Articles | Volume 14, issue 4
https://doi.org/10.5194/essd-14-1869-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-14-1869-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description paper
| 
21 Apr 2022
Data description paper |
| 21 Apr 2022
| 21 Apr 2022
GeoDAR: georeferenced global dams and reservoirs dataset for bridging attributes and geolocations
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Blake A. Walter
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Fangfang Yao
Cooperative Institute for Research in Environmental Sciences (CIRES),
University of Colorado Boulder, Boulder, Colorado, USA
Chunqiao Song
Nanjing Institute of Geography and Limnology, Chinese Academy of
Sciences, Nanjing, China
Meng Ding
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Abu Sayeed Maroof
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Jingying Zhu
Nanjing Institute of Geography and Limnology, Chinese Academy of
Sciences, Nanjing, China
Chenyu Fan
Nanjing Institute of Geography and Limnology, Chinese Academy of
Sciences, Nanjing, China
Jordan M. McAlister
Department of Geography, Oklahoma State University, Stillwater,
Oklahoma, USA
Safat Sikder
Department of Geography and Geospatial Sciences, Kansas State
University, Manhattan, Kansas, USA
Yongwei Sheng
Department of Geography, University of California, Los Angeles (UCLA),
Los Angeles, California, USA
George H. Allen
Department of Geography, Texas A&M University, College Station,
Texas, USA
Jean-François Crétaux
Laboratoire d'Études en Géophysique et Océanographie
Spatiales (LEGOS), Centre National d'Études Spatiales (CNES), Toulouse,
France
Yoshihide Wada
International Institute for Applied Systems Analysis (IIASA),
Laxenburg, Austria
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Cited articles
Allen, G. H. and Pavelsky, T. M.: Global extent of rivers and streams,
Science, 361, 585–587, https://doi.org/10.1126/science.aat0636, 2018.
Belletti, B., Leaniz, C. G. D., Jones, J., Bizzi, S., Börger, L.,
Segura, G., Castelletti, A., van der Bund, W., Aarestrup, K., Barry, J.,
Belka, K., Berkhuysen, A., Birnie-Gauvin, K., Bussettini, M., Carolli, M.,
Consuegra, S., Dopico, E., Feierfeil, T., Fernández, S., Garrido, P. F.,
Garcia-Vazquez, E., Garrido, S., Giannico, G., Gough, P., Jepsen, N., Jones,
P. E., Kemp, P., Kerr, J., King, J., Łapińska, M., Lázaro, G.,
Lucas, M. C., Marcello, L., Martin, P., McGinnity, P., O'Hanley, J., Amo, R.
O. d., Parasiewicz, P., Pusch, M., Rincon, G., Rodriguez, C., Royte, J.,
Schneider, C. T., Tummers, J. S., Vallesi, S., Vowles, A., Verspoor, E.,
Wanningen, H., Wantzen, K. M., Wildman, L., and Zalewski, M.: More than one
million barriers fragment Europe's rivers, Nature, 588, 436–441,
https://doi.org/10.1038/s41586-020-3005-2, 2020.
Biancamaria, S., Lettenmaier, D. P., and Pavelsky, T. M.: The SWOT mission
and its capabilities for land hydrology, Surv. Geophys., 37, 307–337,
https://doi.org/10.1007/s10712-015-9346-y, 2016.
Biemans, H., Haddeland, I., Kabat, P., Ludwig, F., Hutjes, R. W. A., Heinke,
J., von Bloh, W., and Gerten, D.: Impact of reservoirs on river discharge
and irrigation water supply during the 20th century, Water Resour. Res., 47,
W03509, https://doi.org/10.1029/2009WR008929, 2011.
Boulange, J., Hanasaki, N., Yamazaki, D., and Pokhrel, Y.: Role of dams in
reducing global flood exposure under climate change, Nat. Commun., 12, 417,
https://doi.org/10.1038/s41467-020-20704-0, 2021.
