Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3349-2022
© 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-14-3349-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
19 Jul 2022
Data description article |
| 19 Jul 2022
| 19 Jul 2022
A high-resolution inland surface water body dataset for the tundra and boreal forests of North America
Yijie Sui
National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Min Feng
CORRESPONDING AUTHOR
National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Academy of Plateau Science and Sustainability, Qinghai Normal University, Xining 810016, China
University of Chinese Academy Sciences, Beijing 100049, China
Chunling Wang
National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy Sciences, Beijing 100049, China
Xin Li
National Tibetan Plateau Data Center, State Key Laboratory of Tibetan Plateau Earth System, Environment and Resources, Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
University of Chinese Academy Sciences, Beijing 100049, China
Related authors
Jinhao Xu, Min Feng, Yijie Sui, Yanan Su, Xuefei Zhang, Qinglin Wu, Zhimin Hu, and Ruilin Wang
The Cryosphere, 20, 2851–2870, https://doi.org/10.5194/tc-20-2851-2026, https://doi.org/10.5194/tc-20-2851-2026, 2026
Short summary
Short summary
Lakes in alpine periglacial environments are expanding rapidly under climate warming, but their detection and classification remain difficult because of complex terrain and spectral interference. We developed a vision-transformer-based framework to delineate lake boundaries and distinguish contemporary glacial lakes from other lakes. Applied to the southeastern Tibetan Plateau, it identified 3,266 lakes, including many as small as 0.0001 km².
Jinhao Xu, Min Feng, Yijie Sui, Yanan Su, Xuefei Zhang, Qinglin Wu, Zhimin Hu, and Ruilin Wang
The Cryosphere, 20, 2851–2870, https://doi.org/10.5194/tc-20-2851-2026, https://doi.org/10.5194/tc-20-2851-2026, 2026
Short summary
Short summary
Lakes in alpine periglacial environments are expanding rapidly under climate warming, but their detection and classification remain difficult because of complex terrain and spectral interference. We developed a vision-transformer-based framework to delineate lake boundaries and distinguish contemporary glacial lakes from other lakes. Applied to the southeastern Tibetan Plateau, it identified 3,266 lakes, including many as small as 0.0001 km².
Paolo Nasta, Günter Blöschl, Heye R. Bogena, Steffen Zacharias, Roland Baatz, Gabriëlle De Lannoy, Karsten H. Jensen, Salvatore Manfreda, Laurent Pfister, Ana M. Tarquis, Ilja van Meerveld, Marc Voltz, Yijian Zeng, William Kustas, Xin Li, Harry Vereecken, and Nunzio Romano
Hydrol. Earth Syst. Sci., 29, 465–483, https://doi.org/10.5194/hess-29-465-2025, https://doi.org/10.5194/hess-29-465-2025, 2025
Short summary
Short summary
The Unsolved Problems in Hydrology (UPH) initiative has emphasized the need to establish networks of multi-decadal hydrological observatories to tackle catchment-scale challenges on a global scale. This opinion paper provocatively discusses two endmembers of possible future hydrological observatory (HO) networks for a given hypothesized community budget: a comprehensive set of moderately instrumented observatories or, alternatively, a small number of highly instrumented supersites.
