the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
CAMELS-NZ: Hydrometeorological time series and landscape attributes for Aotearoa New Zealand
Abstract. We present the first large-sample catchment hydrology dataset for Aotearoa New Zealand with hourly time series: the Catchment Attributes and Meteorology for Large-Sample Studies – Aotearoa New Zealand (CAMELS-NZ). This dataset provides hourly hydrometeorological time series and comprehensive landscape attributes for 369 catchments across Aotearoa New Zealand, ranging from 1972 to 2024. Hourly records include streamflow, precipitation, temperature, relative humidity and potential evapotranspiration, with more than 65 % of streamflow records exceeding 40 years in length. CAMELS-NZ offers a rich set of static catchment attributes that quantify physical characteristics such as land cover, soil properties, geology, topography, and human impacts, including information on abstractions, dams, groundwater or snowmelt influences, as well as on ephemeral rivers. Aotearoa New Zealand's remarkable gradients in climate, topography, and geology give rise to diverse hydroclimatic landscapes and hydrological behaviours, making CAMELS-NZ a unique contribution to large-scale hydrological studies. Furthermore, Aotearoa New Zealand’s hydrology is defined by highly permeable volcanic catchments, sediment-rich alpine rivers with glacial contributions, and steep, rainfall-driven fast-rising rivers, providing opportunities to study diverse hydrological processes and rapid hydrological responses. CAMELS-NZ adheres to the standards established by most previously published CAMELS datasets, enabling international comparison studies. The dataset fills a critical gap in global hydrology by representing a Pacific Island environment with complex hydrological processes. This dataset supports a wide range of hydrological research applications, including model development and climate impact assessments, predictions in ungauged basins and large-sample comparative studies. The open-access nature of CAMELS-NZ ensures broad usability across multiple research domains, providing a foundation for national water resource and flood management, as well as international hydrological research. By integrating long-term high-resolution data with diverse catchment attributes, we hope that CAMELS-NZ will enable innovative research into Aotearoa New Zealand's hydrological systems while contributing to the global initiative to create freely available large-sample datasets for the hydrological community. The CAMELS-NZ dataset can be accessed at https://doi.org/10.26021/canterburynz.28827644 (Bushra et al., 2025).
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CC1: 'Comment on essd-2025-244', Sacha Ruzzante, 02 Jun 2025
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Overall this seems like a valuable contribution to the growing number of CAMELS datasets. However, I have some suggestions to improve the usefulness of the data and to improve consistency with other CAMELS datasets.
- Can you include time series of glacier evolution, as was done for Camels-CH (Höge et al., 2023)? Or at minimum, have a static attribute that describes glacier cover for each catchment.
- Some of the static attributes are provided as categorical variables that indicate the dominant category (eg. land cover, geology). For many applications it is more useful to know the percentage of the catchment that falls into each category, rather than just the most common category.
- There are many useful static attributes that can be calculated but are not currently included. For example, soil characteristics from SoilGrids (Poggio et al., 2021), catchment average elevation, mean annual temperature, precipitation seasonality, etc. See other camels datasets or the Caravan project (Kratzert et al., 2023) for examples.
- Are there other climate datasets that could be included as well? For machine learning models previous work has shown that including several climate datasets in training usually improves overall model skill. For example, ERA5-Land (Muñoz Sabater, 2019), the New Zealand Reanalysis Dataset (Pirooz et al., 2023) CHIRPS (Funk et al., 2015), CPC (Chen & Xie, 2008), etc. You may want to look at how these were included in other camels datasets such as Camels-BR (Chagas et al., 2020). Some of these provide daily data only, but that is what many users will want anyway. For snow-affected catchments it would be useful to have a SWE product (eg. ERA5-Land).
- It would be useful to also provide daily aggregated streamflow and meteorology data. Most hydrologic models are built on daily data, and for benchmarking models across different research groups it is useful to know that everyone is using exactly the same data. Providing the daily aggregated data helps ensure this.
- The paper states “Information on how to obtain permission [for the 13 gauges that require it] is provided in the readme file”, but this is missing from the readme file.
- I’m not sure what the authors mean by the “original temporal structure” in the following:“All time series data are reported in the local time zone, and include the effects of daylight saving time (DST) where applicable. No corrections or transformations were applied to standardise timestamps across the dataset. This decision was made to preserve the original temporal structure of the observations.” It would be more useful if all timestamps were provided in standard time, and it is quite possible to do this while preserving the temporal structure of the data.
- There are some negative streamflow values. For example, station 29231, which has a number of timestamps with flow of -0.003 cms. What does this mean?
