Environmental controls and predictions of African vegetation

dynamics

Martin Jung, Eric Thomas

Department of Biogeochemical Integration

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

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?

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

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)

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

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

ResultsOverall MEF = 0.91

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

A little excursion…

Simple model based on soil moisture indicator explains 79%

of variance

Very small effect of fire on FAPAR anomalies

Back to the original model…

Local variable importance (sensitivity)

A functional classification

RGB of first 3 PCAs of variable importance (77% of variance

explained)

K-means clustering of variable importance (10 classes)

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

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)

… again just climate!N

orm

aliz

ed v

aria

ble

impo

rtan

ce

5-1525-3545-5565-7585-95

Percentiles std(FAPARANO)

FPAR IAV high when:Intermediate WAI seasonality + Always high air humidity + Large IAV in radiation(but only part of the story!)

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)