the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
24 Mar 2021
24 Mar 2021
GeoDAR: Georeferenced global dam and reservoir dataset for bridging attributes and geolocations
- 1epartment of Geography and Geospatial Sciences, Kansas State University, Manhattan, Kansas, USA
- 2Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, Colorado, USA
- 3Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
- 4Department of Geography, Oklahoma State University, Stillwater, Oklahoma, USA
- 5Department of Geography, University of California, Los Angeles (UCLA), Los Angeles, California, USA
- 6Department of Geography, Texas A&M University, College Station, Texas, USA
- 7Laboratoire d'Études en Géophysique et Océanographie Spatiales (LEGOS), Centre National d'Études Spatiales (CNES), Toulouse, France
- 8International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
- 1epartment of Geography and Geospatial Sciences, Kansas State University, Manhattan, Kansas, USA
- 2Cooperative Institute for Research in Environmental Sciences (CIRES), University of Colorado Boulder, Boulder, Colorado, USA
- 3Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences, Nanjing, China
- 4Department of Geography, Oklahoma State University, Stillwater, Oklahoma, USA
- 5Department of Geography, University of California, Los Angeles (UCLA), Los Angeles, California, USA
- 6Department of Geography, Texas A&M University, College Station, Texas, USA
- 7Laboratoire d'Études en Géophysique et Océanographie Spatiales (LEGOS), Centre National d'Études Spatiales (CNES), Toulouse, France
- 8International Institute for Applied Systems Analysis (IIASA), Laxenburg, Austria
Abstract. Dams and reservoirs are among the most widespread human-made infrastructure on Earth. Despite their societal and environmental significance, spatial inventories of dams and reservoirs, even for the large ones, are insufficient. A dilemma of the existing georeferenced dam datasets is the polarized focus on either dam quantity and spatial coverage (e.g., GOODD) or detailed attributes for limited dam quantity or regions (e.g., GRanD and national inventories). One of the most comprehensive datasets, the World Register of Dams (WRD) maintained by the International Commission on Large Dams (ICOLD), documents nearly 60,000 dams with an extensive suite of attributes. Unfortunately, WRD records are not georeferenced, limiting the benefits of their attributes for spatially explicit applications. To bridge the gap between attribute accessibility and spatial explicitness, we introduce the Georeferenced global Dam And Reservoir (GeoDAR) dataset, created by utilizing online geocoding API and multi-source inventories. We release GeoDAR in two successive versions (v1.0 and v1.1) at https://doi.org/10.6084/m9.figshare.13670527. GeoDAR v1.0 holds 21,051 dam points georeferenced from WRD, whereas v1.1 consists of a) 23,680 dam points after a careful harmonization between GeoDAR v1.0 and GRanD and b) 20,214 reservoir polygons retrieved from high-resolution water masks. Due to geocoding challenges, GeoDAR spatially resolved 40 % of the records in WRD which, however, comprise over 90 % of the total reservoir area, catchment area, and reservoir storage capacity. GeoDAR does not release the proprietary WRD attributes, but upon individual user requests we can assist in associating GeoDAR spatial features with the WRD attribute information that users have acquired from ICOLD. With a dam quantity triple that of GRanD, GeoDAR significantly enhances the spatial details of smaller but more widespread dams and reservoirs, and complements other existing global dam inventories. Along with its extended attribute accessibility, GeoDAR is expected to benefit a broad range of applications in hydrologic modelling, water resource management, ecosystem health, and energy planning.
Jida Wang et al.
Status: final response (author comments only)
-
RC1: 'Comment on essd-2021-58', Anonymous Referee #1, 01 Apr 2021
Wang et al. describe the creation of a new global dam and reservoir dataset. Overall, the paper is well-written and the methods and findings are on the whole very clear. This dataset will be a valuable contribution to the community and will likely be used by scientists across multiple fields.