Busker, T., de Roo, A., Gelati, E., Schwatke, C., Adamovic, M., Bisselink, B., Pekel, J.-F., and Cottam, A.: A global lake and reservoir volume analysis using a surface water dataset and satellite altimetry, Hydrol. Earth Syst. Sci., 23, 669–690, https://doi.org/10.5194/hess-23-669-2019, 2019.
Carpenter, S. R., Stanley, E. H., and Vander Zanden, M. J.: State of the
world's freshwater ecosystems: physical, chemical, and biological changes,
Annu. Rev. Environ. Resour., 36, 75–99,
https://doi.org/10.1146/annurev-environ-021810-094524, 2011.
Chao, B. F., Wu, Y. H., and Li, Y. S.: Impact of artificial reservoir water
impoundment on global sea level, Science, 320, 212–214,
https://doi.org/10.1126/science.1154580, 2008.
Chen, T., Song, C., Ke, L., Wang, J., Liu, K., and Wu, Q.: Estimating
seasonal water budgets in global lakes by using multi-source remote sensing
measurements, J. Hydrol., 593, 125781,
https://doi.org/10.1016/j.jhydrol.2020.125781, 2021.
Cooley, S. W., Ryan, J. C., and Smith, L. C.: Human alteration of global
surface water storage variability, Nature, 591, 78–81,
https://doi.org/10.1038/s41586-021-03262-3, 2021.
Crétaux, J. F., Abarca-del-Rio, R., Berge-Nguyen, M., Arsen, A., Drolon,
V., Clos, G., and Maisongrande, P.: Lake volume monitoring from space, Surv.
Geophys., 37, 269–305, https://doi.org/10.1007/s10712-016-9362-6, 2016.
Crétaux, J. F., Jelinski, W., Calmant, S., Kouraev, A., Vuglinski, V.,
Berge-Nguyen, M., Gennero, M. C., Nino, F., Del Rio, R. A., Cazenave, A.,
and Maisongrande, P.: SOLS: A lake database to monitor in the near real time
water level and storage variations from remote sensing data, Adv. Space.
Res., 47, 1497–1507, https://doi.org/10.1016/j.asr.2011.01.004, 2011.
Crétaux, J. F., Biancamaria, S., Arsen, A., Berge-Nguyen, M., and
Becker, M.: Global surveys of reservoirs and lakes from satellites and
regional application to the Syrdarya river basin, Environ. Res. Lett., 10,
015002, https://doi.org/10.1088/1748-9326/10/1/015002, 2015.
Dams in Japan, Japan Dam Foundation (JDF):
http://damnet.or.jp/Dambinran/binran/TopIndex_en.html, last
access: May 2021.
Degu, A. M., Hossain, F., Niyogi, D., Pielke, R., Shepherd, J. M., Voisin,
N., and Chronis, T.: The influence of large dams on surrounding climate and
precipitation patterns, Geophys. Res. Lett., 38, L04405,
https://doi.org/10.1029/2010GL046482, 2011.
Department of Water and Sanitation (DWS) of South Africa: List of Registered
Dams (LRD), DWS [data set], http://www.dwaf.gov.za/DSO/Publications.aspx, 2019.
Döll, P., Fiedler, K., and Zhang, J.: Global-scale analysis of river flow alterations due to water withdrawals and reservoirs, Hydrol. Earth Syst. Sci., 13, 2413–2432, https://doi.org/10.5194/hess-13-2413-2009, 2009.
Fang, W., Wang, C., Chen, X., Wan, W., Li, H., Zhu, S., Fang, Y., Liu, B.,
and Hong, Y.: Recognizing global reservoirs from Landsat 8 images: a deep
learning approach, IEEE J. Sel. Top. Appl., 12,
3701–3701, https://doi.org/10.1109/JSTARS.2019.2929601, 2019.
Farr, T. G., Rosen, P. A., Caro, E., Crippen, R., Duren, R., Hensley, S., Kobrick, M., Paller, M., Rodriguez, E., Roth, L., Seal, D., Shaffer, S., Shimada, J., Umland, J., Werner, M., Oskin, M., Burbank, D., and Alsdorf, D.: The Shuttle Radar Topography Mission, Rev. Geophys., 45, RG2004, https://doi.org/10.1029/2005RG000183, 2007.
