dataesr/scientific-paragraphs-categorization huggingface dataset:
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.
, 2009; Vorosmarty et al. , 2003; Wang et al. , 2017). Despite such environmental and societal significance, our spatial inventory of global dams and reservoirs, even for the large ones (such as those with a surface area >1 km 2 ), has been insufficient. We still lack a thorough and authoritative dataset that documents both geographic coordinates (latitude and longitude) and standard attributes (e. g. , purpose, reservoir storage capacity, and hydropower capacity) of the existing large dams. One of the most comprehensive datasets, the World Register of Dams (WRD), is regularly updated by the International Commission on Large Dams (ICOLD; https://www. icoldcigb. org), a non-governmental organization dedicated to the global sharing of professional dam/reservoir information. The recent version of ICOLD WRD documents nearly 60,000 "large" dams, defined as those with a wall higher than 15 m or between 5 to 15 m but with a reservoir storage greater than 3 million m 3 (mcm).
, 2015). Other global dam and reservoir datasets that are georeferenced, however, often lack essential attributes. An example is the recently published GlObal geOreferenced Database of Dams (GOODD V1)  (Mulligan et al. , 2020) , which contains 38,667 dam points digitized from Google Earth imagery and their associated catchments delineated from digital elevation models  (DEMs). Despite this dam quantity, GOODD provides no other attribute information. Another inventory, the Global River The current attributes are limited to obstruction types such as locks, weirs, and multiple types of dams. In addition, GROD is tailored for the forthcoming Surface Water and Ocean Topography (SWOT) satellite mission which is designed to observe river reaches wider than 50-100 m  (Biancamaria et al. , 2016). While these rivers are sufficiently captured by GRWL, the obstruction infrastructure identified along the river mask in GRWL excludes many large dams on rivers narrower than 30 m.
In the US, for instance, there are at least 5170 NID-registered dams higher than 15 m (i. e. , large dams according to ICOLD criteria), but less than 8% of these dams intersect with GRWL (i. e. , located on rivers wider than 30 m). Among the few global dam/reservoir datasets that provide both georeferenced locations and essential attributes, are the United Nations Food and Agricultural Organization (FAO) AQUASTAT  (Li et al. , 2011)  and the Global Reservoir and Dam database (GRanD)  (Lehner et al. , 2011). GRanD was constructed by harmonizing AQUASTAT and a wide range of regional gazetteers and inventories. Its latest version, v1. 3, contains 7320 dams as well as their reservoir boundaries and approximately 50 attributes, with a cumulative storage capacity of 6881 km 3. Since its publication, GRanD has been applied extensively by a variety of studies, although its focus is on the world's largest dams (e.
g. , >0. 1 km 3 ) and its quantity (7320 dams) is only a fraction of the 59,000 dams documented in WRD. A spatially resolved inclusion of additional large dams, such as those in compliance with the ICOLD definition, has been increasingly desired by the hydrology community and encouraged by growing collaborations from multiple disciplines such as biogeochemistry, ecology, energy planning, and infrastructure managements  (Belletti et al. , 2020; Boulange et al. , 2021; Grill et al. , 2019; Lin et al. , 2019; Wada et al. , 2017). To further improve our spatial inventory of the world's largest dams, we performed a harmonization between the dam points in GeoDAR v1. 0 and GRanD v1. 3. Through harmonization, we aimed to merge both datasets, remove any duplicates, and build association between new dams supplemented by GRanD and the WRD records.
Version With removal of duplicates, this harmonization led to a total number of 23,680 georeferenced dam points, with an accumulative storage capacity of 7486 km 3 (comparable to that 7388 km 3 in the original WRD). An overview of this harmonization process is illustrated by the Venn diagram in Fig. 2b. Finally, the reservoir polygons for each of the georeferenced dams were retrieved as thoroughly as possible from three global water body datasets: GRanD v1. 3 reservoirs  (Lehner et al. , 2011) , HydroLAKES v1. 0  (Messager et al. , 2016) , and the Landsat-based UCLA Circa-2015 Lake Inventory  (Sheng et al. , 2016). These 24,000 or so dam points (georeferenced from WRD and supplemented by GRanD) and their associated reservoir polygons constituted GeoDAR v1. 1. Details of all processes and their Quality Assurance and Quality Control (QA/QC) are included in the following method sections.
