the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 20 Jul 2026
Decade-long isotope dataset of rainfall and non-rainfall waters in the central Namib Desert
Abstract. Drylands are essential components of the Earth System, 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 has 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 dataset documenting monthly fog and rainfall amounts as well as annual Kuiseb River flooding events at Gobabeb. The detailed stable isotope data included 532 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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Preprint
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Interactive discussion
Status: closed
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RC1: 'Comment on essd-2026-170', Roland Vogt, 27 Aug 2026
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RC2: 'Reply on RC1', Anonymous Referee #2, 28 Aug 2026
The authors collected a series of foundational datasets of both rainfall and non-rainfall water, providing essential records to evaluate the impacts of extreme events on the water cycle of drylands.
Reviewer #1 in the first round of review pointed out that the data have quite some overlap with previous publications. The authors also responded to what overlaps with previous publications and what is new in the datasets. But it is still true that there are quite some overlaps with previous publications.
Reviewer #1 also questioned the fog classification. I have similar comments. In my opinion, fog types should be classified based on meteorological observations, particularly the prevailing meteorological conditions and the temperature, humidity, and wind profiles within the atmospheric boundary layer, for instance, ERA5 reanalysis data as used in Mass et al. 2025 (https://doi.org/10.5194/acp-25-491-2025). Stable isotopes should be used cautiously as supporting evidence rather than as the primary criterion for fog-type classification.
Isotopic compositions mainly provide information about differences in vapor sources and associated atmospheric processes among fog types. However, they do not necessarily fall within distinct or non-overlapping isotopic ranges, nor do they always lie perfectly along a specific local meteoric water line (LMWL). As is well known, isotopic compositions from different water sources can substantially overlap. Therefore, different fog types may exhibit statistically distinct isotopic tendencies, but this does not necessarily imply that each fog type has a unique isotopic range that can be used for unambiguous classification.
Accordingly, the different fog types should preferably be classified based on the meteorological conditions prevailing during their occurrence, rather than being separated according to their isotopic distributions and meteorological conditions. Isotopic data can then be used to characterize or support the inferred differences among fog types.
Furthermore, isotopic measurements are subject to variability and uncertainty arising from differences in vapor sources, atmospheric mixing, sampling procedures, and analytical measurement errors. Consequently, one should not expect fog-water isotopes to exhibit a perfect distribution along a specific meteoric water line. Under supersaturated conditions, for example, radiation fog may also exhibit isotopic compositions to the left of the LMWL. Therefore, I would recommend interpreting the apparently well-separated isotopic distributions shown in Figs. 6 and 10 with caution and avoiding an overly strict classification of fog types based on their isotopic signatures.
Besides, the LMWL itself exhibits substantial variability, with a slope of 5.8 in the submitted manuscript compared with 7.01 reported by Kaseke et al. (2017) and other publications discussed in the submitted manuscript. Therefore, the LMWL slope should not be used as a main indicator for distinguishing radiation fog from other fog types.
Reviewer #2 in the first round raised the concern that groundwater should not be included in the construction of the LMWL. The authors argued that the available precipitation samples were insufficient to establish a robust LMWL. However, they also used the close agreement between the groundwater samples and the LMWL as evidence supporting the LMWL. I find this argument somewhat contradictory. If groundwater samples were incorporated into, or used to constrain, the LMWL because of the limited precipitation dataset, their proximity to the resulting LMWL cannot provide independent evidence for its validity.
Citation: https://doi.org/10.5194/essd-2026-170-RC2 -
AC1: 'Reply on RC2', Lixin Wang, 29 Aug 2026
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2026-170/essd-2026-170-AC1-supplement.pdf
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AC1: 'Reply on RC2', Lixin Wang, 29 Aug 2026
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AC2: 'Reply on RC1', Lixin Wang, 14 Sep 2026
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2026-170/essd-2026-170-AC2-supplement.pdf
-
RC2: 'Reply on RC1', Anonymous Referee #2, 28 Aug 2026
Interactive discussion
Status: closed
-
RC1: 'Comment on essd-2026-170', Roland Vogt, 27 Aug 2026
-
RC2: 'Reply on RC1', Anonymous Referee #2, 28 Aug 2026
The authors collected a series of foundational datasets of both rainfall and non-rainfall water, providing essential records to evaluate the impacts of extreme events on the water cycle of drylands.
