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This study assesses the perception and vulnerability of the farming communities to climate change in the southwestern parts of Ethiopia. Data were collected from 442 households in four districts: Jimma Arjo, Bako Tibe, Chewaka, and Sekoru. The vulnerability of the farming communities was assessed using the households’ livelihood vulnerability index. A total of 40 indicators were applied to calculate household livelihood vulnera-bility to climate change, which were categorized into five major capitals: natural, social, financial, physical, and human. The household percep-tions of climate change results showed that there existed a statistically significant relationship between climate change perceptions and changes in rainfall pattern (75.6%, p

The impacts of various climatic conditions, such as temperature and rainfall variabilities, are very critical and sensitive to rain-fed crop production, particularly over the water stress arid and semi-arid regions of Ethiopia. This study was designed to evaluate the potential impact of climate variability and change on sorghum grain yield in the Babile district of eastern Ethiopia. The study was conducted based on observed and model-based simulated projected rainfall and temperature obtained from the Ethiopian Meteorological Institute and General Circulation Models (GCM) used by the Intergovernmental Panel on Climate Change (IPCC) of the Fifth Assessment Report CMIP5) and Agricultural Model Inter-comparison and Improvement Project (AgMIP). Three GCM models, namely GFDLESM2M, CanESM2, and HadGEM2-ES under RCP4.5, were considered to generate future climate projections for the near-term 21st century. Various univariate and multivariate statistical techniques were employed to compute and identify whether the impacts of climate variability and change on rain-fed sorghum crop performance were reasonably viable over the districts where grain yield is highly stable and productive under normal climate conditions. Our findings revealed that more stable and better rainfall performance from May to September, the season when sorghum crops are normally planted in the Babile district, was positively correlated, while the maximum and minimum temperatures of the season were negatively correlated with sorghum grain yield. A significant association has been detected between sorghum grain yield and its growing period rainfall, number of rainy days, and maximum and minimum temperature with multi-regression analysis. Thus, the variability of rainfall in August, June temperature, and the number of rainy days in September significantly impacted sorghum crop productivity. As a result, the multi-regression model adjusted R-squared indicated that 77% variance in annual sorghum yield performance was explained by rainfall and temperature conditions that prevailed during the crop growing period. During the past period, there was a significant increase in sorghum yields, which are projected to decline during the near term of the 21st century in the future. This revealed that declining and disturbed rainfall performance and increases in temperature are likely to reduce overall sorghum grain yield in the Babile district. We recommend that there is a need to enhance awareness for smallholder farmers on the adverse impact of climate variability and change on sorghum grain yield. In view of this, the farmers need to be geared toward employing climate-smart agriculture as a possible adaptation measure to reduce the negative impacts of climate variability and change on rain-fed crop production practices in the Babile district and other arid and semi-arid parts of eastern Ethiopia.

Climate change has been identified as a major challenge of rainfed agriculture. To contextualize whether there is climate change footprint, identification of rainfall and temperature trend at regional and local scale is helpful for designing long-term adaptation and mitigation strategies. The present study therefore aims to assess evidences of climate change presences in terms of, climate variability and trend in the wettest parts of Southwest Ethiopia. Daily and monthly historical gridded rainfall and temperature data (1983–2016) of ten stations were provided by Ethiopian National Metrological Agency. Moreover, long years historical recorded climate data of Nekemte and Bedele (1971–2020) and Sekoru (1981–2020) were used in the present study. Coefficient of variation, the Mann-Kendall non-parametric statistical test and Sen's slope estimator, linear regression analysis and the precipitation concentration index were applied to detect the presence of climate change in the southwest parts of Ethiopia. In this study, the trend package of open R software employed for trend identification and rate of change per year. The results indicate that the annual rainfall has declining trend at five stations with statistically significant at one station while the mean maximum and minimum temperature shows a statistically significant increasing trend at eight and six stations, respectively. At a seasonal scale, the amount of rainfall in the main rainy season (June to August) is dominated by a downward trend (eight out of ten stations) while, the autumn season (September to November) shows an increasing trend in all stations with statistically significant at one station. The precipitation concentration index analysis revealed that inconsistent and significantly irregular precipitation is observed at six stations (60%) of the ten stations. This study concludes that the climate of the wettest parts of Ethiopia is getting warmer and the amount of rainfall in the main rainy season has declined in the vast majority of the study area., Climate change; Rainfall; Southwest Ethiopia; Temperature; Rainy season; Precipitation concentration index.

