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Exploring differences in spatial patterns and temporal trends of phenological models at continental scale using
Hamed Mehdipoor1, Raul Zurita-Milla2, Ellen-Wien Augustijn2
1Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, PO Box 217, 7500 AE, Enschede, the Netherlands. h.mehdipoor@utwente.nl.
International Journal of Biometeorology
|November 14, 2019
Summary
Different phenological models, despite similar accuracy metrics like root-mean-square error (RMSE), produce significantly different plant development patterns and trends. This highlights the need for new validation metrics for climate impact models.
Area of Science:
- Phenology and Climate Science
- Ecological Modeling and Data Analysis
Background:
- Phenological models are crucial for understanding weather and climate impacts on plant development.
- Model accuracy is often assessed using root-mean-square error (RMSE), but this metric may not capture spatial and temporal variations.
Purpose of the Study:
- To analyze and compare spatial patterns and temporal trends from various temperature-based phenological models.
- To evaluate the impact of different model types on estimating plant leaf onset dates and trends.
Main Methods:
- Calibration of extended spring indices, thermal time, and photothermal time models using lilac leaf onset data (1961-1994).
- Validation using volunteered phenological observations and gridded temperature data.
- Application of the two most accurate models to assess leaf onset patterns and trends in the conterminous US (2000-2014).
Main Results:
- Extended spring indices and thermal time models showed similar RMSE, approximately 2 days lower than other models.
- Despite similar RMSE, model-predicted leaf out dates varied by up to 11 days, and trends by a week per decade.
- The statistical significance of trends was highly dependent on the specific phenological model used.
Conclusions:
- Standard accuracy metrics like RMSE are insufficient for fully validating phenological models.
- There is a critical need for developing new metrics to quantify differences in spatial patterns and temporal trends derived from different models.
- Improved validation metrics are essential for accurately assessing climate change impacts on plant phenology.
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