Environmental controls and predictions of African vegetation dynamics Martin Jung, Eric Thomas Department of Biogeochemical Integration

Africa 2nd largest continent (30 x 10 6 km 2 ) 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 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 ( ) 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: ; Validation period: ; Leave ‘one year out’ forward run using selected variables ( ); 20 Random Forests with 48 trees each using 1000 random locations Evaluation of predicted fAPAR Estimation of variable importances

Prediction of vegetation states Three variants: - cost function of variable selection sensitive to MEF(FAPAR) (fire not included) - cost function of variable selection sensitive to MEF(FAPAR anomalies) (fire not included) - cost function of variable selection sensitive to MEF(FAPAR anomalies) (fire included) Variable selection: ~250 variables, 500 random locations, training set ( ), test set ( ) Forward runs with selected variables: leave one year out cross-validation; 25 Random Forests with 50 trees each and based on 500 random locations each

Results Overall 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%) Normalized variable importance

What controls spatial pattern of interannual variability? * STD(FAPAR Anomalies ) = f(land cover, soil, climate) * 59 candidate predictors * Training on full domain * 9 variables selected (MEF=0.82)

… again just climate! Normalized variable importance

Percentiles std(FAPAR ANO )

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)