Data · dataset · 2026
Reply on RC1 [* Cross-Reference *]
Listed in LORE
Abstract.
Description
Harmonized large-sample datasets have become a central pillar of hydrological research, particularly in the machinelearning era, where data-based algorithms and machine-learning techniques are gaining increasing importance in daily life. The CAMELS-LUX dataset (Catchment Attributes and MEteorology for Large-sample Studies – LUXembourg) described here covers 56 nested catchments (0.46 km2 – 4256.62 km2) that contribute to the Luxembourgish stream network.
While Luxembourg has a relatively homogeneous climate, the physiography varies significantly on a small scale making it a suitable study area for investigating different hydrological processes, such as runoff generation or groundwater recharge. The CAMELS-LUX dataset contains hydrological observations, meteorological data, and atmospheric reanalysis data from 2004–2021. Moreover, comprehensive physiographic catchment characteristics are provided that incorporate geology classes, land use classes, and a range of topographic indices.
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CAMELS-LUX is distinctive as the first dataset in the CAMELS series that offers data at three different temporal resolutions: daily, hourly, and in a 15-minute time step. Furthermore, CAMELS-LUX includes a series of flash floods in 2016 and 2018 as well as major large floods in 2010 and 2021. The extensive information contained in CAMELS-LUX is instrumental in advancing our understanding of varying discharge behaviour within Luxembourg and beyond.
The CAMELS-LUX dataset has been utilized to develop and train a Long-Short-Term-Memory (LSTM) model, that simulates discharge time series, providing a benchmark for subsequent hydrological modelling efforts in the area. The model based on this dataset sufficiently reproduces hydrological rainfall-runoff dependencies and can be applied to simulate discharge in sparsely gauged basins for approximation. The CAMELS-LUX dataset is available on zenodo: doi.org/10.5281/zenodo.13846619 (Nijzink et al., 2024). ***This entry has been automatically imported via OpenAlex by LIST harvest scripts.
Please refer to doi.org/10.5194/essd-2024-482-ac1 for the original and latest version of the publication***
Links
Where it is published
- Dataverse dataset page lore.list.lu/dataset.xhtml?persistentId=perma%3ALIST.VDKRE6 ↗
landing page · from lore list lu
- Persistent identifier lore.list.lu/citation?persistentId=perma%3ALIST.VDKRE6 ↗
DOI / persistent id · from lore list lu
Catalogue records · 1
- Dataverse API lore.list.lu/api/datasets/:persistentId/?persistentId=perma%3ALIST.VDKRE6 ↗
metadata API · from lore list lu
Topics
- Stated by source
- Other
- From keywords
- Computer Science & AI · Earth & Environmental Science · Life Sciences
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Provenance · 1 source records, 10 field assertions
| Source | Key | Last seen | Raw |
|---|---|---|---|
| LORE | perma:LIST.VDKRE6 | 3 h ago | JSON v1 |
| Field | Assertion | Extractor | Evidence |
|---|---|---|---|
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| concepts[field].local:field:computer-science-ai | mapping · lore list lu | connector:lore_list_lu@1.0.0 | /subjects |
| concepts[field].local:field:earth-environmental | mapping · lore list lu | connector:lore_list_lu@1.0.0 | /subjects |
| concepts[field].local:field:life-sciences | mapping · lore list lu | connector:lore_list_lu@1.0.0 | /subjects |
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| description | source · lore list lu | connector:lore_list_lu@1.0.0 | /description |
| publication_date | source · lore list lu | connector:lore_list_lu@1.0.0 | |
| title | source · lore list lu | connector:lore_list_lu@1.0.0 | /name |
| updated_date | source · lore list lu | connector:lore_list_lu@1.0.0 | |
| version_label | source · lore list lu | connector:lore_list_lu@1.0.0 |