Evaluating the response of the land carbon sink to the anomalies in temperature and drought imposed by El Niño events provides insights into the present-day carbon cycle and its climate-driven variability. It is also a necessary step to build confidence in terrestrial ecosystems models' response to the warming and drying stresses expected in the future over many continents, and particularly in the tropics. Here we present an in-depth analysis of the response of the terrestrial carbon cycle to the 2015/2016 El Niño that imposed extreme warming and dry conditions in the tropics and other sensitive regions. First, we provide a synthesis of the spatio-temporal evolution of anomalies in net land–atmosphere CO2 fluxes estimated by two in situ measurements based on atmospheric inversions and 16 land-surface models (LSMs) from TRENDYv6. Simulated changes in ecosystem productivity, decomposition rates and fire emissions are also investigated. Inversions and LSMs generally agree on the decrease and subsequent recovery of the land sink in response to the onset, peak and demise of El Niño conditions and point to the decreased strength of the land carbon sink: by 0.4–0.7 PgC yr−1 (inversions) and by 1.0 PgC yr−1 (LSMs) during 2015/2016. LSM simulations indicate that a decrease in productivity, rather than increase in respiration, dominated the net biome productivity anomalies in response to ENSO throughout the tropics, mainly associated with prolonged drought conditions.This article is part of a discussion meeting issue ‘The impact of the 2015/2016 El Niño on the terrestrial tropical carbon cycle: patterns, mechanisms and implications’.
Forest fires are a recurrent phenomenon in Mediterranean forests, with impacts for human landscapes and communities, which must be understood before they can be managed. This study used the physically based Limburg Soil Erosion Model (LISEM) to simulate rainfall-runoff response, under soil water repellent (SWR) conditions and different stages of vegetation recovery. Five rainfall-runoff events were selected, representing wet and dry conditions, spread over two years after a wildfire which burned eucalypt and maritime pine plantations in the Colmeal experimental micro-catchment, central Portugal. Each event was simulated using three Leaf Area Index (LAI) estimates: indirect field-based measurements (TC-LAI), NDVI-based estimates derived from Landsat-5 TM and Landsat-7 ETM+ imagery (NDVI-LAI), and the LAI of a fully restored canopy to test model sensitivity to interception parameters. LISEM was able to simulate events in relative terms but underestimated peak runoff (r 2 = 0·36, mean error = À31%, and NSE = À0·15) and total runoff (r 2 = 0·52, mean error = À15% and NSE = 0·09), which could be related to the presence of SWR or saturated areas, according to pre-rainfall soil moisture conditions. The model performed better for individual hydrographs, especially under wet conditions. Modelling the full-cover scenario showed minor sensitivity of LISEM to the observed changes in LAI. NDVI-LAI data gave a close to equal model performance with TC-LAI and therefore can be considered a suitable substitute for ground-based measurements in post-fire runoff predictions. However, more attention should be given to representing pre-rainfall soil moisture conditions and especially the presence of SWR.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.