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Timely and accurate agricultural impact assessments for droughts are critical for designing appropriate interventions and policy. These assessments are often ad hoc, late, or spatially imprecise, with reporting at the zonal or regional level. This is problematic as we find substantial variability in losses at the village-level, which is missing when reporting at the zonal level. In this paper, we propose a new data fusion method—combining remotely sensed data with agricultural survey data—that might address these limitations. We apply the method to Ethiopia, which is regularly hit by droughts and is a substantial recipient of ad hoc imported food aid. We then utilize remotely sensed data obtained near mid-season to predict substantial crop losses of greater than or equal to 25% due to drought at the village level for five primary cereal crops. We train machine learning models to predict the likelihood of losses and explore the most influential variables. On independent samples, the models identify substantial drought loss cases with up to 81% accuracy by mid- to late-September. We believe the proposed models could be used to help monitor and predict yields for disaster response teams and policy makers, particularly with further development of the models and integration of soon-to-be available high-resolution, remotely sensed data such as the Harmonized Landsat Sentinel (HLS) data set. [ABSTRACT FROM AUTHOR]

This paper builds on existing theory and proposes a framework to identify vulnerability to climate change in food systems by examining historic cases where common environmental problems caused famine. Cases presented are (1) Ireland's Potato Famine, (2) El Niño induced famines during the Colonial period, and (3) Ethiopia between 1965 and 1997. Three factors stand out as common in each. Prior to each famine: (1) there were very few ways that people could obtain a living in the worst affected regions; (2) livelihoods in famine stricken communities came to depend on highly specialized agro-ecosystems that were sensitive to environmental change; (3) institutions failed to provide adequate safety nets to protect livelihoods from failure. This analysis suggests that vulnerability to climate change in food systems can be assessed by looking at agro-ecosystems, livelihoods and institutions. Local conditions, however, mean that ways of measuring these three factors will vary from place to place. As a result, direct comparisons are difficult. By conceptualizing these three variables as the axes of a three dimensional "vulnerability" space, it is possible to compare regions and look at trends over time by studying the paths through this "space" as traced by changes at the agroecosystem, livelihood, and institutional scale. [ABSTRACT FROM AUTHOR]

The potential impacts of climate change on wheat were assessed for Kulmsa area in central highlands of Ethiopia using the Agricultural Production Systems sIMulator (APSIM)—wheat model. The objectives were to (i) evaluate the performance of wheat under increased temperatures with or without changes in rainfall and carbon dioxide (CO2) levels and (ii) assess the response of different wheat cultivars to projected future climates under improved management practices (optimal nitrogen rate, planting date, and density) (IMPs). The model was first calibrated and used to identify ranges of IMPs. Then, the model was used to (i) conduct sensitivity analysis of wheat in response to assumed elevated temperature (T), with and without change in rainfall and CO2 level, and to (ii) evaluate average effect of future worst-case climate change scenarios as predicted by an ensemble of three global climate models (GCM) under highest Representative Concentration Pathways (RCP8.5) for three future time frames, NF (2010–2039), MC (2040–2069), and EC (2070–2099). The simulation was evaluated for three wheat cultivars ("early", "medium", and "late") relative to their corresponding baseline values under the selected IMPs. The baseline climate was represented by the long-term (1980–2009) dataset and CO2 of 360 μmol/mol. Results showed that increased temperature above 4 °C alone had strong negative impacts on yields under the IMPs when CO2 staying constant at the baseline level. In contrast, climate change simulations with GCM projections under IMPs and elevated CO2 effect showed that wheat yield remained unchanged (− 0.4 to + 9%) for all three genotypes. This suggests that the IMPs and elevated CO2 were able to reduce the negative effect of elevated T on wheat yield as T stress did not go beyond optimal T range for wheat. Overall, climate change may not reduce wheat production in the climate of the location of study in the near future, midcentury, or end century. [ABSTRACT FROM AUTHOR]

The Greater Horn of Africa region increasingly experiences high risk of water scarcity. A combination of frequent droughts, rapid population growth and rising urbanisation has reduced streamflow and intensified water abstraction, causing water and food shortages. Estimates of future streamflow changes in the region have so far been highly uncertain and evaluations using ground-based measurements are still limited. Here, future streamflow changes are estimated using a distributed hydrological model forced with an ensemble of high-resolution climate simulations produced using the European community Earth-System Model v3.1. The simulated streamflow is evaluated using observed data from 29 stations from river basins across different climate zones in the region. Evaluation results show large sub-regional variations in the performance of simulated streamflow. The sign and magnitude of future streamflow changes vary between climate simulations and river basins, highlighting the uncertainties in the hydrologic projections. Overall, the streamflow projections indicate large (seasonal, long-term mean and extreme) streamflow decreases for all major rivers in Ethiopia and increases in the equatorial parts of the region at the end of the century. The ensemble mean shows a 10 to 25% decrease in the long-term mean flow in Ethiopia and a 10% increase in the equatorial part of the region in 2080s. Similarly, there is a substantial change in high flows in 2080s, with up to − 50% reduction in the northern and 50% increase in the equatorial parts of the region. These findings are critical because the rivers provide water supply to a rapidly changing socio-economy of the region. [ABSTRACT FROM AUTHOR]

