Datensatz

Zusammenfassung

Vegetation-greenness distributions (based on remote sensing NDVI) and their change are analyzed as functional vegetation-climate relations in a 2-dimensional eco-hydrological state space spanned by surface flux ratios of energy excess (U, loss by sensible heat H over supply by net radiation N) versus water excess (W, loss by discharge Ro over gain by precipitation P). An eco-hydrologic Ansatz attributes state change trajectories in (U,W)-space to external (or climate) and internal (or anthropogenic) causes jointly with vegetation-greenness interpreted as active tracer. Selecting the Tibetan Plateau with its complex topographic, climate and vegetation conditions as target area, ERA-Interim weather data link geographic and (U,W)-state space, into which local remote sensing GIMMS data (NDVI) are embedded; a first and second period (1982–1993 and 1994-2006) are chosen for change attribution analysis: (i) State space statistics is characterized by a bimodal distribution with two distinct geobotanic regimes (Semidesert and Steppe) of low and moderate vegetation-greenness separated by gaps at aridity D ~ 2 (net radiation over precipitation) and greenness NDVI ~ 0.3. (ii) Changes between the first and second period are attributed to external (about 70%) and internal (30%) processes. (iii) Attribution conditioned joint distributions of NDVI (and its change) show 38.2% decreasing (61.8% increasing) area cover with low (moderate) greenness while high greenness areas are slightly reduced. (iv) Water surplus regions benefit most from climate change (showing vegetation-greenness growth) while the energy surplus change is ambiguous, because eco-hydrological diagnostics attributes high mountainous regions (such as the Himalaya) as internal without considering heat storage deficit due to increasing vegetation.