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

Gregor Ratzmann,Britta Tietjen,Ute Gangkofner,Rasmus Fensholt

doi:10.3390/rs8121026

Abstract

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.

Highlights

The importance of rainfall and water availability for dryland vegetation functioning has long been recognized and shown in numerous studies [1,2,3,4]
The response is regularly estimated from linear models as slope or linear coefficient ingesting rainfall as explanatory and vegetation productivity as the response variable [3,6,16,17,18,19,20]. β translates water availability during a period of net growth into any ecosystem or plant-specific functional response as a function of the integral of other constraints potentially influencing plant growth [21]. β can be interpreted as the Water Use Efficiency (WUE) under preconditions that the ineffective precipitation was accounted for leading to an intercept = 0
The results presented show a distinct difference between vegetation functional response to rainfall (β) during dry and wet periods for the two semi-arid regions (South Western Africa (SWA) and Western Africa (WA))

Summary

Introduction

The importance of rainfall and water availability for dryland vegetation functioning has long been recognized and shown in numerous studies [1,2,3,4]. Climate model predictions of globally altered water availability in the upcoming century [11] make it necessary to enhance the understanding of dryland vegetation response to rainfall and future changes thereof [12,13]. Β can be interpreted as the Water Use Efficiency (WUE) (i.e., the ratio of net photosynthesis to transpiration) under preconditions that the ineffective precipitation (runoff and soil evaporation) was accounted for leading to an intercept = 0. In this case, β becomes a measure of WUE [5,22]. As emphasized in [14,17,23], this may only yield reliable results in cases where the intercept of the relationship between vegetation and water metrics used equals zero (otherwise the β and efficiency will be different entities) which is seldom obtained by using satellite based proxies of vegetation productivity such as annually summed NDVI (normalised difference vegetation index) and annually summed precipitation

Methods

Findings

Discussion

Conclusion