There is increasing concern that avoiding climate change impacts will require proactive adaptation, particularly for infrastructure systems with long lifespans. However, one challenge in adaptation is the uncertainty surrounding climate change projections generated by general circulation models (GCMs). This uncertainty has been addressed in different ways. For example, some researchers use ensembles of GCMs to generate probabilistic climate change projections, but these projections can be highly sensitive to assumptions about model independence and weighting schemes. Because of these issues, others argue that robustness-based approaches to climate adaptation are more appropriate, since they do not rely on a precise probabilistic representation of uncertainty. In this research, we present a new approach for characterizing climate change risks that leverages robust decision frameworks and probabilistic GCM ensembles. The scenario discovery process is used to search across a multi-dimensional space and identify climate scenarios most associated with system failure, and a Bayesian statistical model informed by GCM projections is then developed to estimate the probability of those scenarios. This provides an important advancement in that it can incorporate decision-relevant climate variables beyond mean temperature and precipitation and account for uncertainty in probabilistic estimates in a straightforward way. We also suggest several advancements building on prior approaches to Bayesian modeling of climate change projections to make them more broadly applicable. We demonstrate the methodology using proposed water resources infrastructure in Lake Tana, Ethiopia, where GCM disagreement on changes in future rainfall presents a major challenge for infrastructure planning. [ABSTRACT FROM AUTHOR]

Despite large increases in national cereal production in recent decades, Ethiopia continues to experience regular acute food insecurity crises, often associated with drought events. However, the meteorology of these events is poorly defined and local populations frequently experience food insecurity crises in years when national rainfall and cereal production totals are high. Therefore, looking at national, or even to some extent sub-national, rainfall variability is a misappropriation of climate as a causal factor in food insecurity in Ethiopia. The distinction between 'drought' as catch-all driver of food insecurity and a more nuanced view of the relationship between rainfall variability and food security is necessary both for addressing food insecurity now and for interpreting long-term climate model projections. The on-going recurrence of acute food insecurity is a feature of the heterogeneity of climate and climate variability in Ethiopia, but only in the context of a food system dominated by smallholder farming and climate-sensitive livelihoods. Climate variability has the greatest adverse impact in the most marginal livelihood zones in the drier east of the country. Increasing the resilience of smallholder farmers and pastoralists to climate variability and improvements in early warning and disaster risk response could reduce the frequency and severity of food security crises. However, unless the food system in Ethiopia undergoes transformational adaptation, food insecurity crises will continue to occur, and the opportunity to achieve zero hunger by 2030 will be missed. [ABSTRACT FROM AUTHOR]

Nearly all of Ethiopia's agriculture is dependent on rainfall, particularly the amount and seasonal occurrence. Future climate change predictions agree that changes in rainfall, temperature, and seasonality will impact Ethiopia with dramatic consequences. When, where, and how these changes will transpire has not been adequately addressed. The objective of our study was to model how projected climate change scenarios will spatially and temporally impact cereal production, a dietary staple for millions of Ethiopians. We used Maxent software fit with crop data collected from household surveys and bioclimatic variables from the WorldClim database to develop spatially explicit models of crop production in Ethiopia. Our results were extrapolated to three climate change projections (i.e., Canadian Centre for Climate Modeling and Analysis, Hadley Centre Coupled Model v3, and Commonwealth Scientific and Industrial Research Organization), each having two emission scenarios. Model evaluations indicated that our results had strong predictability for all four cereal crops with area under the curve values of 0.79, 0.81, 0.79, and 0.83 for teff, maize, sorghum, and barley, respectively. As expected, bioclimatic variables related to rainfall were the greatest predictors for all four cereal crops. All models showed similar decreasing spatial trends in cereal production. In addition, there were geographic shifts in land suitability which need to be accounted for when assessing overall vulnerability to climate change. The ability to adapt to climate change will be critical for Ethiopia's agricultural system and food security. Spatially explicit models will be vital for developing early warning systems, adaptive strategies, and policy to minimize the negative impacts of climate change on food production. [ABSTRACT FROM AUTHOR]