000878328 001__ 878328
000878328 005__ 20210130005546.0
000878328 0247_ $$2doi$$a10.3390/hydrology7030050
000878328 0247_ $$2Handle$$a2128/25460
000878328 0247_ $$2WOS$$aWOS:000580073600001
000878328 0247_ $$2altmetric$$aaltmetric:88429984
000878328 037__ $$aFZJ-2020-02780
000878328 082__ $$a550
000878328 1001_ $$00000-0001-7018-3592$$aMobilia, Mirka$$b0$$eCorresponding author
000878328 245__ $$aModelling Actual Evapotranspiration Seasonal Variability by Meteorological Data-Based Models
000878328 260__ $$aBasel$$bMDPI$$c2020
000878328 3367_ $$2DRIVER$$aarticle
000878328 3367_ $$2DataCite$$aOutput Types/Journal article
000878328 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1596808252_3884
000878328 3367_ $$2BibTeX$$aARTICLE
000878328 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000878328 3367_ $$00$$2EndNote$$aJournal Article
000878328 520__ $$aThis study aims at illustrating a methodology for predicting monthly scale actual evapotranspiration losses only based on meteorological data, which mimics the evapotranspiration intra-annual dynamic. For this purpose, micrometeorological data at the Rollesbroich and Bondone mountain sites, which are energy-limited systems, and the Sister site, a water-limited system, have been analyzed. Based on an observed intra-annual transition between dry and wet states governed by a threshold value of net radiation at each site, an approach that couples meteorological data-based potential evapotranspiration and actual evapotranspiration relationships has been proposed and validated against eddy covariance measurements, and further compared to two well-known actual evapotranspiration prediction models, namely the advection-aridity and the antecedent precipitation index models. The threshold approach improves the intra-annual actual evapotranspiration variability prediction, particularly during the wet state periods, and especially concerning the Sister site, where errors are almost four times smaller compared to the basic models. To further improve the prediction within the dry state periods, a calibration of the Priestley-Taylor advection coefficient was necessary. This led to an error reduction of about 80% in the case of the Sister site, of about 30% in the case of Rollesbroich, and close to 60% in the case of Bondone Mountain. For cases with a lack of measured data of net radiation and soil heat fluxes, which are essential for the implementation of the models, an application derived from empirical relationships is discussed. In addition, the study assessed whether this variation from meteorological data worsened the prediction performances of the models.
000878328 536__ $$0G:(DE-HGF)POF3-255$$a255 - Terrestrial Systems: From Observation to Prediction (POF3-255)$$cPOF3-255$$fPOF III$$x0
000878328 588__ $$aDataset connected to CrossRef
000878328 7001_ $$0P:(DE-Juel1)144420$$aSchmidt, Marius$$b1$$ufzj
000878328 7001_ $$00000-0002-1575-0782$$aLongobardi, Antonia$$b2
000878328 773__ $$0PERI:(DE-600)2777964-6$$a10.3390/hydrology7030050$$gVol. 7, no. 3, p. 50 -$$n3$$p50 -$$tHydrology$$v7$$x2306-5338$$y2020
000878328 8564_ $$uhttps://juser.fz-juelich.de/record/878328/files/hydrology-07-00050-v2.pdf$$yOpenAccess
000878328 8564_ $$uhttps://juser.fz-juelich.de/record/878328/files/hydrology-07-00050-v2.pdf?subformat=pdfa$$xpdfa$$yOpenAccess
000878328 909CO $$ooai:juser.fz-juelich.de:878328$$pdnbdelivery$$pVDB$$pVDB:Earth_Environment$$pdriver$$popen_access$$popenaire
000878328 9101_ $$0I:(DE-HGF)0$$60000-0001-7018-3592$$aExternal Institute$$b0$$kExtern
000878328 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144420$$aForschungszentrum Jülich$$b1$$kFZJ
000878328 9101_ $$0I:(DE-HGF)0$$60000-0002-1575-0782$$aExternal Institute$$b2$$kExtern
000878328 9131_ $$0G:(DE-HGF)POF3-255$$1G:(DE-HGF)POF3-250$$2G:(DE-HGF)POF3-200$$3G:(DE-HGF)POF3$$4G:(DE-HGF)POF$$aDE-HGF$$bErde und Umwelt$$lTerrestrische Umwelt$$vTerrestrial Systems: From Observation to Prediction$$x0
000878328 9141_ $$y2020
000878328 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2020-01-03
000878328 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000878328 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2020-01-03
000878328 915__ $$0StatID:(DE-HGF)0112$$2StatID$$aWoS$$bEmerging Sources Citation Index$$d2020-01-03
000878328 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2020-01-03
000878328 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2020-01-03
000878328 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000878328 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bDOAJ : Blind peer review$$d2020-01-03
000878328 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$f2020-01-03
000878328 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2020-01-03
000878328 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2020-01-03
000878328 920__ $$lyes
000878328 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000878328 980__ $$ajournal
000878328 980__ $$aVDB
000878328 980__ $$aUNRESTRICTED
000878328 980__ $$aI:(DE-Juel1)IBG-3-20101118
000878328 9801_ $$aFullTexts