the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 05 Aug 2025
Environment90m – globally standardized environmental variables for spatial freshwater biodiversity science at high spatial resolution
Abstract. The current loss of freshwater habitats and biodiversity calls for an immediate mobilization and application of existing data and tools to contribute to the development of sound strategies for their long-term conservation. However, one particular challenge for obtaining a baseline regarding the spatial distribution of freshwater habitats and biodiversity is the need for standardized high-resolution environmental information, which ideally can provide a characterization of freshwater habitats anywhere in the world. To address this challenge, we present the Environment90m dataset which aggregates a large number of environmental layers into each of the 726 million sub-catchments of the Hydrography90m dataset, corresponding to single stream segments. Specifically, Environment90m includes 45 variables related to topography and hydrography, 19 climate variables for the observation period of 1981–2010, as well as projections for 2041–2070 and 2071–2100 under the Shared Socioeconomic Pathways (SSPs) 1.26, 3.70 and 5.85, and three global circulation models (UKESM, MPI and IPSL). Moreover, Environment90m includes 22 land cover categories for the annual time-series data from 1992–2020. In addition, we provide 15 soil variables and information on aridity and modelled streamflow. Summary statistics (i.e., mean, min, max, range, sd) are provided for all continuous variables while for categorical data, the proportion of each category is calculated within each of the sub-catchments. The data is available at https://hydrography.org/environment90m. To facilitate data download and processing, we provide dedicated functions within the hydrographr R-package. For all underlying calculations, we used the open-source tools GDAL/OGR, GRASS-GIS and AWK, so that custom data can be easily generated using the hydrographr R-package. Environment90m, along with the tools, provides an array of opportunities for research and application in spatial freshwater biodiversity science, specifically biogeographical analyses and conservation in freshwater ecosystems.
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Status: open (until 09 Oct 2025)
- RC1: 'Comment on essd-2025-399', Anonymous Referee #1, 30 Sep 2025 reply
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RC2: 'Comment on essd-2025-399', Anonymous Referee #2, 06 Oct 2025
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Review of “Environment90m – globally standardized environmental variables for spatial freshwater biodiversity science at high spatial resolution”
The manuscript presents Environment90m, a valuable new dataset for global freshwater research. By providing globally standardized environmental variables at high spatial resolution, this dataset addresses a key limitation in large-scale studies of freshwater ecosystems - the lack of consistent, high-resolution environmental data. The integration with the hydrographr R package is particularly commendable, as it facilitates data access and analysis at large scales.
However, before recommendation, I have several concerns regarding the clarity, structure, and presentation of the paper.
Major Concerns
Manuscript structure: The structure of some sections should be revised for better logical flow (see detailed comments below).
Readability: Language and grammar require improvement. Sentences are frequently too long and difficult to follow. A language edit is recommended.
hydrographr package updates: The extent of modifications and extensions made to the hydrographr R package is unclear. A concise summary of newly added functions - beyond those demonstrated in the case study - would be highly valuable to researchers.
Minor Concerns
Citations often appear incorrectly formatted or are replaced by “?”, suggesting broken links to the bibliography. These should be checked and corrected.
The vignette link is not functional. I suggest including “Environment90m” in the vignette title (e.g., “Case study - Danube Basin (Environment90m)”) to make it more easily discoverable.
In the case study (vignette), the paths (working directory setup) appear inconsistent and worked only after adjustments; also, “flow” may need to be replaced by “accumulation” in the function:
download_hydrography90m_tables(subset = c("flow --> accumulation?", "length",
"slope_grad_dw_cel"),
…)
Section-Specific Comments
Introduction
Line 23 - 30: This paragraph is hard to read and it seems establishing a baseline is a major motivation to assemble this data set. I suggest to elaborate on this, as it is not quite clear to me what this baseline is referring to.
Paragraph 2 (Lines 31 - 54): This section currently focuses on methodological difficulties rather than the broader relevance of the dataset. Consider moving this discussion to the Calculation section. Instead, emphasize the scientific and practical value of integrating stream networks with climatological, land cover, and soil data. I.e., I suggest to focus less on the technical challenges and more on the possibilities once these challenges are overcome.
Paragraph 3 (Lines 55 - 60): The comparison with existing datasets is useful but remains vague. Clarify how Environment90m advances beyond these products and articulate the specific knowledge gains it enables.
Line 81: The link is not working.
Environmental Data
Section 2.1: The stream network data are already described in the Hydrography90m publication and may not need to be reiterated here. A concise reference to that paper may suffice.
Section 2.2: Elaborate on the use of the GCMs and why a combination of three models was used.
Section 2.3: How were the 22 categories selected from the original 37 ESA categories? Does the land-use data have a temporal resolution (for the years 1992 to 2020)? Please be consistent when referring to land-use or land-cover data.
Section 2.4: Why did you decide to integrate over all available soil depths?
Section 2.6: I suggest to also provide the variance of stream flow over the selected time period.
Section 2.7: How exactly was AI and PET modeled? Please elaborate on the process.
Calculations
Include a short discussion on how the varying spatial resolutions of the underlying datasets affect Environment90m applications and interpretation - particularly in small headwater catchments.
Use a consistent notation for spatial resolution (either 90 m or 90 m²).
Case Study Workflow
The vignette link is not working.
The case study is an excellent addition. However, please include a (short) dedicated section summarizing the new functions added to hydrographr for handling Environment90m data, possibly including a summary table.
Conclusion
It is not always clear whether the studies cited used Hydrography90m or Environment90m. Please clarify.
New functions for lake processing are introduced only here; these should be documented earlier in a dedicated section.
Figures and Tables
Tables 1–7: Ensure uniform font size.
Figure 1 & 3: Captions should be more descriptive and self-explanatory.
Overall Assessment
Environment90m represents an important contribution to global freshwater biodiversity science. With clearer presentation, improved language, and clearer documentation of the newly added hydrographr functions, this dataset will likely become a foundational resource for future large-scale aquatic research.
Citation: https://doi.org/10.5194/essd-2025-399-RC2
Data sets
Environment90m - globally standardized environmental variables for spatial freshwater biodiversity science at high spatial resolution Jaime R. García Márquez, Afroditi Grigoropoulou, Thomas Tomiczek, Marlene Schürz, Vanessa Bremerich, Yusdiel Torres-Cambas, Merret Buurman, Kristi Bego, Giuseppe Amatulli, Sami Domisch http://doi.org/10.18728/igb-fred-995.0
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After carefully reading the research article “Environment90m - globally standardized environmental variables for spatial freshwater biodiversity science at high spatial resolution”, I see an immense value in the presented dataset. The data presented is not new itself, but the consistent aggregation and re-sampling on a global scale is of high value for further studies. Therefore, it will be of high interest to a huge number of users. The dataset is very well accessibly by tools provided to access the data via an R package (hydrographr), an online platform (GeoFresh), or direct downloads seem very helpful and are well documented with vignettes.
However, I have some suggestion about how to improve the structure und presentation of the dataset and methods, as well as the language, which I will outline in the attached PDF.