Abstract
Dryland ecosystems are a major source of land cover, account for about 40% of Earth's terrestrial surface and net primary productivity, and house more than 30 % of the human population. These ecosystems are subject to climate extremes (e.g. large-scale droughts and extreme floods) that are projected to increase in frequency and severity under most future climate scenarios. In this modelling study we assessed the impact of single years of extreme (high or low) rainfall on dryland vegetation in the Sahel. The magnitude and legacy of these impacts were quantified on both the plant functional type and the ecosystem levels. In order to understand the impact of differences in the rainfall distribution over the year, these rainfall anomalies were driven by changing either rainfall intensity, event frequency or rainy-season length. The Lund–Potsdam–Jena General Ecosystem Simulator (LPJ-GUESS) dynamic vegetation model was parameterized to represent dryland plant functional types (PFTs) and was validated against flux tower measurements across the Sahel. Different scenarios of extreme rainfall were derived from existing Sahel rainfall products and applied during a single year of the model simulation timeline. Herbaceous vegetation responded immediately to the different scenarios, while woody vegetation had a weaker and slower response, integrating precipitation changes over a longer timeframe. An increased season length had a larger impact than increased intensity or frequency, while impacts of decreased rainfall scenarios were strong and independent of the season characteristics. Soil control on surface water balance explains these contrasts between the scenarios. None of the applied disturbances caused a permanent vegetation shift in the simulations. Dryland ecosystems are known to play a dominant role in the trend and variability of the global terrestrial CO2 sink. We showed that single extremely dry and wet years can have a strong impact on the productivity of drylands ecosystems, which typically lasts an order of magnitude longer than the duration of the disturbance. Therefore, this study sheds new light on potential drivers and mechanisms behind this variability.