The manuscript is incredibly detailed – perhaps slightly too much so – but users of the dataset may be grateful for it to be so well-documented. On the one hand, it is commendable that the authors have put so much effort into this dataset and into ensuring the manuscript thoroughly describes the entire process; on the other hand, the manuscript does get so repetitive in places, particularly in the results, that it is challenging for the reader to digest. It is up to the authors’ judgement as to whether or not to shorten the paper, but I would suggest in general thinking about streamlining the paper in places and shortening a few of the most repetitive sections.
Another more general comment is that this paper uses a lot of jargon (particularly geo-matching and geocoding) which are not clearly defined early in the text. Since most readers will not be familiar with geo-matching or geocoding, I suggest clearly defining these terms at some point early in the paper to explain in better detail what exactly they refer to. The methods are eventually pretty clear, and after reading the paper I now understand what these terms refer to, but a lack of understanding of these terms is perhaps likely to confuse the reader at the beginning of the paper.
As for the dataset itself, it appears complete and is easy to visualize and play around with in GIS. The dam/reservoir attributes included for each polygon as well as the readme are very useful and clear. Clearly a lot of thought and careful work has gone into the dataset and it is very well done.
I have several additional, mostly minor, comments listed below.
Specific Comments:
Line 55: It would be helpful here to define what is meant by “attributes” – i.e. reservoir use, storage capacity, dam completion year, etc
Line 107: Change to something like: “Our preference was the former when possible to optimize the georeferencing accuracy”
Line 109: Change “with” to “by”
Line 117-118: The sentence “We acknowledge that although we tried …” is a bit vague. I suggest adding a bit more detail about the challenges associated with duplicate record removal, making the sentence something like: “We acknowledge that owing to the challenge of XXXX, our duplicate removal is not perfect and may have misidentified or missed some duplicate dams”
Line 135: It is unclear to me what you mean by “build associated between new dams supplemented by GRanD and the WRD records”
Figure 2, particularly Figure 2a, is a little hard to interpret. I understand what you are going for here but I find it hard from looking at these Venn diagrams to determine which of these circles represent what is actually included in the GeoDAR datasets. I suggest revising, adding additional description or considering whether this figure is necessary.
Lines 220-231: Can you provide more details about how the QC/QA was performed? How did this analysis lead to determining that 3% of the matched results were matching errors?
Line 247: The phrase “the forward geocoding input the text address of each dam” does not make sense (and is not grammatically correct)
Line 247: Reading this paragraph, I was a bit confused by what is meant by “text address of each dam” and how this can be used to query the longitude and latitude. While this is explained in greater detail and is much clearer in the following paragraph, it made this first paragraph hard to follow. I suggest perhaps reorganizing this section (i.e. putting some specifics from the following paragraph into this paragraph) and/or providing a short primer, either here or earlier in the text, about “forward” vs. “reverse” geocoding so the reader, who is likely unfamiliar with geocoding, can better understand this rather abstract description.
Line 297: Can you clarify, both here and in the above mention of QA/QC (see above comment) whether all reservoirs were manually QCed, or just a subset?
Line 303: This offset for dams in China is interesting and as you probably know likely has to do with China’s GPS shift problem. Perhaps another sentence could be added to explain this in greater detail (i.e. why Google Maps does not work in China like it does in the rest of the world)
Lines 435-440: Was any QA/QC performed for the reservoir/dam matching? What is the likelihood that some dams were incorrectly matched with their reservoir polygons?
Lines 504-505: The sentence starting with “As a result, the average reservoir size decreased…” is a little confusing as I am unsure whether the decrease in mean size is referring to the mean size of reservoirs identified from each data type (i.e. GRanD, HydroLakes) or the mean size of the entire dataset decreasing as the datasets were added in hierarchical order from largest to smallest. I suggest keeping this information in the text but rephrasing to make clearer.
Line 407: Change to the “the retrieved polygons do not always represent the maximum water extents of the reservoirs…”
I don’t think Figure 12 is necessary since it is pretty hard to see differences between the two datasets at the global scale. Figure 13 and Figure 14 are much better illustrations of the differences between the datasets.
