Drivers and Subseasonal Predictability of Heavy Rainfall in Equatorial East Africa and Relationship with Flood Risk

Abstract

Equatorial East Africa (EEA) suffers from significant flood risks. These can be mitigated with pre-emptive action, however currently available early warnings are limited to a few days lead time. Extending warnings using subseasonal climate forecasts could open a window for more extensive preparedness activity. However before these forecasts can be used, the basis of their skill and relevance for flood risk must be established. Here we demonstrate that subseasonal forecasts are particularly skillful over EEA. Forecasts can skillfully anticipate weekly upper quintile rainfall within a season, at lead times of two weeks and beyond. We demonstrate the link between the Madden-Julian Oscillation (MJO) and extreme rainfall events in the region, and confirm that leading forecast models accurately represent the EEA teleconnection to the MJO. The relevance of weekly rainfall totals for fluvial flood risk in the region is investigated using a long record of streamflow from the Nzoia river in Western Kenya. Both heavy rainfall and high antecedent rainfall conditions are identified as key drivers of flood risk, with upper quintile weekly rainfall shown to skillfully discriminate flood events. We additionally evaluate GloFAS global flood forecasts for the Nzoia basin. Though these are able to anticipate some flooding events with several weeks lead time, analysis suggests action based on these would result in a false alarm more than 50% of the time. Overall, these results build on the scientific evidence base that supports the use of subseasonal forecasts in EEA, and activities to advance their use are discussed.

Advances in the Application and Utility of Subseasonal-to-Seasonal Predictions

“…The trial also documented examples of good predictability beyond week 2 for intraseasonal periods with rainfall above the upper tercile, generally when the MJO was predicted to be active in a rainfall-favoring phase. This supports the expectation that while, on average, skill drops sharply beyond 2 weeks lead time (MacLeod et al 2021a), an active MJO can provide a 'window of opportunity' for longer-lead warning (Kilavi et al 2018). These results give clear indications that S2S predictions can assist KMD in strengthening its season onset predictions.…”

Subseasonal Predictability of the July 2021 Extreme Rainfall Event Over Henan China in S2S Operational Models

“…The S2S prediction project releases operational forecasts and reforecasts, with particular emphasis on high‐impact weather events (Vitart et al., 2017), which provide a valuable tool for risk management and communication with policy‐makers and other stakeholders (Macleod et al., 2021; VanBuskirk et al., 2021). Assessing the predictability of extreme rainfall events can demonstrate the potential benefits of S2S model forecasts (Merryfield et al., 2020).…”

“…policy-makers and other stakeholders (Macleod et al, 2021;VanBuskirk et al, 2021). Assessing the predictability of extreme rainfall events can demonstrate the potential benefits of S2S model forecasts (Merryfield et al, 2020).…”

Drivers of Subseasonal Forecast Errors of the East African Short Rains

“…The lack of subseasonal forecast provision is unfortunate, as compared to most of the globe East Africa is a hotspot of forecast skill (de Andrade et al., 2020; Vigaud et al., 2019). The strong predictability arises in large part from the Madden‐Julian Oscillation (MJO), which shows a strong teleconnection with regional rainfall: models with skillful MJO predictions also capture the teleconnection to rainfall over East Africa (MacLeod et al., 2021).…”