A high-frequency, long-term data set of hydrology and sediment yield: The alpine badland catchments of Draix-Bléone Observatory
Abstract. Draix-Bléone critical zone observatory was created in 1983 to study erosion processes in a mountainous badland region of the French Southern Alps. Six catchments of varying size (0.001 to 22 km2) and vegetation cover are equipped to measure water and sediment fluxes, both as bedload and suspended load. This paper presents the core dataset of the observatory, including rainfall and meteorology, high-frequency discharge and suspended sediment concentration and event-scale bedload volumes. The longest records span almost 40 years. Measurement and data processing methods are presented, as well as data quality assessment procedures and examples of results. All the data presented in this paper is available on the open repository https://doi.org/10.17180/obs.draix (Draix-Bleone Observatory, 2015) and a 5-year snapshot is available for review at https://doi.org/10.57745/BEYQFQ (Klotz et al, 2023).
Sebastien Klotz et al.
Status: open (until 08 Jun 2023)
- RC1: 'Comment on essd-2023-34', Anonymous Referee #1, 09 May 2023 reply
- RC2: 'Review comments on essd-2023-34', Jens Turowski, 17 May 2023 reply
- RC3: 'Comment on essd-2023-34', Anonymous Referee #3, 26 May 2023 reply
Sebastien Klotz et al.
2015-2019 subset of rainfall, meteorology, discharge and sediment yield data from Draix-Bleone Observatory https://doi.org/10.57745/BEYQFQ
Observatoire hydrosedimentaire de montagne Draix-Bleone https://dx.doi.org/10.17180/obs.draix
Sebastien Klotz et al.
Viewed (geographical distribution)
Klotz et al. have posted a rich set of meteorological, discharge and sediment yield data for 6 French alpine catchments containing badlands from the Draix-Bléone Observatory. Given the difficulty of conducting such monitoring in a mountainous environment, the temporal span of the proposed chronicles makes this dataset quite unique and therefore very valuable. The authors have extensively illustrated, via previously published studies, the potential of the proposed dataset to contribute to a variety of issues related to runoff and erosion in this specific mountainous environment.
The article is well structured and clear. The data are readily accessible and usable in their current format and size, either by using the 5-year snapshot that is available for review or by using the BDOH web interface for the full dataset (requires registration, but has powerful features that make exploration of the dataset quite user-friendly).
I very much appreciated the significant effort made by the authors to explain the complex procedures for collecting, processing and qualifying hydrosedimentary data and I think this manuscript is acceptable for publication with minor revision.
My main suggestions are intended to provide additional information to help those who would like to use these data sets or replicate similar collection and processing procedures. In addition to minor editorial suggestions, they focus on the following two points: (i) the description of the data processing and qualification procedures, which is sometimes too brief or a bit confusing, and (ii) how the quality codes assigned to the different data can or should be used.
My suggestions for improving the paper are detailed below in three comment sections (general/specific/editorial)
All the data proposed in this document have been qualified with a quality code -presented in Table 2- in the form of 4 levels (good - intermediate - uncertain - poor) if we exclude the codes for missing data and data without quality code. The effort made by the data producers to generate such detailed quality grades and to attempt to formalize the attribution rules is noteworthy. However, nothing is said about how to take these different levels into account when analysing these data. It would be greatly appreciated if the authors could provide guidelines to future users of the data on how to take these different levels into account. For example, do they advise to associate different levels of uncertainty to them? If yes, could the authors provide an idea of uncertainties associated to each level (at least as a range, i.e. [Min; Max]) ? Since uncertainties may depend on the configuration of each data monitoring system, it would probably be appropriate to include this information in the header of each data file. In addition, I would suggest including in paragraph 4 a small discussion of how existing studies that have used these data have accounted for these different levels of quality...
L41-42: I would not use "are only available in..." as other datasets combining records of suspended and bedload may exist (e.g., DOI: 10.2478/johh-2019-0003).
