Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya

TL;DR

This paper introduces a novel forecasting method for vegetation condition indicators in Kenya, enabling early drought warnings that can significantly aid in disaster preparedness and mitigation for vulnerable pastoral communities.

Contribution

It presents a new approach combining linear autoregression and Gaussian process models to forecast vegetation conditions several weeks in advance, improving early warning systems for droughts.

Findings

High forecasting accuracy several weeks ahead

89% hit rate for drought alert 4 weeks in advance

Low false alarm rates around 4-6%

Abstract

Droughts are a recurring hazard in sub-Saharan Africa, that can wreak huge socioeconomic costs.Acting early based on alerts provided by early warning systems (EWS) can potentially provide substantial mitigation, reducing the financial and human cost. However, existing EWS tend only to monitor current, rather than forecast future, environmental and socioeconomic indicators of drought, and hence are not always sufficiently timely to be effective in practice. Here we present a novel method for forecasting satellite-based indicators of vegetation condition. Specifically, we focused on the 3-month Vegetation Condition Index (VCI3M) over pastoral livelihood zones in Kenya, which is the indicator used by the Kenyan National Drought Management Authority(NDMA). Using data from MODIS and Landsat, we apply linear autoregression and Gaussian process modeling methods and demonstrate high forecasting skill several weeks ahead. As a benchmark we predicted the drought alert marker used by NDMA (VCI3M<35). Both of our models were able to predict this alert marker four weeks ahead with a hit rate of around 89% and a false alarm rate of around 4%, or 81% and 6% respectively six weeks ahead. The methods developed here can thus identify a deteriorating vegetation condition well and sufficiently in advance to help disaster risk managers act early to support vulnerable communities and limit the impact of a drought hazard.