National Meteorological Agency Research Articles

Groundwater Potential Assessment Using Integrated Geospatial and Analytic Hierarchy Process Techniques (AHP) in Chemoga Watershed, Upper Blue Nile Basin, Ethiopia

Groundwater is an invaluable natural resource that sustains human life and supports the economic development of nations. However, its unsustainable utilization has emerged as a critical issue, particularly in developing countries. This study investigates the groundwater potential of the Chemoga watershed to address these challenges. Conventional groundwater assessments have typically relied on labor-intensive and time-consuming field surveys, which are resource-demanding and often fail to provide accurate estimates due to the inherent complexity of groundwater systems. In response, this research utilizes geospatial and analytic hierarchy process (AHP) techniques to assess groundwater potential in the Chemoga Watershed, aiming to overcome these challenges. Eight critical biophysical and environmental factors: geology, slope, rainfall, land use/land cover (LULC), soil type, elevation, lineament density, and drainage density were selected for analysis using Saaty’s AHP methodology. Data was gathered from satellite imagery, existing thematic maps, local water offices, and national meteorological agencies. The integration of these thematic maps was performed through a weighted overlay analysis using ArcGIS 10.8, which resulted in the delineation of groundwater potential zones (GWPZ). The model was validated by cross-referencing the generated GWPZ with existing data from dug wells and boreholes. The results reveal five groundwater potential zones: very high (0.73%), high (24.39%), moderate (43.38%), poor (31.25%), and very poor (0.25%). The most suitable zones are in the south, southeast, and southwest of the watershed, particularly near Debre Markos Town. These high-potential zones were validated with a significant 81.5% match to ground truth data from shallow wells. The findings of this study provide crucial insights for decision-makers, enabling the formulation of more effective groundwater management strategies. By identifying cost-effective and suitable well sites, this research contributes to ensuring a sustainable water supply for Debre Markos Town.

Development of Rainfall Intensity Duration Frequency Curves for Debre Tabor Town, Ethiopia Using Non-stationary Method

Stationary rainfall intensity duration frequency curves have historically influenced urban infrastructure designs. In contrast to the stationary model, which takes constant parameters into account throughout the observation periods, the non-stationary method takes into account changes in the extreme parameters that determine the distribution of precipitation over time. The parameters were estimated using maximum likelihood estimator method. The best model were computed using the R-studio software by comparing information criteria then model parameters, return levels, rainfall intensity are computed. The National Meteorological Agency, situated in Addis Ababa, Ethiopia, provided the essential historical rainfall data of the Debre Tabor rainfall station for this study, Tests and trends were looked for in the rainfall data. Due to its ability to produce the lowest Akaike, corrected Akaike information criteria, and diagnosis test of goodness of fitness Model Type-MV was chosen for Debre Tabor stations. The parameters of the best models were used to forecast the return levels for each of the following return periods: 2, 5, 10, 25, 50, and 100 years. Because the non-stationary technique has varied intensity levels over time, the annual maximum rainfall from the best appropriate model was calculated using its exceedance probability. Using the 95% of exceedance of the return level, the highest rainfall in each fit was determined. In comparison to the stationary model, the non-stationary model produced higher rainfall intensity values. Therefore, when developing IDF curves, the non-stationary approach should be taken into consideration.

Investigating Effective Reconnaissance Drought Index Ability to Reproduce Drought Signature over the Massili Basin (Burkina Faso)

Drought is a significant natural hazard particularly in arid and semi-arid regions where water resources management is already challenging. Burkina Faso, a landlocked country located in the Sahel region, is highly vulnerable to drought due to its arid climate. The country has experienced recurrent droughts since the 1970s, with significant impacts on its population and economy. To develop effective drought mitigation strategies, a comprehensive understanding of drought characteristics is required. This study investigates historical long-term drought trends in the Massili basin located in central Burkina Faso. For this purpose, drought features has been analyzed based on the Effective Reconnaissance Drought Index (eRDI) at various months of accumulation. To calculate the Effective Reconnaissance Drought Index for the Massili Basin, monthly precipitation (Prct), minimum temperature (Tmin), and maximum temperature (Tmax) data spanning from 1960 to 2021 were obtained from the National Meteorological Agency of Burkina Faso. The Potential evapotranspiration (ETP) was estimated using the Hargreaves method. Our findings indicate that under eRDI-3, 1964 (1.86), 2020 (1.53), and 2021 (0.63) are the wettest years, while 1963 (-0.65) and 1998 (-0.76) are the driest. Under eRDI-12, a significant portion of the values falls within the range of -0.14 to 0.03. In the case of eRDI-24, a substantial number of the values cluster between -0.08 and 0.08. This distribution highlights near-normal drought conditions (-0.99 to 0.99) as the most frequent occurrence within the watershed. The desertification of the Sahel area has been a topic of discussion for decades. However, these findings of this study reinforce the prevailing belief in a partial re-greening of the Sahel region.

