the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multi-Element Dataset Across Diverse Climatic Zones and Soil Profiles in China’s Mountains
Abstract. Mountain ecosystems are crucial for global biodiversity conservation and climate regulation, yet their response to environmental change remains poorly understood due to limited high-resolution, multi-element datasets. Here, we present a comprehensive geochemical dataset comprising more than 1,300 soil samples collected from 166 sites across 30 mountain regions in China, spanning five major climatic zones and representative vegetation types. Soil samples were systematically collected from three standardized horizons (organic, surface mineral, and parent material), and analyzed for the concentrations of 24 elements, including macronutrients (e.g., phosphorus, potassium, calcium, magnesium), micronutrients (e.g., iron, molybdenum, manganese, copper), and trace metals (e.g., cadmium, chromium, lead, antimony). To support integrated Earth system analyses, the dataset is accompanied by key site-specific environmental variables, including climate parameters (temperature, precipitation, aridity index), normalized difference vegetation index, soil physicochemical properties (pH, moisture, bulk density), atmospheric nitrogen deposition, and chemical weathering index. The dataset reveals significant vertical stratification in element distributions, with organic horizon enriched in biogenic elements, and deeper horizons dominated by lithogenic components. Spatial patterns along latitudinal, longitudinal, and altitudinal gradients underscore the influence of climate and geology on soil chemistry. This open-access dataset provides a valuable resource for parameterizing and validating biogeochemical models, assessing soil quality in mountain regions, and improving predictions of ecosystem responses to global change. The dataset can be accessed via https://doi.org/10.11888/Terre.tpdc.302620 (Wu et al., 2025b).
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Status: open (until 18 Jul 2025)
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RC1: 'Comment on essd-2025-302', Anonymous Referee #1, 24 Jun 2025
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This manuscript presents an exceptionally comprehensive soil geochemical dataset that addresses a critical gap in global biogeochemical databases by systematically characterizing 1,300+ samples across 30 mountain regions spanning five climatic zones in China. The authors’ methodological rigor is evident in their stratified sampling design across three pedogenic horizons (A, B, and C), standardized analytical protocols for 24 macro- and microelements, and integration with ancillary environmental variables including climatic indices, vegetation parameters, and human activity factor. The dataset’s particular strength lies in its unprecedented spatial coverage of montane ecosystems, combined with vertical resolution that captures pedogenic gradients crucial for understanding soil formation processes and biogeochemical cycling.
Overall, the authors’ efforts in assembling this high-resolution, multi-horizon, and climatically contextualized soil dataset are timely and scientifically significant for researchers in soil science, biogeochemistry, ecology, and Earth system modeling. Moreover, the manuscript is generally well organized, and it is suitable for publication in the journal after some minor revisions. Please find my comments below.
Specific comments:
I recommend the authors should stored the valuable data in the Zendo website.
Line 123: Replace “was” with “were”. Please check other grammar issues in the manuscript.
Line 132: Please specify the extraction method for pH measurement (e.g., water, KCl, or CaCl₂). This is essential for comparability with other pH datasets and can influence interpretation of cation exchange and element mobility.
Lines 154-158: The calculation of the Chemical Index of Alteration (CIA) should be more explicitly explained. Please clarify how CaO* was estimated, and whether the method has followed that of Nesbitt & Young (1982) directly or been corrected.
Lines 164-165: The strict coordination has been carried out, but it was not clearly defined. Does this refer to harmonization of sampling protocols across sites, or post-hoc statistical adjustments (e.g., normalization, transformation, unit standardization) to ensure cross-site comparability?
Line 103: The manuscript would benefit from a concise description of the statistical or visualization methods used to generate Figures 2-6. This addition will help readers better interpret the trends and distributions presented.
Line 260: The authors provided horizon-level sampling and vertical stratification but did not elucidate the implications for soil development modeling. Given the presence of C-horizon data and CIA indices, this dataset could serve as a valuable benchmark for soil formation modeling (e.g., using SoilGen or CLORPT frameworks). A short paragraph in Section 4 may highlight this point.
Line 316: Add a sentence summarizing the dataset structure (e.g., file formats, variable descriptions, metadata schema) to assist users in quickly understanding how to work with the data.
Line 249: The value “Fe (>200%)” as explanatory power in redundancy analysis seems inconsistent (R² cannot exceed 100%). Please double-check this statement or clarify if it refers to cumulative variance.
Tables 1 and 2: Several abbreviations used in these tables (e.g., MAT, MAP) are not defined within the table notes. As tables should be interpretable independently of the main text, please add a legend or footnotes explaining all abbreviations.
Figures 2 and 3: Both figures lack x-axis labels, which impairs interpretability. Ensure all figures include complete and clear axis annotations, including units.
Citation: https://doi.org/10.5194/essd-2025-302-RC1
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