A1 Vertaisarvioitu data-artikkeli tieteellisessä lehdessä
CAMELS-FI : hydrometeorological time series and landscape properties for 320 catchments in Finland; 
Tekijät: Seppä, Iiro; Gonzales Inca, Carlos; Uusikivi, Jari; Alho, Petteri
Kustantaja: Copernicus GmbH
Julkaisuvuosi: 2026
Lehti: Earth System Science Data
Vuosikerta: 18
Numero: 7
Aloitussivu: 4745
Lopetussivu: 4769
ISSN: 1866-3508
eISSN: 1866-3516
DOI: https://doi.org/10.5194/essd-18-4745-2026
Julkaisun avoimuus kirjaamishetkellä: Avoimesti saatavilla
Julkaisukanavan avoimuus : Kokonaan avoin julkaisukanava
Verkko-osoite: https://doi.org/10.5194/essd-18-4745-2026
Rinnakkaistallenteen osoite: https://research.utu.fi/converis/portal/detail/Publication/527174843
Rinnakkaistallenteen lisenssi: CC BY
Rinnakkaistallennetun julkaisun versio: Kustantajan versio
Linkki tutkimusdataan: https://doi.org/10.5281/zenodo.15853357
Comprehensive, large-sample hydrological datasets, such as CAMELS (Catchment Attributes and MEteorology for Large-sample Studies), have provided the basis for advances in many aspects of hydrological research in recent years. They can be utilised for several purposes, such as training or calibrating hydrological models, comparisons between regions dominated by different types of hydrological processes and testing of general validity of hydrological theories. The value of these datasets is in combining a multitude of data sources into one easily accessible and usable, harmonised high-quality package. We present CAMELS-FI, an extensive dataset for 320 catchments in Finland. It combines hydrological and meteorological time series with biophysical and human influence catchment attributes in a format that enables comparisons between catchments within the dataset but also between earlier CAMELS datasets. CAMELS-FI includes a diverse set of catchments with human influence varying from near natural to heavily regulated. CAMELS-FI is available at https://doi.org/10.5281/zenodo.15853357 (Seppä et al., 2025).
Ladattava julkaisu This is an electronic reprint of the original article. |  |
Julkaisussa olevat rahoitustiedot:
This research was a part of the Ministry of Education and Culture's Doctoral Education Pilot under Decision no. VN/3137/2024-OKM-6 (Digital Waters (DIWA) Doctoral Education Pilot related to the DIWA Flagship (decision no. 359247) funded by the Research Council of Finland's Flagship Programme) and with Flagship Programme funding granted by the Research Council of Finland for Digital Waters Flagship (decision no. 359247).
