the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 23 Oct 2025
Hydrologic, biogeochemical, microbial, and macroinvertebrate responses to network expansion, contraction, and disconnection across headwater stream networks with distinct physiography in Alabama, USA
Abstract. Here we present a comprehensive dataset of hydrologic, biogeochemical, microbial, and macroinvertebrate community measurements from a set of multi-year, co-occurring, watershed studies in non-perennial stream networks that dynamically expand and contract over space and time. The data were collected over the 2022–2024 water years across three stream networks draining watersheds with a similar humid, subtropical climate but distinct physiographies (i.e., Piedmont, Appalachian Plateau, Coastal Plain) in Alabama, USA. Our goal was to characterize the spatiotemporal patterns and drivers of how non-perennial stream networks expand and contract, as well as the biogeochemical, microbial, and macroinvertebrate dynamics associated with changes in network connectivity and water availability. We used a combination of spatial, temporal, and spatiotemporal sampling and sensor-based monitoring approaches to capture hydrologic, biogeochemical, and ecological responses to network expansion and contraction in each watershed. This manuscript describes the overall study design, monitoring network and sampling approaches, data and sample collection and analysis, and specific datasets generated. All data products are publicly available through the Hydroshare data repository for hydrologic, biogeochemical, and macroinvertebrate data (https://www.hydroshare.org/group/247) and through the NCBI data repository for microbial data. All data product-specific DOIs and repository-specific unique IDs are cited in Appendix A (Table A1, Table A3).
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Status: final response (author comments only)
- RC1: 'Comment on essd-2025-559', Anonymous Referee #1, 25 Nov 2025
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RC2: 'Comment on essd-2025-559', Anonymous Referee #2, 05 Jan 2026
I would like to congratulate the authors for this tremendous effort into collecting a very comprehensible 3-year dataset of intermittent streams in three watersheds located in Alabama, USA. This dataset is very valuable due to its broad coverage of hydrological, biogeochemical and ecological data including macroinvertebrate, microbial, and fungal community sampling. The authors claim that a high resolution dataset as such is highly rare, and necessary due to long-term monitoring traditionally focusing on perennial rivers. However, as a previous reviewer has mentioned earlier, the usability of this dataset may be limited due to multiple different sampling designs and temporal resolutions applied for the individual datasets.
A major confusion in this dataset are the different sampling/monitoring designs utilized, which are named Approach 1, 2 and 3. These are firstly introduced in Table 1, however, are only named in the Methods section. A quick check of data products also revealed that some samples do not belong to any of the approaches, which adds to the confusion. These approaches should be clearly introduced, explained and named in the last paragraph of the introduction to prepare the reader for the subsequent figures/tables and sections. Meaningful identifiers (e.g. watershed outlet, seasonal sampling etc.) rather than numbers would be preferable, for the reader to intuitively distinguish the approaches used.
Many acronyms are used in this manuscript, which easily becomes overwhelming. Some acronyms are also not defined in the data products (e.g. ‘sublocation’ in ENVI_SE_TAL.xlsx). A major confusion is that the watersheds themselves are referred to by the physiographical region and their acronyms do not follow the same pattern. If I googled correctly, the acronyms derive after a city in the watershed. This is highly non-intuitive. Watershed-scale studies typically name their watershed after the biggest river in the watershed. Namely the river, where the watershed outlet is located. Failing to do so makes searching for this dataset difficult, especially for scientist who work on a global scale and are unfamiliar with this specific region.
Furthermore, as another reviewer has pointed out, many sensors are used in this dataset and their error estimates are not given or discussed in the manuscript. The accuracy and potential drift that may have been observed over the years is not mentioned. It is highly unlikely that over the extensive temporal period, no drift was observed in any of the sensors.
Finally, Table 2, 3 and 4 are especially important tables as they are overviews of what variables are in each data product and every reader will skim over these to evaluate whether the dataset is suitable for their needs. Keeping this in mind, beyond variables taken and their units, it would be especially helpful to disclose the temporal resolution sampled for each data product, the time periods covered, and what the total sample size in each watershed is.
Specific comments:
Please treat each display product as stand-alone, and define acronyms in the table or figure captions for readers who do not read your entire manuscript.
The sampling dates in Table 1 are hard to comprehend (e.g. asterisks, bold distinction). It would be easier to read if the two approaches had their individual rows and sampling dates are separately disclosed.
Figure 1: I am assuming that the 5 distinct colours in the left map are physiographical areas. Please add a legend to define them, or remove the colours that are not relevant to this study.
Figure 2, 3, 4: Do the contours refer to elevation? Please described in figure captions.
427-430: Please provide error ranges for variables that were measured with two different methods or instruments. Were any statistical tests done to statistically support that there were no differences?
501: When I first read ‘composite sample’ I understood that the four sample types (litter, biofilm, surface water and sediment) were merged together in the end, and sequenced together as one sample. However, reading the DNA extraction and sequencing procedure, it seems that the sample types were extracted and sequenced separately. In this case, I would recommend the authors not to refer to them as ‘composite’ samples but just refer to them as four different sample types that were sampled, extracted and analyzed separately.
Citation: https://doi.org/10.5194/essd-2025-559-RC2
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The datasets are particularly valuable because hydrological, biogeochemical, and ecological observations are rarely collected together. However, there are inconsistencies in the spatial and temporal resolutions among the datasets. Not all observations share the same spatiotemporal resolution, which represents a major limitation and may hinder usability for other researchers. The authors should also explicitly discuss these limitations in the text.
The data methods are clearly articulated; however, there is no discussion of uncertainty estimates. Although many sensors and analytical methods have been employed, the authors have not reported the associated accuracy and uncertainty values that users need to consider. This information is essential for evaluating spatiotemporal heterogeneity. Without it, we cannot determine whether observed differences across space or time are truly significant or simply within the bounds of measurement uncertainty.
The authors claim “novelty” at Line 73, but it is not clearly established. The first two paragraphs primarily emphasize the need for this work, which is not the same as demonstrating its novelty. Could the authors explicitly articulate what makes this dataset novel?
There is an inconsistency regarding the study period: the abstract states 2022, while the main text (Line 239) indicates 2021. Please clarify which year is correct and ensure consistency throughout the manuscript.
Overall, the structure and organization of the subsections are good. However, there are too many abbreviations, making it difficult to follow the text. I strongly recommend reducing the number of abbreviations to improve readability.