TY - JOUR
T1 - An integrated phenology modelling framework in r
AU - Hufkens, Koen
AU - Basler, David
AU - Milliman, Tom
AU - Melaas, Eli K.
AU - Richardson, Andrew D.
N1 - Funding Information:
The Richardson Lab acknowledges support from the NSF Macrosystems Biology programme (awards EF-1065029 and EF-1702697). D.B. acknowledges the Harvard Forest Bullard Fellowship programme. K.H. acknowledges support from the LabEx COTE MicroMic project and BELSPO Brain programme (project BR/175/A3/COBECORE). We thank ORNL, the Daymet team and Michele M. Thornton for the continued support in developing daymetr. We thank the World Climate Research Program's Working Group on Coupled Modelling, which is responsible for CMIP, and the climate modelling groups for producing and making available their model output. We are grateful to the US Department of Energy's Program for Climate Model Diagnosis and Inter-comparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. Climate scenarios used were from the NEX-GDDP dataset, prepared by the Climate Analytics Group and NASA Ames Research Center using the NASA Earth Exchange, and distributed by the NASA Center for Climate Simulation (NCCS). We thank the NASA Earth Exchange project for making these data available. Finally, we thank our many collaborators, including site PIs and technicians, for their efforts in support of PhenoCam.
Publisher Copyright:
© 2018 The Authors. Methods in Ecology and Evolution © 2018 British Ecological Society
PY - 2018/5
Y1 - 2018/5
N2 - 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 (PEP725), MODIS phenology (MCD12Q2) 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.
AB - 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 (PEP725), MODIS phenology (MCD12Q2) 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.
KW - MODIS land surface phenology
KW - PEP725
KW - PhenoCam
KW - USA-NPN
KW - modelling
KW - phenology
KW - r package
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U2 - 10.1111/2041-210X.12970
DO - 10.1111/2041-210X.12970
M3 - Article
AN - SCOPUS:85041923561
SN - 2041-210X
VL - 9
SP - 1276
EP - 1285
JO - Methods in Ecology and Evolution
JF - Methods in Ecology and Evolution
IS - 5
ER -