CROP YIELDS AND FLOOD IMPACTS DERIVED FROM LARGE-SCALE CLIMATE INDICES
Description
Probabilities of high-impact extremes (such as extended floods or crop yield failures) are related to governing large-scale climatic patterns, such as the North Atlantic Oscillation (NAO). NAO is indicative for anomalously wet or dry conditions in specific European segments and seasons.
Advanced machine learning has been used to relate influential climate patterns to the occurrence of both floods and crop yield anomalies in different European case study areas. Flood occurrence and damage in Southern and Eastern Europe, for instance, are significantly related to winter and summer NAO.
(Potential) Categories of users
Water resource managers, agricultural companies, local authorities
(Potential) applications to decision making in the water sector:
- Flood predictions based on large-scale climate indices
- Advance prediction of anomalies in the production of certain crops (for instance, for some regions where sugar beet is harvested, predictions can be made up to six months before the start of harvesting season.
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Added value of this modelling approach/model/results
The work shifts the focus from forecasting hazards towards forecasting impacts; extensions to include additional bottom-up local knowledge and stakeholder information are ongoing.
What decision can be improved with this modelling approach:
Flood protection measures, agricultural practices, crop irrigation decisions