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

Barrett, Adam B, Duivenvoorden, Steven, Salakpi, Edward E, Muthoka, James M, Mwangi, John, Oliver, Seb and Rowhani, Pedram (2020) Forecasting vegetation condition for drought early warning systems in pastoral communities in Kenya. Remote Sensing of Environment. a111886. ISSN 0034-4257

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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 modelling methods and demonstrate high forecasting skill several weeks ahead. As a bench mark 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.

Item Type: Article
Schools and Departments: School of Global Studies > Geography
Research Centres and Groups: Sussex Sustainability Research Programme
SWORD Depositor: Mx Elements Account
Depositing User: Mx Elements Account
Date Deposited: 14 May 2020 09:06
Last Modified: 20 Aug 2020 09:47
URI: http://sro.sussex.ac.uk/id/eprint/91240

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