the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 08 Aug 2025
Decade-long isotope dataset of rainfall and non-rainfall waters in the central Namib Desert
Abstract. Drylands are essential Earth System components, and dryland dynamics are strongly controlled by water availability. Long-term ground observations of hydrological parameters are often lacking in most dryland ecosystems. A series of foundational and unique water input data, including both rainfall and non-rainfall (i.e., fog and dew) components, in hyper-arid desert environments have been presented in this database. These observations provide comprehensive isotope measurements of rainfall and non-rainfall water resources, which are essential to evaluate the impacts of extreme climate events on the water cycle. The database comprised three key components: (1) a decade-long (2014−2023) event-based stable isotopes (δ2H, δ18O, and d-excess) in rainfall and non-rainfall waters at Gobabeb, (2) a two-month spatial isotope dataset (δ2H, δ18O, and d-excess) of fog collected from the central Namib Desert in 2016 and 2017, and (3) a six-decade (1963−2023) temporal data documenting monthly fog and rainfall amounts as well as annual Kuiseb River flooding events at Gobabeb. The detailed stable isotope data included 585 fog samples, 71 rainfall samples, 115 dew samples, and 13 groundwater samples over the past ten years (2014−2023) in the Namib Desert. Detailed descriptions of the study sites, sampling procedures, analytical methods, and data quality control are provided in this study. The uniqueness of our long-term dataset makes it an important resource for future studies investigating hydrological processes in drylands and their responses to climate change. The DOIs of the dataset can be obtained in the “Data availability” section.
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Status: closed
- RC1: 'Comment on essd-2025-245', Anonymous Referee #1, 09 Sep 2025
-
RC2: 'Comment on essd-2025-245', Anonymous Referee #2, 09 Sep 2025
Review of “Decade-long isotope dataset of rainfall and non-rainfall waters in the central Namib Desert”
Summary: The authors describe a newly released dataset that includes rain, fog, dew, and groundwater samples analyzed for the stable isotope ratios of oxygen and hydrogen. They also include a 6-decade event dataset, documenting the presence of rain or fog. The data are presented and described, though I expect more quality assurance and contextualization of the dataset than was presented by the authors. Please see annotated pdf for additional comments.
Main points:
- Please present the data in the context of other regional datasets. i.e., the data indicate higher/lower/ similar d2H / d18O / dxs / LMWLs compared to other precipitation collected in Namibia, Botswana and/or South Africa. I recognize that they shouldn’t be expected to match, but knowing whether they plot as expected relative to those other collections will be important.
- Please include uncertainty in the estimate of the LMWL. Since that estimate is used to categorize the fog types, being careful about how it is defined, and the authors level of certainty is critical. Also, why was groundwater included in the LMWL? It likely reflects precip that fell upstream and was transported downstream and recharged the aquifer. In this sense, it isn’t a realistic quantity to include in the LMWL. Please provide a physical reason for inclusion.
- Was any QA applied to the data, if so, what? And why? What proportion of total rain and fog or dew events were captured?
- The discussion does not add much and doesn’t appear to be required for this type of paper. I would omit. UNLESS the authors offer context relative to other datasets. IN which case, the relationship of this dataset to those may be discussed in the discussion section.
- Some notes on the data access: I appreciate the self-describing nature of the data file. However, it is not easily machine readable without deleting the whole header. Additionally - the use of special characters in the header (delta symbol, per mille symbol) makes it more difficult to load in and work with programatically. The column 'Event' seems like it should be 'Site Name'. And the column 'Samp type' should be simplified to 'Dew', 'Fog', 'Rain', or 'Groundwater'. The temporal or spatial campaign can be noted in a column called 'comments' or 'Sample campaign'. I also noticed inconsistencies in the isotope ratio column headers: 'δD [‰ SMOW]', 'δ18O [‰]', 'd xs [‰]'. Note that only one references SMOW. All should, or none should. It would be my preference (for ease of use) to convert these headers to 'd2H', 'd18O', and 'd', with the headers explained in the metadata. This would allow the data to be more easily machine readable.
Status: closed
-
RC1: 'Comment on essd-2025-245', Anonymous Referee #1, 09 Sep 2025
Review of «Decade-long isotope dataset of rainfall and non-rainfall waters in the central Namib Desert» by Li et al.