For these additional regional registers, it was either difficult to bulk-download the dam records, or we were legally restricted from releasing their dam coordinates, as was the case for China, and therefore, we only used these registers for the purpose of validation. The procedure of geo-matching is illustrated in Fig. 3. Given each regional register, our goal was to find its matching records from the subset of ICOLD WRD for the same region, by cross-checking value similarities for several key attributes between the two datasets. On one hand, the compared attributes must be mutually available in both datasets. On the other hand, the attributes should cover various themes so that in combination, they are able to disambiguate records that represent different dams but may coincide in certain attributes.
However, this ideal scenario was often unnecessary and sometimes impossible. One of the reasons is that the key attributes do not always have valid values. In WRD, for instance, the values of "nearest town" for nearly all (>99%) US dams are null. While this attribute is valid for most other dams, the nearest town/city in WRD is not necessarily the division that administrates or contains the dam as is the case in the township in some regional registers. Another reason is that our collected multi-source datasets were not collated by a universal standard. As a result, inherent discrepancies of the attribute definitions and/or values may exist among the datasets. One example is the dam's "completion year", which could be ambiguous between the year when the dam construction was concluded and the year when the dam operation was initiated or commissioned.
3 ). Y Y Y/na Y Y Y Y Y Y/na Y Y N/na Y Y Y/na Y N/na Y M2 6557 dams Y Y Y Y na na Y Y Y N/na Y Y M3 2510 dams Y Y na Y na na Y Y na N/na Y Y Y Y Y/na N/na Y N/na Y Y Y/na N/na N/na Y Note: In column "Quality level", the initial letter "M" symbolizes QA levels for geo-matching (as opposed to "C" for geocoding in Table  5 ). "Y" indicates that attribute values in WRD and the regional register agree with each other, "N" represents disagreement, and "na" indicates attribute values are not available in either or both datasets. Scenarios with "River" values as "Y" do not apply to Cambodia as river names are missing in the regional register/inventory.
(2017)  with those in WRD and identified 31 of them exhibiting substantial discrepancies between the two datasets. Considering that the storage capacity values in  Wada et al. (2017)  have been verified with other data sources, we used them to replace the original WRD values of these 31 dams. The entire supplementation process, including adding new dams, updating existing dam coordinates, and correcting reservoir storage capacities, increased the total storage capacity of our georeferenced dams by 19%, and 90% of the capacity increase comes from the 43 added large dams. For improved clarity, it is worth reiterating that all dams supplemented by  Wada et al. (2017)  were documented in ICOLD WRD. The combined results of geo-matching and geocoding, after the supplementation from  Wada et al. (2017) , defines GeoDAR v1.
0. For the remaining 3062 dams in GRanD, Step 2 utilized their reservoir polygons to spatially intersect with the dam points in GeoDAR v1. 0. A distance tolerance of 500 m was applied in the spatial intersection to account for occasional geographic offsets in GeoDAR v1. 0 (such as in mainland China; see Section 2. 4). As part of the QC, the attribute values of each intersecting pair (one from GRanD and the other from WRD) were manually compared to determine whether they are indeed the same dam. This step identified another 433 overlapping dams between the two datasets. In total, we found that GeoDAR v1. 0 overlaps 4691 out of the 7320 dams in GRanD, and their spatial coordinates were updated to be consistent with those in GRanD. Second, for the remaining 2629 dams in GRanD that do not overlap GeoDAR v1.
