continental phenology trends and modeling · 2017-02-06 · study questions / goals •perform...
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![Page 1: Continental Phenology Trends and Modeling · 2017-02-06 · Study Questions / Goals •Perform Retrospective Trend Analysis –How has timing of SOS changed across N. America during](https://reader033.vdocument.in/reader033/viewer/2022060209/5f04425f7e708231d40d180b/html5/thumbnails/1.jpg)
Using three decades of Landsat data
to characterize trends and
interannual variation in
boreal and temperate forest
phenology
Eli Melaas, Damien Sulla-Menashe,
& Mark Friedl
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Background
• Spring phenology is: – Coherent fingerprint of climate change
– Tightly coupled with land-atmosphere exchange of carbon and water
• Most retrospective analyses of continental trends in phenology use AVHRR – Potential AVHRR issues:
• Snow cover
• Land cover heterogeneity
• Low sensor quality
– Resolved using Landsat TM/ETM+ data: • Fmask screening of snow/clouds
• Medium resolution data
• High sensor quality
• Disturbance detection
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Wang et al. 2011 PNAS
Sp
rin
g N
et E
cosy
stem
Ex
chan
ge
(gC
m-2
)
Spring phenology date (DOY)
Keenan et al. 2014 NCC
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• Annual timing of spring is driven by combination of: – cold Twinter (chilling)
– warm Tspring (forcing)
– photoperiod
• Zohner et al. (2016) Nature Climate Change: – Species from shorter winters (≤ 6 months
with Tavg < 5°C) rely on photoperiodism
– Photoperiod may only constrain climate shifts in spring phenology at lower latitudes
Background
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Study Questions / Goals
• Perform Retrospective Trend Analysis
– How has timing of SOS changed across N. America during 1982-2013?
– What is the statistical significance of this trend?
• Test Zohner et al.’s working hypothesis:
– Warm temperate species spring phenology driven by photoperiod
– Cold temperate/boreal species spring phenology driven by spring warming
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Study Region / Methods
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• Exclude Landsat pixels with:
- Inconsistent seasonality
- Insufficient EVI amplitude
- Agriculture (NLCD / EOSD)
One Landsat pixel
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1. Spring Warming
(Sarvas et al. 1972)
Phenology Models
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R f =
28.4
1+ exp(3.4 - 0.185*T )T > 0
0 T £ 0
ì
íï
îï
SOS = R fp0=12.5
200
å
R f =T - 5 T > 0
0 T £ 0
ìíî
SOS = R fFT
230
å
R f =
28.4
1+ exp(3.4 - 0.185*T )
daylength
10
æ
èç
ö
ø÷
3.9
T > 0
0 T £ 0
ì
íï
îï
SOS = R fp0=11.7
761
å
2. Freeze-Thaw
(Barr et al. 2004; Kim et al. 2012)
3. Photoperiod (Blumel & Chmielewski 2012)
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Methods
Assessment
• Compare Landsat phenology with ground observations (Flux tower photosynthesis)
Trend Analysis
• Estimate magnitude (Theil-Sen) and significance (Mann-Kendall) of SOS trend across Landsat pixels within 500 m grid cells for each sidelap region
Modeling
• Train phenology models using PhenoCam and surface meteorological data
• Run models using NARR 2 m Tair data (32 km) and compare predictions with Landsat SOS
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Location of PhenoCam sites
Sample NARR data
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Landsat Phenology Assessment
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Melaas et al. 2016 RSE
EOS
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Results: SOS Trends
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−6 −4 −2 0 2 4 6
1982−2013 Trend in AGDD for DOY 91−150 (GDD/yr)
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Results
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Results
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Take Home Messages:
• Divergence in long-term SOS trend across North American forests
• Our evidence supports Zohner et al.’s hypothesis that warm temperate species phenology will be photoperiod-constrained