The interannual variability of Africa's ecosystem productivity: a multi-model analysis

We are comparing spatially explicit process-model based estimates of the terrestrial carbon balance and its components over Africa and confront them with remote sensing based proxies of vegetation productivity and atmospheric inversions of land-atmosphere net carbon exchange. Particular emphasis is on characterizing the patterns of interannual variability of carbon fluxes and analyzing the factors and processes responsible for it. For this purpose simulations with the terrestrial biosphere models ORCHIDEE, LPJ-DGVM, LPJ-Guess and JULES have been performed using a standardized modeling protocol and a uniform set of corrected climate forcing data. While the models differ concerning the absolute magnitude of carbon fluxes, we find several robust patterns of interannual variability among the models. Models exhibit largest interannual variability in southern and eastern Africa, regions which are primarily covered by herbaceous vegetation. Interannual variability of the net carbon balance appears to be more strongly influenced by gross primary production than by ecosystem respiration. A principal component analysis indicates that moisture is the main driving factor of interannual gross primary production variability for those regions. On the contrary in a large part of the inner tropics radiation appears to be limiting in two models. These patterns are corroborated by remotely sensed vegetation properties from the SeaWiFS satellite sensor. Inverse atmospheric modeling estimates of surface carbon fluxes are less conclusive at this point, implying the need for a denser network of observation stations over Africa.JRC.DDG.H.3-Global environement monitorin

Modeling the Effects of Future Growing Demand for Charcoal in the Tropics

Global demand for charcoal is increasing mainly due to urban population in developing countries. More than half the global population now lives in cities, and urban-dwellers are restricted to charcoal use because of easiness of production, access, transport, and tradition. Increasing demand for charcoal, however, may lead to increasing impacts on forests, food, and water resources, and may even create additional pressures on the climate system. Here we assess how different charcoal scenarios based on the Shared Socio-economic Pathways (SSP) relate to potential biomass supply. For this, we use the energy model TIMER to project the demand for fuelwood and charcoal for different socio-economic pathways for urban and rural populations, globally, and for four tropical regions (Central America, South America, Africa and Indonesia). Second, we assess whether the biomass demands for each scenario can be met with current and projected forest biomass estimated with remote sensing and modeled Net Primary Productivity (NPP) using a Dynamic Global Vegetation Model (LPJ-GUESS). Currently one third of residential energy use is based on traditional bioenergy, including charcoal. Globally, biomass needs by urban households by 2100 under the most sustainable scenario, SSP1, are of 14.4 mi ton biomass for charcoal plus 17.1 mi ton biomass for fuelwood (31.5 mi ton biomass in total). Under SSP3, the least sustainable scenario, we project a need of 205 mi tons biomass for charcoal plus 243.8 mi ton biomass for fuelwood by 2100 (total of 450 mi ton biomass). Africa and South America contribute the most for this biomass demand, however, all areas are able to meet the demand. We find that the future of the charcoal sector is not dire. Charcoal represents a small fraction of the energy requirements, but its biomass demands are disproportionate and in some regions require a large fraction of forest. This could be because of large growing populations moving to urban areas, conversion rates, production inefficiencies, and regions that despite available alternative energy sources still use a substantial amount of charcoal. We present a framework that combines Integrated Assessment Models and local conditions to assess whether a sustainable sector can be achieved

Modeling the vertical soil organic matter profile using Bayesian parameter estimation

The vertical distribution of soil organic matter (SOM) in the profile may constitute an important factor for soil carbon cycling. However, the formation of the SOM profile is currently poorly understood due to equifinality, caused by the entanglement of several processes: input from roots, mixing due to bioturbation, and organic matter leaching. In this study we quantified the contribution of these three processes using Bayesian parameter estimation for the mechanistic SOM profile model SOMPROF. Based on organic carbon measurements, 13 parameters related to decomposition and transport of organic matter were estimated for two temperate forest soils: an Arenosol with a mor humus form (Loobos, the Netherlands), and a Cambisol with mull-type humus (Hainich, Germany). Furthermore, the use of the radioisotope Pb-210(ex) as tracer for vertical SOM transport was studied. For Loobos, the calibration results demonstrate the importance of organic matter transport with the liquid phase for shaping the vertical SOM profile, while the effects of bioturbation are generally negligible. These results are in good agreement with expectations given in situ conditions. For Hainich, the calibration offered three distinct explanations for the observations (three modes in the posterior distribution). With the addition of Pb-210(ex) data and prior knowledge, as well as additional information about in situ conditions, we were able to identify the most likely explanation, which indicated that root litter input is a dominant process for the SOM profile. For both sites the organic matter appears to comprise mainly adsorbed but potentially leachable material, pointing to the importance of organo-mineral interactions. Furthermore, organic matter in the mineral soil appears to be mainly derived from root litter, supporting previous studies that highlighted the importance of root input for soil carbon sequestration. The Pb-210(ex) measurements added only slight additional constraint on the estimated parameters. However, with sufficient replicate measurements and possibly in combination with other tracers, this isotope may still hold value as tracer for SOM transport

Effects of Total Resources, Resource Ratios, and Species Richness on Algal Productivity and Evenness at Both Metacommunity and Local Scales

The study of the interrelationship between productivity and biodiversity is a major research field in ecology. Theory predicts that if essential resources are heterogeneously distributed across a metacommunity, single species may dominate productivity in individual metacommunity patches, but a mixture of species will maximize productivity across the whole metacommunity. It also predicts that a balanced supply of resources within local patches should favor species coexistence, whereas resource imbalance would favor the dominance of one species. We performed an experiment with five freshwater algal species to study the effects of total supply of resources, their ratios, and species richness on biovolume production and evenness at the scale of both local patches and metacommunities. Generally, algal biovolume increased, whereas algal resource use efficiency (RUE) and evenness decreased with increasing total supply of resources in mixed communities containing all five species. In contrast to predictions for biovolume production, the species mixtures did not outperform all monocultures at the scale of metacommunities. In other words, we observed no general transgressive overyielding. However, RUE was always higher in mixtures than predicted from monocultures, and analyses indicate that resource partitioning or facilitation in mixtures resulted in higher-than-expected productivity at high resource supply. Contrasting our predictions for the local scale, balanced supply of resources did not generally favor higher local evenness, however lowest evenness was confined to patches with the most imbalanced supply. Thus, our study provides mixed support for recent theoretical advancements to understand biodiversity-productivity relationships