Climate Change Trends And Its Effects On The Production Of Selected Food Crops: Evidence From Amaro Kelle District Of Southern Ethiopia

Amaro Kelle district of Southern Ethiopia is a rainfall-based agricultural districts that is vulnerable to the impacts of climate change and risk. This study analyzed climate change trends and effects on the selected food crops production: evidence from the Amaro Kelle district of Southern Ethiopia. Secondary data on climate change trends and food crop production from the Ethiopian national meteorological agency and the Amaro Kelle agricultural and rural development office were used for the study. Microsoft Excel was used to analyze the data on temperature, rainfall and crop production trends. The result on Trend analysis showed an increase in maximum and minimum temperatures at the rate of 0.056 and 0.0787 0C per 32 years, while there is a decrease in annual rainfall at the rate of -7.6012 mm per 32 years. These climatic conditions increased maize and sorghum productivity at the rate of 713.69 and 376.56 tonnes per 12 years, respectively, and decreased productivity of teff and wheat at the rate of -2618.5 and -30.892 tonnes per 12 years, respectively. These findings indicated that climate change positively and negatively affected the district's food crop production and food security. Finally, the study recommends that farmers in the district should focus on the cultivation of those crops that correlate positively with increasing temperature and decreasing rainfall trend in the study area.

Choices of Adaptation Strategies to Climate Variability and Its Determinants: Evidence from Farm Households of Benishangul Gumuz Regional State, Western Ethiopia

Climate variability and change is a serious threat to the livelihoods of rural communities because they are very sensitive to such changes. This study identified farmers’ choice of and factors determining adaptation strategies to climate variability and change in Benishangul Gumuz regional state, western Ethiopia which is harshly affected by climate change stresses. Both primary and secondary data were used for the study. Primary data were collected from a randomly selected 395 sample households through interviewed using field-based questionnaires and focus group discussions. Relevant secondary data were also obtained from Benishangul Gumuz region Agriculture and Natural resource Bureau, national meteorological agency and different reports. Descriptive statistics were used to describe farmers’ adaptation strategies to climate change. Multivariate probit model was estimated to identify the factors determining households’ choice of adaptation strategies to climate change. The results of the model pointed out that the likelihood of households to adopt soil and water conservation practice, crop diversity, small scale irrigation, improved crop varieties, agrochemical applications and adjusting planting date were 64.7%, 70.4%, 65.5%, 64.2%, 63.6% and 58.9% respectively. The results also indicated that the joint likelihood of using all adaptation strategies was only 2.13% and the joint likelihood of failure to adopt all of the adaptation strategies was 2.82%. Moreover, Multivariate probit model confirmed that age, sex, education status, family size, dependency ratio, total land holding, farming experience, credit access, frequency of extension contacts, distance to the market, total livestock holding, farm income and off/non-farm income have a statistically significant impact on climate adaptation strategies. Therefore, policy makers should focus on towards supporting improved extension service, facilitating the availability of credit especially to adaptation technologies, improving farmers farm income earning opportunities, improving their literacy status, and improving their access to markets.

Effectiveness of climate information services: an evaluation of the accuracy and socio-economic benefits for smallholder farmers in Niger and Mali

Climate information services are foundational means of building the farmer’s resilience. However, studies are scarce about the accuracy of climate information services in dryland regions such as the West Africa Sahel, like in Mali and Niger. Thus, this study examined the accuracy of climate forecasts and their socio-economic benefits in these two countries. For rainfall forecasts and alerts, we collected the 2022 data from the ‘SMS Sandji’ platform in Mali (Nara) and the national meteorological agency alert database in Niger (Zinder). The socio-economic benefits of climate information were determined using a sample of 900 individuals in Niger and 227 in Mali. The results indicate that both seasonal and daily climate forecasts have high to moderate accuracy from 0.7 to 0.58 for CSI and 0.11 to 0.43 for BS index in Niger, and 0.94 to 0.91 for CSI, and 0.06 to 0.25 for BS in Mali. The results of field survey show that, in general, 87 to 100% of the respondents in Niger and 100% in Mali received the seasonal forecasts. ANOVA also reveals with high significance (p value = 0.0001) that the utilization of climate information plays a crucial role in improving farmers’ average financial incomes with FCFA 24,943 per hectare at season onset to FCFA 15,355 per hectare during the cropping season, and FCFA 6204 per hectare at the end of the season, and time-saving of 36 h per hectare to 8 h per hectare, depending on the period when the information was used. Globally, this work underscores the importance of climate information services and highlights their positive socio-economic impacts to the livelihood of farmers.

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.

Variability of climatic parameters in Tiefora, Burkina Faso: Scientific aspects and farmers' perceptions

The evolution of climatic parameters is of major importance for the populations of the Sahel, and in particular Burkina Faso, whose agricultural production is predominantly rain-fed. The aim of this study is to analyze the variation in climatic parameters in the rural commune of Ti?fora in Burkina Faso. Climatic data from 1991-2020 were collected from the National Meteorological Agency (ANAM). In addition, individual questionnaire surveys were carried out among 100 local people. The results show that from 1991 to 2020, 19 out of 30 years had rainfall in excess of 1000 mm. The area experienced 16 years of rainfall surpluses, compared with 14 years of rainfall deficits. In terms of temperature, the annual average rose from 27.08?C to 28.43?C from 1991 to 2020, a difference of 1.35?C. This indicates a rise in temperature. Other climatic parameters (wind, humidity and ETP) have also varied in the area, with an increase. The people of Ti?fora perceive this rise in temperature and a fall in rainfall.

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.