Matches in SemOpenAlex for { <https://semopenalex.org/work/W4225984022> ?p ?o ?g. }
- W4225984022 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, Et) 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 (St), i.e., the reduction of Et from its theoretical maximum. Then, we embed the new St formulation within a process-based model of E to yield a global hybrid E model. In this hybrid model, the St 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 St 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." @default.
- W4225984022 created "2022-05-05" @default.
- W4225984022 creator A5017362795 @default.
- W4225984022 creator A5019243789 @default.
- W4225984022 creator A5030207043 @default.
- W4225984022 creator A5060456665 @default.
- W4225984022 creator A5087368097 @default.
- W4225984022 date "2022-04-08" @default.
- W4225984022 modified "2023-10-09" @default.
- W4225984022 title "A deep learning-based hybrid model of global terrestrial evaporation" @default.
- W4225984022 cites W1864007918 @default.
- W4225984022 cites W1930871163 @default.
- W4225984022 cites W1973082817 @default.
- W4225984022 cites W1975768883 @default.
- W4225984022 cites W1979723077 @default.
- W4225984022 cites W1981213426 @default.
- W4225984022 cites W2016044589 @default.
- W4225984022 cites W2022224360 @default.
- W4225984022 cites W2048243194 @default.
- W4225984022 cites W2073773661 @default.
- W4225984022 cites W2077968790 @default.
- W4225984022 cites W2081105229 @default.
- W4225984022 cites W2098787944 @default.
- W4225984022 cites W2112084935 @default.
- W4225984022 cites W2121069783 @default.
- W4225984022 cites W2123558695 @default.
- W4225984022 cites W2129859781 @default.
- W4225984022 cites W2138763184 @default.
- W4225984022 cites W2149662495 @default.
- W4225984022 cites W2160741281 @default.
- W4225984022 cites W2172396214 @default.
- W4225984022 cites W2196961118 @default.
- W4225984022 cites W2276195374 @default.
- W4225984022 cites W2359292793 @default.
- W4225984022 cites W2485420366 @default.
- W4225984022 cites W2588003345 @default.
- W4225984022 cites W2592541999 @default.
- W4225984022 cites W2600777534 @default.
- W4225984022 cites W2601923741 @default.
- W4225984022 cites W2742433978 @default.
- W4225984022 cites W2744649443 @default.
- W4225984022 cites W2785846558 @default.
- W4225984022 cites W2808626200 @default.
- W4225984022 cites W2810551812 @default.
- W4225984022 cites W2887626628 @default.
- W4225984022 cites W2904321320 @default.
- W4225984022 cites W2911777435 @default.
- W4225984022 cites W2913323966 @default.
- W4225984022 cites W2916882450 @default.
- W4225984022 cites W2922004833 @default.
- W4225984022 cites W2936453454 @default.
- W4225984022 cites W2954648193 @default.
- W4225984022 cites W2974527409 @default.
- W4225984022 cites W2981104727 @default.
- W4225984022 cites W2989857225 @default.
- W4225984022 cites W2991713810 @default.
- W4225984022 cites W2995511918 @default.
- W4225984022 cites W2997282111 @default.
- W4225984022 cites W3006332078 @default.
- W4225984022 cites W3036708439 @default.
- W4225984022 cites W3036878551 @default.
- W4225984022 cites W3040739689 @default.
- W4225984022 cites W3044041163 @default.
- W4225984022 cites W3049729109 @default.
- W4225984022 cites W3092376471 @default.
- W4225984022 cites W3102368172 @default.
- W4225984022 cites W3131921307 @default.
- W4225984022 cites W3152553619 @default.
- W4225984022 cites W3152729261 @default.
- W4225984022 cites W3161201474 @default.
- W4225984022 cites W4206112603 @default.
- W4225984022 doi "https://doi.org/10.1038/s41467-022-29543-7" @default.
- W4225984022 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/35395845" @default.
- W4225984022 hasPublicationYear "2022" @default.
- W4225984022 type Work @default.
- W4225984022 citedByCount "21" @default.
- W4225984022 countsByYear W42259840222022 @default.
- W4225984022 countsByYear W42259840222023 @default.
- W4225984022 crossrefType "journal-article" @default.
- W4225984022 hasAuthorship W4225984022A5017362795 @default.
- W4225984022 hasAuthorship W4225984022A5019243789 @default.
- W4225984022 hasAuthorship W4225984022A5030207043 @default.
- W4225984022 hasAuthorship W4225984022A5060456665 @default.
- W4225984022 hasAuthorship W4225984022A5087368097 @default.
- W4225984022 hasBestOaLocation W42259840221 @default.
- W4225984022 hasConcept C105795698 @default.
- W4225984022 hasConcept C111919701 @default.
- W4225984022 hasConcept C121332964 @default.
- W4225984022 hasConcept C1276947 @default.
- W4225984022 hasConcept C134306372 @default.
- W4225984022 hasConcept C157517311 @default.
- W4225984022 hasConcept C178650346 @default.
- W4225984022 hasConcept C182365436 @default.
- W4225984022 hasConcept C183688256 @default.
- W4225984022 hasConcept C19269812 @default.
- W4225984022 hasConcept C26517878 @default.
- W4225984022 hasConcept C33923547 @default.
- W4225984022 hasConcept C38652104 @default.
- W4225984022 hasConcept C39432304 @default.
- W4225984022 hasConcept C41008148 @default.