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dc.contributor.authorHu, Xiangping
dc.contributor.authorCherubini, Francesco
dc.contributor.authorVezhapparambu, Sajith
dc.contributor.authorStrømman, Anders Hammer
dc.identifier.citationJournal of Advances in Modeling Earth Systems. 2018, 10 (10), 2495-2513.nb_NO
dc.description.abstractThe importance to consider changes in surface albedo and go beyond simple carbon accounting when assessing climate change impacts of forestry and land use activities is increasingly recognized. However, representation of albedo changes in climate models is complex and highly parameterized, thereby limiting their applications in climate impact studies. The availability of simple yet reliable albedo models can enhance consideration of albedo changes in land use studies. We propose a set of simplified models for estimating surface albedo in a boreal forest. We process and harmonize datasets of remotely‐sensed albedo estimates, forest structure parameters, and meteorological records for different forest locations in Norway. By combining linear unmixing with nonlinear programming, we simultaneously produce albedo estimates at the same resolution of the land cover dataset (16 m, notably higher than satellite retrievals) and a variety of flexible models for albedo predictions. We test different combinations of functional forms, variables, and constraints, including variants specific for snow‐free conditions. We find that models capture the seasonal pattern of surface albedo and the interactive effect of forest structures and meteorological parameters, and many of them show good statistical scores. The cross‐validation exercise shows that the models derived from one area perform reasonably well when applied to other forested areas in Norway, regardless of the temporal and spatial scales. By incorporating changes in forest structure and climate conditions as explicit variables, these models are simple to be used in different applications aiming at estimating albedo changes from forest management and climate change.nb_NO
dc.publisherAmerican Geophysical Union (AGU)nb_NO
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internasjonal*
dc.titleFrom remotely‐sensed data of Norwegian boreal forests to fast and flexible models for estimating surface albedonb_NO
dc.typeJournal articlenb_NO
dc.typePeer reviewednb_NO
dc.source.journalJournal of Advances in Modeling Earth Systemsnb_NO
dc.relation.projectNorges forskningsråd: 209697nb_NO
dc.relation.projectNorges forskningsråd: 244074nb_NO
dc.description.localcode© 2018. The Authors. This is an open access article under the terms of the Creative Commons Attribution‐NonCommercial‐NoDerivs License.nb_NO
cristin.unitnameInstitutt for energi- og prosessteknikk

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Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal
Med mindre annet er angitt, så er denne innførselen lisensiert som Attribution-NonCommercial-NoDerivatives 4.0 Internasjonal