Model-Data Integration

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

Team

Other members of the department actively involved in MDI activities

Weijie Zhang, Basil Kraft, Nora Linscheid

Guests

Name
Shanning Bao
Simon Besnard
Naixin Fan
Doctoral Researcher
Catarina Moura

Key Papers

Besnard, S.; Carvalhais, N.; Arain, A.; Black, A.; de Bruin, S.; Buchmann, N.; Cescatti, A.; Chen, J.; Clevers, J. G. P. W.; Desai, A. R. et al.: Quantifying the effect of forest age in annual net forest carbon balance. Environmental Research Letters 13, 124018 (2018), doi.org/10.1088/1748-9326/aaeaeb

Fan, N.; Koirala, S.; Reichstein, M.; Thurner, M.; Avitabile, V.; Santoro, M.; Ahrens, B.; Weber, U.; Carvalhais, N.: Apparent ecosystem carbon turnover time: uncertainties and robust features. Earth System Science Data 12 (4), pp. 2517 - 2536 (2020), doi.org/10.5194/essd-12-2517-2020

Jenny, J.-P.; Koirala, S.; Gregory-Eaves, I.; Francus, P.; Niemann, C.; Ahrens, B.; Brovkin, V.; Baud, A.; Ojala, A. E. K.; Normandeau, A. et al.: Human and climate global-scale imprint on sediment transfer during the Holocene. Proceedings of the National Academy of Sciences of the United States of America 116 (46), pp. 22972 - 22976 (2019), doi.org/10.1073/pnas.1908179116

Koirala, S.; Jung, M.; Reichstein, M.; de Graaf, I. E. M.; Camps-Valls, G.; Ichii, K.; Papale, D.; Raduly, B.; Schwalm, C. R.; Tramontana, G. et al.: Global distribution of groundwater-vegetation spatial covariation. Geophysical Research Letters 44 (9), pp. 4134 - 4142 (2017), doi/10.1002/2017GL072885

Current MDI Projects

Earth System Deep Learning for Seasonal Fire Forecasting in Europe (SeasFire)
Start: March 2022
End: March 2023
PI / Co-PI: Nuno Carvalhais
Participating BGI member: Nuno Carvalhais, Lazaro Alonso
Funding: ESA more
Earth System Models for the Future
Duration: 06/2021 - 05/2025
PI/Co-PIs: Nuno Carvalhais, Sönke Zaehle

ESM2025 – Earth system models for the future is an ambitious European research project on Earth System modelling that will build a novel generation of Earth system models fitted to support the development of mitigation and adaptation strategies in line with the commitments of the Paris Agreement.

Participating department members: Nuno Carvalhais, Sönke Zaehle more
Project Office Biomass
Duration: 07/2020 - 04/2023
PI/Co-PIs: Nuno Carvalhais, Markus Reichstein

Within ESA’s Earth Explorers, the primary objective of BIOMASS is to improve our understanding on the role of the land biosphere on the global carbon cycle, with clear implications in a context of climate change. Project Office BIOMASS aims to synthesize, produce and disseminate relevant information within the biomass community; identify gaps and investigate solutions within the context of BIOMASS; and establish communication to maintain a necessary dialogue within the broader context of the BIOMASS mission.

Participating department members: Hui Yang, Stefanie Burkert, Nuno Carvalhais, Siyuan Wang, Markus Reichstein more
DeepCube
Duration: 01/2021 - 12/2023
PI/Co-PIs: Nuno Carvalhais

DeepCube is a research project that aims to explore and develop new deep learning approaches to address relevant Earth system dynamics related to localized impacts of extreme events (drought and heat waves) and ecosystem disturbances (wildfires), including impacts of climate variability on migration flows, through AI-based understanding, quantification and prediction.

Participating department members: Nuno Carvalhais, Fabian Gans and Markus Reichstein, Christian Requeña-Mesa, Dushyant Kumar more
SINDBAD

SINDBAD

We have been working closely with GDM in building Strategies to Integrate Data and Biogeochemical models in Development (SINDBAD), a modular framework for model-data-integration. Currently, SINDBAD supports several projects on water-carbon coupling and vegetation dynamics across scales: Graph by Sujan Koirala and Martin Jung (GDM), and the PhD projects of Tina Trautmann, Hoontaek Lee and Siyuan Wang.

Analysis of climate constraints on gross primary productivity based on light use efficiency models : Here, Shanning analyzes the patterns of climate sensitivities of gross primary productivity using FLUXNET data and global sun-induced fluorescence data. The project focuses on the evaluation and prediction of climate sensitivity functions and model parameters based on model-data fusion strategy.