I’m not sure the section title “Improved spatial details over GRanD” is the right phrasing. I understand what you actually mean but to me this phrasing implies that you have improved the detail of the spatial attributes of GRanD reservoirs, not actually increased the number and decreased the size of resolved reservoirs. I’m not sure exactly what it should be changed to, but I would consider rephrasing. Similarly, throughout the text, I would suggest rephrasing or coming up with another term to replace “enhancing the spatial detail” since this could mean different things to the reader.
I’m not sure what the takeaway from Figure 16 is intended to be, other than perhaps showing that in general the datasets don’t necessarily agree and include different dams/reservoirs? The color scheme and size of the dots also make it very hard to distinguish against the colored background. I suggest either removing this figure entirely or at the very least changing the color scheme.
The section 3.4.2 (pages 33-41) is very long and detailed, particularly when it can perhaps be summarized in one sentence as something like “GeoDAR contains substantially more small dams/reservoirs than GRanD, and therefore while the total capacity of the reservoirs in GeoDAR and GRanD is similar, the main advantage of GeoDAR is that it is more spatially extensive by including many more small dams and reservoirs.” It is up to your judgement, of course, but I suggest considering shortening this section – much of the info here is perhaps interesting, but it is very repetitive and therefore hard to read in places.
The conclusion is really well-written and does a very good job of highlighting the advantages and novelty of this dataset. It is a bit repetitive but I think it is appropriate here because it is more of a summary than a conclusion (and that works for this sort of paper).
-
AC1: 'Reply on RC1', Jida Wang, 03 Apr 2021
We are very grateful for this reviewer's poisitve comments on the value of our dataset and very constructive suggestions to improve the readiability and clarity of our manuscript. Thank you. We will start to incorporate these comments in the coming weeks, and will later submit a detailed revision response (together with the responses to the second reviewer as well).
-
AC1: 'Reply on RC1', Jida Wang, 03 Apr 2021
-
RC2: 'Comment on essd-2021-58', Anonymous Referee #2, 22 Apr 2021
Wang et al., describe a new global georeferenced database of dams based on geo matching attributes from the proprietary ICOLD database with publicly available sources to match attributes to spatial locations of dams.
This database complements existing geo-referenced databases as it a) expands on the number of dams for which more attributes such as reservoir storage are available and b) allows for connecting spatial locations of dams with attributes from the ICOLD database. As such I believe this database is a valuable addition to the growing number of global dam datasets (e.g. see www.globaldamwatch.org).
Whilst this database certainly has merits I think it promises more than it delivers. The authors frame the paper as a significant improvement over other dam databases in that it includes more attributes as there are already other global datasets (e.g. GOODD and GROD) that include more dams but lack such attributes. However, the majority of the paper is focused on identifying the spatial location of dams and improvements in quantity and spatial locations of dams. E.g. line 812 “GeoDAR’s major improvement lies on the quantity or spatial details of the dams”. This framing is understandable given the challenge of linking to a guarded proprietary database. However, this limits the use of the database as only people who have purchased access from ICOLD may be able to connect the attributes with the spatial location of the dams. Whilst this point is made clear in the conclusions it could be made more clear in abstract and introduction and the overall framing of the paper. It might be worth focusing on potential applications of the dataset.
Overall, it is also not entirely clear to me why there are two versions of the dataset released simultaneously. It seems to me that V1.1 supersedes v1.0 in that it includes more dams and associated reservoirs and the harmonising with GranD is just part of the method. The authors in line 929 also refer to V1.1 as “our end product”.
As noted by the authors, (line 967) connecting the dam locations with a hydrographic network would enable research into hydrological implications and ecological connectivity. This would greatly enhance the utility of the dataset.