Gao, H., Birkett, C., and Lettenmaier, D. P.: Global monitoring of large
reservoir storage from satellite remote sensing, Water Resour. Res., 48,
W09504, https://doi.org/10.1029/2012WR012063, 2012.
Grill, G., Lehner, B., Thieme, M., Geenen, B., Tickner, D., Antonelli, F.,
Babu, S., Borrelli, P., Cheng, L., Crochetiere, H., Macedo, H. E.,
Filgueiras, R., Goichot, M., Higgins, J., Hogan, Z., Lip, B., McClain, M.
E., Meng, J., Mulligan, M., Nilsson, C., Olden, J. D., Opperman, J. J.,
Petry, P., Liermann, C. R., Saenz, L., Salinas-Rodriguez, S., Schelle, P.,
Schmitt, R. J. P., Snider, J., Tan, F., Tockner, K., Valdujo, P. H., van
Soesbergen, A., and Zarfl, C.: Mapping the world's free-flowing rivers,
Nature, 569, 215–221, https://doi.org/10.1038/s41586-019-1111-9, 2019.
Habets, F., Molenat, J., Carluer, N., Douez, O., and Leenhardt, D.: The
cumulative impacts of small reservoirs on hydrology: a review, Sci. Total
Environ., 643, 850–867, https://doi.org/10.1016/j.scitotenv.2018.06.188,
2018.
Latrubesse, E. M., Arima, E. Y., Dunne, T., Park, E., Baker, V. R., d'Horta,
F. M., Wight, C., Wittmann, F., Zuanon, J., Baker, P. A., Ribas, C. C.,
Norgaard, R. B., Filizola, N., Ansar, A., Flyvbjerg, B., and Stevaux, J. C.:
Damming the rivers of the Amazon basin, Nature, 546, 363–369,
https://doi.org/10.1038/nature22333, 2017.
Lehner, B., Verdin, K., and Jarvis, A.: New global hydrography derived from
spaceborne elevation data, Eos T. Am. Geophys. Un.,
89, 93–104, https://doi.org/10.1029/2008eo100001, 2008.
Lehner, B., Liermann, C. R., Revenga, C., Vörösmarty, C., Fekete,
B., Crouzet, P., Döll, P., Endejan, M., Frenken, K., Magome, J.,
Nilsson, C., Robertson, J. C., Rodel, R., Sindorf, N., and Wisser, D.:
High-resolution mapping of the world's reservoirs and dams for sustainable
river-flow management, Front. Ecol. Environ., 9, 494–502,
https://doi.org/10.1890/100125, 2011.
Li, B., Yan, Q., and Zhang, L.: Flood monitoring and analysis over the
middle reaches of Yangtze River basin using MODIS time-series imagery, in:
2011 IEEE International Geoscience and Remote Sensing Symposium, Vancouver,
British Columbia, Canada, 24–29 July 2011, 807–810,
https://doi.org/10.1109/IGARSS.2011.6049253, 2011.
Li, Y., Gao, H., Zhao, G., and Tseng, K. H.: A high-resolution bathymetry
dataset for global reservoirs using multi-source satellite imagery and
altimetry, Remote Sens. Environ., 244, 111831,
https://doi.org/10.1016/j.rse.2020.111831, 2020.
Lin, P., Pan, M., Beck, H. E., Yang, Y., Yamazaki, D., Frasson, R., David,
C. H., Durand, M., Pavelsky, T. M., Allen, G. H., Gleason, C. J., and Wood,
E. F.: Global reconstruction of naturalized river flows at 2.94 million
reaches, Water Resour. Res., 55, 6499–6516,
https://doi.org/10.1029/2019WR025287, 2019.