Reviewer #1 in the first round of review pointed out that the data have quite some overlap with previous publications. The authors also responded to what overlaps with previous publications and what is new in the datasets. But it is still true that there are quite some overlaps with previous publications.
Reviewer #1 also questioned the fog classification. I have similar comments. In my opinion, fog types should be classified based on meteorological observations, particularly the prevailing meteorological conditions and the temperature, humidity, and wind profiles within the atmospheric boundary layer, for instance, ERA5 reanalysis data as used in Mass et al. 2025 (https://doi.org/10.5194/acp-25-491-2025). Stable isotopes should be used cautiously as supporting evidence rather than as the primary criterion for fog-type classification.
Isotopic compositions mainly provide information about differences in vapor sources and associated atmospheric processes among fog types. However, they do not necessarily fall within distinct or non-overlapping isotopic ranges, nor do they always lie perfectly along a specific local meteoric water line (LMWL). As is well known, isotopic compositions from different water sources can substantially overlap. Therefore, different fog types may exhibit statistically distinct isotopic tendencies, but this does not necessarily imply that each fog type has a unique isotopic range that can be used for unambiguous classification.
Accordingly, the different fog types should preferably be classified based on the meteorological conditions prevailing during their occurrence, rather than being separated according to their isotopic distributions and meteorological conditions. Isotopic data can then be used to characterize or support the inferred differences among fog types.
Furthermore, isotopic measurements are subject to variability and uncertainty arising from differences in vapor sources, atmospheric mixing, sampling procedures, and analytical measurement errors. Consequently, one should not expect fog-water isotopes to exhibit a perfect distribution along a specific meteoric water line. Under supersaturated conditions, for example, radiation fog may also exhibit isotopic compositions to the left of the LMWL. Therefore, I would recommend interpreting the apparently well-separated isotopic distributions shown in Figs. 6 and 10 with caution and avoiding an overly strict classification of fog types based on their isotopic signatures.
Besides, the LMWL itself exhibits substantial variability, with a slope of 5.8 in the submitted manuscript compared with 7.01 reported by Kaseke et al. (2017) and other publications discussed in the submitted manuscript. Therefore, the LMWL slope should not be used as a main indicator for distinguishing radiation fog from other fog types.
Reviewer #2 in the first round raised the concern that groundwater should not be included in the construction of the LMWL. The authors argued that the available precipitation samples were insufficient to establish a robust LMWL. However, they also used the close agreement between the groundwater samples and the LMWL as evidence supporting the LMWL. I find this argument somewhat contradictory. If groundwater samples were incorporated into, or used to constrain, the LMWL because of the limited precipitation dataset, their proximity to the resulting LMWL cannot provide independent evidence for its validity.
Citation: https://doi.org/10.5194/essd-2026-170-RC2 -
AC1: 'Reply on RC2', Lixin Wang, 29 Aug 2026
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2026-170/essd-2026-170-AC1-supplement.pdf
-
AC1: 'Reply on RC2', Lixin Wang, 29 Aug 2026
-
AC2: 'Reply on RC1', Lixin Wang, 14 Sep 2026
The comment was uploaded in the form of a supplement: https://essd.copernicus.org/preprints/essd-2026-170/essd-2026-170-AC2-supplement.pdf
-
RC2: 'Reply on RC1', Anonymous Referee #2, 28 Aug 2026
Data sets
Decade-long isotope dataset (δ2H and δ18O) of rainfall and non-rainfall water samples in the central Namib Desert Yue Li, Eugene Marais, and Lixin Wang https://doi.org/10.6084/m9.figshare.31441114
Annual and monthly fog amount from 1962 to 2023 at Gobabeb, Namibia Yue Li, Eugene Marais, and Lixin Wang https://doi.org/10.6084/m9.figshare.31441123
Annual and monthly rainfall amount from 1962 to 2023 at Gobabeb, Namibia Yue Li, Eugene Marais, and Lixin Wang https://doi.org/10.6084/m9.figshare.31441141
Annual total ocurrence of Kuiseb River flooding from 1962 to 2023 at Gobabeb, Namibia Yue Li, Eugene Marais, and Lixin Wang https://doi.org/10.6084/m9.figshare.31441165
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