This paper presents a database consisting of three main components:
(1) a decade-long (2014−2023) event-based stable isotope record of rainfall and non-rainfall waters at Gobabeb,
(2) a two-month spatial isotope dataset of fog collected in the central Namib Desert in 2016 and 2017, and
(3) a six-decade (1963−2023) temporal dataset documenting monthly fog and rainfall amounts as well as annual Kuiseb River flooding events at Gobabeb.The isotope dataset itself has been published on Pangaea (Li et al. 2025b, hereafter Li25b), and the present paper can be viewed as an extended description of that dataset. There is, however, substantial overlap with Li et al. (2025a, hereafter Li25a), in which components (1)–(3) are already described in detail. The only substantive addition in the current paper is the inclusion of data from 2022 and 2023. While the publication of this isotope dataset is certainly valuable, several important issues require attention.
First, the dataset does not include the fog categories (radiation, mixed, advection) that the authors have assigned to individual events. Since these categories are derived from the isotope data itself, and their meteorological basis is, in some cases, questionable, they should be made available alongside the dataset. For example, the event on 16.12.2014 is classified as radiation fog, despite being associated with a wind speed of 7 m/s (Kaseke et al. 2017). Making these classifications transparent is essential so that their validity can be independently evaluated.
My principal concern relates to the description of dataset (1). In Li25a, the authors state with regard to the isotope data collected at Gobabeb: “Additionally, we opportunistically recorded event‐based fog amount data using the 1 m² SFC as a backup between October 2014 and December 2022.” Similarly, Li25b reports that the temporal fog samples at Gobabeb were collected using a standard fog collector (SFC). However, it now appears that from 2020 onwards, the samples were not obtained with an SFC, but rather from rooftop drippings collected by one of the co-authors. The number of fog days based on these rooftop samples is much higher than that derived from fog precipitation measured with the Juvik collector. Moreover, there are several cases where fog samples are reported on days when meteorological data at Gobabeb show no indication of fog (e.g. in June 2023, 6 out of 15 reported events lack supporting evidence from fog precipitation, visibility, cloud base height, air temperature cooling rate, or longwave downward radiation).
There is no reason to doubt that water was indeed dripping from the roof, and these observations may point to interesting scientific questions—for example, whether they represent dewfall triggered by a nearby fog front, or processes related to cooling or changes in specific humidity. Nevertheless, omitting this information about the sampling method is problematic, as it affects the interpretation and comparability of the dataset.
Finally, the explanation given in Li25a—that the higher number of fog days is due to the greater sensitivity of the SFC to low-intensity southerly fog compared to the Juvik collector—appears inconsistent with the actual sampling methods employed after 2020. This discrepancy is confusing and, in its current form, undermines confidence in the dataset description.
Kaseke K. F., Wang L. X. and Seely M. K. (2017): Nonrainfall water origins and formation mechanisms, Science Advances, 3(3) (doi: 10.1126/sciadv.1603131).
Li Y., Wang L., Diersing C., Qiao N., Yi L. Maggs-Kölling G., Marais E. (2025a): El Niño intensified fog formation in the Namib Desert, Earth's Future (doi.org/10.1029/2024EF005725).
Li, Y., Marais, E., Wang, L. (2025b): Decade-long isotope dataset (δ2H and δ18O) of rainfall and non-rainfall water samples in the central Namib Desert, Namibia [dataset]. PANGAEA, (doi.pangaea.de/10.1594/PANGAEA.981180).
Citation: https://doi.org/10.5194/essd-2025-245-RC1 -
RC2: 'Comment on essd-2025-245', Anonymous Referee #2, 09 Sep 2025
Review of “Decade-long isotope dataset of rainfall and non-rainfall waters in the central Namib Desert”
Summary: The authors describe a newly released dataset that includes rain, fog, dew, and groundwater samples analyzed for the stable isotope ratios of oxygen and hydrogen. They also include a 6-decade event dataset, documenting the presence of rain or fog. The data are presented and described, though I expect more quality assurance and contextualization of the dataset than was presented by the authors. Please see annotated pdf for additional comments.
Main points:
- Please present the data in the context of other regional datasets. i.e., the data indicate higher/lower/ similar d2H / d18O / dxs / LMWLs compared to other precipitation collected in Namibia, Botswana and/or South Africa. I recognize that they shouldn’t be expected to match, but knowing whether they plot as expected relative to those other collections will be important.
- Please include uncertainty in the estimate of the LMWL. Since that estimate is used to categorize the fog types, being careful about how it is defined, and the authors level of certainty is critical. Also, why was groundwater included in the LMWL? It likely reflects precip that fell upstream and was transported downstream and recharged the aquifer. In this sense, it isn’t a realistic quantity to include in the LMWL. Please provide a physical reason for inclusion.
- Was any QA applied to the data, if so, what? And why? What proportion of total rain and fog or dew events were captured?
- The discussion does not add much and doesn’t appear to be required for this type of paper. I would omit. UNLESS the authors offer context relative to other datasets. IN which case, the relationship of this dataset to those may be discussed in the discussion section.