L85-86. The sentence "Mean annual rainfall for the observatory... and mean annual temperature..." would require you to provide the method you used to estimate a average value from multiple measurement locations. Instead, I suggest that you provide an average value at a single observatory location inside the observatory and indicate the time period used to estimate the average.
L96-97, Table 1 and Legend of Figure 1: At the end of the introduction the authors explain that the data from the Galabre catchment area, which belongs to the Draix-Bléone observatory, will not be presented in this datapaper because they have already been published. Therefore, I suggest not to mention it again here, nor to present it in Table 1, nor to mention that it is not represented in Figure 1.
Table 1: please consider i) dropping the line for Galabre; ii) adding a column indicating the % of badland area for each catchment; iii) providing one sentence to describe how the average catchment slope was evaluated (from which data ? which spatial resolution if derived from DEM...); iv) specify in Line 91 that the vegetation cover was stable during the whole period of monitoring (if not, provide a range of value instead of a unique value)
Table 3: add the resolution for the Archail raingauge
L129-131 & 140: please provide more details on how the raingauge data were corrected because I didn't see in the data I consulted any adjustments of the bucket volume.
Figure 5: please consider using a ratio 1:2 between y1 and y2 (i.e. P = 2*T) as usually done to present this king of graph in the Mediterranean context (here you used P = 4T)
Table 5: add the acquisition frequency for the Bouinenc station
L229 & 233: Please specify the difference between noise suppression (L233) and oscillation suppression (L229) and/or consider merging the two steps.
L234-235: Please provide more detail on how the time series of recorded water levels are corrected for inconsistencies between some occasional scale readings (or manual bucket measurements) and the continuously recorded data.
Section 3.3.1: Can you provide a table summarizing the data set for suspended load, as was done for all other data categories?
L285-286: How many samples are enough to establish an event-specific relationship?
L285-295: the combination of procedures (and the priorities between each step) need to be clarified (see comments below).
-- Step 2: Consider merging step2 into step4.
-- Step 3: is is not clear if all sediment concentrations derived from the voltage/concentration (or the turbidity/concentration) relation are included in the reconstructed concentration time serie. If only part of them are included, please detail the criteria used. What happens if no sample is available ?
-- Step 4: Are concentrations derived from discharge/concentration relations included only when turbidimetric measurements are not available (i.e., shallow water depth, or concentration below 10 g/L, or device failure) ? are there other cases where they are included in the final reconstructed concentration dataset ?
-- Step 5: What is the decision criterion for moving from the Step 4 to the Step 5 ?
L300: consider changing "volume" by "mass" in this sentence, as multiplying the discharge (l/s) and the concentraztion (g/l) provides a flux (g/s), and then integrating gives a mass.
L301: consider switching "volume" and "mass" in this sentence.
L304: "When enough samples are available" is vague... please try to provide an idea on how many samples are required ?
L313-314 & legend of Figure 7: please specify whether instantaneous or event-scale average concentrations are concerned
L352-354: please explicit the rule(s) for assigning a volume of sediment deposits to a series of floods.
L364: Would it be possible to mention the long-term bedload's contribution to total exports (in addition of the value for 2013) ?
Section 4: as already said, the author have extensively illustrated, via previously published studies, the potential of the proposed dataset to contribute to a variety of issues related to runoff and erosion in this specific mountainous environment. Surprisingly, the authors did not provide perspectives on potential future uses of their datasets. Please could you discuss a bit more potenital future works. I also suggest to ad one or two sentences on the future of data collection.
Section 5: I haven't found a way to access GIS files, such as catchment boundaries or device locations, either in BDOH or in the 5 year snapshot provided for the review process. If these data already exist somewhere in access, I suggest mentioning the links to access them, otherwise I suggest adding them to the datasets proposed in this datapaper.
L64: Drop “^” from “Legoût”
L333: Change "Fig.9" into "Fig.8"
References: please carefully check the list of references as I identified the following issues: i) (Le Bouteiller et al., 2019) and (Gras et al., 2007) to be added ; ii) alphabetical sorting problem for Klotz at al.; iii) reference (Olivier et al., 1995) is presented in two parts (L534 & 553-554)