| 02 Sep 2025
Recent summer soil moisture drying in Switzerland based on measurements from the SwissSMEX network
Abstract. Notably drier summers and more frequent droughts were reported in Switzerland in the last decades. Here, we present curated timeseries of in situ soil moisture measurements from the Swiss Soil Moisture Experiment (SwissSMEX) network, which as of now cover 15 years. We demonstrate the potential of this comprehensive network for analysing the documented drying trends. At 12 grassland stations, SwissSMEX provides data on the volumetric soil water content at various depths in the soil profile, which can be used to calculate integrated soil water content down to 50 cm depth as an indicator of root zone water. We document recent measures that have been taken to secure the SwissSMEX network and to ensure the continuity of its long-term soil moisture timeseries. These timeseries are used to analyse trends in summer and summer half-year anomalies of integrated soil water content, and to investigate the robustness of the recent drying based on different sets of Swiss Plateau stations. Furthermore, the SwissSMEX-based trends are compared with those from soil moisture of a widely used land reanalysis product (ERA5-Land) and of a merged passive microwave remote sensing product (European Space Agency Climate Change Initiative ESA CCI).
There is good agreement between the temporal evolution and the drying tendency of SwissSMEX in situ soil moisture based on different sets of Swiss Plateau stations, which vary in their temporal coverage due to sensor failures. Comparing the in situ timeseries from stations with best temporal coverage with ERA5-Land and ESA CCI PASSIVE soil moisture from the corresponding grid cells also reveals a good agreement between these three independent data sources, with correlations of 0.85 or higher for the median timeseries calculated across stations. Based on these stations, the drying over the common 2010–2023 period amounts to ‑2.0 mm yr-1 for the absolute summer soil moisture anomalies of SwissSMEX and ERA5-Land (or ‑1.2 % yr‑1 for the percentage anomalies), and to -0.9 mm yr-1 (or ‑0.6 % yr‑1) for ESA CCI PASSIVE. The summer half-year trends are about half of those from the summer values for SwissSMEX (-1.0 mm yr-1, p < 0.05 in this case) and ERA5-Land (‑1.1 mm yr-1), while ESA CCI PASSIVE shows similar summer and summer half-year trends. Although most of the trends are not significant over the short 2010–2023 period, trends in summer half-year soil moisture anomalies from ERA5-Land become significant for certain time frames when the period for the trend test calculation is extended to years before 2010. Although the SwissSMEX network indicates that summer soil drying has increased in recent years, the 15 years of in situ data currently available are in many cases not yet sufficient to robustly estimate a significant trend. This highlights the importance of sustaining ongoing measurements to ensure a seamless continuation of soil moisture monitoring in Switzerland.
This paper presents 15 years of curated in situ soil moisture time series from the SwissSMEX network and demonstrates how data from 12 grassland stations, providing volumetric soil water content at multiple depths and integrated values down to 50 cm, can be used to analyze drying trends in soil water content. The data set is of broad interest given the increasing occurrence of drought in many regions worldwide under global climate change and fits well within the scope of ESSD. The presented data product will be valuable to the scientific community working on global change and water scarcity issues in alpine regions. It would be great to also include the data in the International Soil Moisture Network (Dorigo et al., 2021).
Unfortunately, the manuscript exhibits several substantial shortcomings with respect to its methods, content, overall structure, and clarity of writing, which need to be addressed in detail (see specific comments below). I have suggested improvements to the text in some places. However, the entire text should be revised by a native speaker. When revising the manuscript, authors should bear in mind that ESSD focuses on the data set rather than on the interpretation of the data.
General comments:
The Abstract is overly long and not suitable for ESSD. It should be 150–200 words and focus on the dataset rather than scientific interpretation, describing the data type, size, and spatial and temporal coverage, explaining how the data were collected, briefly outlining quality-control procedures, and clearly indicating where the dataset is available (e.g., repository or DOI).
The Introduction section is too long and includes discussion, which is inappropriate for ESSD. It should instead concisely provide context and justify the dataset by highlighting gaps in existing data, clearly state the dataset’s purpose, scope, key variables, and coverage, and emphasize its value.
The Conclusion section should be shortened and focus on the dataset rather than scientific interpretations, emphasizing its main contributions, unique features or improvements over existing datasets, and briefly noting potential applications.
The manuscript does not provide sufficient references and details for the statistical methods used. Even well-known methods such as the Mann-Kendall trend test should be properly cited and the implementation described (e.g., software/library and version). For example: “The Mann-Kendall test (Mann, 1945; Kendall, 1975) detects monotonic changes over time and is robust to non-normally distributed data. Calculations were performed using the Python library pyMannKendall (vX.Y.Z) at α = 0.05, with missing values handled according to the library’s default options.”
