the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 18 Sep 2026
A long-term multiscale Critical Zone dataset integrating environmental monitoring and an open-air laboratory: The Alento River Catchment Observatory
Abstract. This paper presents an open-access dataset from the Alento River Catchment (ARC) in southern Italy, a strategic Mediterranean Critical Zone observatory integrating long-term environmental monitoring with an open-air laboratory. Long-term observations of weather variables, streamflow, reservoir water level, groundwater depth, soil water content, and soil matric potential are complemented by targeted field campaigns involving hydrogeophysical surveys, stable water isotope analyses, soil physical and hydraulic measurements, spectral measurements, and high-resolution UAV-based mapping. The principal value of the ARC dataset lies in the coordinated integration of continuous and campaign-based observations across multiple spatial and temporal scales and Critical Zone compartments, providing a basis for linking localized soil and subsurface processes with field- and catchment-scale hydrological responses. Since 2016, the ARC has been a component of the European TERrestrial Environmental Observatories (TERENO) network. The dataset includes both directly measured and derived products, with documented processing procedures, quality-control information, calibration results, and missing-data records. The principal value of the ARC dataset lies in the coordinated integration of continuous and campaign-based observations across multiple spatial and temporal scales and Critical Zone compartments. The combined observations can support the development, calibration, evaluation, and intercomparison of process-based hydrological and Critical Zone models, the evaluation of Earth Observation products, and the investigation of scale-dependent relationships among point-, field-, hillslope-, and catchment-scale observations. The complete dataset is openly accessible through Figshare under a CC BY 4.0 license. (Romano et al., 2026; https://doi.org/10.6084/m9.figshare.31229608).
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Status: open (until 20 Nov 2026)
- RC1: 'Comment on essd-2026-725', Anonymous Referee #1, 20 Oct 2026 reply
Data sets
Complete dataset reflecting the spatial and thematic components of the Alento critical zone (CZ) observatory. N. Romano et al. https://doi.org/10.6084/m9.figshare.31229608
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- 1
General comments
The paper presents a dataset collected in the Alento catchment, a 411 km² catchment in southern Italy, that is relevant for critical zone studies in a Mediterranean context. The basis of the dataset is hydrometeorological and hydrological data dating back to 1994 for the oldest station. This basis has been complemented in recent years with sub-catchments (~10 ha) field campaigns to document other variables such as soil moisture, isotope concentrations in various compartments, geophysical data on more limited durations. This additional data provides information that transforms the Alento catchment into a critical zone observatory (CZO). Unfortunately, in the provided data set, discharge measurements on the two MFG2 and GOR1 catchments are not provided, although streamflow gauges are mentioned in Figure 3. This considerably lowers the value of the dataset, as it does not allow to relate discharge with soil moisture dynamics (as an example).
Data from critical zone observatories such as the Alento catchment are always worth being shared, as they represent a very long-term investment to collect valuable data. This is particularly true for Mediterranean regions where such datasets are scarce. The data can be used to perform comparative studies between catchments or to calibrate and assess models of various complexities. Soil moisture data are also relevant for the calibration of satellite products.
I fully support the effort of the authors to publish this dataset. However, several points, detailed below, must be addressed before possible publication of the paper.
A general point concerns the time step at which the data are provided. The authors have chosen to provide daily aggregated time series, even if the data are acquired at shorter time scales. One of the interests of the dataset is to be located in a Mediterranean catchment, where rainfall events are often of short duration. Potential users of the data include researchers interested in flash floods and the daily time step does not allow capturing the rapid dynamics of the hydrological response. Daily time step data remain useful to study water resources and droughts, but if the authors were providing sub-daily data, this would enhance the value of the data set.
The authors also mention a full weather station, including the four components of the radiative budget, recorded at a hourly time step, but they only provide daily reference evapotranspiration ET0. Once again, the full hourly dataset would be interesting for future users. So why do they only provide daily ET0?
Another limit of the current presentation of the dataset is the description of the quality assessment/quality control which is often not very detailed. Accuracy of the measurements should be provided is possible.
Specific comments
Line 139: Part of the dataset is already described in Romano et al. (2018), but the dataset presented in the present paper is longer. Could you precise in the manuscript which variables continue to be acquired and provide a contact if readers are interested in updated time series.
Line 170: is it possible to get the discharge corresponding to the water level times series?
Line 191: water level is interesting but discharge is much more interesting for computing water balance.
Line 222: define water-yield the first time it is used (instead of line 229).
Line 257: Nasta et al. (2017)? (Nasta et al. (2018) not cited in the reference)
Line 286: not consistent to provide daily data for a Mediterranean catchment with intense rainfall events and quick dynamics.
Lines 303-304: “Quality assurance procedures included routine inspection of the time series to identify physically unrealistic values and periods affected by instrument malfunction, data logger interruptions, power supply failures, or communication problems.”. This sentence is not very precise and more information about data quality check could be provided, together with estimates of uncertainties. This sentence is found several times in the paper and the remark is valid for all the occurrence of the sentence (e.g lines 329-330,
Line 309: was Eq. (3) verified using in situ sampling?