Zhen Zhang, Benjamin Poulter, Joe R. Melton, William J. Riley, George H. Allen, David J. Beerling, Philippe Bousquet, Josep G. Canadell, Etienne Fluet-Chouinard, Philippe Ciais, Nicola Gedney, Peter O. Hopcroft, Akihiko Ito, Robert B. Jackson, Atul K. Jain, Katherine Jensen, Fortunat Joos, Thomas Kleinen, Sara H. Knox, Tingting Li, Xin Li, Xiangyu Liu, Kyle McDonald, Gavin McNicol, Paul A. Miller, Jurek Müller, Prabir K. Patra, Changhui Peng, Shushi Peng, Zhangcai Qin, Ryan M. Riggs, Marielle Saunois, Qing Sun, Hanqin Tian, Xiaoming Xu, Yuanzhi Yao, Yi Xi, Wenxin Zhang, Qing Zhu, Qiuan Zhu, and Qianlai Zhuang
Biogeosciences, 22, 305–321, https://doi.org/10.5194/bg-22-305-2025, https://doi.org/10.5194/bg-22-305-2025, 2025
Short summary
Short summary
This study assesses global methane emissions from wetlands between 2000 and 2020 using multiple models. We found that wetland emissions increased by 6–7 Tg CH4 yr-1 in the 2010s compared to the 2000s. Rising temperatures primarily drove this increase, while changes in precipitation and CO2 levels also played roles. Our findings highlight the importance of wetlands in the global methane budget and the need for continuous monitoring to understand their impact on climate change.
Yaoming Ma, Zhipeng Xie, Yingying Chen, Shaomin Liu, Tao Che, Ziwei Xu, Lunyu Shang, Xiaobo He, Xianhong Meng, Weiqiang Ma, Baiqing Xu, Huabiao Zhao, Junbo Wang, Guangjian Wu, and Xin Li
Earth Syst. Sci. Data, 16, 3017–3043, https://doi.org/10.5194/essd-16-3017-2024, https://doi.org/10.5194/essd-16-3017-2024, 2024
Short summary
Short summary
Current models and satellites struggle to accurately represent the land–atmosphere (L–A) interactions over the Tibetan Plateau. We present the most extensive compilation of in situ observations to date, comprising 17 years of data on L–A interactions across 12 sites. This quality-assured benchmark dataset provides independent validation to improve models and remote sensing for the region, and it enables new investigations of fine-scale L–A processes and their mechanistic drivers.
Shaomin Liu, Ziwei Xu, Tao Che, Xin Li, Tongren Xu, Zhiguo Ren, Yang Zhang, Junlei Tan, Lisheng Song, Ji Zhou, Zhongli Zhu, Xiaofan Yang, Rui Liu, and Yanfei Ma
Earth Syst. Sci. Data, 15, 4959–4981, https://doi.org/10.5194/essd-15-4959-2023, https://doi.org/10.5194/essd-15-4959-2023, 2023
Short summary
Short summary
We present a suite of observational datasets from artificial and natural oases–desert systems that consist of long-term turbulent flux and auxiliary data, including hydrometeorological, vegetation, and soil parameters, from 2012 to 2021. We confirm that the 10-year, long-term dataset presented in this study is of high quality with few missing data, and we believe that the data will support ecological security and sustainable development in oasis–desert areas.
Yaozhi Jiang, Kun Yang, Youcun Qi, Xu Zhou, Jie He, Hui Lu, Xin Li, Yingying Chen, Xiaodong Li, Bingrong Zhou, Ali Mamtimin, Changkun Shao, Xiaogang Ma, Jiaxin Tian, and Jianhong Zhou
Earth Syst. Sci. Data, 15, 621–638, https://doi.org/10.5194/essd-15-621-2023, https://doi.org/10.5194/essd-15-621-2023, 2023
Short summary
Short summary
Our work produces a long-term (1979–2020) high-resolution (1/30°, daily) precipitation dataset for the Third Pole (TP) region by merging an advanced atmospheric simulation with high-density rain gauge (more than 9000) observations. Validation shows that the produced dataset performs better than the currently widely used precipitation datasets in the TP. This dataset can be used for hydrological, meteorological and ecological studies in the TP.
Yanxing Hu, Tao Che, Liyun Dai, Yu Zhu, Lin Xiao, Jie Deng, and Xin Li
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2022-63, https://doi.org/10.5194/essd-2022-63, 2022
Preprint withdrawn
Short summary
Short summary
We propose a data fusion framework based on the random forest regression algorithm to derive a comprehensive snow depth product for the Northern Hemisphere from 1980 to 2019. This new fused snow depth dataset not only provides information about snow depth and its variation over the Northern Hemisphere but also presents potential value for hydrological and water cycle studies related to seasonal snowpacks.
