 10% of the extreme indices exhibited significant increasing trends at p < 0.01. In Simada, 20% of the extreme indices showed significant increasing trends at p < 0.05. Overall, the results showed that the ITA method can identify a variety of significant trends that the MK test misses.

Evaluation of historical CMIP6 model simulations and future climate change projections in the Baro River Basin

Abstract This study evaluated the performance of five Global Climate Model (GCM) outputs from the Coupled Model Intercomparison Project phase 6 (CMIP6) in reproducing the historical precipitation and temperature. Observational data from the National Meteorological Agency are used for model evaluation and bias correction. Then, the projections from representative GCMs are used to understand the future climate (2031–2060) of the Baro River Basin under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) with respect to the historical datasets (1985–2014). Statistical metrics (percent of bias, root mean square error, and coefficient of determination) are used to assess the model's performance in reproducing precipitation and temperature and Compromise Programming (CP) was used in ranking GCMs. GFDL-CM4, INM-CM5-0, and INM-CM4-8 models for precipitation; CMCC-ESM2, MRI-ESM2-0, and INM-CM4-8 for maximum temperature; and GFDL-CM4, INM-CM4-8, and INM-CM5-0 for minimum temperature were selected based on their better simulation. The projected annual precipitation shows increases of 6% under SSP2-4.5 and 16.46% under SSP5-8.5. The mean annual maximum and minimum temperature show increases of 1.43 and 1.96 °C under SSP2-4.5, and 1.81 and 3.11 °C under SSP5-8.5, respectively. Overall, the ensemble of three models outperforms the ensemble of all models for the Baro River Basin when utilising the representative GCMs.

Trends and impacts of climate change on crop production in Burkina Faso

Abstract Understanding past climate trends and their impacts in the Sahel region is fundamental for climate change (CC) adaptation and mitigation. This study analyses climate trends from 1961 to 2020 in three climatic zones in Burkina Faso and the impacts of CC on five major crops production. Long time series of daily rainfall and temperature data from National Meteorology Agency for the period 1961 to 2020 has been compiled. Crop production data (1984–2020) were retrieved from the agriculture department. Climate temporal variations in each climatic zone were analyzed using extreme climate indices and principal component analysis. Linear regression was used to assess climate impacts on crop production. The results showed a high rainfall variability and changes in temperature extremes in the three zones. The climate window, 1991–2020, was hotter than 1961–1990, while the last decade (2011–2020) was the wettest. Most climate indices (67%) showed significant correlations with crop yields. Dry spells, cool days, cold nights, average daily wet days and rainfall intensity showed positive and negative effects on maize, cowpea, millet and sorghum yields. This study highlights the importance of climate-smart policy promoting drought-resistant and short-duration varieties in addressing the adverse effects of CC on crop production.

El Niño and other climatic drivers of epidemic malaria in Ethiopia: new tools for national health adaptation plans

BackgroundEthiopia has a history of climate related malaria epidemics. An improved understanding of malaria–climate interactions is needed to inform malaria control and national adaptation plans.MethodsMalaria–climate associations in Ethiopia were assessed using (a) monthly climate data (1981–2016) from the Ethiopian National Meteorological Agency (NMA), (b) sea surface temperatures (SSTs) from the eastern Pacific, Indian Ocean and Tropical Atlantic and (c) historical malaria epidemic information obtained from the literature. Data analysed spanned 1950–2016. Individual analyses were undertaken over relevant time periods. The impact of the El Niño Southern Oscillation (ENSO) on seasonal and spatial patterns of rainfall and minimum temperature (Tmin) and maximum temperature (Tmax) was explored using NMA online Maprooms. The relationship of historic malaria epidemics (local or widespread) and concurrent ENSO phases (El Niño, Neutral, La Niña) and climate conditions (including drought) was explored in various ways. The relationships between SSTs (ENSO, Indian Ocean Dipole and Tropical Atlantic), rainfall, Tmin, Tmax and malaria epidemics in Amhara region were also explored.ResultsEl Niño events are strongly related to higher Tmax across the country, drought in north-west Ethiopia during the July–August–September (JAS) rainy season and unusually heavy rain in the semi-arid south-east during the October–November–December (OND) season. La Niña conditions approximate the reverse. At the national level malaria epidemics mostly occur following the JAS rainy season and widespread epidemics are commonly associated with El Niño events when Tmax is high, and drought is common. In the Amhara region, malaria epidemics were not associated with ENSO, but with warm Tropical Atlantic SSTs and higher rainfall.ConclusionMalaria–climate relationships in Ethiopia are complex, unravelling them requires good climate and malaria data (as well as data on potential confounders) and an understanding of the regional and local climate system. The development of climate informed early warning systems must, therefore, target a specific region and season when predictability is high and where the climate drivers of malaria are sufficiently well understood. An El Niño event is likely in the coming years. Warming temperatures, political instability in some regions, and declining investments from international donors, implies an increasing risk of climate-related malaria epidemics.

