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 We have applied novel data processing and machine-learning techniques to Earth Observation data to measure and forecast an indicator of drought the vegetation condition index (VCI). Our forecasts are more accurate than any that were available before. These drought indicators are relevant for e.g. pastoralists in Kenya and our forecasts have benefits for drought Early Warning Systems.
Exploitation Route We are working with agencies in Kenya and beyond to incorporate our methods in their early warning systems
Sectors Agriculture, Food and Drink,Environment

URL http://astrocast.org.uk/
 
Description Droughts are a recurring hazard in sub-Saharan Africa, that can wreak huge socioeconomic costs. Acting promptly through early warning systems (EWS) can provide substantial mitigation. Sussex research has improved the way drought forecast information is produced and used in Africa. Novel satellite-telescope data analysis by astrophysicists, collaborating with geographers and the Kenya National Drought Management Authority, has produced new, highly accurate, short-term forecasts. Through partnership between researchers, forecast producers and decision-makers these forecasts are now included in EWS that are used by government agencies, national and international NGOs and populations at risk to trigger earlier and more appropriate action. This has supported improved capacity in these organisations to manage and interpret data, and a shift to anticipatory responses in near term disaster risk management, with associated funding mechanisms.
First Year Of Impact 2019
Sector Agriculture, Food and Drink,Environment
Impact Types Societal

 
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 provided motivation for the work in our paper. NDMA put our forecasts into their monthly drought bulletins as part of their Early Warning System. RCMRD use our processing code to produce VCI forecasts for Kenya and East African countries. KRCS disseminate our forecasts and provide stakeholder engagement.
Impact The paper. VCI forecasts in NDMA drought bulletins. VCI reports with forecasts.
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 provided motivation for the work in our paper. NDMA put our forecasts into their monthly drought bulletins as part of their Early Warning System. RCMRD use our processing code to produce VCI forecasts for Kenya and East African countries. KRCS disseminate our forecasts and provide stakeholder engagement.
Impact The paper. VCI forecasts in NDMA drought bulletins. VCI reports with forecasts.
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 provided motivation for the work in our paper. NDMA put our forecasts into their monthly drought bulletins as part of their Early Warning System. RCMRD use our processing code to produce VCI forecasts for Kenya and East African countries. KRCS disseminate our forecasts and provide stakeholder engagement.
Impact The paper. VCI forecasts in NDMA drought bulletins. VCI reports with forecasts.
Start Year 2018