Model-Data Integration

Dr. Nuno Carvalhais

Mission

In general, we are curious about interactions in carbon and water dynamics in terrestrial ecosystems in an Earth system context, and are keen on learning by bringing together models and observations. We want to advance understanding in interactions between terrestrial ecosystems and climate with a particular relevance to the global carbon and water biogeochemical cycles. The MDI group explores strategies and develops methods to extract and transfer information from data to models to investigate the functioning --dynamics and sensitivities-- of terrestrial ecosystems and its representation in modeling structures. Ultimately, we aim at identifying approaches that are robust against inevitable challenges in data and models to represent complex Earth system processes.

Focus areas

Model-data-integration : Exploration of multiple-constraint model-data assimilation approaches using in situ and Earth Observation data-streams of water and carbon states and fluxes

We have been investigating in situ dynamics of carbon and water fluxes for parameter inversion, for model selection and for understanding parameter variability; and, at global scales, to investigate controls on trends in [CO2 atm] and evaluate contrasting descriptions of large scale hydrological dynamics. The focus on "multiple-constraints" approaches and combining classical inverse parameter estimation techniques with machine learning approaches paves way to SINDBAD framework (see below), and the development of hybrid modeling approaches therein. We are very interested in methodological aspects of breeding model-data-assimilation and machine learning approaches to maximize the information flow from observations to models.

Terrestrial C-H2O dynamics across scales : Exploration of observation-driven approaches to diagnose (1) determinants of carbon sink/source strength and (2) carbon-water coupling strength at ecosystem and watershed scales.

Based on statistical approaches, we study carbon sink/source strength for the FLUXNET domain, and investigate, in addition to climate, the importance of stand age and biomass dynamics and distribution. The approach paves way for improvements in observation-based upscaling of NEP fluxes. In a strong collaboration with GDM, we support the development of observational-based approaches to diagnose physiological responses to water stress and the use of different approaches to partition surface-atmosphere water fluxes.

Ecosystem carbon turnover times: Diagnosing and interpreting mechanisms underlying the environmental controls on ecosystem turnover times of carbon.

We investigate carbon turnover times as an observational diagnostic on the strength of the land-atmosphere carbon cycle coupling. We have been working on breaking down an whole ecosystem estimate into different components (vegetation and soil), revisiting the importance of scale and equilibrium assumption to investigate the robust features in the observation-derived turnover times. We are interested in observational and methodological aspects to understand biotic and abiotic controls on temporal dynamics of carbon turnover; in developing further concepts to constraint projections from Earth System Models; in contributing to observational dataset development

About joining us...

Over the years we have been hosting internships, students working on their Masters thesis, and mostly the development of PhD and Postdoc research projects. There have been opportunities for training and for the development of collaborative research that have been mutually appreciated, and where I have seen that we have grown as a group. If you are interested in working with us, feel free to get in touch. We announce opportunities here and in the context of the IMPRS for PhD projects.

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