Performance assessment of bias correction methods using observed and regional climate model data in different watersheds, Ethiopia

Abstract Bias correction methods are used to compensate for any tendency to overestimate or underestimate the downscaled variables. Rainfall, maximum, and minimum temperatures are the key climate variables where the socioeconomic activities of the regions are principally based on rain-fed agriculture. This paper compares the performance of regional climate models (RCMs) and bias correction methods in Gelana and Deme watersheds in Ethiopia during the base period of 1988–2019. Observed data obtained from the Ethiopian National Meteorological Agency were used for performance evaluation of the RCM outputs. The performance of the three selected RCMs and four bias correction methods were evaluated by using four statistical indicators: Pearson correlation coefficient (R), root mean square error, Nash–Sutcliffe efficiency, and percent bias. The results show that the RACMO22T and HIRHAM5 models performed better than the RCA4 model in reproducing daily precipitation, and maximum and minimum temperatures in the Deme and Gelana watersheds. Similarly, the empirical quantile mapping method for precipitation and maximum temperature bias correction, and the distribution mapping method for minimum temperature bias correction, were well performed and preferable to adjust the climate variables of the future periods in these watersheds. Moreover, all RCMs performed better in the Deme watershed than in the Gelana watershed.

Farmers’ perceptions of climate variability and adaptation strategies in the rural areas of Dire Dawa administration, eastern Ethiopia

Climate variability has significant impact on agricultural production especially in low-income countries where agriculture largely relies on rainfall, but only a few studies explored this issue at local scale. Therefore, this study was conducted to characterize local climate and assess farmers' perceptions and adaptation strategies to climate variability in the rural areas of Dire Dawa administration. Historical rainfall and temperature data (1987–2017) were obtained from National Meteorological Agency (NMA) of Ethiopia, while data of farmers' perceptions and adaptation strategies were collected from a total of 120 household heads through survey questionnaire, key informant interviews and focus group discussions. The results revealed that the area received an average annual rainfall of 568.3 mm with main rainy season (kiremt) contributing 70.7% to annual rainfall. The earliest and latest onset dates of kiremt season were 15th of April and 2nd of August, respectively. The amount of annual and kiremt rainfall totals showed low and medium variability with a coefficient variability (CV) of 18.3% and 27.7%, respectively, whereas short rainy season (belg) rainfall had high variability with a CV of 43.9%. Climate variability perception analysis showed that an overwhelming majority of the respondents (90%) perceived a decrease in the annual rainfall and 91.7% detected an increase in annual average temperature in the study area. Farmers of the study area were well aware of the changes in rainfall and temperature and thus employed a range of adaptation practices. Soil and water conservation practices (100%), off-farm income diversification (63%), planting drought-tolerant varieties (50%) and changing of planting date (45%) were the main adaptation strategies employed in the study area to avert the negative effects of climate variability. The findings imply that the area has been experiencing palpable changes in climate variables during the study period against which farmers exercised multiple adaptation strategies. However, farmers in the area are still face hardship as a result of climate variability which necessitates improving farmers’ resilience through innovative mechanisms and better extension services.

Spatiotemporal variability and trends of rainfall and temperature in the tropical moist montane ecosystem: Implications to climate-smart agriculture in Geshy watershed, Southwest Ethiopia

