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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/99007 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/99007 http://eprints.org/relation/ismediumThumbnailVersionOf https://pure.iiasa.ac.at/id/document/99007 99012 2 303906 document 99012 small.jpg image/png 376a51f2eb2b23c5cc6f8b1d6147c9dd MD5 1966 2022-09-23 08:46:53 https://pure.iiasa.ac.at/id/eprint/18244/6/small.jpg 18244 6 6 image/png other Thumbnails conversion from text to thumbnail_small en public

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http://eprints.org/relation/isVersionOf https://pure.iiasa.ac.at/id/document/99007 http://eprints.org/relation/isVolatileVersionOf https://pure.iiasa.ac.at/id/document/99007 http://eprints.org/relation/issmallThumbnailVersionOf https://pure.iiasa.ac.at/id/document/99007 archive 353 disk0/00/01/82/44 2022-09-23 08:38:08 2022-09-23 08:38:08 2022-09-23 08:38:08 article show Kallio M. 9337 Guillaume J.H.A. Burek P. 2091 0000-0001-6390-8487 Tramberend S. 1614 0000-0002-7024-1075 Smilovic M. 2211 0000-0001-9651-8821 Horton A.J. Virrantaus K. pub BNR WAT prog_ysp Bias correction; Dasymetric modelling; Downscaling; Areal interpolation; Environmental modelling; meta-Modelling Complex environmental model outputs used to inform decisions often have systematic errors and are of inappropriate resolution, requiring downscaling and bias correction for local applications. Here we provide a new interpretation of dasymetric modelling (DM) as a spatial bias correction framework useful in environmental modelling. DM is based on areal interpolation where estimates of some variable at target zones are obtained from overlapping source zones using ancillary information. We explore DM by downscaling runoff output from a distributed hydrological model using two meta-models and describe the properties of the methodology in detail. Consistent with properties of linear scaling bias correction, results show that the methodology 1) reduces errors compared to the source data and meta-models, 2) improve the spatial structure of the estimates, and 3) improve the performance of the downscaled estimates, particularly where meta-models perform poorly. The framework is simple and useful in ensuring spatial coherence of downscaled products. 2022-11 published Elsevier doi:10.1016/j.envsoft.2022.105511 3174 46 312 3079 public Environmental Modelling & Software 157 e105511 TRUE 1873-6726 no info:eu-repo/semantics/article