A new study in Journal of Hydrometeorology explored how accurately modeling vegetation can improve flash drought predictions. The researchers found that integrating real-time satellite data on vegetation into models offers more accuracy than older models that rely on less timely or less accurate plant data. Compared to models that use predicted plant growth, models that incorporate satellite measurements of plant cover improve tracking of drought duration. This model improvement is crucial for agriculture, as better prediction can help farmers protect their crops and manage water resources more efficiently.
Authors Ali Fallah, Christopher Skinner, Mathew Barlow, and Laurie Agel of the University of Massachusetts Lowell along with Justin Mankin of Dartmouth College received funding for this work from the Climate Program Office’s Modeling, Analysis, Predictions, and Projections (MAPP) program and the National Integrated Drought Information System (NIDIS). These researchers continue to work toward a MAPP and NIDIS initiative to improve how we understand and anticipate U.S. droughts in the context of weather and climate changes. This work aims to link science to practical NIDIS applications.
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