This paper examines the spatio-temporal trends and variability of temperature and rainfall in Geshi Watershed of South Western Ethiopia. The available gridded daily rainfall and temperature data managed by Ethiopian National Meteorological Agency (NMA) for 1986–2020 was used. Spatial variability and temporal trends of temperature and rainfall were analyzed using XLSTAT and ArcGIS 10.8 software. The Mann-Kendall test was used for trend analysis. Variability analysis was also done using the coefficient of variation and precipitation concentration index. The study reveals constant increasing trends in rainfall; and insignificant and high variability but increasing trends of temperature. The variabilities and trends justify the spatio-temporal differences along different seasons (spring locally known as ‘belg’, summer locally known as ‘kiremt’, and annual). The watershed is characterized by high to moderate rainfall coefficient of variation, significant years of high concentration of rainfall, and substantial negative annual rainfall anomalies; that severe variability was found in the summerseason. These variabilities and trends, especially late onset and early cessation of rainfall and increase in temperature created favorable conditions for insect and disease proliferation that eventually reduces agricultural productivity. Household income, which is solely based on agricultural production, declines which in turn affects household resilience. Maladaptation in the form of deforestation, fuelwood and charcoal selling, agricultural land expansion, and practicing input-intensive agriculture that contributes to greenhouse gas (GHG) emissionsare taken as an alternative livelihood strategy. In conclusion, spatiotemporal variabilities and trends of rainfall and temperature affect agricultural productivity, reduced household resilience leading to low adaptive capacity, and increased GHG emissions, which are known to be major pillars of climate-smart agriculture (CSA). In response, area-specific CSA technologies such as small-scale irrigation, improved animal husbandry, efficient inorganic fertilizer use, using manure, crop residue management, using high-yielding improved crop varieties, and using disease-resistant varieties are recommended.

Trends in Rainfall and Temperature Extremes in Ethiopia: Station and Agro-Ecological Zone Levels of Analysis

Climate extreme events have been observed more frequently since the 1970s throughout Ethiopia, which adversely affects the socio-economic development of the country, as its economy depends on agriculture, which, in turn, relies heavily on annual and seasonal rainfall. Climate extremes studies conducted in Ethiopia are mainly limited to a specific location or watershed, making it difficult to have insights at the national level. The present study thus aims to examine the observed climate extreme events in Ethiopia at both station and agro-ecological zone (AEZ) levels. Daily rainfall and temperature data for 47 and 37 stations, respectively (1986 up to 2020), were obtained from the National Meteorology Agency (NMA). The Modified Mann–Kendall (MMK) trend test and the Theil–Sen slope estimator were employed to estimate the trends in rainfall and temperature extremes. This study examines trends of 13 temperature and 10 rainfall extreme indices using RClimDex in R software. The results revealed that most of the extreme rainfall indices showed a positive trend in the majority of the climate stations. For example, an increase in consecutive dry days (CDD), very heavy rainfall days (R20), number of heavy rainfall days (R10) and consecutive wet days (CWD) were exhibited in most climate stations. In relation to AEZs, the greater number of extreme rainfall indices illustrated an upward trend in cool and sub-humid, cool and humid, and cool and moist AEZs, a declining trend in hot arid AEZ, and equal proportions of increasing and decreasing trends in warm semi-arid AEZs. Concerning extreme temperature indices, the result indicated an increasing trend of warm temperature extreme indices and a downward trend of cold temperature extreme indices in most of the climate stations, indicating the overall warming and dryness trends in the country. With reference to AEZs, an overall warming was exhibited in all AEZs, except in the hot arid AEZ. The observed trends in the rainfall and temperature extremes will have tremendous direct and indirect impacts on agriculture, water resources, health, and other sectors in the country. Therefore, the findings suggest the need for identifying and developing climate change adaptation strategies to minimize the ill effects of these extreme climate events on the social, economic, and developmental sectors.

Spatiotemporal dynamics of vegetation in response to climate variability in the Borana rangelands of southern Ethiopia

In this paper, we analyze the spatiotemporal dynamics of vegetation and its association with climate variables in the Borana rangelands of southern Ethiopia. Moderate resolution imaging spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data, namely, MODIS/TERRA, MOD09Q1 16-day L3 Global 250 m Version 6 from National Aeronautics and Space Administration (NASA) United States Geological Survey (USGS), and climate data from 2000 to 2018 obtained from the National Meteorological Agency (NMA) of Ethiopia are used to determine the vegetation dynamics both at annual and seasonal timescales. The results indicate that the NDVI varies between .31 and .41, and the study area is dominated by sparse vegetation, especially shrubs and grasslands. The Borana rangelands were previously dominated by open grasslands, perennial herbaceous and woody vegetations, and some encroaching woody vegetations. The NDVI revealed poorer vegetation growth than the vegetation condition index (VCI) between 2000 and 2018, although variations were observed spatiotemporally. Rainfall was more highly variable both at annual and seasonal scales than the temperature during the studied years. The climate variables significantly determined the condition of vegetation, particularly during the growing seasons compared to other periods. A significant strong positive correlation was observed between the NDVI, VCI, and climate variables during the main (Belg) and short (Meher) rainy seasons in the Borana rangelands. The results of this study are beneficial for the development of proper intervention policies and provide information for sustainable rangeland management.