Ecosystem transpiration from eddy covariance, where Jake develops and assesses methods for partitioning the evapotranspiration flux measured from eddy covariance into the transpiration and abiotic evaporation components. The methods are applied to the FLUXNET dataset, giving a global perspective on plant water use.

Ecological process understanding across time scales: In her PhD project, Nora explores time-scale dependencies between vegetation and climate globally, to answer questions such as: How does the decadal sensitivity of vegetation to climate differ from seasonal sensitivity? Can we extrapolate atmosphere-biosphere relations from one time scale to the other? Where and when do we need to account for such time-scale dependencies in vegetation modeling and monitoring?

Water-carbon-cycle interactions across scales : In his PhD project, Hoontaek attempts to answer 1) how interactions between water and carbon cycles form observations of each cycle and 2) who drives whom at which scales. To do so, Hoontaek will take advantage of the stream of global datasets and a model-data-fusion framework.

Change in global vegetation : Here Siyuan explores data-assimilation to study vegetation processes and ecosystem responses to environment at different spatio-temporal scales. The project investigates the links between ecosystem carbon fluxes and changes in vegetation carbon states, integrating vegetation mortality and metabolic activity based on a novel modular modeling framework and different observational data streams.

Inter-annual variability in terrestrial ecosystem fluxes, in this PhD project, Ranit endeavours to develop a hybrid modelling approach to simulate water and carbon fluxes at the ecosystem level. The project will exploit large multi-variate Earth Observation datasets to explore causal linkages between changing climate and ecosystem responses by taking advantage of both physically based models (physically interpretable) and machine learning approaches (data-adaptive)

original

Work in progress.

https://agupubs.onlinelibrary.wiley.com/doi/10.1002/2017GL072885

Click for paper.

Work in progress.

Click for paper.

Work in progress.

Work in progress.

Alumni

Alumni

Name

Data & Tools

Carbon turnover times: Naixin has synthesized and processed multiple global datasets of gross primary productivity, soil carbon, and vegetation carbon, to generate a large ensemble of global whole ecosystem carbon turnover times (Fan et al., 2020). The previous dataset developed by Nuno has already been used for evaluation of Earth System Models (Carvalhais et al., 2014). Both these data are publicly available through the data portal of Max Planck Institute for Biogeochemistry.

ESMValTool: The Earth System Model Evaluation Tool (ESMValTool) is a community-development that aims at improving, diagnosing and understanding the causes and effects of model biases and inter-model spread. Within CRESCENDO project, Sujan contributed an ESMValTool “recipe” that diagnoses the ESM simulations of 𝝉.

Forest age: based on forest inventory data, Simon developed a ML model to derive forest stand age from global remote sensing and climate data.

Global Aboveground Biomass: we collaborated with GAMMA Remote Sensing, within the GlobBiomass ESA DUE consortium, to develop a global high resolution map of above ground biomass. The data can be found here: https://doi.pangaea.de/10.1594/PANGAEA.894711.

Lambda: Çağlar (GDM/MDI) developed a new ecohydrological dataset of metrics characterizing the vegetation dynamics under moisture limitation in Africa using daily time series of Fraction of Vegetation Cover (FVC) from the geostationary METEOSAT satellite (Küçük et al., 2020).

TEA: Transpiration Estimation Algorithm: Jake (GDM/MDI) makes available data (https://zenodo.org/record/3978408#.YECpgWLPx_V), the algorithms and some of the tools developed in investigating the evapotranspiration flux partitioning from eddy covariance data (https://github.com/jnelson18/).

Publications

Journal Article (94)

