Which hydro-meteorological variables control large-scale photosynthesis?
- Department of Biogeochemical Integration, Max Planck Institute for Biogeochemistry, Jena, Germany (wantong@bgc-jena.mpg.de)
Vegetation dynamics are determined by a multitude of hydro-meteorological variables, and this interplay changes in space and time. Due to its complexity, it is still not fully understood at large spatial scales. This knowledge gap contributes to increased uncertainties in future climate projections because large-scale photosynthesis is influencing the exchange of energy and water between the land surface and the atmosphere, thereby potentially impacting near-surface weather. In this study, we explore the relative importance of several hydro-meteorological variables for vegetation dynamics. For this purpose, we infer the correlations of anomalies in temperature, precipitation, soil moisture, VPD, surface net radiation and surface downward solar radiation with respective anomalies of photosynthetic activity as inferred from Sun-Induced chlorophyll Fluorescence (SIF). To detect changing hydro-meteorological controls across different climate conditions, this global analysis distinguishes between climate regimes as determined by long-term mean aridity and temperature. The results show that soil moisture was the most critical driver with SIF in the simultaneous correlation with dry and warm conditions, while temperature and VPD was both influential on cold and wet regimes during the study period 2007-2018. We repeat our analysis by replacing the SIF data with NDVI, as a proxy for vegetation greenness, and find overall similar results, except for surface net radiation expanding controlled regions on cold and wet regimes. As the considered hydro-meteorological variables are inter-related, spurious correlations can occur. We test different approaches to investigate and account for this phenomenon. The results can provide new insight into mechanisms of vegetation-water-energy interactions and contribute to improve dynamic global vegetation models.
How to cite: Li, W., Migliavacca, M., Luo, Y., and Orth, R.: Which hydro-meteorological variables control large-scale photosynthesis?, EGU General Assembly 2020, Online, 4–8 May 2020, EGU2020-15936, https://doi.org/10.5194/egusphere-egu2020-15936, 2020
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Great study thanks! How did you control for the colinearity of the input features? For example the Soil Levels 1-4 are highly correlated variables. Disentangling the importance of each feature separately can be difficult when included in the same model. How did you overcome this issue?
Great work really cool!
Thank you for your question! We didn’t handle the potential problem of collinearity in Random forest, while we did contrast methods based on machine learning and correlation analysis, and they showed similar results. This support the patterns of main drivers that we identified by RF.
I'd like to give some complements:
We cannot fully overcome the problem of collinearity is a limitation of our study.
We try to mitigate the problem by using state-of-the-art machine learning (random forests), and removing the seasonal cycles in our next step.
In your slides you mention: "Here we use unit-adjusted net radiation to replace potential evapotranspiration" why is that? Potential evaporation is available as a field in ERA5?
Nice question! There are many ways to calculate aridity index. We simply choose the method of using united-adjusted solar radiation devided by precipitation, because we already have solar radiation and precipitation data in global ERA5 datasets, and also the uncertainties of different aridity index hardly impact our main analysis.
Another point is that, potential evapotranspiration is hard to validate, so the net radiation from ERA5 datasets can be more reliable that the calculation of PET.