A canary, a coal mine, and imperfect data: determining the efficacy of open-source climate change models in detecting and predicting extreme weather events in Northern and Western Kenya

Abstract

Climate models, by accurately forecasting future weather events, can be a critical tool in developing countermeasures to reduce crop loss and decrease adverse effects on animal husbandry and fishing. In this paper, we investigate the efficacy of various regional versions of the climate models, RCMs, and the commonly available weather datasets in Kenya in predicting extreme weather patterns in northern and western Kenya. We identified two models that may be used to predict flood risks and potential drought events in these regions. The combination of artificial neural networks (ANNs) and weather station data was the most effective in predicting future drought occurrences in Turkana and Wajir with accuracies ranging from 78 to 90%. In the case of flood forecasting, isolation forests models using weather station data had the best overall performance. The above models and datasets may form the basis of an early warning system for use in Kenya’s agricultural sector.

Hydrologic Extremes in a Changing Climate: a Review of Extremes in East Africa

“…However, the application of these approaches in the Eastern African region is challenged by different factors. For instance, hydroclimatic extremes prediction and assessment are challenged by limited data availability, process understanding, modeling and prediction methods, and definition of extreme events that can affect the outcome of extreme value analysis [65,89,90].…”