Sara E. Miller

Kenya is highly dependent on precipitation for both food and water security. Farmers and pastoralists rely on rain to provide water for crops and vegetation to feed herds. As such, precipitation forecasts can be useful tools to inform decision makers and potentially allow the preparation for such events as drought. This study assessed the predictability of a seasonal forecast (CFSv2) and a short-term precipitation forecast (CHIRPS-GEFS) over Kenya. The short-term forecast was assessed on its ability to predict the onset date of the rainy season, and the skill of the seasonal forecast in predicting abnormal precipitation patterns. CHIRPS-GEFS provided a useful starting point to estimate the onset date, but during the long rains in the southwest, where agriculture is concentrated, differences between the predicted and actual onset dates were large (over 20 days). Assessments for CFSv2 generally displayed lower forecast skill over highlands and coastal regions at a seasonal scale. The CFSv2 forecast skill varied widely over individual months and lead times, but over whole rainy seasons, CFSv2 was more skillful than a random forecast at all lead times in the major agricultural areas of Kenya. This research fills a critical research and application gap in understanding the forecast precipitation skill for onset and sub-seasonal prediction.

In arid and semi-arid regions of Eastern and Southern Africa, drought can be devastating to pastoralists who depend on healthy vegetation for their herds. The Kenya Livestock Insurance Program (KLIP) addresses this challenge through its insurance program that relies on a vegetation index product derived from eMODIS NDVI (enhanced Normalized Difference Vegetation Index). Insurance payouts are triggered when index values fall below a certain threshold for a Unit Area of Insurance (UAI). The objective of this study is to produce an updated, cloud-based NDVI product, potentially allowing for earlier payouts that may help herders to prevent, minimize, or offset drought-induced losses. The new product, named reNDVI (rapid enhanced NDVI), provides an updated cloud filtering algorithm and brings the entire processing chain to the cloud. Access to the scripts used for the processing described and resulting data is openly available. To test the performance of the new product, we provide a robust evaluation of reNDVI and eMODIS NDVI and their derived payout indices against historical drought, payouts provided, and mortality data. The implications of potential payout differences are also discussed. The products show good comparability; the monthly average NDVI per UAI has correlation values over 0.95 and MAPD under 5% for most UAIs. However, there are moderate differences when assessing year-to-year payout amounts triggered. Because the payouts are currently calculated based on the 20th and first percentile of index values from 2003–2016, payouts are very sensitive to even small changes in NDVI. Where livestock mortality was available, payouts for reNDVI and eMODIS had similar correlations (r = 0.453 and r = 0.478, respectively) with mortality rates. Therefore, with the potential reduced latency and updated cloud filtering, the reNDVI product could be a suitable replacement for eMODIS in the Kenya Livestock Insurance Program. The updated reNDVI product shows promise as a vegetation index that could address a pressing drought insurance challenge.