1.
Pacheco-Labrador, J.; Migliavacca, M.; Ma, X.; Mahecha, M. D.; Carvalhais, N.; Weber, U.; Benavides, R.; Bouriaud, O.; Barnoaie, I.; Coomesl, D. A. et al.; Bohn, F. J.; Kraemer, G.; Heide, U.; Huth, A.; Wirth, C.: Challenging the link between functional and spectral diversity with radiative transfer modeling and data. Remote Sensing of Environment 280, 113170 (2022)
2.
Son, R.; Ma, P.-L.; Wang , H.; Rasch, P. J.; Wang, S.-Y. (.; Kim, H.; Jeong , J.-H.; Lim , K.-S. S.; Yoon, J.-H.: Deep Learning provides substantial improvements to county-level fire weather forecasting over the western United States. Journal of Advances in Modeling Earth Systems 14 (10), e2022MS002995 (2022)
3.
Küçük, Ç.; Koirala, S.; Carvalhais, N.; Miralles, D. G.; Reichstein, M.; Jung, M.: Observation-based assessment of secondary water effects on seasonal vegetation decay across Africa. Frontiers in Big Data 5, 967477 (2022)
4.
Anderegg, W. R. L.; Wu, C.; Acil, N.; Carvalhais, N.; Pugh, T. A. M.; Sadler, J. P.; Seidl, R.: A climate risk analysis of Earth’s forests in the 21st century. Science 377 (6610), pp. 1099 - 1103 (2022)
5.
Kondylatos, S.; Prapas, I.; Ronco , M.; Papoutsis , I.; Camps-Valls , G.; Piles , M.; Fernández-Torres , M.-Á.; Carvalhais, N.: Wildfire danger prediction and understanding with deep learning. Geophysical Research Letters (accepted)
Show more

About joining our team....

Over the years we have been hosting internships, students working on their Masters thesis, and mostly the development of PhD and Postdoc research projects. These 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.

CRESCENDO
Duration: 11/2015 - 03/2021

In CRESCENDO, Sujan and Nuno evaluate the carbon cycle dynamics in European Earth System Models and contribute to the implementation of model evaluation diagnostics of carbon turnover times in the ESMValTool.

Contact: Sönke Zaehle more
Duration: 11/2018 - 10/2019

In this project, we'll work on creating a multi-decadal record of biomass estimates from multiple Earth Observation datasets and investigate the integration of these new remote sensing data streams into an analysis of ecosystem dynamics and modelling

Contact: Nuno Carvalhais more
Duration: 04/2018 - 09/2019

The Earth System Data Lab (ESDL) is a multi-variate data set of essential Earth System variables on a common grid and sharing a common data model. The main objective of the ESDL activity is to establish and operate a service to the scientific community that greatly facilitates access and exploitation of the multivariate data set in the ESDL and by this means advances the understanding of the interactions between the ocean-land-atmosphere system and society.

Contact: Miguel Mahecha more
Duration: 01/2015 - 12/2017

The main purpose of the ESA DUE GlobBiomass project is to better characterise and to reduce uncertainties of AGB estimates by developing an innovative synergistic mapping approach in five regional sites for the epochs 2005, 2010 and 2015 and for one global map for the year 2010.

Contact: Nuno Carvalhais, Markus Reichstein more
Duration: 06/2009 - 05/2013

Carbo Extremes aims to improve our understanding of the terrestrial carbon cycle in response to climate variability and extreme events

Contact: Christian Beer, Nuno Carvalhais, Dorothea Frank, Miguel Mahecha, Markus Reichstein, Xiuchen Wu, Jakob Zscheischler more
Duration: 11/2010 - 2/2016

EMBRACE brings together the leading Earth System Models (ESMs) in Europe around a common set of objectives to improve our ability to simulate the Earth System and make reliable projections of future global change.

Contact: Nuno Carvalhais, Martin Jung, Markus Reichstein, Sönke Zaehle more
Duration: 04/2010 - 03/2013

Apparent ecosystem carbon turnover time: uncertainties and robust features

Contact: Matthias Forkel, Markus Reichstein more

CARBONES

Duration: 04/2010 - 03/2013

A novel approach for quantifying and understanding CO2 surface fluxes is proposed with CARBONES. It is a global information system that will address in a comprehensive and accurate way the quantification and understanding of the distribution of CO2 fluxes, carbon pools and underlying processes.

Contact: Matthias Forkel, Markus Reichstein

GEOCARBON

Duration: 10/2011 - 09/2014

GEOCARBON aims at designing a coordinated Global Carbon Observation and Analysis System, addressing the climate targets of the Group on Earth Observations (GEO) toward building a Global Earth Observation System of Systems (GEOSS) for carbon.

Contact: Nuno Carvalhais, Christoph Gerbig, Martin Heimann, Martin Jung, Christoph Köstler, Manos Kalomenopoulos, Miguel Mahecha, Markus Reichstein, Gregor Schürmann, Sönke Zaehle

GHG-Europe

Duration: 01/2010 - 09/2013

Greenhouse gas management in European land use systems

Contact: Martin Heimann, Christoph Gerbig, Jošt Valentin Lavric, Antje Moffat, Markus Reichstein, Enrico Tomelleri, Sönke Zaehle
Go to Editor View