000902320 001__ 902320
000902320 005__ 20211130111105.0
000902320 0247_ $$2doi$$a10.3390/rs13163068
000902320 0247_ $$2Handle$$a2128/28969
000902320 0247_ $$2WOS$$aWOS:000689918200001
000902320 037__ $$aFZJ-2021-04177
000902320 082__ $$a620
000902320 1001_ $$0P:(DE-Juel1)176864$$aZhao, Haojin$$b0$$eCorresponding author
000902320 245__ $$aThe Importance of Subsurface Processes in Land Surface Modeling over a Temperate Region: An Analysis with SMAP, Cosmic Ray Neutron Sensing and Triple Collocation Analysis
000902320 260__ $$aBasel$$bMDPI$$c2021
000902320 3367_ $$2DRIVER$$aarticle
000902320 3367_ $$2DataCite$$aOutput Types/Journal article
000902320 3367_ $$0PUB:(DE-HGF)16$$2PUB:(DE-HGF)$$aJournal Article$$bjournal$$mjournal$$s1636464249_21290
000902320 3367_ $$2BibTeX$$aARTICLE
000902320 3367_ $$2ORCID$$aJOURNAL_ARTICLE
000902320 3367_ $$00$$2EndNote$$aJournal Article
000902320 520__ $$aLand surface models (LSMs) simulate water and energy cycles at the atmosphere–soil interface, however, the physical processes in the subsurface are typically oversimplified and lateral water movement is neglected. Here, a cross-evaluation of land surface model results (with and without lateral flow processes), the National Aeronautics and Space Administration (NASA) Soil Moisture Active/Passive (SMAP) mission soil moisture product, and cosmic-ray neutron sensor (CRNS) measurements is carried out over a temperate climate region with cropland and forests over western Germany. Besides a traditional land surface model (the Community Land Model (CLM) version 3.5), a coupled land surface-subsurface model (CLM-ParFlow) is applied. Compared to CLM stand-alone simulations, the coupled CLM-ParFlow model considered both vertical and lateral water movement. In addition to standard validation metrics, a triple collocation (TC) analysis has been performed to help understanding the random error variances of different soil moisture datasets. In this study, it is found that the three soil moisture datasets are consistent. The coupled and uncoupled model simulations were evaluated at CRNS sites and the coupled model simulations showed less bias than the CLM-standalone model (−0.02 cm3 cm−3 vs. 0.07 cm3 cm−3), similar random errors, but a slightly smaller correlation with the measurements (0.67 vs. 0.71). The TC-analysis showed that CLM-ParFlow reproduced better soil moisture dynamics than CLM stand alone and with a higher signal-to-noise ratio. This suggests that the representation of subsurface physics is of major importance in land surface modeling and that coupled land surface-subsurface modeling is of high interest
000902320 536__ $$0G:(DE-HGF)POF4-2173$$a2173 - Agro-biogeosystems: controls, feedbacks and impact (POF4-217)$$cPOF4-217$$fPOF IV$$x0
000902320 588__ $$aDataset connected to CrossRef, Journals: juser.fz-juelich.de
000902320 7001_ $$0P:(DE-Juel1)129506$$aMontzka, Carsten$$b1
000902320 7001_ $$0P:(DE-Juel1)144513$$aBaatz, Roland$$b2
000902320 7001_ $$0P:(DE-Juel1)129549$$aVereecken, Harry$$b3
000902320 7001_ $$0P:(DE-Juel1)138662$$aFranssen, Harrie-Jan Hendricks$$b4
000902320 773__ $$0PERI:(DE-600)2513863-7$$a10.3390/rs13163068$$gVol. 13, no. 16, p. 3068 -$$n16$$p3068 -$$tRemote sensing$$v13$$x2072-4292$$y2021
000902320 8564_ $$uhttps://juser.fz-juelich.de/record/902320/files/remotesensing-13-03068-v4.pdf$$yOpenAccess
000902320 909CO $$ooai:juser.fz-juelich.de:902320$$pdnbdelivery$$pdriver$$pVDB$$popen_access$$popenaire
000902320 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)176864$$aForschungszentrum Jülich$$b0$$kFZJ
000902320 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129506$$aForschungszentrum Jülich$$b1$$kFZJ
000902320 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)144513$$aForschungszentrum Jülich$$b2$$kFZJ
000902320 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)129549$$aForschungszentrum Jülich$$b3$$kFZJ
000902320 9101_ $$0I:(DE-588b)5008462-8$$6P:(DE-Juel1)138662$$aForschungszentrum Jülich$$b4$$kFZJ
000902320 9131_ $$0G:(DE-HGF)POF4-217$$1G:(DE-HGF)POF4-210$$2G:(DE-HGF)POF4-200$$3G:(DE-HGF)POF4$$4G:(DE-HGF)POF$$9G:(DE-HGF)POF4-2173$$aDE-HGF$$bForschungsbereich Erde und Umwelt$$lErde im Wandel – Unsere Zukunft nachhaltig gestalten$$vFür eine nachhaltige Bio-Ökonomie – von Ressourcen zu Produkten$$x0
000902320 9141_ $$y2021
000902320 915__ $$0StatID:(DE-HGF)0200$$2StatID$$aDBCoverage$$bSCOPUS$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0160$$2StatID$$aDBCoverage$$bEssential Science Indicators$$d2021-05-04
000902320 915__ $$0LIC:(DE-HGF)CCBY4$$2HGFVOC$$aCreative Commons Attribution CC BY 4.0
000902320 915__ $$0StatID:(DE-HGF)0600$$2StatID$$aDBCoverage$$bEbsco Academic Search$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0100$$2StatID$$aJCR$$bREMOTE SENS-BASEL : 2019$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0501$$2StatID$$aDBCoverage$$bDOAJ Seal$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0500$$2StatID$$aDBCoverage$$bDOAJ$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0113$$2StatID$$aWoS$$bScience Citation Index Expanded$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0700$$2StatID$$aFees$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0150$$2StatID$$aDBCoverage$$bWeb of Science Core Collection$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)9900$$2StatID$$aIF < 5$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0510$$2StatID$$aOpenAccess
000902320 915__ $$0StatID:(DE-HGF)0030$$2StatID$$aPeer Review$$bASC$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0561$$2StatID$$aArticle Processing Charges$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)1150$$2StatID$$aDBCoverage$$bCurrent Contents - Physical, Chemical and Earth Sciences$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0300$$2StatID$$aDBCoverage$$bMedline$$d2021-05-04
000902320 915__ $$0StatID:(DE-HGF)0199$$2StatID$$aDBCoverage$$bClarivate Analytics Master Journal List$$d2021-05-04
000902320 920__ $$lyes
000902320 9201_ $$0I:(DE-Juel1)IBG-3-20101118$$kIBG-3$$lAgrosphäre$$x0
000902320 980__ $$ajournal
000902320 980__ $$aVDB
000902320 980__ $$aUNRESTRICTED
000902320 980__ $$aI:(DE-Juel1)IBG-3-20101118
000902320 9801_ $$aFullTexts