Periodic Reporting for period 1 - FIBER (Understanding soil fertility impacts on terrestrial biomass production in a changing environment)
Periodo di rendicontazione: 2017-02-28 al 2019-02-27
This projected addressed how droughts affect photosynthesis, vegetation productivity and the global carbon cycle. Remote-sensing based models are widely used for estimating terrestrial photosynthesis. These commonly use information about the greenness of the Earth surface, incoming solar radiation and some measure of dryness. Due to the limitation that soil moisture is particularly challenging to observe from satellites, these models have commonly resorted to using information only on the atmospheric dryness (vapour pressure deficit). In view of known physiological mechanisms by which plants respond to drying soils, the question is: Does vapour pressure deficit alone provide enough information to simulate drought impacts on vegetation?
Understanding vegetation sensitivity to droughts is important for estimating the impacts of extreme events and the year-to-year variations of the terrestrial carbon balance. Dry and hot periods like last year’s summer in northern Europe can negatively affect the C balance of a whole continent and thereby accelerate the man-made increase in atmospheric CO2.
The objective of this project was to develop a new method for detecting water stress on vegetation productivity, to test the widely-used assumption that atmospheric humidity provides sufficient information to capture drought effects, including soil water stress, and to characterise the drought-sensitivity of ecosystems across a wide range of climates.
Understanding vegetation sensitivity to droughts is important for estimating the impacts of extreme events and the year-to-year variations of the terrestrial carbon balance. Dry and hot periods like last year’s summer in northern Europe can negatively affect the C balance of a whole continent and thereby accelerate the man-made increase in atmospheric CO2.
The objective of this project was to develop a new method for detecting water stress on vegetation productivity, to test the widely-used assumption that atmospheric humidity provides sufficient information to capture drought effects, including soil water stress, and to characterise the drought-sensitivity of ecosystems across a wide range of climates.
We collected data from the FLUXNET research network of ecosystem flux measurements, paired CO2 exchange flux measurements with remotely sensed vegetation greenness data around respective sites, and soil moisture estimates from a set of sources and models. We then developed a machine learning approach (based on Artificial Neural Networks), designed to detect and quantify soil moisture effects on the efficiency by which vegetation converts CO2 and absorbed light energy into photosynthates (referred to as light use efficiency). This allowed us to isolate the soil moisture effect on photosynthesis from other drivers, including the (partly co-varying) vapour pressure deficit. By analysing patterns in the data from flux measurements and satellites, we found a clear additional effect by soil moisture that substantially reduces photosynthesis during “soil moisture droughts” - an effect that cannot be estimated when solely relying on information on vapour pressure deficit. We quantified this effect for a large number of sites across the globe and characterised sites with respect to their sensitivity to low soil moisture. We found that there is a tendency of sites in relatively wet climates to exhibit a low sensitivity to a drying of the topsoil (max. top 1 m), possibly due to access to water in deeper soil layers. This research was published in New Phytologist (Stocker et al. 2018).
In a next step, we evaluated a set of widely-used remote sensing-based models for estimating vegetation productivity across the globe and over time. The question we asked was whether these models can accurately simulate photosynthesis during soil moisture drought periods, which we have managed to identify precisely in our previous study (Stocker et al. 2018). This was an obvious question to ask because these remote sensing-based models do not account for soil moisture directly, but use information on atmospheric humidity as a proxy for dryness in general. We found that all the models we investigated had a very clear relationship with the timing and magnitude of the apparent soil moisture stress we identified earlier. This indicates that information on soil moisture should be included explicitly in estimates of vegetation productivity - not just atmospheric humidity and remotely sensed vegetation greenness. This research was published in Nature Geoscience (Stocker et al., 2019).