Drought is an extreme event that causes great economic and environmental damage. The main objective of this study is to evaluate sensitivity, characterization and propagation of drought in the Upper Blue Nile. Drought indices: standardized precipitation index (SPI) and the recently developed standardized reconnaissance drought index (RDIst) are applied for five weather stations from 1980 to 2015 to evaluate RDIst applicability in the Upper Blue Nile. From our analysis both SPI and RDIst applied for 3-, 6-, 12 month of time scales follow the same trend, but in some time steps the RDIst varies with smaller amplitude than SPI. The severity and longer duration of drought compared with others periods of meteorological drought is found in the years 1984, 2002, 2009, 2015 including five weather stations and entire Upper Blue Nile. For drought relationships the correlation analysis is made across the time scales to evaluate the relationship between meteorological drought (SPI), soil moisture drought (SMI), and hydrological drought (SRI). We found that the correlation between three indices (SPI, SMI and SRI) at different time scales the 24-month time scale is dominant and are given by 0.82, 0.63 and 0.56.

The Upper Blue Nile region in Ethiopia, frequently affected by drought conditions and much of its water flows from highland regions to territorial countries. Hence to characterize, monitor and forecast drought, an advanced drought index which considers the combination of different climatic conditions in this region is required. The main objective of this article is to develop new drought indices by considering with and without climate signal contribution which is precious in decision making of policy makers and scientific community. In order to estimate the quality and performance of the developed indices we implemented statistical methods of RMSE, MAE and bias with the existing index of SPI across different time scales. Infact we developed two drought indices by considering SST into the meteorological variables (MVDIstS) and without considering SST (MVDIst0). Both drought indices show drought severity, larger duration than that of SPI.

Projections of future climate change trends in four urban centers of southwest Ethiopia were examined under a high Representative Concentration Pathways (RCP8.5) scenario for near- (2030), mid- (2050), and long-term (2080) periods based on high-resolution (0.220) Coordinated Regional Climate Downscaling Experiment (CORDEX) for Africa data. The multi-model ensemble projects annual maximum and minimum temperatures increasing by 0.047 °C per year (R2 > 0.3) and 0.038 °C per year (R2 > 0.7), respectively, with the rates increased by a factor of 10 for decadal projections between the 2030s and 2080s. The monthly maximum temperature increase is projected to be 1.41 °C and 2.82 °C by 2050 and 2080, respectively. In contrast, the monthly minimum temperature increase is projected to reach +3.2 °C in 2080. The overall seasonal multi-model ensemble average shows an increment in maximum temperature by +1.1 °C and +1.9 °C in 2050 and 2080, with the highest change in the winter, followed by spring, summer, and autumn. Similarly, the future minimum temperature is projected to increase across all seasons by 2080, with increases ranging from 0.4 °C (2030s) to 3.2 °C (2080s). All models consistently project increasing trends in maximum and minimum temperatures, while the majority of the models projected declining future precipitation compared to the base period of 1971–2005. A two-tailed T-test (alpha = 0.05) shows a significant change in future temperature patterns, but no significant changes in precipitation were identified. Changes in daily temperature extremes were found in spring, summer, and autumn, with the largest increases in extreme heat in winter. Therefore, our results support proactive urban planning that considers suitable adaptation and mitigation strategies against increasing air temperatures in urban centers in southwest Ethiopia. Future work will examine the likely changes in temperature and precipitation extremes.

