Upscaling models, downscaling data or the right model for the right scale of application?

Plant epidemiological models are used in a range of applications, from detailed simulation models that closely follow pathogen infection and dispersal, to generic template-based models for rapid assessment of invasive species. There is increasing interest in applying small scale models - e.g., based on tissue, organ or whole plants - using remotely collected daily data, to generate regional risk information (e.g., maps). The assumption made is that such small scale models “scale-up” appropriately to regional, continental or even global scale. However, these models are often constructed using locally collected, hourly data. By necessity data available are often at much coarser scale, both temporally and spatially, than the data used to develop the model. Computational requirements increase considerably when more detailed models that require fine resolution data (if available) are applied to large areas, while small scale models often add little useful information at these scales and may lead to error propagation. Ideally, detailed models should be used at small temporal and spatial scales and less detailed models used for larger temporal and spatial scales. This paper presents examples of different approaches for changing scales - including upscaling models, downscaling data, and developing new models - and the issues that these approaches create or solve, along with ideas about how we can ensure that the scale of model and data match the desired application.

Transcript

1. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Where do you put the uncertainty?
2. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Upscaling models, downscaling data or the right model for the right scale of application? A/Prof Adam H Sparks, Prof Karen Garrett, Prof Chris Gilligan, Prof Andrew Nelson, Dr Keith Pembleton
3. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Multi-scale influence of weather (data) on pathogens & disease development (models)
4. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Data are common Correlative models Resolution of Predictor Variables (often weather data) monthly HOURLY Extent of Predictor Variables plant GLOBAL
5. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Data becoming more common High-res processes Better understanding Resolution of Predictor Variables (often weather data) monthly HOURLY Extent of Predictor Variables plant GLOBAL
6. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Uncommon data Uncommon models Resolution of Predictor Variables (often weather data) monthly HOURLY Extent of Predictor Variables plant GLOBAL
7. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 GCM outputs, climatic averages Mainly correlative models Resolution of Predictor Variables (often weather data) monthly HOURLY Extent of Predictor Variables plant GLOBAL
8. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 How do you handle this?
9. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 APSIM# #Agricultural Production Systems sIMulator *
10. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 RICPEST (and WHEATPEST) *
11. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 EPIRICE *
12. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 SimCast *
13. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Resolution of Predictor Variables (often weather data) monthly HOURLY Extent of Predictor Variables plant GLOBAL
14. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Generate the Data
15. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 GEOSIMCAST *
16. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Modify the Model
17. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 SimCastmeta *
18. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 What’s gained or lost?
19. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 What’s Changing?
20. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
21. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
22. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
23. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
24. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
25. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081
26. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Resolution of Predictor Variables (often weather data) monthly HOURLY Extent of Predictor Variables plant GLOBAL
27. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 Acknowledgements My co-authors for the conversations, their inputs and ideas that inspired me Ross Darnell - Data61, CSIRO, Australia Emerson Del Ponte - Universidade Federal de Viçosa, Brazil Greg Forbes - CIP, Peru Robert Hijmans - UC Davis, USA Serge Savary - INRA, France Laetitia Willocquet – INRA, France
28. CRICOS QLD00244B NSW 02225M TEQSA:PRF12081 @adamhsparks