Lin, P., Pan, M., Wood, E. F., Yamazaki, D., and Allen, G. H.: A new
vector-based global river network dataset accounting for variable drainage
density, Sci. Data, 8, 28, https://doi.org/10.1038/s41597-021-00819-9, 2021.
Liu, K., Song, C., Wang, J., Ke, L., Zhu, Y., Zhu, J., Ma, R., and Luo, Z.:
Remote sensing-based modeling of the bathymetry and water storage for
channel-type reservoirs worldwide, Water Resour. Res., 56, e2020WR027147,
https://doi.org/10.1029/2020WR027147, 2020.
Lyons, E. A. and Sheng, Y.: LakeTime: Automated seasonal scene selection for
global lake mapping using Landsat ETM+ and OLI, Remote Sensing, 10, 54,
https://doi.org/10.3390/rs10010054, 2018.
Mady, B., Lehmann, P., Gorelick, S. M., and Or, D.: Distribution of small
seasonal reservoirs in semi-arid regions and associated evaporative losses,
Environ. Res Commun., 2, 061002, https://doi.org/10.1088/2515-7620/ab92af,
2020.
Managing Aquatic ecosystems and water Resources under multiple Stress
project (MARS): MARS GeoDatabase (MARSgeoDB) version 2 [data set],
http://www.mars-project.eu/index.php/databases.html (last access: 4 February 2021), 2017.
Map World (Tianditu), National Platform for Common Geospatial Information
Services (NPCGIS): https://map.tianditu.gov.cn, last access: July 2021.
Messager, M. L., Lehner, B., Grill, G., Nedeva, I., and Schmitt, O.:
Estimating the volume and age of water stored in global lakes using a
geo-statistical approach, Nat. Commun., 7, 13603,
https://doi.org/10.1038/ncomms13603, 2016.
Mulligan, M., van Soesbergen, A., and Saenz, L.: GOODD, a global dataset of
more than 38,000 georeferenced dams, Sci. Data, 7, 31,
https://doi.org/10.1038/s41597-020-0362-5, 2020.
Mulligan, M., Lehner, B., Zarfl, C., Thieme, M., Beames, P., van Soesbergen,
A., Higgins, J., Januchowski-Hartley, S. R., Brauman, K. A., De Felice, L.,
Wen, Q., de Leaniz, C. G., Belletti, B., Mandle, L., Yang, X., Wang, J., and
Mazany-Wright, N.: Global Dam Watch: curated data and tools for management
and decision making, Environ. Res. Infrastruct. Sustain., 1, 033003,
https://doi.org/10.1088/2634-4505/ac333a, 2021.
National Register of Large Dams (NRLD): Government of India, Central Water Commission, Central Dam Safety Organization, New Delhi, 300 pp., June 2019.
Natural Resources Canada (NRC): CanVec 1M Man-Made Features – Dam version 1.0.1, NRC [data set], Data catalogue date: 7 April 2017, originally accessed from http://geogratis.gc.ca/api/en/nrcan-rncan/ess-sst/0c78d7fe-100b-5937-b74e-7590a03a6244.html, last access: September 2017.
Nilsson, C. and Berggren, K.: Alterations of riparian ecosystems caused by
river regulation, Bioscience, 50, 783–792,
https://doi.org/10.1641/0006-3568(2000)050[0783:AORECB]2.0.CO;2, 2000.
Open Development Cambodia (ODC): Hydropower dams 1993–2014, ODC [data set],
https://data.opendevelopmentmekong.net/en/dataset/hydropower-2009-2014 (5 September 2019),
2015.
Open Development Myanmar (ODM): Myanmar Dams, ODM [data set],
https://data.opendevelopmentmekong.net/en/dataset/myanmar-dams (last access: 5 September 2019), 2018.
Paredes-Beltran, B., Sordo-Ward, A., and Garrote, L.: Dataset of Georeferenced Dams in South America (DDSA), Earth Syst. Sci. Data, 13, 213–229, https://doi.org/10.5194/essd-13-213-2021, 2021.
