Does objective cluster analysis serve as a useful precursor to seasonal precipitation prediction at local scale? Application to western Ethiopia

Abstract

Abstract. Prediction of seasonal precipitation can provide actionable information to guide management of various sectoral activities. For instance, it is often translated into hydrological forecasts for better water resources management. However, many studies assume homogeneity in precipitation across an entire study region, which may prove ineffective for operational and local-level decisions, particularly for locations with high spatial variability. This study proposes advancing local-level seasonal precipitation predictions by first conditioning on regional-level predictions, as defined through objective cluster analysis, for western Ethiopia. To our knowledge, this is the first study predicting seasonal precipitation at high resolution in this region, where lives and livelihoods are vulnerable to precipitation variability given the high reliance on rain-fed agriculture and limited water resources infrastructure. The combination of objective cluster analysis, spatially high-resolution prediction of seasonal precipitation, and a modeling structure spanning statistical and dynamical approaches makes clear advances in prediction skill and resolution, as compared with previous studies. The statistical model improves versus the non-clustered case or dynamical models for a number of specific clusters in northwestern Ethiopia, with clusters having regional average correlation and ranked probability skill score (RPSS) values of up to 0.5 and 33 %, respectively. The general skill (after bias correction) of the two best-performing dynamical models over the entire study region is superior to that of the statistical models, although the dynamical models issue predictions at a lower resolution and the raw predictions require bias correction to guarantee comparable skills. Primer on prediction models and cluster analysisSeasonal precipitation prediction can provide potentially actionable information to guide management of various sectoral activities. For instance, precipitation prediction is often translated into a hydrological forecast, which can be used to optimize reservoir operations, provide early flood or drought warning, inform waterway navigation, etc. As a primary input to soil moisture, precipitation prediction is also essential to agricultural management -farmers can take advantage of anticipated preferable climatic conditions or avoid unnecessary costs under expected undesirable conditions. Two types of models are commonly used for seasonal precipitation prediction: statistical and dynamical. Dynamical models, such as general circulation models (GCMs), include complex physical climate processes, while statistical models are purely data driven, relating observations and hydroclimate variables directly.While both modeling approaches have produced skillful seasonal predictions for a variety of applications (e.g., Barrett, 1993;Hammer et al., 2000;Shukla et al., 2016), each has noteworthy drawbacks. Dynamical models often require a great amount of time to build and parameterize, whereas statistical models require c...

A Grand Prediction: Communicating and Evaluating 2018 Summertime Upper Blue Nile Rainfall and Streamflow Forecasts in Preparation for Ethiopia's New Dam

“…In this region, most rainfall occurs during the summer kiremt season, which extends from June-September. Strong spatial and temporal variability in rainfall exists across the mountainous basin, with an average seasonal rainfall of 760 mm (Zhang et al, 2018). Given the relative wealth in water resources, a majority of the nation's agriculture and infrastructure are located in this basin (Awulachew et al, 2007).…”