ALBERT

Description: In recent years, there has been an increase in the number of publications of tthe application of artificial neural networks (ANNs) has been investigated in a variety of hydrological contexts. However, how does the performance of ANNs compare with more traditional approaches? Here, we illustrate the respective error rates for a state-of-the-art evolutionary neural network (EANN), and the global GR4J and TOPMODEL approaches to streamflow prediction.The EANN demonstrates superior average performance (NRMSE = 0.4692 compared to 0.508 and 0.496 for GR4J and TOPMODEL respectively) for relatively short-range prediction (1 year ahead), but significantly underperforms for longer-range prediction (NRMSE = 0.4821 compared to 0.273 and 0.279 for GR4J and TOPMODEL respectively for two years ahead; NRMSE = 0.474 compared to 0.334 and 0.339 for GR4J and TOPMODEL respectively for three years ahead).Interestingly, for longer range prediction (2 and 3 years ahead), for which the global models yield a lower overall error, the EANN overestimates high peak flows, whereas the conceptual models underestimate high peak flows. For 2 years ahead, the EANN has an NRMSE of 0.0287 for high peak flows compared to 0.1877 and 0.0671 for GR4J and TOPMODEL respectively. For 3 years ahead, the EANN has an NRMSE of 0.0156 for high peak flows compared to 0.2480 and 0.0725 for GR4J and TOPMODEL respectively.These results suggest that the EANN may be a more reliable flood predictor despite greater overall error rates. We will be investigating how these trends hold up for longer prediction periods (5 to 15 years ahead).

Description: Forests are a substantial terrestrial carbon sink, but anthropogenic changes in land use and climate have considerably reduced the scale of this system1. Remote-sensing estimates to quantify carbon losses from global forests2,3,4,5 are characterized by considerable uncertainty and we lack a comprehensive ground-sourced evaluation to benchmark these estimates. Here we combine several ground-sourced6 and satellite-derived approaches2,7,8 to evaluate the scale of the global forest carbon potential outside agricultural and urban lands. Despite regional variation, the predictions demonstrated remarkable consistency at a global scale, with only a 12% difference between the ground-sourced and satellite-derived estimates. At present, global forest carbon storage is markedly under the natural potential, with a total deficit of 226 Gt (model range = 151–363 Gt) in areas with low human footprint. Most (61%, 139 Gt C) of this potential is in areas with existing forests, in which ecosystem protection can allow forests to recover to maturity. The remaining 39% (87 Gt C) of potential lies in regions in which forests have been removed or fragmented. Although forests cannot be a substitute for emissions reductions, our results support the idea2,3,9 that the conservation, restoration and sustainable management of diverse forests offer valuable contributions to meeting global climate and biodiversity targets.

Description: Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17–34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.