An integrated phenology modelling framework in r
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 climat...
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| Vydáno v: | Methods in ecology and evolution Ročník 9; číslo 5; s. 1276 - 1285 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
London
John Wiley & Sons, Inc
01.05.2018
Wiley Wiley-Blackwell |
| Témata: | |
| ISSN: | 2041-210X, 2041-210X |
| On-line přístup: | Získat plný text |
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| 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 (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. |
|---|---|
| AbstractList | 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
<styled-content style='fixed-case'>USA</styled-content>
National Phenology Network (
<styled-content style='fixed-case'>USA</styled-content>
‐
<styled-content style='fixed-case'>NPN</styled-content>
), the Pan European Phenology Project (
<styled-content style='fixed-case'>PEP</styled-content>
725),
<styled-content style='fixed-case'>MODIS</styled-content>
phenology (
<styled-content style='fixed-case'>MCD</styled-content>
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 (
<styled-content style='fixed-case'>RMSE</styled-content>
) 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. 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. 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. 1. 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. 2. 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. 3. 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. 4. 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. 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. |
| Author | Milliman, Tom Melaas, Eli K. Hufkens, Koen Basler, David Richardson, Andrew D. Goslee, Sarah |
| Author_xml | – sequence: 1 givenname: Koen orcidid: 0000-0002-5070-8109 surname: Hufkens fullname: Hufkens, Koen email: koen.hufkens@gmail.com organization: Harvard University – sequence: 2 givenname: David surname: Basler fullname: Basler, David organization: Harvard University – sequence: 3 givenname: Tom surname: Milliman fullname: Milliman, Tom organization: University of New Hampshire – sequence: 4 givenname: Eli K. surname: Melaas fullname: Melaas, Eli K. organization: Boston University – sequence: 5 givenname: Andrew D. surname: Richardson fullname: Richardson, Andrew D. organization: Northern Arizona University – sequence: 6 givenname: Sarah surname: Goslee fullname: Goslee, Sarah |
| BackLink | https://hal.inrae.fr/hal-02622109$$DView record in HAL https://www.osti.gov/biblio/1420342$$D View this record in Osti.gov |
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| Snippet | Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and... 1. Phenology is a first-order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and... Abstract Phenology is a first‐order control on productivity and mediates the biophysical environment by altering albedo, surface roughness length and... |
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| SubjectTerms | Albedo Biosphere Climate models Climate system Climatic data Data collection Environmental Sciences Evapotranspiration Life Sciences modelling MODIS land surface phenology PEP725 PhenoCam Phenology r package Reproducibility Surface roughness USA‐NPN Vegetation |
| Title | An integrated phenology modelling framework in r |
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