Unpacking dasymetric modelling to correct spatial bias in environmental model outputs

Marko Kallio*, Joseph Guillaume, Peter Burek, Sylvia Tramberend, Mikhail Smilovic, Alexander Horton, Kirsi-Kanerva Virrantaus

*Corresponding author for this work

Research output: Contribution to journalArticleScientificpeer-review

1 Citation (Scopus)
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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.
Original languageEnglish
Article number105511
Number of pages12
JournalEnvironmental Modelling & Software
Publication statusPublished - Nov 2022
MoE publication typeA1 Journal article-refereed


  • Bias correction
  • dasymetric modelling
  • downscaling
  • areal interpolation
  • environmental modelling
  • meta-modelling


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