(2017)  were overwritten by those of the overlapping dams in GRanD. This correction led to a minor decrease of 30 km 3 (less than 1%) in the total reservoir storage capacity. Eventually, the remaining 1111 dams in GRanD, which were not found in ICOLD WRD, were appended to the 22,569 georeferenced WRD dams so that our final inventory absorbed the entire dataset of GRanD. It is worth noting that similar to geo-matching (Section 2. 3), our attribute association here could be conservative, meaning that some of the dams appended from GRanD might be documented in the remaining WRD (the subset not georeferenced successfully). The harmonized dataset, GeoDAR v1. 1, contains a total of 23,680 georeferenced dam points, including 16,360 from WRD alone, 6209 shared between WRD and GRanD, and the other 1111 from GRanD alone (Fig.
Retrieving reservoir boundaries In addition to the 23,680 georeferenced dam points, GeoDAR v1. 1 also includes their associated reservoir boundaries which we retrieved as thoroughly as possible from three global water body datasets: GRanD reservoirs  (Lehner et al. , 2011) , HydroLAKES v1. 0  (Messager et al. , 2016) , and UCLA Circa-2015 Lake Inventory  (Sheng et al. , 2016). These three water body datasets exhibit an increasing spatial resolution: from 7000+ polygons in GRanD reservoirs provided exclusively for GRanD's dam points, to millions of water body polygons, including both natural lakes and reservoirs, in the other two datasets. While HydroLAKES documents 1. 4 million water bodies larger than 0. 1 km 2 (10 ha), the Landsat-based UCLA Circa-2015 Lake Inventory further reduced the minimum size to only 0.
004 km 2 (0. 4 ha), resulting in another 7. 7 million water bodies on the global continental surface. Accordingly, we implemented a hierarchical procedure, where the three water body datasets were applied in ascending order of spatial resolution to retrieve the reservoir boundaries with an overall decreasing size. Specifically, GRanD v1. 3 provides 7250 reservoir polygons for the 7320 collected dam points. The remaining 70 dams without reservoir polygons are either river barrages and thus have no proper reservoirs, or infrastructures that were too recent to have filled impoundments. Other rarer cases also include dams that were abandoned or to be constructed  (Lehner et al. , 2011). These 7250 reservoir polygons were assigned to their associated dam points in GeoDAR v1. 1 through GRanD IDs.
Similarly, only the subset that does not intersect with the 6902 HydroLAKES polygons was considered, in order to avoid duplicates in the retrieved reservoirs from different datasets. The use of UCLA Circa-2015 Lake Inventory retrieved another 6062 reservoirs. Combining the results from all three water body datasets, 20,214 (85%) out of the 23,680 georeferenced dams were paired with their reservoir polygons. To this end, both of the dam points and their associated reservoir polygons were considered as the final product components in GeoDAR v1. 1.
1 (reservoir areas without polygons are based on ICOLD WRD attributes). These reservoir polygons also correspond to a cumulative storage capacity of 7168 km 3 , accounting for nearly 96% of the total storage capacity in v1. 1. These statistics indicate that the reservoirs whose boundaries were retrieved unsuccessfully were mostly small in area and storage. Access Earth System Science Data Discussions Preprint. The numbers of reservoir polygons retrieved from each of the three water body datasets are fairly comparable (6000 to 7000 each), but the total reservoir storage capacity and area both decrease drastically with the increasing spatial resolution of the water body datasets (Table  6 ). As a result, the average reservoir size decreased from 65 km 2 in those from GRanD, to 2 km 2 from HydroLAKES and then less than 1 km 2 from the UCLA Circa-2015 Lake Inventory.
In addition, we foresee several other applications of our produced reservoir polygons in GeoDAR v1. 1. These 20,000 plus reservoirs, which are mostly distributed in the populated middle-latitude regions (Fig. 7 ), reveal an unprecedented detail of human footprints on the natural surface hydrology. Together with other high-resolution surface water data such as the UCLA Circa-2015 Lake Inventory and the Joint Research Centre's Global Water Database  (Pekel et al. , 2016) , these reservoir polygons can help us better disambiguate artificial water impoundments from natural lakes and free-flowing river reaches. This water body separation is overdue, as it provides a fundamental base map for assessing and modelling how human water regulations alter the natural surface water regimes. By flagging more reservoirs from natural lakes, this map also expanded the global training pool for machine learning algorithms that aim to thoroughly classify or detect reservoirs from remote sensing images.