As also noted by an earlier reviewer, the paper is very detailed and quite repetitive. I think it could be significantly shortened to make it more readable. In particular, the methods section is very detailed. Whilst this may be useful for some readers, the majority of readers will not require such extensive detail and could perhaps be referred to supplementary material if more detail is required. The methods section already includes a lot of the numbers later presented in results and discussion while some validation methods get introduced in the results section so some re-organisation would be required.
Specific comments:
I’m surprised that only about 60% of dams from GRanD were found in GeoDAR considering GRanD dams tend to be the largest and usually well documented dams and as such I would expect their attributes to be easily found.
I was wondering if there could be a potential bias in WRD data since this is a volunteered database? Are there any countries not included because they don’t contribute to ICOLD?
Line 54. “inaccessible” in what sense? I believe many WRD coordinates can be made available at cost. Suggest change to e.g. not freely or publicly available. In particular as the point about public availability is made in line 58 for regional registers.
Line 93. We may decrypt? Perhaps link to more detail provided in sections 4 and 5.
Line 98. How is it possible that about 1/3 of the WRD dams (v1.1) capture a similar total storage capacity as the full WRD inventory of ~60,000 dams. Is this because the remaining ~40k dams in WRD are non-reservoir dams? Please explain this in this section. Also would be good to provide the total storage in WRD here (which is only provided in line 138)
Line 118: “We acknowledge…” I suggest rephrasing this sentence to something like: “Whilst we have made every endeavour to remove duplicates, we acknowledge that some duplicates may remain in the dataset”
Line 138. 7388 km3 in original WRD. Is this the figure for all WRD dams or for the cleaned version of 56,783 dams?
Line 139: I don’t think the Venn diagrams are very clear. Not sure if they are even needed as the text explains the process. A simple flowchart might be easier to understand.
Line 239: “rest parts of the world” is a strange phrase
Line 322: ICOLD storage capacity erroneous. See earlier comment (line 98) on ICOLD reported storage. This can also explain the discrepancy. Note that Mulligan et al (2020) also note erroneous reporting of catchments in ICOLD.
Line 525-562, section 3.2 in results and discussion seems to introduce more methods on validation. This should be moved to methods.
Line 797: I find the term (global) capacity improvement a bit confusing. I guess what is meant is a higher reporting of total dam storage capacity by country or globally which is hardly surprising given that more dams and reservoirs are included.
Technical corrections:
Numbers in some cases use thousand separator (e.g. 7,163 line 238) but not in others. Please be consistent throughout
Line 163. “NID records were accessed”
Line 223 “This led to a conservative success rates”
Line 258: “this process was repeated”
Line 570 “We believed”
Line 700: Mulligan et al (2020)
Line 744: GeoDAR
References: line 39 Doll should be Döll, line 42 Vorosmarty should be: Vörösmarty and there may be others.
-
AC2: 'Reply on RC2', Jida Wang, 22 Apr 2021
We very much appreciate these detailed and constructive comments from Reviewer 2, which will certainly help us improve the clarity and readability of our data paper. We will try to absorb the reviewer's comments in the revision stage. Thank you again.
-
AC2: 'Reply on RC2', Jida Wang, 22 Apr 2021
Jida Wang et al.
Data sets
Georeferenced global Dams And Reservoirs (GeoDAR) Jida Wang, Blake A. Walter, Fangfang Yao, Chunqiao Song, Meng Ding, Md Abu Sayeed Maroof, Jingying Zhu, Chenyu Fan, Aote Xin, Jordan M. McAlister, Md Safat Sikder, Yongwei Sheng, George H. Allen, Jean-François Crétaux, and Yoshihide Wada https://doi.org/10.6084/m9.figshare.13670527
Jida Wang et al.
Viewed
| HTML | XML | Total | BibTeX | EndNote | |
|---|---|---|---|---|---|
| 599 | 289 | 20 | 908 | 9 | 14 |
- HTML: 599
- PDF: 289
- XML: 20
- Total: 908
- BibTeX: 9
- EndNote: 14
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1