the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
The Forest Soil Moisture Monitoring Network (FSMMN): Multi-depth soil moisture, matric potential, and temperature data from three U.S. experimental forests
Abstract. The Forest Soil Moisture Monitoring Network (FSMMN) provides a coordinated, multi-depth dataset of soil moisture and temperature measurements from three eastern U.S. Forest Service experimental forests, including Hubbard Brook (New Hampshire), Fernow (West Virginia), and Coweeta (North Carolina). This latitudinal gradient spans distinct soil types, vegetation communities, and precipitation regimes across the Appalachian Mountains, capturing the variability in soil water dynamics. The network currently includes 44 monitoring sites and 262 soil moisture sensors distributed across the three forests, with hourly records beginning in 2022 at Coweeta and Fernow, and in 2023 at Hubbard Brook. At each forest, paired in situ volumetric water content (VWC) and soil matric potential (SMP) sensors were installed at three depth intervals (10–20 cm, 50 cm, and 60–100 cm) within multiple soil profiles across several catchments, enabling the characterization of soil moisture dynamics and hydraulic function at multiple scales. The data undergo automated and manual quality control procedures described in this paper and are updated annually in public data repositories. The dataset enhances the spatiotemporal coverage of soil moisture and temperature observations in forested headwater catchments where long-term, spatially distributed soil moisture records have historically been scarce. By capturing both vertical and lateral soil water variability across contrasting forest ecosystems, the FSMMN provides a foundation for cross-site studies linking soil hydraulic properties and catchment water balance at scales relevant to ecological and hydrological modeling.
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- RC1: 'Comment on essd-2026-28', Philipp Kraft, 01 Oct 2026 reply
Data sets
NRCS-USFS Soil Moisture Measurements - Hubbard Brook Experimental Forest, 2023-2025 NRCS-USFS Forest Soil Moisture Monitoring Network https://doi.org/10.6073/pasta/2eb8ea3a25e81ead1188af94ccfede72
NRCS-USFS Soil Moisture Measurements - Fernow Experimental Forest, WV, 2022-2025 NRCS-USFS Forest Soil Moisture Monitoring Network https://doi.org/10.6073/pasta/3394903db59772e9aef31c7b9628fb42
NRCS-USFS Soil Moisture Measurements - Coweeta Hydrologic Laboratory, NC, 2022-2025 NRCS-USFS Forest Soil Moisture Monitoring Network https://doi.org/10.6073/pasta/3e11ea6cf7bcfe8791a3abd29c9b7638
Interactive computing environment
FSMMN - QC Emily Piche https://doi.org/10.5281/zenodo.18202619
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General Comments
The manuscript is well-written and provides essential context for the newly established Forest Soil Moisture Monitoring Network (FSMMN) datasets. The authors provide a particularly strong characterization of soil types across the study sites, which is fundamental for understanding soil water dynamics. The dataset itself is unique, highly relevant, and provides a valuable contribution to the field. However, to reach its full potential for modeling and process-oriented studies, the integration of meteorological data and additional vegetation parameters must be addressed.
Specifically, a major limitation is the lack of a direct, documented connection to meteorological observations. While the authors mention existing monitoring programs and networks such as RAWS, SCAN, and NEON (Lines 79–81, 90–91), the manuscript lacks specific details regarding the local weather stations used for the interpretation of the FSMMN datasets. It remains unclear where these stations are located, what specific parameters they record, and how they spatially align with the soil moisture sensors. Providing the specific DOIs for these meteorological datasets would also greatly enhance the reproducibility and usability of the work. As soil moisture interpretation is highly dependent on local weather conditions, this information is crucial for the scientific community.
Furthermore, to improve the utility of the dataset for ecohydrological modeling, the authors are encouraged to include or link data regarding vegetation structure. Providing quantitative metrics such as tree height, canopy cover, Leaf Area Index (LAI), and understory coverage would significantly enhance the dataset's ability to be used in studies of water storage and atmospheric feedback.
Finally, Section 5 ("Summary and Conclusions") currently functions more as a "Discussion" than a summary. While it does not introduce new empirical findings, it expands significantly on the scientific implications and the utility of the FSMMN in addressing existing knowledge gaps (e.g., the energy state of soil water). If the journal requires a strict distinction between "Discussion" and "Conclusion", the authors should consider renaming this section or restructuring it to clearly separate the summary of facts from the interpretation of implications.
Technical Corrections