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ARS Home » Northeast Area » Beltsville, Maryland (BARC) » Beltsville Agricultural Research Center » Hydrology and Remote Sensing Laboratory » Research » Publications at this Location » Publication #374754
Research Project: Integrating Remote Sensing, Measurements and Modeling for Multi-Scale Assessment of Water Availability, Use, and Quality in Agroecosystems

Location: Hydrology and Remote Sensing Laboratory

Title: Why predict climate hazards if we need to understand impacts? Mobile technologies could put humans back into the equation

Author
item ENENKEL, M. - Columbia University - New York
item BROWN, M.E. - University Of Maryland
item VOGT, J.V. - European Commission-Joint Research Centre (JRC)
item MCCARTY, J.L. - University Of Miami
item BELL, A. - New York University
item GUHA-SAPIR, D. - Universite Catholique
item DORIGO, W.A. - Vienna University Of Technology
item VASILAKY, K. - Columbia University - New York
item SVOBODA, M. - University Of Nebraska
item BONIFACIO, R. - World Food Programme
item Anderson, Martha
item FUNK, C. - Collaborator
item OSGOOD, D. - Columbia University - New York
item HAIN, C. - Nasa Marshall Space Flight Center
item VINCK, P. - Collaborator

Submitted to: Climatic Change
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/25/2020
Publication Date: 10/9/2020
Citation: Enenkel, M., Brown, M., Vogt, J., Mccarty, J., Bell, A., Guha-Sapir, D., Dorigo, W., Vasilaky, K., Svoboda, M., Bonifacio, R., Anderson, M.C., Funk, C., Osgood, D., Hain, C., Vinck, P. 2020. Why predict climate hazards if we need to understand impacts? Mobile technologies could put humans back into the equation. Climatic Change. 162:1161–1176. https://doi.org/10.1007/s10584-020-02878-0.
DOI: https://doi.org/10.1007/s10584-020-02878-0

Interpretive Summary: Drought is a well-studied climatic phenomenon with many indices that track the drivers and development of drought using geospatial datasets. Such datasets include precipitation and soil moisture deficits, and impacts on vegetation cover and crop conditions. Less well-tracked is geospatial information on socioeconomic impacts of drought, yet such information is critical for planning effective global drought humanitarian response and famine early warning systems. This paper describes an approach for collecting information on socioeconomic and environmental outcomes of drought via mobile technologies. Machine learning approaches can be implemented to identify and predict hot spots of impact using climate and vegetation forecasts as a trigger, toward better targeting of future response.

Technical Abstract: Virtually all climate monitoring and forecasting efforts concentrate on hazards rather than impacts, which are a priority for planning response activities and the evaluation of mitigation strategies. Effective disaster risk management strategies need to consider the prevailing ‘human terrain’ to predict who is at risk and how communities will be affected. There has been little effort to align the spatiotemporal granularity of socioeconomic assessments with environmental monitoring, leaving methodical approaches like machine learning virtually untapped for pattern recognition of extreme climate impacts. While the request for “better” socioeconomic data is not new, we highlight the need to collect and analyse environmental and socioeconomic data together and discuss novel strategies for coordinated data collection via mobile technologies from a drought risk management perspective. A better temporal, spatial and contextual understanding of socioeconomic impacts of extreme climate conditions will help to establish complex causal pathways and quantitative proof about climate-attributable livelihood impacts.