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Decadal trends in evaporation from global energy and water balances

MPG-Autoren
http://pubman.mpdl.mpg.de/cone/persons/resource/persons62425

Jung,  Martin
Research Group Biogeochemical Model-data Integration, Dr. M. Reichstein, Max Planck Institute for Biogeochemistry, Max Planck Society;

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Zitation

Zhang, Y., Leuning, R., Chiew, F. H. S., Wang, E., Zhang, L., Liu, C., et al. (2012). Decadal trends in evaporation from global energy and water balances. Journal of Hydrometeorology, 13(1), 379-391. doi:10.1175/JHM-D-11-012.1.


Zitierlink: http://hdl.handle.net/11858/00-001M-0000-000E-DDD6-5
Zusammenfassung
Satellite and gridded meteorological data can be used to estimate evaporation (E) from land surfaces using simple diagnostic models. Two satellite datasets indicate a positive trend (first time derivative) in global available energy from 1983 to 2006, suggesting that positive trends in evaporation may occur in “wet” regions where energy supply limits evaporation. However, decadal trends in evaporation estimated from water balances of 110 wet catchments do not match trends in evaporation estimated using three alternative methods: 1) , a model-tree ensemble approach that uses statistical relationships between E measured across the global network of flux stations, meteorological drivers, and remotely sensed fraction of absorbed photosynthetically active radiation; 2) , a Budyko-style hydrometeorological model; and 3) , the Penman–Monteith energy-balance equation coupled with a simple biophysical model for surface conductance. Key model inputs for the estimation of and are remotely sensed radiation and gridded meteorological fields and it is concluded that these data are, as yet, not sufficiently accurate to explain trends in E for wet regions. This provides a significant challenge for satellite-based energy-balance methods. Trends in for 87 “dry” catchments are strongly correlated to trends in precipitation (R2 = 0.85). These trends were best captured by , which explicitly includes precipitation and available energy as model inputs.