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Connecting the Next Generation of Seasonal and Sub-seasonal Forecasts to Vegetation Stress Indices

Authors: Diego Pons*, Columbia University
Topics: Climatology and Meteorology, Agricultural Geography, Food Systems
Keywords: Seasonal Climate Forecasts, Climate Variability, Climate Change, Vegetation Stress Monitoring, Latin America
Session Type: Paper
Day: 4/8/2020
Start / End Time: 1:45 PM / 3:00 PM
Room: Virtual Track 3
Presentation File: No File Uploaded

The new seasonal and sub-seasonal forecast systems developed by the meteorological institutes around the world has opened new avenues for state-of-the-art research and applied science that has the potential to transform policy-making processes in these territories and help local governments achieve their developmental goals. Agriculture for food production is a large component of the economies in several countries currently facing climate variability and change. Often, the agricultural landscapes in rural areas of these countries are dependent on precipitation for crop irrigation. In the face of on-going climate variability and change, decision-making processes at both institutional and farm level are becoming more complex. An informed risk-management strategy in climate-vulnerable agricultural landscapes could benefit from anticipating the effects of a potential drought to food production. Current efforts for monitoring vegetation stress in agricultural areas around the world have been successfully implemented and used by leading global development (FAO) and humanitarian (WFP) agencies. Yet, these tools currently monitoring agricultural production could benefit from incorporating seasonal and sub-seasonal precipitation forecasts to move from monitoring to forecasting agricultural droughts months in advance, which could inform risk-management strategies at both farm and institutional level. In this paper, we show the advantages of calibrating independent dynamic models from the NMME to generate an ensemble with enhance predictability skill to forecast precipitation at a country and sub-country level. We then use the case of Guatemala to demonstrate the usefulness of these forecasts at sub-country level to anticipate vegetation stress in agricultural landscapes with diverse crop calendars.

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