Recent rainfall trends between 1990 and 2020: Contrasting characteristics between two climate zones in Burkina Faso (West Africa)

The northern region (municipality of Samba) and the southwestern region (municipality of Gaoua) are agricultural production areas, which are however dependent on rain-fall. Therefore, knowledge of rainfall characteristics is essential for good agricultural planning. Thus, the objective of this study is to analyse the recent evolution of rainfall between 1990 and 2020 in Burkina Faso. To this end, monthly rainfall data were acquired from the National Meteorological Agency of Burkina Faso. Statistical methods for detecting breaks in time series, standardised rainfall indices, rainfall extremes and rainfall concentration were applied to the data collected. The study shows that annual rainfall totals are increasing between 1990 and 2020, but are marked by alternating wet and dry periods. Moreover, precipitation is more concentrated in a few months (July, August, September) in the northern region since the rainfall concentration index (RCI) is ? 25% between 1990 and 2020, and precipitation is more spread over several months (May, June, July, August, September, October) in the southwestern region, since the RCI oscillates between 18.13% and 19.09%, except for the decade 2000-2010 when the RCI is 20.3%. Therefore, the northern region is exposed to extreme precipitation (increase in total wet days (precipitation ?1 mm) (JP), frequency of intense rainfall (P95(day), intensity of rainy days (SDII (mm/day), maximum daily precipitation (PXJA (mm)) more than the southwestern region (decrease in frequency of intense rainfall, maximum daily precipitation). It is therefore imperative for the national authorities to initiate resilience actions in favour of farmers in the northern region.

Evaluating Agronomic Onset Definitions in Senegal through Crop Simulation Modeling

Rainfed agriculture in Senegal is heavily affected by weather-related risks, particularly timing of start/end of the rainy season. For climate services in agriculture, the National Meteorological Agency (ANACIM) of Senegal has defined an onset of rainy season based on the rainfall. In the field, however, farmers do not necessarily follow the ANACIM’s onset definition. To close the gap between the parallel efforts by a climate information producer (i.e., ANACIM) and its actual users in agriculture (e.g., farmers), it is desirable to understand how the currently available onset definitions are linked to the yield of specific crops. In this study, we evaluated multiple onset definitions, including rainfall-based and soil-moisture-based ones, in terms of their utility in sorghum production using the DSSAT–Sorghum model. The results show that rainfall-based definitions are highly variable year to year, and their delayed onset estimation could cause missed opportunities for higher yields with earlier planting. Overall, soil-moisture-based onset dates determined by a crop simulation model produced yield distributions closer to the ones by semi-optimal planting dates than the other definitions, except in a relatively wet southern location. The simulated yields, particularly based on the ANACIM’s onset definition, showed statistically significant differences from the semi-optimal yields for a range of percentiles (25th, 50th, 75th, and 90th) and the means of the yield distributions in three locations. The results emphasize that having a good definition and skillful forecasts of onset is critical to improving the management of risks of crop production in Senegal.

Observed time series trend analysis of climate variability and smallholder adoption of new agricultural technologies in west Shewa, Ethiopia

This study was aimed at analyzing time-series trends and variability analysis of observed rainfall and temperature records from 16 stations during the 1985–2015 period. Besides, it focused on the smallholders’ perceptions, including the analysis of their response strategies to both observed and perceived climate variability and its determinants. Both purposive and random sampling procedures were used. Mixed methods (quantitative and qualitative) of the research were used. The sample household survey method was used to collect primary data from smallholder farmers, while key informant interviews were used to gather qualitative data from the experts. A semi-structured questionnaire was used to collect quantitative data, whereas a discussion guide checklist was used to gather qualitative data. A total of 397 respondents and 24 key informants were used as sources of primary data. In addition, visits to the National Meteorology Agency of Ethiopia were made to collect recorded climate data. Descriptive statistics and econometric models were used to analyze quantitative data, whereas thematic categorization was used to analyze and report qualitative data. The results show that both the summer and annual rainfall have an increasing trend but are not statistically significant. With regards to variability, low to moderate levels of variability were recorded according to the seasons and annual rainfall. The result of the Mann-Kendall test shows that the mean minimum temperature was raised by 0.05 °C while the maximum temperature rose by 0.03 °C/30 years. The monthly maximum temperature also shows an increasing trend, with the lowest record during August (22.05 °C) and the highest in March (26.49 °C. Besides, smallholder farmers’ perceptions were in accordance with recorded station data. In response to both observed and perceived climate variability and its extremes, smallholder farmers in the study area were striving to adopt different adaptation strategies. Rain variability, frequency of extension contact, years of farming experience, access to institutions and infrastructure, wealth status, market access, and the number of active labor force within the household all hampered smallholder farmers' efforts to implement potential measures. In contrast, the high prices of chemicals and improved seed, access to credit services, land size, and limited access to accurate and timely early warning information all negatively and significantly influenced the adaptation decisions of smallholder farmers in the area. Therefore, the impacts of abnormal trends or shifts in observed rainfall and temperature patterns, along with socio-economic, technological, and behavioral factors, call for policymakers to set strategies that will enable smallholders to improve their livelihoods, ensure food security, and build resilience in the face of climatic extremes.