Ethiopia experiences significant climate-induced drought and stress on crop and livestock productivity, contributing to widespread food insecurity. Here, we present subseasonal crop water stress analyses that indicate degrading, growing conditions along Ethiopia’s eastern highlands, including productive and populated highland regions. These seasonally shifting areas of increasing water stress stretch from the north to south across eastern Ethiopia, intersecting regions of acute food insecurity and/or high population. Crop model simulations indicate that between 1982 and 2014, parts of eastern Amhara and eastern Oromia experienced increasing water deficits during the critical sowing, flowering, and ripening periods of crop growth. These trends occurred while population in these regions increased by 143% between 2000 and 2015. These areas of enhanced crop water stress in south-central Ethiopia coincide with regions of high population growth and ongoing crop extensification. Conversely, large regions of relatively unpopulated western Ethiopia are becoming wetter. These areas may therefore be good targets for agricultural development.

Demonstrating Effects of Current and Projected Rainfall Characteristics on Wheat yield has been investigated in Sinana district for the period 1995-2016. Data on rainfall and crop yield for the period 1995-2016 were obtained from National Meteorological Agency and Sinana District Agricultural Offices, respectively. Following data quality checking, rainfall data (current and future), correlation and regression studies were analyzed using Statistical software like Instat V3.36 and SPSS V20. Downscaling the output of CSIRO-Mk3.6.0 GCM model (daily rainfall data) for RCP8.5 emission scenario using a web based software tool (Marksim GCM) for the period 2020-2049 were used to determine seasonal total rainfall and ascertain its impact on yield. This study used mean, coefficient of variation, correlation and regression analysis to ascertain the relation, cause and effect relationship between rainfall characteristics and wheat yields. The results indicated that the mean onset date of the main rainy season (JJAS) for Robe and Sinana station was June 30 and July 3. Furthermore, the results of Pearson Correlation Coefficients indicated that kiremt rainfall total (JJAS) had moderate positive relationship (r = 0.499) with wheat yield in the study area. It was also observed that nearly fifty percent of total variance of crop yield is explained jointly by kiremt rainfall total and rainy day (R2 value was 47.9%). The result of projected wheat yield indicated that there will be a slight decrease in wheat yield (qt/ha) after 2030 years due to the impact of expected weakening of kiremt rainfall total. Taking in to account the above findings, it could be suggested that the farmers’ community will be encouraged encouraged to utilize timely climate information issued from National Meteorological Agency of Ethiopia (NMA) and other centers for farm level decision to enhance their crop production.

On a planet with a population of more than 7 billion, how do we identify the millions of drought-afflicted people who face a real threat of livelihood disruption or death without humanitarian assistance? Typically, these people are poor and heavily dependent on rainfed agriculture and livestock. Most live in Africa, Central America, or Southwest Asia. When the rains fail, incomes diminish while food prices increase, cutting off the poorest (most often women and children) from access to adequate nutrition. As seen in Ethiopia in 1984 and Somalia in 2011, food shortages can lead to famine. Yet these slow-onset disasters also provide opportunities for effective intervention, as seen in Ethiopia in 2015 and Somalia in 2017. Since 1985, the U.S. Agency for International Development’s Famine Early Warning Systems Network (FEWS NET) has been providing evidence-based guidance for effective humanitarian relief efforts. FEWS NET depends on a Drought Early Warning System (DEWS) to help understand, monitor, model, and predict food insecurity. Here we provide an overview of FEWS NET’s DEWS using examples from recent climate extremes. While drought monitoring and prediction provides just one part of FEWS NET’s monitoring system, it draws from many disciplines—remote sensing, climate prediction, agroclimatic monitoring, and hydrologic modeling. Here we describe FEWS NET’s multiagency multidisciplinary DEWS and Food Security Outlooks. This DEWS uses diagnostic analyses to guide predictions. Midseason droughts are monitored using multiple cutting-edge Earth-observing systems. Crop and hydrologic models can translate these observations into impacts. The resulting information feeds into FEWS NET reports, helping to save lives by motivating and targeting timely humanitarian assistance.

