Contrasting responses of woody and herbaceous vegetation to altered rainfall characteristics in the Sahel
Résumé
Abstract. Dryland ecosystems form a major land cover, accounting for about 40 % of Earth's terrestrial surface and net primary productivity, and housing more than 30 % of the human population. These ecosystems are subject to climate extremes (e.g. large-scale droughts) 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 signature of the rainy season characteristics, these rainfall anomalies were driven by changing either rainfall intensity, event frequency or 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 fluxtower measurements across the Sahel. Different scenarios of extreme rainfall were derived from existing Sahel rainfall products such that meteorological consistency was maintained, 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. Semi-arid ecosystems are known to play a dominant role in the trend and variability of the terrestrial CO2 sink. We showed that single extremely dry and wet years can have strong and long-term impact on the productivity of drylands ecosystems, shedding new light on potential drivers and mechanisms behind this variability.