Estimating disease risk at the whole plant level with General Circulation Models

Area of Science:

• Agricultural Meteorology
• Plant Pathology
• Climate Modeling

Background:

• General Circulation Models (GCMs) offer limited plant-level weather data for climate change impact assessments.
• Global climate change may increase extreme weather events, impacting plant production.
• Accurate plant-scale weather data is crucial for predicting plant and disease development.

Purpose of the Study:

• To develop and test hierarchical mesoscale modeling approaches for downscaling weather information to the whole-plant level.
• To improve the estimation of plant-environment interactions, particularly surface wetness duration (SWD), under changing climate conditions.
• To enhance the assessment of climate change impacts on plant disease risk.

Main Methods:

• Developed two hierarchical mesoscale modeling systems: Localized Mesoscale Forecast System (LMFS) and Canopy-Mesoscale Forecast System (CMFS).
• Tested downscaling methods using vineyard data, focusing on surface wetness duration (SWD).
• Compared model-derived SWD and atmospheric variables against on-site sensor data and observations.

Main Results:

• Successfully downscaled weather information to the whole-plant level using mesoscale models.
• Demonstrated the utility of LMFS and CMFS in simulating spatially and temporally variable SWD.
• Validated model forecasts against on-site observations, showing promise for extension to GCMs.

Conclusions:

• Hierarchical mesoscale modeling provides a viable solution for obtaining plant-scale weather data from global climate models.
• This approach significantly improves the assessment of climate change impacts on plant disease risk.
• The developed methods can be extended to GCMs for more accurate predictions of canopy-level climate variables.