A map showing the locations of the measurement stations should be included, along with a table summarizing key soil properties, such as soil type and texture.
Specific comments:
L10: “curated time series of in-situ”
L16: “vertically integrated” or “root-zone soil moisture”
L16: “the robustness of recent drying trends”
L44-59: This section reads more like a discussion and is not directly related to the data presented; it should be rewritten in a more concise way.
L45: Is the increase in evapotranspiration significant?
L53: “soil moisture”
L117: Please explain the trapezoidal method.
L115-143: This section on data processing is very confusing. The processing steps are not clearly presented, and information on data processing, e.g. sensor calibration and data correction, is missing. It needs to be rewritten in a clearer and more concise manner, with the focus on how the data was processed.
L164: “0–0.5 m (merged)” should be removed, as this information is unnecessary; the acronym IWC is self-explanatory.
L165: Please clarify how this percentage was determined. Was a statistical analysis performed to derive it, or was it determined in another way?
L184-186: This section should be moved to Chapter 2.1.2. In addition, the weighting function should be presented as a numbered equation. Note that the weights in the equation should not have units of length, otherwise the unit of IWC would be mm·m rather than mm. Combining several measurement depths into a single layer (e.g., 7–28 cm) causes measurements at 5 cm depth to be weighted more heavily. It would be preferable to weigh each measurement depth individually when calculating IWC.
L186: This is a strong assumption and should be tested using periods for which deeper soil moisture measurements are available.
L188–193: Since these data are not part of the dataset provided with this submission, this section is too excessive and should be reduced to a single sentence.
L199: “in-situ time series”
L201: "available on a 0.25° × 0.25° latitude–longitude grid with daily temporal resolution."
L211: “Figure 3 presents the individual soil moisture anomaly time series of the summer IWC at SwissSMEX stations (2010–2024) for each station combination (Table 1), along with the respective median anomalies.”
L214–215: The median anomalies appear to exhibit different trends. Please add trend lines to make these differences more clearly visible.
L234: Please explain “Theil-Sen trend”
L284-285: “ERA5-Land and ESA CCI PASSIVE also show good agreement in the long-term monthly variations of Swiss Plateau soil moisture since 1991…”
L295–299: This summary section does not fit in the Discussion and should be removed.
L329–330: Domínguez-Niño et al. (2019) found no evidence of decreasing sensitivity of the 10HS sensors in detailed laboratory experiments with different soil materials. However, using only the factory calibration led to much higher measurement errors compared to sensor- and soil-specific calibrations, which could falsely suggest a decrease in sensitivity. This important clarification should be included here.
L341-342: “We present a curated and comprehensive set of in situ soil moisture time series from the SwissSMEX stations on the Swiss Plateau.”
Figure 1: This figure only shows snapshots of data availability. A time series diagram illustrating the availability of measurements over time would be more informative (see, for example, Fig. 2 in Bogena et al., 2022).
Figure 2 does not effectively present the data. The many overlaid lines make it difficult to distinguish individual series, and the thick black line is superfluous, as it merely duplicates other datasets. I suggest presenting the data in two additional subplots. In addition, the legend could also be placed outside the plot area so that the data range does not have to be unnecessarily restricted. Finally, similar plots should be provided for the remaining stations in the supplement.
Figure 4: The numbers inside the circular areas are not all easily readable and should be made clearer.
Literature
Bogena, H.R., M. Schrön, J. Jakobi, P. Ney, S. Zacharias, M. Andreasen, R. Baatz, … and H. Vereecken (2022): COSMOS-Europe: A European network of Cosmic-Ray Neutron Soil Moisture Sensors. Earth Syst. Sci. Data 14: 1125–1151. DOI: 10.5194/essd-14-1125-2022
Domínguez-Niño, J.M., H.R. Bogena, J.A. Huisman, B. Schilling and J. Casadesús (2019): On the accuracy of factory-calibrated low-cost soil water content sensors. Sensors 19(14): 3101. DOI:10.3390/s19143101
Dorigo, W., I. Himmelbauer, D. Aberer, L. Schremmer, I. Petrakovic, L. Zappa, W. Preimesberger, A. Xaver, F. Annor, J. Ardö, D. Baldocchi, M. Bitelli, G. Blöschl, … and R. Sabia (2021): The International Soil Moisture Network: serving Earth system science for over a decade. Hydrol. Earth Syst. Sci. 25: 5749–5804. DOI:10.5194/hess-25-5749-2021