[1] Operational rainfall forecasts using the analog method have been issued in Ethiopia since 1987. We evaluate the performance of the forecast system for February–May and June–September rainy seasons over the period 1999–2011. Verification is performed using rainfall data obtained from Ethiopian meteorological stations covering eight homogeneous rainfall regions used in the forecasts. The results reveal that forecasts issued by the National Meteorological Agency (NMA) of Ethiopia, for the past 12 years have a weak positive skill for all eight regions compared with climatology. In terms of ranked probability skill scores, the values are all lower than 10% indicating that the forecast skill is modest. The results further suggest that the forecasting system has bias toward forecasting near-normal conditions and has problems in capturing below average events. In contrast, the forecast has some positive skills in ranking the wet years of February–May season, particularly over the regions where there is high seasonal rainfall variability with significantly positive rank correlations for the above average years. For the main season, however, the forecast is not able to rank wet years or dry years. The extreme low and high rainfall events are mostly missed by the forecast scheme. The results indicate rather low forecast skill for extreme rainfall events in both seasons. Generally, the results indicate that NMA's forecasts have low but positive skill as it is common with results from other forecasting systems for the Greater Horn of Africa region. The underforecasting of dry events is the most serious shortcoming of the system.

In 2011, drought in the Horn of Africa again made news headlines. This study aims to quantify the meteorological component of this and other drought episodes in Ethiopia since 1971. A monthly precipitation data set for 14 homogeneous rainfall zones was constructed based on 174 gauge observations. As a measure of drought, the Standardized Precipitation Index was calculated on seasonal, annual and biannual timescales for each zone. The results point to 2009 as a year of exceptionally widespread drought. All zones experienced some degree of drought at the annual scale, although in most zones, previous droughts were more extreme. On the national level, 2009 was the second driest year in the period, surpassed only by the historic drought year 1984. Linear regression analysis indicates a general decline in precipitation in southern Ethiopia, during both February–May and June–September. In the central and northern highlands, the trend analysis did not provide evidence of similar tendencies. However, spring droughts have appeared more frequently in all parts of Ethiopia during the last 10–15 years than during the previous decades. acceptedVersion

In much of Ethiopia, similar to the Sahelian countries to its west, rainfall from June to September contributes the majority of the annual total, and is crucial to Ethiopia’s water resource and agriculture operations. Drought-related disasters could be mitigated by warnings if skillful summer rainfall predictions were possible with sufficient lead time. This study examines the predictive potential for June–September rainfall in Ethiopia using mainly statistical approaches. The skill of a dynamical approach to predicting the El Niño–Southern Oscillation (ENSO), which impacts Ethiopian rainfall, is assessed. The study attempts to identify global and more regional processes affecting the large-scale summer climate patterns that govern rainfall anomalies. Multivariate statistical techniques are applied to diagnose and predict seasonal rainfall patterns using historical monthly mean global sea surface temperatures and other physically relevant predictor data. Monthly rainfall data come from a newly assembled dense network of stations from the National Meteorological Agency of Ethiopia. Results show that Ethiopia’s June–September rainy season is governed primarily by ENSO, and secondarily reinforced by more local climate indicators near Africa and the Atlantic and Indian Oceans. Rainfall anomaly patterns can be predicted with some skill within a short lead time of the summer season, based on emerging ENSO developments. The ENSO predictability barrier in the Northern Hemisphere spring poses a major challenge to providing seasonal rainfall forecasts two or more months in advance. Prospects for future breakthroughs in ENSO prediction are thus critical to future improvements to Ethiopia’s summer rainfall prediction.

