the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Discrete Global Grid System-based Flow Routing Datasets in the Amazon and Yukon Basins
Abstract. Discrete Global Grid systems (DGGs) are emerging spatial data structures widely used to organize geospatial datasets across scales. While DGGs have found applications in various scientific disciplines, including atmospheric science and ecology, their integration into physically based hydrologic models and Earth System Models (ESMs) has been hindered by the lack of flow-routing datasets based on DGGs. In response to this gap, this study pioneers the development of new flow routing datasets using Icosahedral Snyder Equal Area (ISEA) DGGs and a novel mesh-independent flow direction model. We present flow routing datasets for two large basins, the tropical Amazon River Basin and the Arctic Yukon River Basin. These datasets demonstrate the potential of DGGs-based flow routing datasets to enhance the performance of hydrologic models and provide observationally-based flow routing inputs for immediate application to the Amazon and Yukon River Basins. The data are available at https://doi.org/10.5281/zenodo.8377765 (Liao, 2023).
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RC1: 'Comment on essd-2023-398', Anonymous Referee #1, 16 May 2024
The authors describe the creation of DGGS-based hydrography data. While aiming at DGGS, the authors only mention ISEA3H, without other meaningful information (index type, seqnum or z3 or other, comparing with other hexagonal DGGS types like ISEA4H or ISEA7H). Thus, the DGGS integration for an advanced user would still be potentially tricky.
The chosen resolutions are fairly coarse, the authors don't discuss MERIT Hydro or Hydrography90m https://hydrography.org/hydrography90m/
Only two river basins are used, overall this could be considered a prototype or proof-of-concept, but to be a meaningful dataset there should be many more basins, and higher resolutions. The choice of data formats is awkward, GeoJSON is very inefficient, and (Geo)Parquet is very new. Better to go with e.g. GeoPackage and even some tiling or partitioning (which would go well with DGGS cells).
Is this about a dataset or the software? the datasets do not have the quality or meaningfulness to be deposited in ESSD. The methodology is mostly well described, the initial integration of the DEM data into the hexagonal grid is not explained (I read zonal stats later somewhere?).
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- unconventional abbrev DGGs, is it meant as DGG Discrete Global Grid's (plural) or should it be DGGS Discrete Global Grid System, with plural DGGS's?
- citation irregular format Matthew B. J. Purss et al., 2016 ll14, (Kevin Sahr, 2019), ll 16
- unclear ll33 ".., which does not support vector-based datasets"
- ll35 "within a DGGs-based framework"
- abbrev PCS ever used again?
- ll37 "improved numerical performance for surface and subsurface hydrologic models (Liao et al., 2020)" how?Â
- ll39, "3) more flexibility in spatial resolution due to their hierarchical data structure" how does that hold true when compared against rasters?
- "ISEA3H" better than ISEA4H or ISEA7H? The reasoning here is only for hexagonal in general
- "ISEA streamlines calculations of conserved quantities" ... using ISEA cells, yes, ISEA itself does not streamline. Please avoid too sensationalist wording, overall the concept is sound and stands by itself.
- ll44: "This study breaks new ground by developing new flow routing datasets using the ISEA3H DGGs and our newly developed mesh-independent flow direction model." really strange wording, the second part soe not fit well. make separate sentence?
- ll70: " Yukon Basin at 15-arc-second (∼ 500 m) ", 15 arc seconds is slightly less than 500m at the equator, but almost half at e.g. 60 degrees, this might be something to consider when deciding for an ISEA3H resolution, please review with your methodology and elaborate.
- ll83: Â REACH library (Engwirda, 2023), does simplification use which priority, strahler or other channel order systems?
- Figure 2, why these resolutions shown? Not explained, ll92 "user-defined tolerance" ... at least example to reduce the feeling of arbitrariness? Why can there be isolated river segments if you redo routing and use the catchment boundary... somewhat inconsistent feeling here
- ll96: "DGGRID is an open-source library developed by Kevin Sahr in 2003" the development started maybe in 2003, but it is still actively developed. Please rephrase.
- check  (Sahr, 2015), suggest additional Sahr
- version 7.0? not even 7.6, 7.6 or 7.8 from the last years?
- table 1: Showing internode spacing (distance between centroids?) and a square root of area (for a close-to-circle shape) is probably not meaningful (they are almost the same for the purpose here). You could rather keep the actual cell area in km2 or ha, and clarify that internode spacing could also be interpreted as analog to "pixel" size of conventional raster, and that it closely corresponds to a diameter of a circle of the same area, so the reader can understand the spatial resolution of the grid cells.