Apart from data analysis, this project yielded both new methods for data analysis, and new model development for global vegetation productivity simulations using a new modular modelling framework (SOFUN):
- Benjamin Stocker. (2018, April 6). sofun: v1.1.0 (Version v1.1.0). Zenodo. http://doi.org/10.5281/zenodo.1213758
All code and model outputs are made freely accessible through github (https://github.com/stineb/soilm_global https://github.com/stineb/nn_fluxnet2015 https://github.com/stineb/fvar) and on the open access permanent data repository Zenodo:
- Stocker, Benjamin. (2018). fLUE [Data set]. New Phytologist. Zenodo. http://doi.org/10.5281/zenodo.1158524
- Benjamin Stocker. (2018, February 20). nn_fluxnet2015: Final release (Version submission_3). Zenodo. http://doi.org/10.5281/zenodo.1181691
- Benjamin Stocker. (2018, April 6). sofun: v1.1.0 (Version v1.1.0). Zenodo. http://doi.org/10.5281/zenodo.1213758
- Stocker, Benjamin. (2018). GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci. (Version v1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1423484
- Benjamin Stocker. (2019, January 18). soilm_global: Code for Stocker et al. (2019) Nature Geosci. (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.2543324
In a next step, we evaluated a set of widely-used remote sensing-based models for estimating vegetation productivity across the globe and over time. The question we asked was whether these models can accurately simulate photosynthesis during soil moisture drought periods, which we have managed to identify precisely in our previous study (Stocker et al. 2018). This was an obvious question to ask because these remote sensing-based models do not account for soil moisture directly, but use information on atmospheric humidity as a proxy for dryness in general. We found that all the models we investigated had a very clear relationship with the timing and magnitude of the apparent soil moisture stress we identified earlier. This indicates that information on soil moisture should be included explicitly in estimates of vegetation productivity - not just atmospheric humidity and remotely sensed vegetation greenness. This research was published in Nature Geoscience (Stocker et al., 2019).
Apart from data analysis, this project yielded both new methods for data analysis, and new model development for global vegetation productivity simulations using a new modular modelling framework (SOFUN):
- Benjamin Stocker. (2018, April 6). sofun: v1.1.0 (Version v1.1.0). Zenodo. http://doi.org/10.5281/zenodo.1213758
All code and model outputs are made freely accessible through github (https://github.com/stineb/soilm_global https://github.com/stineb/nn_fluxnet2015 https://github.com/stineb/fvar) and on the open access permanent data repository Zenodo:
- Stocker, Benjamin. (2018). fLUE [Data set]. New Phytologist. Zenodo. http://doi.org/10.5281/zenodo.1158524
- Benjamin Stocker. (2018, February 20). nn_fluxnet2015: Final release (Version submission_3). Zenodo. http://doi.org/10.5281/zenodo.1181691
- Benjamin Stocker. (2018, April 6). sofun: v1.1.0 (Version v1.1.0). Zenodo. http://doi.org/10.5281/zenodo.1213758
- Stocker, Benjamin. (2018). GPP: Site-scale and global model outputs from P-model used for Stocker et al. (2019) Nature Geosci. (Version v1.0) [Data set]. Zenodo. http://doi.org/10.5281/zenodo.1423484
- Benjamin Stocker. (2019, January 18). soilm_global: Code for Stocker et al. (2019) Nature Geosci. (Version v1.0). Zenodo. http://doi.org/10.5281/zenodo.2543324
The method developed by FIBER provides a powerful approach to tackle the long-standing challenge of separating effects from partly co-varying drivers of vegetation productivity during droughts, in particular atmospheric humidity and soil moisture content. This is a crucial insight needed for the formulation of powerful models, in particular the design of remote sensing-based methods and serves as a key model benchmark. This method also enabled us to detect the timing of apparent soil moisture droughts. This information can serve as a “ground-truth” for methods that aim at detecting soil moisture droughts not just around the sites we investigated (where ecosystem flux measurements are available), but across space based on other, indirect measures (e.g. surface reflectance, sun-induced fluorescence, or surface temperature). Insights gained by the project FIBER add to fundamental knowledge required to improve European economies’ resilience to climate risk by understanding vulnerability of terrestrial ecosystem productivity to climate change.