Chagas, V. B. P., Chaffe, P. L. B., Addor, N., Fan, F. M., Fleischmann, A. S., Paiva, R. C. D., & Siqueira, V. A. (2020). CAMELS-BR: Hydrometeorological time series and landscape attributes for 897 catchments in Brazil. Earth System Science Data 12(3), 2075–2096. https://doi.org/10.5194/essd-12-2075-2020
Chen, M., & Xie, P. (2008, January 8). CPC unified gauge-based analysis of global daily precipitation. Western Pacific Geophysics Meeting, Cairns, Australia.
Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., Husak, G., Rowland, J., Harrison, L., Hoell, A., & Michaelsen, J. (2015). The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Scientific Data, 2(1), 150066. https://doi.org/10.1038/sdata.2015.66
Höge, M., Kauzlaric, M., Siber, R., Schönenberger, U., Horton, P., Schwanbeck, J., Floriancic, M. G., Viviroli, D., Wilhelm, S., Sikorska-Senoner, A. E., Addor, N., Brunner, M., Pool, S., Zappa, M., & Fenicia, F. (2023). CAMELS-CH: Hydro-meteorological time series and landscape attributes for 331 catchments in hydrologic Switzerland. Earth System Science Data, 15(12), 5755–5784. https://doi.org/10.5194/essd-15-5755-2023
Kratzert, F., Nearing, G., Addor, N., Erickson, T., Gauch, M., Gilon, O., Gudmundsson, L., Hassidim, A., Klotz, D., Nevo, S., Shalev, G., & Matias, Y. (2023). Caravan—A global community dataset for large-sample hydrology. Scientific Data, 10(1), 61. https://doi.org/10.1038/s41597-023-01975-w
Muñoz Sabater, J. (2019). ERA5-Land monthly averaged data from 1950 to present [Dataset]. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). https://doi.org/10.24381/cds.68d2bb30
Pirooz, A., Moore, S., Carey-Smith, T., Turner, R., & Su, C.-H. (2023). The New Zealand Reanalysis (NZRA): Development and preliminary evaluation. Weather and Climate, 42(1), 58–74. https://doi.org/10.2307/27226715
Poggio, L., de Sousa, L. M., Batjes, N. H., Heuvelink, G. B. M., Kempen, B., Ribeiro, E., & Rossiter, D. (2021). SoilGrids 2.0: Producing soil information for the globe with quantified spatial uncertainty. SOIL, 7(1), 217–240. https://doi.org/10.5194/soil-7-217-2021Citation: https://doi.org/10.5194/essd-2025-244-CC1 -
RC1: 'Comment on essd-2025-244', Anonymous Referee #1, 17 Jun 2025
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This paper describes a new open access dataset that will be of great value to hydrologists and others in the earth sciences. It is clear and well written and includes excellent background on NZ's climate, landscape and geology. I recommend that the paper is published if the following minor comments are addressed satisfactorily.
Lines 140-145: I would recommend using a more recent source for future estimates of temperature and rainfall changes instead of King 2010 as this is based on NIWA's modelling done nearly 20 years ago for AR4. For instance:
Peter Gibson, et al., 2025, Downscaled CMIP6 future climate projections for New Zealand: climatology and extremes, Weather and Climate Extremes, https://doi.org/10.1016/j.wace.2025.100784.
Also when discussing changes in climate, the reference period needs to be included; e.g. "expected to warm by 1 degC by 2040 relative to the 1986-2005 average". In addition, as currently written only a single scenario has been explored (probably King 2010's mid-range scenario), but this choice needs to be highlighted and the scenario information included for context.In the context of this dataset, it might also be of interest to describe how NZ's climate has changed over the past 60 years (for example referencing the 'seven station series' (https://niwa.co.nz/climate-and-weather/nz-temperature-record/seven-station-series-temperature-data) or the following recent research on changing climate normals, although there are many other studies that could be used.
Srinivasan, R., et al. (2024). Moving to a new normal: Analysis of shifting climate normals in New Zealand. International Journal of Climatology, 44(10), 3240–3263. https://doi.org/10.1002/joc.8521Figure 4 and related text: Fig 4a units should be in %. Coefficient of variation is a normalised ratio of SD to MEAN, so either expressed as a fraction or %. The incorrect units are also in Table 3.
Line 253: Related to above, the sentence "Rainfall variability is highest in the eastern parts of both of the North and South Island" is not correct. Annual rainfall variability relative to the annual mean is highest in east (i.e. coefficient of variation). Variability (e.g. variance or standard deviation) of annual rainfall is greatest in the higher elevation parts of the West Coast. Without showing a plot of annual mean rainfall, a reader unfamiliar with NZ, might come away from this part of the paper not realising that the highest rainfalls are on the west. I recommend that Fig 4 also include a map of mean annual rainfall.Citation: https://doi.org/10.5194/essd-2025-244-RC1
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