- Some notes on the data access: I appreciate the self-describing nature of the data file. However, it is not easily machine readable without deleting the whole header. Additionally - the use of special characters in the header (delta symbol, per mille symbol) makes it more difficult to load in and work with programatically. The column 'Event' seems like it should be 'Site Name'. And the column 'Samp type' should be simplified to 'Dew', 'Fog', 'Rain', or 'Groundwater'. The temporal or spatial campaign can be noted in a column called 'comments' or 'Sample campaign'. I also noticed inconsistencies in the isotope ratio column headers: 'δD [‰ SMOW]', 'δ18O [‰]', 'd xs [‰]'. Note that only one references SMOW. All should, or none should. It would be my preference (for ease of use) to convert these headers to 'd2H', 'd18O', and 'd', with the headers explained in the metadata. This would allow the data to be more easily machine readable.
Data sets
Decade-long isotope dataset of rainfall and non-rainfall waters in the central Namib Yue Li, Eugene Marais, Lixin Wang https://doi.pangaea.de/10.1594/PANGAEA.981180
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Review of «Decade-long isotope dataset of rainfall and non-rainfall waters in the central Namib Desert» by Li et al.
This paper presents a database consisting of three main components:
(1) a decade-long (2014−2023) event-based stable isotope record of rainfall and non-rainfall waters at Gobabeb,
(2) a two-month spatial isotope dataset of fog collected in the central Namib Desert in 2016 and 2017, and
(3) a six-decade (1963−2023) temporal dataset documenting monthly fog and rainfall amounts as well as annual Kuiseb River flooding events at Gobabeb.
The isotope dataset itself has been published on Pangaea (Li et al. 2025b, hereafter Li25b), and the present paper can be viewed as an extended description of that dataset. There is, however, substantial overlap with Li et al. (2025a, hereafter Li25a), in which components (1)–(3) are already described in detail. The only substantive addition in the current paper is the inclusion of data from 2022 and 2023. While the publication of this isotope dataset is certainly valuable, several important issues require attention.
First, the dataset does not include the fog categories (radiation, mixed, advection) that the authors have assigned to individual events. Since these categories are derived from the isotope data itself, and their meteorological basis is, in some cases, questionable, they should be made available alongside the dataset. For example, the event on 16.12.2014 is classified as radiation fog, despite being associated with a wind speed of 7 m/s (Kaseke et al. 2017). Making these classifications transparent is essential so that their validity can be independently evaluated.
My principal concern relates to the description of dataset (1). In Li25a, the authors state with regard to the isotope data collected at Gobabeb: “Additionally, we opportunistically recorded event‐based fog amount data using the 1 m² SFC as a backup between October 2014 and December 2022.” Similarly, Li25b reports that the temporal fog samples at Gobabeb were collected using a standard fog collector (SFC). However, it now appears that from 2020 onwards, the samples were not obtained with an SFC, but rather from rooftop drippings collected by one of the co-authors. The number of fog days based on these rooftop samples is much higher than that derived from fog precipitation measured with the Juvik collector. Moreover, there are several cases where fog samples are reported on days when meteorological data at Gobabeb show no indication of fog (e.g. in June 2023, 6 out of 15 reported events lack supporting evidence from fog precipitation, visibility, cloud base height, air temperature cooling rate, or longwave downward radiation).
There is no reason to doubt that water was indeed dripping from the roof, and these observations may point to interesting scientific questions—for example, whether they represent dewfall triggered by a nearby fog front, or processes related to cooling or changes in specific humidity. Nevertheless, omitting this information about the sampling method is problematic, as it affects the interpretation and comparability of the dataset.
Finally, the explanation given in Li25a—that the higher number of fog days is due to the greater sensitivity of the SFC to low-intensity southerly fog compared to the Juvik collector—appears inconsistent with the actual sampling methods employed after 2020. This discrepancy is confusing and, in its current form, undermines confidence in the dataset description.
Kaseke K. F., Wang L. X. and Seely M. K. (2017): Nonrainfall water origins and formation mechanisms, Science Advances, 3(3) (doi: 10.1126/sciadv.1603131).
Li Y., Wang L., Diersing C., Qiao N., Yi L. Maggs-Kölling G., Marais E. (2025a): El Niño intensified fog formation in the Namib Desert, Earth's Future (doi.org/10.1029/2024EF005725).
Li, Y., Marais, E., Wang, L. (2025b): Decade-long isotope dataset (δ2H and δ18O) of rainfall and non-rainfall water samples in the central Namib Desert, Namibia [dataset]. PANGAEA, (doi.pangaea.de/10.1594/PANGAEA.981180).