- p6/7: DGGRID Â Application Programming Interface (API) (Liao, 2022a)? ... "implemented several APIs to set up a DGGRID model run"
- ll139: " topological relationship-based reconstruction method" is that explained somewhere under the same term?
- ll154: GCS and GeoJSON, which GCS, probably WGS84, EPSG:4326? please name it explicitly.
- in section 3 all subsections start with the same "The variable_polygon.geojson is a polygon-based GeoJSON data file", I think that can be introduced once, and then does not need to be repeated for each variable in that file. On another note, using GeoJSON for these larger types of data feels fairly ineffective and wastes a lot of storage space, why not use something robust like GeoPackage or FlatGeobuf? Everything beyond 5000-10000 cells/geometries people are not loading into a browser, so why GeoJSON?
- ll177: "special attention was paid to the consistency across different spatial resolutions." How? Consistency is not really elaborated on in the following sections.Â
- ll185: To which "zonal mean resampling procedure" are you referring here, please be specific (refer to methods sections), and which biases? please elaborate
- Figure 13 and 14: When you name "modeled surface elevation" you refer to the elevation in the hex grid cells, which have been post-processed (depression filling etc) after their initial "ingestion" (from which source again?)
- ll223 " use high-resolution meshes (such as the ISEA3H resolution level 13) " ? the 4 resolutions in the paper are (29.42 km, 16.99 km, 9.81 km, and 5.66 km) how is that "high-resolution" when Hydrosheds data is available at 500m / 1km scales?
- ll241 " Topographic Wetness Index (TWI)" ... how can a user derive these indicators from the hex mesh dataset?
- Table A2 why does flow direction data type not applicable? Units would also be good, km2 or m, slope in degrees or percent ... etc.Â
Citation: https://doi.org/10.5194/essd-2023-398-RC1 -
RC2: 'Comment on essd-2023-398', Anonymous Referee #2, 08 Jun 2024
The paper develops new flow routing datasets using Icosahedral Snyder Equal Area (ISEA) DGGs and a novel mesh-independent flow direction model. Technical validation evaluates the proposed datasets from the surface elevation, flow direction, and drainage area. The paper demonstrates the potential of DGGS in the field of hydrologic models.
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Comments:
- The authors conducted a visual examination of the modeled flow directions using the simplified HydroSHEDS river networks in section 4.2. The drainage area was used to validate these flow directions. Actually, characteristic lines of terrain can also be used for validation, such as areas of differences between the fitting valley lines and true lines. If feasible, it is recommended to include more quantifiable
- The authors verified the proposed dataset using surface elevation, flow direction, drainage area and travel distance. However, they did not mention the time cost of constructing the dataset. A good algorithm should balance both computational efficiency and accuracy. As the subdivision level increases, the number of cells in DGGS grows exponentially, significantly impacting the computational efficiency. It is recommended that the authors include a discussion on computational efficiency.
- There is a limitation on watershed extraction in flat areas. The authors did not discuss this issue. It is recommended to include relevant discussion in the Technical Validation
Minor comments:
Unable to directly link to relevant figures by the contents in the manuscript.
Citation: https://doi.org/10.5194/essd-2023-398-RC2 -
RC3: 'Comment on essd-2023-398', Anonymous Referee #2, 08 Jun 2024
The paper develops a new flow routing datasets using Icosahedral Snyder Equal Area (ISEA) DGGs and a novel mesh-independent flow direction model. Technical validation evaluates the proposed datasets from the surface elevation, flow direction and drainage area. The paper demonstrates the potential of DGGS in the field of hydrologic models.
Â
Comments:
- The authors conducted a visual examination of the modeled flow directions using the simplified HydroSHEDS river networks in section 4.2. The drainage area was used to validate these flow directions. Actually, characteristic lines of terrain can also be used for validation, such as areas of differences between the fitting valley lines and true lines. If feasible, it is recommended to include more quantifiable
- The authors verified the proposed dataset using surface elevation, flow direction, drainage area and travel distance. However, they did not mention the time cost of constructing the dataset. A good algorithm should balance both computational efficiency and accuracy. As the subdivision level increases, the number of cells in DGGS grows exponentially, significantly impacting the computational efficiency. It is recommended that the authors include a discussion on computational efficiency.
- There is a limitation on watershed extraction in flat areas. The authors did not discuss this issue. It is recommended to include relevant discussion in the Technical Validation
Minor comments:
Unable to directly link to relevant figures by the contents in the manuscript.
Citation: https://doi.org/10.5194/essd-2023-398-RC3
Data sets
Discrete Global Grid System-based Flow Routing Datasets in the Amazon and Yukon Basins Chang Liao https://doi.org/10.5281/zenodo.8377765
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