Sound climate risk management requires access to the best available decision-relevant climate information and the ability to use such information effectively. The availability and access of such information and the ability to use it is challenging, particularly throughout rural Africa. A gap analysis published by the International Research Institute for Climate and Society (IRI) and the Global Climate Observing System (GCOS) in 2005 explored these challenges in detail and identified four key gaps: (i) gaps in integration of climate into policy; (ii) gaps in integration of climate into practice at scale; (iii) gaps in climate services; and (iv) gaps in climate data. Though this document was published nearly nine years ago, the gaps it highlighted are still relevant today. In the last decade, IRI has been making efforts to address these critical issues in a systematic way through projects and partnerships in Africa. This paper describes IRI’s efforts in Ethiopia, a country particularly prone to climate related risks. Here we outline a creative solution to bridge the gaps in the availability, access and use of national climate information through the Enhancing National Climate Services (ENACTS) initiative. We then discuss how policy and practice has changed as a result of IRI engagement in the development of climate services in the water, public health and agricultural sectors. The work in Ethiopia is indicative of the efforts IRI is implementing in other countries in Africa and in other parts of the world.

Ethiopian agriculture and Nile River flows are heavily dependent upon the Kiremt season (June–September) precipitation in the upper Blue Nile basin, as a means of rain-fed irrigation and streamflow contribution, respectively. Climate diagnostics suggest that the El Niño–Southern Oscillation phenomenon is a main driver of interannual variability of seasonal precipitation in the basin. One-season (March–May) lead predictors of the seasonal precipitation are identified from the large-scale ocean–atmosphere–land system, including sea level pressures, sea surface temperatures, geopotential height, air temperature, and the Palmer Drought Severity Index. A nonparametric approach based on local polynomial regression is proposed for generating ensemble forecasts. The method is data driven, easy to implement, and provides a flexible framework able to capture any arbitrary features (linear or nonlinear) present in the data, as compared to traditional linear regression. The best subset of predictors, as determined by the generalized cross-validation (GCV) criteria, is selected from the suite of potential large-scale predictors. A simple technique for disaggregating the seasonal precipitation forecasts into monthly forecasts is also provided. Cross-validated forecasts indicate significant skill in comparison to climatological forecasts, as currently utilized by the Ethiopian National Meteorological Services Agency. This ensemble forecasting framework can serve as a useful tool for water resources planning and management within the basin.

Study region: The Blue Nile river basin in East Africa. Study focus: This review paper presents the current understanding of hydrological extremes in the Blue Nile River basin under historic and future climate conditions, largely drawing on research outputs over the past decade. Characteristics of precipitation and streamflow extremes, including historic trends and future projections, are considered. New hydrological insights: The review illustrates some discrepancy among research outputs. For the historical context, this is partially related to the period and length of data analyzed and the failure to consider the influence of multi-decadal oscillations. Consequently, we show that annual cycle of Blue Nile flow has not changed in the past five decades. For the future context, discrepancy is partially attributable to the various and differing climate and hydrological models included and the downscaling techniques applied. The need to prudently consider sources of uncertainty and potential causes of bias in historical trend and climate change impact research is highlighted.status: publishe

Defining homogeneous precipitation regions is fundamental for hydrologic applications, yet nontrivial, particularly for regions with highly varied spatial–temporal patterns. Traditional approaches typically include aspects of subjective delineation around sparsely distributed precipitation stations. Here, hierarchical and nonhierarchical (k means) clustering techniques on a gridded dataset for objective and automatic delineation are evaluated. Using a spatial sensitivity analysis test, the k-means clustering method is found to produce much more stable cluster boundaries. To identify a reasonable optimal k, various performance indicators, including the within-cluster sum of square errors (WSS) metric, intra- and intercluster correlations, and postvisualization are evaluated. Two new objective selection metrics (difference in minimum WSS and difference in difference) are developed based on the elbow method and gap statistics, respectively, to determine k within a desired range. Consequently, eight homogenous regions are defined with relatively clear and smooth boundaries, as well as low intercluster correlations and high intracluster correlations. The underlying physical mechanisms for the regionalization outcomes not only help justify the optimal number of clusters selected, but also prove informative in understanding the local- and large-scale climate factors affecting Ethiopian summertime precipitation. A principal component linear regression model to produce cluster-level seasonal forecasts also proves skillful.

The upper Blue Nile Basin in Ethiopia harbors considerable untapped potential for irrigation and large-scale hydropower development and expansion. Numerous water resources system models have been developed to evaluate these resources, yet often fail to adequately address critical aspects, including the transient (e.g., filling) stages of reservoirs, relevant streamflow retention policies and downstream consequences, construction staggering, and the implications of stochastic modeling of variable climate and climate change. This omission has clear economic impacts on benefits and costs and could be pivotal in national policy and decision making. The Investment Model for Planning Ethiopian Nile Development dynamic water resources system model is outlined and applied to address these aspects. For the hydropower and irrigation development projects specified, model results disregarding transient and construction stagger aspects demonstrate overestimations of $6 billion in benefits and 170% in downstream flows ...