Projekteja vuodessa
Abstrakti
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.
Alkuperäiskieli | Englanti |
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Artikkeli | 105511 |
Sivumäärä | 12 |
Julkaisu | Environmental Modelling & Software |
Vuosikerta | 157 |
DOI - pysyväislinkit | |
Tila | Julkaistu - marrask. 2022 |
OKM-julkaisutyyppi | A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä |
Sormenjälki
Sukella tutkimusaiheisiin 'Unpacking dasymetric modelling to correct spatial bias in environmental model outputs'. Ne muodostavat yhdessä ainutlaatuisen sormenjäljen.Tietoaineistot
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Data and code for: Unpacking dasymetric modelling to correct spatial bias in environmental model outputs
Kallio, M. (Creator), Zenodo, 16 tammik. 2022
DOI - pysyväislinkki: 10.5281/zenodo.5857188
Tietoaineisto: Dataset
Projektit
- 1 Päättynyt
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Vesi ja haavoittuvuus hauraissa yhteiskunnissa
Kummu, M. (Vastuullinen tutkija)
01/03/2018 → 28/02/2022
Projekti: Academy of Finland: Other research funding