Releasing global forests from human management: How much more carbon could be stored?

Carbon storage in forests is a cornerstone of policy-making to prevent global warming from exceeding 1.5°C. However, the global impact of management (for example, harvesting) on the carbon budget of forests remains poorly quantified. We integrated global maps of forest biomass and management with machine learning to show that by removing human intervention, under current climatic conditions and carbon dioxide (CO2) concentration, existing global forests could increase their aboveground biomass by up to 44.1 (error range: 21.0 to 63.0) petagrams of carbon. This is an increase of 15 to 16% over current levels, equating to about 4 years of current anthropogenic CO2 emissions. Therefore, without strong reductions in emissions, this strategy holds low mitigation potential, and the forest sink should be preserved to offset residual carbon emissions rather than to compensate for present emissions levels.

The quest for the missing plastics: Large uncertainties in river plastic export into the sea.

Plastic pollution in the natural environment is causing increasing concern at both the local and global scale. Understanding the dispersion of plastic through the environment is of key importance for the effective implementation of preventive measures and cleanup strategies. Over the past few years, various models have been developed to estimate the transport of plastics in rivers, using limited plastic observations in river systems. However, there is a large discrepancy between the amount of plastic being modelled to leave the river systems, and the amount of plastic that has been found in the seas and oceans. Here, we investigate one of the possible causes of this mismatch by performing an extensive uncertainty analysis of the riverine plastic export estimates. We examine the uncertainty from the homogenisation of observations, model parameter uncertainty, and underlying assumptions in models. To this end, we use the to-date most complete time-series of macroplastic observations (macroplastics have been found to contain most of the plastic mass transported by rivers), coming from three European rivers. The results show that model structure and parameter uncertainty causes up to four orders of magnitude, while the homogenisation of plastic observations introduces an additional three orders of magnitude uncertainty in the estimates. Additionally, most global models assume that variations in the plastic flux are primarily driven by river discharge. However, we show that correlations between river discharge (and other environmental drivers) and the plastic flux are never above 0.5, and strongly vary between catchments. Overall, we conclude that the yearly plastic load in rivers remains poorly constrained.

Disentangling Variability in Riverbank Macrolitter Observations.

Anthropogenic macrolitter (>0.5 cm) in rivers is of increasing concern. It has been found to have an adverse effect on riverine ecosystem health, and the livelihoods of the communities depending on and living next to these ecosystems. Yet, little is known on how macrolitter reaches and propagates through these ecosystems. A better understanding of macrolitter transport dynamics is key in developing effective reduction, preventive, and cleanup measures. In this study, we analyzed a novel dataset of citizen science riverbank macrolitter observations in the Dutch Rhine-Meuse delta, spanning two years of observations on over 200 unique locations, with the litter categorized into 111 item categories according to the river-OSPAR protocol. With the use of regression models, we analyzed how much of the variation in the observations can be explained by hydrometeorology, observer bias, and location, and how much can instead be explained by temporal trends and seasonality. The results show that observation bias is very low, with only a few exceptions, in contrast with the total variance in the observations. Additionally, the models show that precipitation, wind speed, and river flow are all important explanatory variables in litter abundance variability. However, the total number of items that can significantly be explained by the regression models is 19% and only six item categories display an R2 above 0.4. This suggests that a very substantial part of the variability in macrolitter abundance is a product of chance, caused by unaccounted (and often fundamentally unknowable) stochastic processes, rather than being driven by the deterministic processes studied in our analyses. The implications of these findings are that for modeling macrolitter movement through rivers effectively, a probabilistic approach and a strong uncertainty analysis are fundamental. In turn, point observations of macrolitter need to be planned to capture short-term variability.