A deep learning-based hybrid model of global terrestrial evaporation.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 35395845.
- Also identified by DOI 10.1038/s41467-022-29543-7 and PMC identifier 8993934.
- Licence recorded as CC BY.
- The licence permits redistribution, so the abstract is shown in full and the full text is available from the publisher.
Abstract
Terrestrial evaporation (E) is a key climatic variable that is controlled by a plethora of environmental factors. The constraints that modulate the evaporation from plant leaves (or transpiration, E<sub>t</sub>) are particularly complex, yet are often assumed to interact linearly in global models due to our limited knowledge based on local studies. Here, we train deep learning algorithms using eddy covariance and sap flow data together with satellite observations, aiming to model transpiration stress (S<sub>t</sub>), i.e., the reduction of E<sub>t</sub> from its theoretical maximum. Then, we embed the new S<sub>t</sub> formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the S<sub>t</sub> formulation is bidirectionally coupled to the host model at daily timescales. Comparisons against in situ data and satellite-based proxies demonstrate an enhanced ability to estimate S<sub>t</sub> and E globally. The proposed framework may be extended to improve the estimation of E in Earth System Models and enhance our understanding of this crucial climatic variable.
Medical subject headings
- Deep Learning
- Plant Transpiration