Background: The first part of this study aimed to develop a model for Anopheles gambiae s.l. with separate parametrization schemes for Anopheles gambiae s.s. and Anopheles arabiensis. The characterizations were constructed based on literature from the past decades. This part of the study is focusing on the model’s ability to separate the mean state of the two species of the An. gambiae complex in Africa. The model is also evaluated with respect to capturing the temporal variability of An. arabiensis in Ethiopia. Before conclusions and guidance based on models can be made, models need to be validated. Methods: The model used in this paper is described in part one (Malaria Journal 2013, 12:28). For the validation of the model, a data base of 5,935 points on the presence of An. gambiae s.s. and An. arabiensis was constructed. An additional 992 points were collected on the presence An. gambiae s.l.. These data were used to assess if the model could recreate the spatial distribution of the two species. The dataset is made available in the public domain. This is followed by a case study from Madagascar where the model’s ability to recreate the relative fraction of each species is investigated. In the last section the model’s ability to reproduce the temporal variability of An. arabiensis in Ethiopia is tested. The model was compared with data from four papers, and one field survey covering two years. Results: Overall, the model has a realistic representation of seasonal and year to year variability in mosquito densities in Ethiopia. The model is also able to describe the distribution of An. gambiae s.s. and An. arabiensis in sub-Saharan Africa. This implies this model can be used for seasonal and long term predictions of changes in the burden of malaria. Before models can be used to improving human health, or guide which interventions are to be applied where, there is a need to understand the system of interest. Validation is an important part of this process. It is also found that one of the main mechanisms separating An. gambiae s.s. and An. arabiensis is the availability of hosts; humans and cattle. Climate play a secondary, but still important, role. publishedVersion

Agriculture is one of the sectors most vulnerable to climate change impact. The main economy of the study area is mainly dependent on rain-fed agriculture, the failure or the goodness of seasonal and annual rainfall amount is extremely critical because the study areas socio economic functioning particularly, food production. Changes in annual, June–September and March–May rainfall and rainy days herein (defined as a day with rainfall greater than 0.1 mm) have been analyzed based on 4 stations located in North Oromia zone (R4) of different climatic zones for the common period 1982–2014. The aim of this study was to characterize the climate of the study areas. This study was conducted in Northern Oromia region part of Ethiopia. Historical climate data (1982-2014), were collected from National Meteorological Agency. Instat+v.3.37 and Excel 2010 were used for analysis of rainfall and temperature variability and trend analysis. Time series anomaly plots for temperature showed that the mean maximum temperature over Filiklik, Werejarso, Gerbeguracha and Fitche has increased by about 0.5 to 2oc, 0.5 to 1.55oc and 0.5 to 1.5oc and 0.5 to 1.5 oc respectively. Whereas minimum temperature also increased by 0.5 to 2oc, 0.5 to 1.5oc and 0.5 to 1.5oc over Filiklik, Gerbeguracha and Fitche respectively but in the past few recent years minimum temperature showed slight decrement at Werejarso. While the rainfall amount and number of rainy days showed slight variability during the period of 1982-2014. The number of rainy days shows that decrement by 5.8% and 4.2% over Filiklik and Werejarso. On the hand numbers of rainy days were stationary over Gerbeguracha and Fitche for the last thirty two years. Strengthen of further research on the impact of climate change and variability on different socio-economic activities of the societies is very crucial.

While El Niño is known to cause failure of Kiremt (boreal summer) rainfall in Ethiopia, the mechanisms are not fully understood. Here we use the ECHAM5 Atmospheric General Circulation Model to investigate the physical link between Pacific sea surface temperature (SST) anomalies and Kiremt rainfall. We compare ECHAM5 simulations forced with reconstructed SST data, to gauge-based rainfall observations and atmospheric reanalysis for the time period of 1961–2009. We perform composite analysis and sensitivity experiments driven only with equatorial Pacific SST anomalies. Our results show warm SST anomalies in the equatorial Pacific drive a corresponding large-scale circulation anomaly with subsidence over Ethiopia in dry Kiremt seasons. Horizontal wind fields show a slow-down of the whole Indian monsoon system with a weaker Tropical Easterly Jet and a weaker East African Low-Level Jet in these summers. These changes can be seen as an anomalous circulation cell over northern Africa with westerlies at 100–200 hPa and easterlies below 500 hPa. Surface easterlies might reduce the moisture inflow from the Atlantic and Congo basin into Ethiopia. This and the general subsidence over the region could explain the reduction in Kiremt rainfall. Our results suggest up to 50% of the Kiremt rainfall anomalies is driven by equatorial Pacific SST variability. publishedVersion