the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
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: open (until 06 Jan 2026)
- RC1: 'Comment on essd-2025-559', Anonymous Referee #1, 25 Nov 2025 reply
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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.