Dryland Vegetation Functional Response to Altered Rainfall Amounts and Variability Derived from Satellite Time Series Data

Vegetation productivity is an essential variable in ecosystem functioning. Vegetation dynamics of dryland ecosystems are most strongly determined by water availability and consequently by rainfall and there is a need to better understand how water limited ecosystems respond to altered rainfall amounts and variability. This response is partly determined by the vegetation functional response to rainfall (β) approximated by the unit change in annual vegetation productivity per unit change in annual rainfall. Here, we show how this functional response from 1983 to 2011 is affected by below and above average rainfall in two arid to semi-arid subtropical regions in West Africa (WA) and South West Africa (SWA) differing in interannual variability of annual rainfall (higher in SWA, lower in WA). We used a novel approach, shifting linear regression models (SLRs), to estimate gridded time series of β. The SLRs ingest annual satellite based rainfall as the explanatory variable and annual satellite-derived vegetation productivity proxies (NDVI) as the response variable. Gridded β values form unimodal curves along gradients of mean annual precipitation in both regions. β is higher in SWA during periods of below average rainfall (compared to above average) for mean annual precipitation <600 mm. In WA, β is hardly affected by above or below average rainfall conditions. Results suggest that this higher β variability in SWA is related to the higher rainfall variability in this region. Vegetation type-specific β follows observed responses for each region along rainfall gradients leading to region-specific responses for each vegetation type. We conclude that higher interannual rainfall variability might favour a more dynamic vegetation response to rainfall. This in turn may enhance the capability of vegetation productivity of arid and semi-arid regions to better cope with periods of below average rainfall conditions.