Pekel, J. F., Cottam, A., Gorelick, N., and Belward, A. S.: High-resolution
mapping of global surface water and its long-term changes, Nature, 540,
418–422, https://doi.org/10.1038/nature20584, 2016.
Schwatke, C., Dettmering, D., Bosch, W., and Seitz, F.: DAHITI – an innovative approach for estimating water level time series over inland waters using multi-mission satellite altimetry, Hydrol. Earth Syst. Sci., 19, 4345–4364, https://doi.org/10.5194/hess-19-4345-2015, 2015.
Sheng, Y., Song, C., Wang, J., Lyons, E. A., Knox, B. R., Cox, J. S., and
Gao, F.: Representative lake water extent mapping at continental scales
using multi-temporal Landsat-8 imagery, Remote Sens. Environ., 185, 129–141,
https://doi.org/10.1016/j.rse.2015.12.041, 2016.
Shin, S., Pokhrel, Y., and Miguez-Macho, G.: High-resolution modeling of
reservoir release and storage dynamics at the continental scale, Water
Resour. Res., 55, 787–810, https://doi.org/10.1029/2018WR023025, 2019.
Sistema Nacional de Informações sobre Segurança de Barragens
(SNISB, Brazilian National Dam Safety Information System): Relatório de
Segurança de Barragens 2017 (Dams Safety Report 2017), SNISB [data set],
http://www.snisb.gov.br/portal/snisb/relatorio-anual-de-seguranca-de-barragem/2017 (last access: 31 August 2019),
2017.
Tilt, B., Braun, Y., and He, D.: Social impacts of large dam projects: A
comparison of international case studies and implications for best practice,
J. Environ. Manage., 90, S249–S257, 2009.
Tobler, W. R.: Computer Movie Simulating Urban Growth in Detroit Region,
Econ. Geogr., 46, 234–240, https://doi.org/10.2307/143141, 1970.
United States Army Coprs of Engineers (USACE): National Inventory of Dams
(NID), USACE [data set], https://nid.usace.army.mil (last access: 20 March 2021), 2018.
Vörösmarty, C. J., Meybeck, M., Fekete, B., Sharma, K., Green, P.,
and Syvitski, J. P. M.: Anthropogenic sediment retention: major global
impact from registered river impoundments, Global Planet. Change, 39, 169–190,
https://doi.org/10.1016/S0921-8181(03)00023-7, 2003.
Wada, Y., Reager, J. T., Chao, B. F., Wang, J., Lo, M. H., Song, C., Li, Y.
W., and Gardner, A. S.: Recent changes in land water storage and its
contribution to sea level variations, Surv. Geophys., 38, 131–152,
https://doi.org/10.1007/s10712-016-9399-6, 2017.
Wang, J., Sheng, Y., and Wada, Y.: Little impact of the Three Gorges Dam on
recent decadal lake decline across China's Yangtze Plain, Water Resour.
Res., 53, 3854–3877, https://doi.org/10.1002/2016WR019817, 2017.
Wang, J., Walter, B. A., Yao, F., Song, C., Ding, M., Maroof, A. S., Zhu, J., Fan, C., McAlister, J. M., Sikder, S., Sheng, Y., Allen, G. H., Crétaux, J.-F., and Wada, Y.: GeoDAR: Georeferenced global Dams And Reservoirs dataset for bridging attributes and geolocations, in: Earth System Science Data (v1.1; v1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.6163413, 2022.
Whittemore, A., Ross, M. R. V., Dolan, W., Langhorst, T., Yang, X., Pawar,
S., Jorissen, M., Lawton, E., Januchowski-Hartley, S., and Pavelsky, T.: A
participatory science approach to expanding instream infrastructure
inventories, Earth's Future, 8, e2020EF001558,
https://doi.org/10.1029/2020EF001558, 2020.
Yamazaki, D., Ikeshima, D., Tawatari, R., Yamaguchi, T., O'Loughlin, F.,
Neal, J. C., Sampson, C. C., Kanae, S., and Bates, P. D.: A high-accuracy
map of global terrain elevations, Geophys. Res. Lett., 44, 5844–5853,
https://doi.org/10.1002/2017GL072874, 2017.
