Applying Astronomy Data Analysis to enhance disaster forecasting

Lead Research Organisation: University of Sussex
Department Name: Sch of Mathematical & Physical Sciences

Abstract

Livestock accounts for 37.5% of Kenya's land area, 12% of its GDP and 40% of its agricultural sector, but is susceptible to frequent droughts and periods of overgrazing. In this context, this project will assess the potential of new Earth observation datasets to deliver near real-time monitoring and prediction of useful and accessible biomass for pastoralism.

Drought and flood events are a major threat in sub-Saharan Africa (SSA) causing substantial losses of life, assets and livelihoods, and weakened national economic performance. Hazard early warning and disaster risk preparedness actions can be effective in reducing these losses (as much as 20 times more effective than post-disaster relief). In this project we will apply advanced data analysis techniques used in astronomy to facilitate improved hazard early warning models in Kenya.

This pilot project brings together STFC funded Astronomers at University of Sussex with a strong track record in data analysis with a world-leading interdisciplinary team in Climate Change and Developmental studies.

Several global to regional pasture monitoring systems exist that are based on Earth observation data, which are used in early warning systems. These systems tend to rely on coarse resolution data (250m - 8km) to provide near real-time information on vegetation health, and are combined with mechanistic models or expert knowledge to forecast seasonal outcomes. However, this spatial scale is unable to adequately distinguish pastures from scrublands, small farms and woody vegetation, and is unable to provide meaningful information on the onset of vegetation stresses. This project will use data from the Copernicus Sentinel mission (data provided every 5-12 days at 10-20m resolution) with key information on vegetation state.

The ultimate outcome of this research will support pastoralists communities Kenya, to decide the suitability and location of pastureland for their various livestock through: a) improved understanding of spatio-temporal distribution of pastures; b) improved understanding of ecological changes and resilience of pastures; and c) near-future predictions of pasture suitability. This will enhance their livelihood resilience in the wake of large and extensive droughts, overgrazing, and land cover change.

Planned Impact

The ultimate impact will be improve the livelihood resilience of pastoralists communities Kenya.

We are undertaking research that will improve the early warning system that will predict events and their consequences to their livelihoods. Our pathways to impact cover how such early warning systems can be improved and implemented in real-time systems. The pathway also indicate how those systems can be incorporportated into response strategies. Responses to warnings are found to be >20 more effective than post-disaster relief.

Publications

10 25 50
 
Description AstroCast 
Organisation International Committee of the Red Cross
Department Kenya Red Cross Society
Country Kenya 
Sector Charity/Non Profit 
PI Contribution We provide Vegetation Condition Index forecasts algorithms, code, and forecast skill assessment. We currently provide weekly VCI forecast reports. We provide training to build capacity in our partners in general and specifically to implement our models.
Collaborator Contribution NDMA expect to put our forecasts into their monthly drought bulletins. RCMRD expect to use our processing code to produce VCI forecasts for Kenya and East African countries.
Impact The paper.
Start Year 2018
 
Description AstroCast 
Organisation National Drought Management Authority
Country Kenya 
Sector Public 
PI Contribution We provide Vegetation Condition Index forecasts algorithms, code, and forecast skill assessment. We currently provide weekly VCI forecast reports. We provide training to build capacity in our partners in general and specifically to implement our models.
Collaborator Contribution NDMA expect to put our forecasts into their monthly drought bulletins. RCMRD expect to use our processing code to produce VCI forecasts for Kenya and East African countries.
Impact The paper.
Start Year 2018
 
Description AstroCast 
Organisation Regional Centre For Mapping Resource For Development
Department RCMRD
Country Kenya 
Sector Academic/University 
PI Contribution We provide Vegetation Condition Index forecasts algorithms, code, and forecast skill assessment. We currently provide weekly VCI forecast reports. We provide training to build capacity in our partners in general and specifically to implement our models.
Collaborator Contribution NDMA expect to put our forecasts into their monthly drought bulletins. RCMRD expect to use our processing code to produce VCI forecasts for Kenya and East African countries.
Impact The paper.
Start Year 2018