the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 12 Mar 2025
Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America
Abstract. We present a new data set aimed at hydrologic studies across North America, with a particular focus on facilitating spatially distributed studies. The data set includes basin outlines, stream observations, meteorological data and geospatial data for 1426 basins in the United States and Canada. To facilitate a wide variety of studies, we provide the basin outlines at a lumped and semi-distributed resolution; streamflow observations at daily and hourly time steps; variables suitable for running a wide range of models obtained and derived from different meteorological data sets at daily (1 data set) and hourly (3 data sets) time steps; and geospatial data and derived attributes from 11 different data sets that broadly cover climatic conditions, vegetation properties, land use, and subsurface characteristics. Forcing data are provided at their native gridded resolution, as well as averaged at the basin and sub-basin level. Geospatial data are provided as maps per basin, as well as summarized as catchment attributes at the basin and sub-basin level with various statistics. Attributes are further complemented with statistics derived from the forcing data and streamflow, and have a particular focus on quantifying the variability of natural processes and catchment characteristics in space and time. Our goal with this data set is to build upon existing large-sample data sets and provide the means for more detailed investigation of hydrologic behavior across large geographical scales. In particular, we hope that this data sets provide others with the data needed to implement a wide range of modeling approaches, and to investigate the impact of basin heterogeneity on hydrologic behaviour and similarity. The CAMELS-SPAT (Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis) is available at: https://dx.doi.org/10.20383/103.01216.
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Notice on discussion status
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
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Preprint
(15456 KB)
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Supplement
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The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(15456 KB) - Metadata XML
-
Supplement
(5840 KB) - BibTeX
- EndNote
- Final revised paper
Journal article(s) based on this preprint
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-893', Anonymous Referee #1, 14 Apr 2025
- AC1: 'Reply on RC1', Wouter Knoben, 08 May 2025
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RC2: 'Comment on egusphere-2025-893', Anonymous Referee #2, 15 Apr 2025
- AC2: 'Reply on RC2', Wouter Knoben, 08 May 2025
Interactive discussion
Status: closed
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RC1: 'Comment on egusphere-2025-893', Anonymous Referee #1, 14 Apr 2025
Overall, the authors did an excellent job explaining the processing of the CAMEL-SPAT geospatial dataset. The narrative is clear and provides sufficient details and explanation to understand the basic process for preparing the dataset(s). The tables are very detailed and helpful in visualizing the different models, model requirements, and the dataset(s).
Overall, I have relatively minor edits, as explained below:
Abstract
Line 14: small typo (This data set)
Line 1: I think the first line of the abstract should make it clear that the dataset builds on the CAMELS dataset; perhaps expand on “new dataset” such as “new dataset that builds on the CAMELs dataset”.
Line 171: small type “area”
Line 185: Please provide more details on the accuracy assessment for the basin methods. Specifically, provide details on what you mean by “evidence suggests”. What statistical/quantitative measurement did you use to identify your “confidence ratings”?
Figure 3: Please clarify a few elements of this figure in the caption: The figure has 3 colors (pink, blue, maroon) – please explain maroon. Also, explain the record length and how it differs from missing values in the caption and somewhere in the paragraph from line 240-249.
Line 210 – Methods and Outcomes section for Streamflow observations: I would recommend creating a CDF figure similar to Figure 2 in Newman et al., 2015 (see below) in order to show the streamflow distribution of the new dataset that you present here.
Data citation: A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR. https://dx.doi.org/10.5065/D6MW2F4D
348: I’d recommend also referencing Figure 1, as that visualizes the “processing steps” referred to in this line.
Figure 7 caption: provide more details similar to those presented in the Figure 5 and 6 captions. For example, I recommend at the least, including the dataset used to create the figures and how/where the statistics were derived.
Figure 8 caption: Same as above (e.g. dataset and statistics explanation), but also include explanation of prediction uncertainty
Citation: https://doi.org/10.5194/egusphere-2025-893-RC1 - AC1: 'Reply on RC1', Wouter Knoben, 08 May 2025
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RC2: 'Comment on egusphere-2025-893', Anonymous Referee #2, 15 Apr 2025
- AC2: 'Reply on RC2', Wouter Knoben, 08 May 2025
Peer review completion
Journal article(s) based on this preprint
Data sets
Catchment Attributes and MEteorology for Large-Sample SPATially distributed analysis (CAMELS-SPAT): Streamflow observations, forcing data and geospatial data for hydrologic studies across North America Wouter Knoben and Kasra Keshavarz https://doi.org/10.20383/103.01216
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Wouter J. M. Knoben
Kasra Keshavarz
Laura Torres-Rojas
Cyril Thébault
Nathaniel W. Chaney
Alain Pietroniro
Martyn P. Clark
The requested preprint has a corresponding peer-reviewed final revised paper. You are encouraged to refer to the final revised version.
- Preprint
(15456 KB) - Metadata XML
-
Supplement
(5840 KB) - BibTeX
- EndNote
- Final revised paper
Overall, the authors did an excellent job explaining the processing of the CAMEL-SPAT geospatial dataset. The narrative is clear and provides sufficient details and explanation to understand the basic process for preparing the dataset(s). The tables are very detailed and helpful in visualizing the different models, model requirements, and the dataset(s).
Overall, I have relatively minor edits, as explained below:
Abstract
Line 14: small typo (This data set)
Line 1: I think the first line of the abstract should make it clear that the dataset builds on the CAMELS dataset; perhaps expand on “new dataset” such as “new dataset that builds on the CAMELs dataset”.
Line 171: small type “area”
Line 185: Please provide more details on the accuracy assessment for the basin methods. Specifically, provide details on what you mean by “evidence suggests”. What statistical/quantitative measurement did you use to identify your “confidence ratings”?
Figure 3: Please clarify a few elements of this figure in the caption: The figure has 3 colors (pink, blue, maroon) – please explain maroon. Also, explain the record length and how it differs from missing values in the caption and somewhere in the paragraph from line 240-249.
Line 210 – Methods and Outcomes section for Streamflow observations: I would recommend creating a CDF figure similar to Figure 2 in Newman et al., 2015 (see below) in order to show the streamflow distribution of the new dataset that you present here.
Data citation: A. Newman; K. Sampson; M. P. Clark; A. Bock; R. J. Viger; D. Blodgett, 2014. A large-sample watershed-scale hydrometeorological dataset for the contiguous USA. Boulder, CO: UCAR/NCAR. https://dx.doi.org/10.5065/D6MW2F4D
348: I’d recommend also referencing Figure 1, as that visualizes the “processing steps” referred to in this line.
Figure 7 caption: provide more details similar to those presented in the Figure 5 and 6 captions. For example, I recommend at the least, including the dataset used to create the figures and how/where the statistics were derived.
Figure 8 caption: Same as above (e.g. dataset and statistics explanation), but also include explanation of prediction uncertainty