1 / 23

Environmental controls and predictions of African vegetation dynamics

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

gaurav
Download Presentation

Environmental controls and predictions of African vegetation dynamics

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

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

  2. 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

  3. 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?

  4. Approach Mean seasonal cycle Mean annual Lag Raw Meteorology (7 x 4 + 7 x 24 ) Cumulative Lag Lag Anomalies Random forests Land use (8) Cumulative Lag Remotely sensed fAPAR Soil (10) Variable selection based on Genetic Algorithm

  5. 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)

  6. 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

  7. 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 fAPAR Estimation of variable importances

  8. Results Overall MEF = 0.91

  9. Approach fails in some locations of massive transformations MEF low, RMS high MEF intermediate, RMS intermediate MEF high, RMS low MEF low, RMS low Color composite of MEF and RMS

  10. A little excursion…

  11. Simple model based on soil moisture indicator explains 79% of variance

  12. Very small effect of fire on FAPAR anomalies

  13. Back to the original model…

  14. Local variable importance (sensitivity)

  15. A functional classification RGB of first 3 PCAs of variable importance (77% of variance explained) K-means clustering of variable importance (10 classes)

  16. 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

  17. 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)

  18. … again just climate! Normalized variable importance

  19. 5-15 25-35 45-55 65-75 85-95 Percentiles std(FAPARANO)

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

  21. 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)

More Related