0 were georeferenced independently from GRanD and those shared with GRanD reflect our georeferencing accuracy for the world's largest dams. However, for each regional sample, we limited the number of GRanD-overlapping dams to no more than 30% of the entire regional sample size (Table  7 ). This was to comply with the size ratio between GRanD and GeoDAR v1. 0 (about 1:3) so that our validation still emphasized smaller dams newly georeferenced in our dataset. We also randomly selected 30 out of the 124 large WRD dams supplemented by  Wada et al. (2017) , considering that they are part of GeoDAR v1. 0 and the supplementation was based on attribute association that is similar to regional geo-matching. In total, 192 dams were selected for validating the geo-matching accuracy. For each dam, we manually checked whether its spatial coordinates in GeoDAR v1.
0 are consistent with those documented in the geomatching source (see source references in Table  2 ). From the remaining subset of GeoDAR v1. 0 produced by geocoding, we followed the same stratified sampling scheme and randomly selected 200 or so dam points for each of the validation regions: China, Indian, Japan, and the other part of the world as a whole (Table  7 ). Compared to geo-matching which was based on attribute association with georeferenced regional registers, the geocoding process was more complicated, and relied largely on the geographic information repository in Google Maps and its embedded geocoding algorithms. To increase our confidence in the geocoding results, we therefore purposefully enlarged the sample size for each validation region. As described in Section 2. 3, three additional georeferenced datasets from authoritative registries in China, Indian, and Japan were used exclusively for the purpose of geocoding validation (refer to Table  2  for register details).
Data attributes and usage The GeoDAR dataset, including dam points for v1. 0 and both dam points and reservoir polygons for v1. 1, is provided as three separate shapefiles. For user convenience, we also duplicated the two dam point shapefiles in the comma-separated values (csv) format. The file names and attributes are explained in Table  8. Although most of our dam points were georeferenced using WRD records, our published GeoDAR complies with the proprietary rights of ICOLD and does not directly release any attribute from WRD. The attributes we provide in GeoDAR, as listed in Table  8 , are only limited to our georeferencing methods, QA/QC, validation, and other information (such as spatial coordinates and part of the reservoir storage capacities) that is already open source or has been permitted for use by the original producers.
Although WRD attributes are not directly available in GeoDAR, we suggest two possible ways for users to acquire at least some of the essential attributes. Upon the user's reasonable request, we can decrypt GeoDAR IDs (Table  8 ) to ICOLD's International Codes, and using the International Codes, the user can link each of the dams/reservoirs in GeoDAR to the entire 40 or so proprietary attributes in WRD. However, this is based on the premise that the user needs to acquire the WRD attribute data from ICOLD, and that the user agrees to not release the decryption key or the WRD attributes to the public. Alternatively, since we imposed no usage restrictions on our spatial features (geometric dam points and reservoir polygons), users are free to integrate them with other datasets and tools, such as remote sensing observations and modelling, to acquire the needed attributes, particularly those not yet documented in ICOLD WRD.
Comparisons with other global dam and reservoir datasets To better understand the improvements and potential applications of GeoDAR, we compare it with three major global dam and reservoir datasets: the complete ICOLD WRD, GRanD (v1. 3), and GOODD (V1). To recap the pros and cons of each dataset, ICOLD WRD documents over 56,000 unique dam records with a broad suite of attributes, but the provided dam records are not georeferenced. GOODD depicts the spatial details of more than 38,000 dam points and their catchments but does not include any other attribute. GRanD is georeferenced and provides multiple essential attributes, but the records are limited to 7320 large dams. Accordingly, our comparison first emphasized the aspects of dam quantity, reservoir area, and if applicable, the spatial pattern and distribution of the dams.
ones, has been increasingly needed by hydrologic modelling and watershed managements  (Grill et al. , 2019; Lin et al. , 2019). To evaluate how GeoDAR spatially resolved WRD in this aspect, we directly used the values of the attribute "catchment area" provided in WRD. As many records in WRD are missing catchment areas, we combined the available values in both WRD and GRanD, and when a dam has catchment areas in both datasets, we preferred the value in GRanD. As reported in Table  9 , the subset of WRD georeferenced in GeoDAR v1. 1 has a total catchment area of 134 million km 2 , which covers 92% of the total catchment area in the entire WRD. The remaining 8% catchment area was compensated for by the inclusion of the remaining non-WRD dams from GRanD. It is worth mentioning that these statistics do not take into account the dams without valid catchment areas.
