0000000000148706
AUTHOR
Nuno Carvalhais
Environment-sensitivity functions for gross primary productivity in light use efficiency models
International audience; The sensitivity of photosynthesis to environmental changes is essential for understanding carbon cycle responses to global climate change and for the development of modeling approaches that explains its spatial and temporal variability. We collected a large variety of published sensitivity functions of gross primary productivity (GPP) to different forcing variables to assess the response of GPP to environmental factors. These include the responses of GPP to temperature; vapor pressure deficit, some of which include the response to atmospheric CO2 concentrations; soil water availability (W); light intensity; and cloudiness. These functions were combined in a full fact…
Deep learning and process understanding for data-driven Earth system science
Machine learning approaches are increasingly used to extract patterns and insights from the ever-increasing stream of geospatial data, but current approaches may not be optimal when system behaviour is dominated by spatial or temporal context. Here, rather than amending classical machine learning, we argue that these contextual cues should be used as part of deep learning (an approach that is able to extract spatio-temporal features automatically) to gain further process understanding of Earth system science problems, improving the predictive ability of seasonal forecasting and modelling of long-range spatial connections across multiple timescales, for example. The next step will be a hybri…
Global Groundwater-Vegetation Relations
Groundwater is an integral component of the water cycle, and it also influences the carbon cycle by supplying moisture to ecosystems. However, the extent and determinants of groundwater-vegetation interactions are poorly understood at the global scale. Using several high-resolution data products, we show that the spatial patterns of ecosystem gross primary productivity and groundwater table depth are correlated during at least one season in more than two-thirds of the global vegetated area. Positive relationships, i.e., larger productivity under shallower groundwater table, predominate in moisture-limited dry to mesic conditions with herbaceous and shrub vegetation. Negative relationships, …