Effects of climatic factors on COVID-19 transmission in Ethiopia

Climatic conditions play a key role in the transmission and pathophysiology of respiratory tract infections, either directly or indirectly. However, their impact on the COVID-19 pandemic propagation is yet to be studied. This study aimed to evaluate the effects of climatic factors such as temperature, rainfall, relative humidity, sunshine duration, and wind speed on the number of daily COVID-19 cases in Addis Ababa, Ethiopia. Data on confirmed COVID-19 cases were obtained from the National Data Management Center at the Ethiopian Public Health Institute for the period 10th March 2020 to 31st October 2021. Data for climatic factors were obtained from the Ethiopia National Meteorology Agency. The correlation between daily confirmed COVID-19 cases and climatic factors was measured using the Spearman rank correlation test. The log-link negative binomial regression model was used to fit the effect of climatic factors on COVID-19 transmission, from lag 0 to lag 14 days. During the study period, a total of 245,101 COVID-19 cases were recorded in Addis Ababa, with a median of 337 new cases per day and a maximum of 1903 instances per day. A significant correlation between COVID-19 cases and humidity was observed with a 1% increase in relative humidity associated with a 1.1% [IRRs (95%CI) 0.989, 95% (0.97–0.99)] and 1.2% [IRRs (95%CI) 0.988, (0.97–0.99)] decrease in COVID-19 cases for 4 and 5 lag days prior to detection, respectively. The highest increase in the effect of wind speed and rainfall on COVID-19 was observed at 14 lag days prior to detection with IRRs of 1.85 (95%CI 1.26–2.74) and 1.078 (95%CI 1.04–1.12), respectively. The lowest IRR was 1.109 (95%CI 0.93–1.31) and 1.007 (95%CI 0.99–1.02) both in lag 0, respectively. The findings revealed that none of the climatic variables influenced the number of COVID-19 cases on the day of case detection (lag 0), and that daily average temperature and sunshine duration were not significantly linked with COVID-19 risk across the full lag period (p > 0.05). Climatic factors such as humidity, rainfall, and wind speed influence the transmission of COVID-19 in Addis Ababa, Ethiopia. COVID-19 cases have shown seasonal variations with the highest number of cases reported during the rainy season and the lowest number of cases reported during the dry season. These findings suggest the need to design strategies for the prevention and control of COVID-19 before the rainy seasons.

Integrating climate information into decision making for building resilience: A case study on farming communities in Bihar, India

Effective utilization of available weather and climate information is important to develop efficient and sustainable agricultural production systems. National meteorological agencies supported by global and regional centers are trying to generate climate forecasts tailored to sector-specific operational applications for a wide range of potential users. However, the big challenge is to develop a mechanism that will effectively communicate the information in a language that could be easily understood at different levels to obtain the maximum benefits from the information they receive.The International Research and Applications Project (IRAP) was a research experiment aimed at improving the livelihoods of rural farmers in Bihar, India, by producing and providing them with tailored weather and climate information. Forecast Application for Risk Management in Agriculture (FARM) schools were conducted to improve the awareness of farmers about the need for climate information and climate adaptation interventions which was applied for Kharif crops in summer monsoon 2018. A communication mechanism was established with the support of local partners to disseminate the tailored forecast and advisories to farmers. Assessment results revealed that the climate information is strongly valued by farmers if provided through a mechanism that they trust. More vulnerable farmers have limited access and ability to use the information, thus capacity building efforts like FARM schools are immensely helpful. Farmers recognize the critical role of climate variability in determining livelihood outcomes and actively seek out information to manage variability. This intervention helped trained farmers to better adjust their farming decisions through enhanced uptake of weather and climate information.