As a result, the WRD portion of GeoDAR v1. 1 (with 22,569 dams) matches the full storage capacity of GRanD but is triple GRanD's dam count. This comparison suggests that the improvement of GeDAR is mainly manifested as the increased dam locations or spatial details, rather than reservoir storage capacity. With the inclusion of the remaining 1111 large dams from GRanD, the number of dams in GeoDAR v1. 1 (23,680) reaches 323% of that in GRanD, with a total reservoir storage capacity (7486 Gt) also exceeding 9% of that in GRanD. https://doi. org/10. 5194/essd-2021-58 Open  The improved spatial detail in GeoDAR is also revealed by the distribution of individual reservoir storage capacities worldwide (Fig. 12 ). Since GeoDAR v1. 1 has absorbed GRanD v1. 3, the global patterns for capacious reservoirs are overall similar between the two datasets.
https://doi. org/10. 5194/essd-2021-58 started: 24 March 2021 c Author(s) 2021. CC BY 4. 0 License. 2. 5. 2 Harmonization with GRanD: forming GeoDAR v1. 1While GeoDAR v1. 0 largely exceeds GRanD in dam count, a visual comparison of their spatial distributions revealed that the latter is often complementary to (instead of completely duplicated by) the former in many regions of the world. This motivated us to perform a systematic harmonization between the two datasets. The merged version, which we entitled GeoDAR v1. 1, combines the merits of GRanD in accurately documenting the world's largest dams and GeoDAR v1. 0 in providing unprecedented spatial details of smaller but more widespread dams (see Disclaimer section for citation courtesy).
started: 24 March 2021 c Author(s) 2021. CC BY 4. 0 License. 20,000+ geo-matched and geocoded dams. Although the regional registers used for geo-matching only cover seven countries, the dams in GeoDAR v1. 0, as shown in Fig. 6b, are distributed in 148 out of the 164 countries in WRD (including ICOLD member and non-member countries), largely owing to our geocoding efforts through Google Maps API. Again, GeoDAR v1. 0 was produced independently from other comprehensive global dam datasets such as GRanD. While its dam quantity can be further expanded, this version provides users a flexibility to choose the optimal dataset based on their specific purposes or study regions. Validation of our georeferencing accuracy for v1. 0 is provided in Section 3. 2.
Acknowledgements The work was in part supported by Kansas State University faculty start-up fund to JW and NASA Surface Water and Ocean Topography (SWOT) Grant (#80NSSC20K1143) to JW. The authors would like to acknowledge ICOLD for providing WRD and the Central Office of ICOLD for informing data dissemination policies and for allowing us to release the position information of WRD we georeferenced. The authors are also grateful to Bernhard Lehner at McGill University for his constructive suggestions and comments on data curation, usage, and dissemination. We also acknowledge Google Maps Platform (https://cloud. google. com/maps-platform) for providing the geocoding API.
Conclusions and Outlooks We have produced a comprehensive and spatially resolved dam and reservoir dataset, GeoDAR, which complementarily improved the existing global inventories of large dams. We demonstrated that the production of GeoDAR is not a direct compilation or collation of existing dam datasets. Instead, it involved a first-known effort to georeference ICOLD WRD. This was jointly enabled by geo-matching (or table-associating) multi-source regional registers and geocoding descriptive attributes through the Google Maps API. This georeferencing effort resulted in GeoDAR v1. 0 which contains 21,051 spatially resolved dam points, each associated with a WRD record, with an overall accuracy of 96%. Each of the georeferenced records was also labelled with a QA score, providing users a reference to the qualities of individual dam locations.