Yamazaki, D., Ikeshima, D., Sosa, J., Bates, P. D., Allen, G. H., and
Pavelsky, T. M.: MERIT Hydro: A high-resolution global hydrography map based
on latest topography dataset, Water Resour. Res., 55, 5053–5073,
https://doi.org/10.1029/2019WR024873, 2019.
Yang, X., Pavelsky, T. M., Ross, M. R. V., Januchowski-Hartley, S. R.,
Dolan, W., Altenau, E. H., Belanger, M., Byron, D., Durand, M., Van Dusen,
I., Galit, H., Jorissen, M., Langhorst, T., Lawton, E., Lynch, R.,
Mcquillan, K. A., Pawar, S., and Whittemore, A.: Mapping flow-obstructing
structures on global rivers, Water Resour. Res., 58, e2021WR030386,
https://doi.org/10.1029/2021WR030386, 2022.
Yao, F., Wang, J., Wang, C., and Crétaux, J. F.: Constructing long-term
high-frequency time series of global lake and reservoir areas using Landsat
imagery, Remote Sens. Environ., 232, 111210,
https://doi.org/10.1016/j.rse.2019.111210, 2019.
Yassin, F., Razavi, S., Elshamy, M., Davison, B., Sapriza-Azuri, G., and Wheater, H.: Representation and improved parameterization of reservoir operation in hydrological and land-surface models, Hydrol. Earth Syst. Sci., 23, 3735–3764, https://doi.org/10.5194/hess-23-3735-2019, 2019.
Yigzaw, W., Li, H. Y., Demissie, Y., Hejazi, M. I., Leung, L. R., Voisin,
N., and Payn, R.: A new global storage-area-depth data set for modeling
reservoirs in land surface and earth system models, Water Resour. Res., 54,
10372–10386, https://doi.org/10.1029/2017WR022040, 2018.
Zarfl, C., Lumsdon, A. E., Berlekamp, J., Tydecks, L., and Tockner, K.: A
global boom in hydropower dam construction, Aquat. Sci., 77, 161–170,
https://doi.org/10.1007/s00027-014-0377-0, 2015.
Zhan, S., Song, C., Wang, J., Sheng, Y., and Quan, J.: A global assessment
of terrestrial evapotranspiration increase due to surface water area change,
Earth's Future, 7, 266–282, https://doi.org/10.1029/2018EF001066, 2019.
Zhang, S., Gao, H., and Naz, B. S.: Monitoring reservoir storage in South
Asia from multisatellite remote sensing, Water Resour. Res., 50, 8927–8943,
https://doi.org/10.1002/2014WR015829, 2014.
Zhang, W., Pan, H., Song, C., Ke, L., Wang, J., Ma, R., Deng, X., Liu, K.,
Zhu, J., and Wu, Q. H.: Identifying emerging reservoirs along regulated
rivers using multi-source remote sensing observations, Remote Sens.,
11, 25, https://doi.org/10.3390/rs11010025, 2019.
Zhao, G. and Gao, H.: Estimating reservoir evaporation losses for the United
States: Fusing remote sensing and modeling approaches, Remote Sens.
Environ., 226, 109–124, https://doi.org/10.1016/j.rse.2019.03.015, 2019a.
Zhao, G. and Gao, H.: Towards global hydrological drought monitoring using
remotely sensed reservoir surface area, Geophys. Res. Lett., 46,
13027–13035, https://doi.org/10.1029/2019GL085345, 2019b.
Short summary
Improved water infrastructure data on dams and reservoirs remain to be critical to hydrologic modeling, energy planning, and environmental conservation. We present a new global dataset, GeoDAR, that includes nearly 25 000 georeferenced dam points and their associated reservoir boundaries. A majority of these features can be linked to the register of the International Commission on Large Dams, extending the potential of registered attribute information for spatially explicit applications.
Improved water infrastructure data on dams and reservoirs remain to be critical to hydrologic...
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