Line 342: provide the manufacturers here.
Line 345: precision about the sampled compartments (rainfall, discharge??)
Line 356: why do you only provide daily aggregated data if hourly values are available. The short time scale variability is interesting in Mediterranean catchments.
Line 385: same remark as above for soil moisture: quick variation of soil moisture would be interesting to follow soil infiltration during intense events.
Line 400: in general soil moisture is also sampled closer to the surface (for instance for the calibration of remote sensing products). Why only 15 and 30 cm depth?
Line 416: Is the range of matrix potential for the MPS-6 sensor exact?
Line 431: is it necessary to have specific softwares to visualize/analyze such data?
Line 437: the interest of geophysical methods is to explore deep layers, and in particular the weathered soil. So why did you only explore the first 3m depths?
Line 480: provide the accuracy of the isotope analyses.
Figure 5C: the catchment on the right bottom side is not legible
Line 530: you provide the density of the emitted points. The relevant information to characterize the accuracy of the DTM is the density of points at the soil surface.
Line 6607: why did the authors not include the retention and hydraulic conductivity curves?
Line 610: long-term environmental observatory and not repository?
Line 629: I agree that high-intensity events are relevant for the study site, so why did the authors not provide sub-daily data?
Figure 7: discharge is provided, but, in the paper, there is no information on how discharge is obtained from water level measurements.
Figure 7: the elevation of the groundwater depth sensor should be provided in the metadata, not in a figure caption.
Figure 8: there are differences between CNRS data and the GS3 sensors for the years 2020-2022. This is not commented in the paper.
Figure 9: same remark as for Figure 8
Line 735: the break mentioned here is not so obvious in Figure 13c
Line 1244: the reference that appears in FigShare (Nasta, Paolo (2026). The Alento Observatory for Agro-Environmental Research in the Mediterranean: Integrating Critical Zone Monitoring and Open-Air Laboratory Data (Dataset). figshare. Dataset. https://doi.org/10.6084/m9.figshare.31229608.v8) is different from what is provided in the paper (titles and authors are different). Furthermore, good practice when publishing a data set is to cite all the persons who contributed to data acquisition, not only the person that formatted them.
References
Romano N., Nasta, P., Bogena, H.R., De Vita, P., Stellato, L., and Vereecken, H.: Monitoring hydrological processes for land and water resources management in a Mediterranean ecosystem: The Alento River catchment observatory, Vadose Zone J., 17, 80042. doi:10.2136/vzj2018.03.0042, 2018.
Analysis of the data set
The data set is provided on FigShare as a .zip file organized in several sub-directories with the DEM, and the data from the various sub-catchments.
The data set in the .zip file is not documented enough to be usable by other persons and it must be revised substantially. Ideally, the information provided in the .zip file should be sufficient so that a person that does not know the data set is able to understand the content of each file (location, measured variables and units). If the reader is interested in more detailed information about the data processing, it can refer to the data paper. But the .zip file should be as auto-sufficient as possible. So most of the information provided in the tables provided in Appendix should appear in the ReadMe files.
Formats: First of all, the dataset contains some files in formats that are not open: the readme is a .docx file and should be a .txt file. There are also excel files that should be converted to .csv files to fulfill with the criteria of open formats. For the geophysical files, dates in the name of the files should be in SI format: YYYY-MM-DD.
ReadMe files: The authors have provided a file named readme that includes information about the whole data set content, but some information is missing (see below) and when the future users open the individual files, it is not always possible to understand what is its content (see below). I recommend adding ReadMe files in all the sub-directories, and to add metadata in each individual file (information presently included in the paper tables provided in appendix). Indeed, some files do not include any metadata and it is not possible to know what is the content of the columns content and the units. To overcome this problem, I would recommend the addition of metadata at the beginning of each .csv files with # at the beginning of each line, including
Additional comments
In all files, provide the coordinate system and a description of the columns and units.
DEM sub-directory: the coordinate system should be provided.
EMI and ERT sub-directories: the content of the columns and the units must be specified. The content of the columns seems to be specific of the data type and should be explained for non-specialists. If processed data are also included, they software used to obtain them should be specified.
Isotopes’ measurements: provide the locations where the data were measured and provide the data in .csv not in .xlsx
Water potential (pot) and water content (theta): provide the location of the measurement points and provide information of the content and units of the columns
MFC1/ MFC1_Q_DtGW: in the .docx readme file, only two columns are described but the file contains 4 columns. Water table depth appears as negative values. Are they depth relative to the soil surface? If yes, could you provide the absolute altitude of the surface? This information is necessary if the various levels are used to map the groundwater level.
MCF2/Coordinates_weather_stations: what is the coordinate system and the reference for altitudes? In addition, provide the data in .csv not in .xlsx
MCF2/soil properties: impossible to understand the content of this file as there is no information about the content of the columns
MCF2/Spectra: impossible to understand the content of this file as there is no information about the content of the columns
MCF2/TDR: provide information on the coordinate system
For discharge data, add in the metadata the catchment size.