An integrated phenology modelling framework in r

Journal Article · Tue Feb 13 00:00:00 EST 2018 · Methods in Ecology and Evolution (Online)

DOI:https://doi.org/10.1111/2041-210X.12970· OSTI ID:1420342

Hufkens, Koen  ^[1]; Basler, David ^[2]; Milliman, Tom ^[3]; Melaas, Eli K. ^[4]; Richardson, Andrew D. ^[5]; Goslee, ed., Sarah

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1. INRA UMR ISPA Villenave d'Ornon France, Department of Applied Ecology and Environmental Biology Ghent University Aquitaine Belgium, Department of Organismic and Evolutionary Biology Harvard University Cambridge MA USA
2. Department of Organismic and Evolutionary Biology Harvard University Cambridge MA USA
3. Earth Systems Research Center University of New Hampshire Durham NH USA
4. Department of Earth &, Environment Boston University Boston MA USA
5. Department of Organismic and Evolutionary Biology Harvard University Cambridge MA USA, School of Informatics, Computing and Cyber Systems Northern Arizona University Flagstaff AZ USA, Center for Ecosystem Science and Society Northern Arizona University Flagstaff AZ USA

Abstract Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and evapotranspiration. Accurate and transparent modelling of vegetation phenology is therefore key in understanding feedbacks between the biosphere and the climate system. Here, we present the phenor r package and modelling framework. The framework leverages measurements of vegetation phenology from four common phenology observation datasets, the PhenoCam network, the USA National Phenology Network ( USA ‐ NPN ), the Pan European Phenology Project ( PEP 725), MODIS phenology ( MCD 12Q2) combined with (global) retrospective and projected climate data. We show an example analysis, using the phenor modelling framework, which quickly and easily compares 20 included spring phenology models for three plant functional types. An analysis of model skill using the root mean squared ( RMSE ) error shows little or no difference regardless of model structure, corroborating previous studies. We argue that addressing this issue will require novel model development combined with easy data assimilation as facilitated by our framework. In conclusion, we hope the phenor phenology modelling framework in the r language and environment for statistical computing will facilitate reproducibility and community driven phenology model development, in order to increase their overall predictive power, and leverage an ever growing number of phenology data products.

• Sponsoring Organization: USDOE
• OSTI ID: 1420342
• Journal Information: Methods in Ecology and Evolution (Online), Journal Name: Methods in Ecology and Evolution (Online) Vol. 9 Journal Issue: 5; ISSN 2041-210X
• Publisher: Wiley-Blackwell
• Country of Publication: United Kingdom
• Language: English