Unpacking dasymetric modelling to correct spatial bias in environmental model outputs
Loading...
Access rights
openAccess
publishedVersion
URL
Journal Title
Journal ISSN
Volume Title
A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä
This publication is imported from Aalto University research portal.
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
View publication in the Research portal (opens in new window)
View/Open full text file from the Research portal (opens in new window)
Date
Department
Major/Subject
Mcode
Degree programme
Language
en
Pages
12
Series
Environmental Modelling & Software, Volume 157
Abstract
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.Description
Other note
Citation
Kallio, M, Guillaume, J, Burek, P, Tramberend, S, Smilovic, M, Horton, A & Virrantaus, K-K 2022, 'Unpacking dasymetric modelling to correct spatial bias in environmental model outputs', Environmental Modelling & Software, vol. 157, 105511. https://doi.org/10.1016/j.envsoft.2022.105511