A Regional Earth System Data Lab for Understanding Ecosystem Dynamics: An Example from Tropical South America

Abstract

Tropical ecosystems experience particularly fast transformations largely as a consequence of land use and climate change. Consequences for ecosystem functioning and services are hard to predict and require analyzing multiple data sets simultaneously. Today, we are equipped with a wide range of spatio-temporal observation-based data streams that monitor the rapid transformations of tropical ecosystems in terms of state variables (e.g., biomass, leaf area, soil moisture) but also in terms of ecosystem processes (e.g., gross primary production, evapotranspiration, runoff). However, the underexplored joint potential of such data streams, combined with deficient access to data and processing, constrain our understanding of ecosystem functioning, despite the importance of tropical ecosystems in the regional-to-global carbon and water cycling. Our objectives are: 1. To facilitate access to regional “Analysis Ready Data Cubes” and enable efficient processing 2. To contribute to the understanding of ecosystem functioning and atmosphere-biosphere interactions. 3. To get a dynamic perspective of environmental conditions for biodiversity. To achieve our objectives, we developed a regional variant of an “Earth System Data Lab” (RegESDL) tailored to address the challenges of northern South America. The study region extensively covers natural ecosystems such as rainforest and savannas, and includes strong topographic gradients (0–6,500 masl). Currently, environmental threats such as deforestation and ecosystem degradation continue to increase. In this contribution, we show the value of the approach for characterizing ecosystem functioning through the efficient implementation of time series and dimensionality reduction analysis at pixel level. Specifically, we present an analysis of seasonality as it is manifested in multiple indicators of ecosystem primary production. We demonstrate that the RegESDL has the ability to underscore contrasting patterns of ecosystem seasonality and therefore has the potential to contribute to the characterization of ecosystem function. These results illustrate the potential of the RegESDL to explore complex land-surface processes and the need for further exploration. The paper concludes with some suggestions for developing future big-data infrastructures and its applications in the tropics.