environmental controls and predictions of african vegetation dynamics martin jung, eric thomas...
TRANSCRIPT
![Page 1: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/1.jpg)
Environmental controls and predictions of African vegetation
dynamics
Martin Jung, Eric Thomas
Department of Biogeochemical Integration
![Page 2: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/2.jpg)
Africa
• 2nd largest continent (30 x 106 km2)
• Lots of people (~1 billion)
• Comparatively little known
• All about water
• hyper-arid to tropical climate
• Hot-spots of interannual variability
• Vulnerable to climate change
![Page 3: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/3.jpg)
Research questions
• Can we predict (forecast) seasonal and interannual vegetation dynamics?
• Which factors control vegetation dynamics (and where)?
• Can we generate an objective functional classification of the African vegetation?
• What causes large interannual variability?
![Page 4: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/4.jpg)
Approach
Meteorology(7 x 4 + 7 x
24 )
Land use (8)
Soil (10)
Remotely sensed fAPAR
Remotely sensed fAPAR
Mean annual
Mean seasonal
cycle
Anomalies
Raw
Random forests
Lag
Cumulative Lag
Lag
Cumulative Lag
Variable selection based on Genetic Algorithm
![Page 5: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/5.jpg)
Data & Methods
• Vegetation state = f(climate, land cover, soil)• Vegetation state: monthly FAPAR (1999-2009) from
SeaWiFS/MERIS (Gobron et al 2006, 2008)
• f: Random Forrests algorithm (Breimann 2000)
• Variable selection: Guided hybrid genetic algorithm (Jung & Zscheischler 2013)
• Climate: ERA-Interim (bias corrected), TRMM (rainfall)• Land cover: SYNMAP (Jung et al 2006) + FAO based land use
(Ramankutty & Foley 1999, updated)
• Soil: global harmonized world soil data base• Fire: GFED (Van der Werf et al)
![Page 6: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/6.jpg)
Variables
• Climate: Tmin, Tmax, Precip, WAI, Rh, Rg, PET– Normal, mean annual, mean seasonal cycle,
anomalies– For normal and anomalies lag variables upto a lag of
6 months: lag, cumulative lag
Land use fractions: evergreen forest, deciduous forest, shrub, C3 grass, C4 grass, C3 crop, C4 crop, barren
• Soil: sand, silt clay, plant awailable water, Corg• Elevation, burned area
![Page 7: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/7.jpg)
Experimental set-up
Variable selection using GHGA based on 500 randomly chosen locations
Training period: 1999-2004; Validation period: 2005-2009;
Leave ‘one year out’ forward run using selected variables (1999-2009);
20 Random Forests with 48 trees each using 1000 random locations
Evaluation of predicted fAPAREstimation of variable importances
![Page 8: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/8.jpg)
ResultsOverall MEF = 0.91
![Page 9: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/9.jpg)
Approach fails in some locations of massive transformations
MEF low, RMS high
MEF high, RMS low
MEF low, RMS low
MEF intermediate, RMS intermediate
Color composite of MEF and RMS
![Page 10: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/10.jpg)
![Page 11: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/11.jpg)
![Page 12: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/12.jpg)
A little excursion…
![Page 13: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/13.jpg)
Simple model based on soil moisture indicator explains 79%
of variance
![Page 14: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/14.jpg)
Very small effect of fire on FAPAR anomalies
![Page 15: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/15.jpg)
Back to the original model…
![Page 16: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/16.jpg)
Local variable importance (sensitivity)
![Page 17: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/17.jpg)
A functional classification
RGB of first 3 PCAs of variable importance (77% of variance
explained)
K-means clustering of variable importance (10 classes)
![Page 18: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/18.jpg)
Just climate discriminates the groups!
* Groups = f(land cover, soil, climate)* 59 candidate predictors* Stratified random sampling (100 per class)* 6 variables selected (Overall accuracy of 78%)
Nor
mal
ized
var
iabl
e im
port
ance
![Page 19: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/19.jpg)
What controls spatial pattern of interannual variability?
* STD(FAPARAnomalies) = f(land cover, soil, climate)* 59 candidate predictors* Training on full domain* 9 variables selected (MEF=0.82)
![Page 20: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/20.jpg)
… again just climate!N
orm
aliz
ed v
aria
ble
impo
rtan
ce
![Page 21: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/21.jpg)
5-1525-3545-5565-7585-95
Percentiles std(FAPARANO)
![Page 22: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/22.jpg)
FPAR IAV high when:Intermediate WAI seasonality + Always high air humidity + Large IAV in radiation(but only part of the story!)
![Page 23: Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration](https://reader036.vdocuments.site/reader036/viewer/2022062518/56649e955503460f94b99cf2/html5/thumbnails/23.jpg)
Outlook
• Potential of seasonal forecasting of FAPAR for early warning systems
• Long-term historical and future changes in FAPAR dynamics (e.g. changing patterns of distribution of functional groups, IAV)