Youhua Ran, Xin Li, Guodong Cheng, Jingxin Che, Juha Aalto, Olli Karjalainen, Jan Hjort, Miska Luoto, Huijun Jin, Jaroslav Obu, Masahiro Hori, Qihao Yu, and Xiaoli Chang
Earth Syst. Sci. Data, 14, 865–884, https://doi.org/10.5194/essd-14-865-2022, https://doi.org/10.5194/essd-14-865-2022, 2022
Short summary
Short summary
Datasets including ground temperature, active layer thickness, the probability of permafrost occurrence, and the zonation of hydrothermal condition with a 1 km resolution were released by integrating unprecedentedly large amounts of field data and multisource remote sensing data using multi-statistical\machine-learning models. It updates the understanding of the current thermal state and distribution for permafrost in the Northern Hemisphere.
Guoqing Zhang, Youhua Ran, Wei Wan, Wei Luo, Wenfeng Chen, Fenglin Xu, and Xin Li
Earth Syst. Sci. Data, 13, 3951–3966, https://doi.org/10.5194/essd-13-3951-2021, https://doi.org/10.5194/essd-13-3951-2021, 2021
Short summary
Short summary
Lakes can be effective indicators of climate change, especially over the Qinghai–Tibet Plateau. Here, we provide the most comprehensive lake mapping covering the past 100 years. The new features of this data set are (1) its temporal length, providing the longest period of lake observations from maps, (2) the data set provides a state-of-the-art lake inventory for the Landsat era (from the 1970s to 2020), and (3) it provides the densest lake observations for lakes with areas larger than 1 km2.
Yongkang Xue, Tandong Yao, Aaron A. Boone, Ismaila Diallo, Ye Liu, Xubin Zeng, William K. M. Lau, Shiori Sugimoto, Qi Tang, Xiaoduo Pan, Peter J. van Oevelen, Daniel Klocke, Myung-Seo Koo, Tomonori Sato, Zhaohui Lin, Yuhei Takaya, Constantin Ardilouze, Stefano Materia, Subodh K. Saha, Retish Senan, Tetsu Nakamura, Hailan Wang, Jing Yang, Hongliang Zhang, Mei Zhao, Xin-Zhong Liang, J. David Neelin, Frederic Vitart, Xin Li, Ping Zhao, Chunxiang Shi, Weidong Guo, Jianping Tang, Miao Yu, Yun Qian, Samuel S. P. Shen, Yang Zhang, Kun Yang, Ruby Leung, Yuan Qiu, Daniele Peano, Xin Qi, Yanling Zhan, Michael A. Brunke, Sin Chan Chou, Michael Ek, Tianyi Fan, Hong Guan, Hai Lin, Shunlin Liang, Helin Wei, Shaocheng Xie, Haoran Xu, Weiping Li, Xueli Shi, Paulo Nobre, Yan Pan, Yi Qin, Jeff Dozier, Craig R. Ferguson, Gianpaolo Balsamo, Qing Bao, Jinming Feng, Jinkyu Hong, Songyou Hong, Huilin Huang, Duoying Ji, Zhenming Ji, Shichang Kang, Yanluan Lin, Weiguang Liu, Ryan Muncaster, Patricia de Rosnay, Hiroshi G. Takahashi, Guiling Wang, Shuyu Wang, Weicai Wang, Xu Zhou, and Yuejian Zhu
Geosci. Model Dev., 14, 4465–4494, https://doi.org/10.5194/gmd-14-4465-2021, https://doi.org/10.5194/gmd-14-4465-2021, 2021
Short summary
Short summary
The subseasonal prediction of extreme hydroclimate events such as droughts/floods has remained stubbornly low for years. This paper presents a new international initiative which, for the first time, introduces spring land surface temperature anomalies over high mountains to improve precipitation prediction through remote effects of land–atmosphere interactions. More than 40 institutions worldwide are participating in this effort. The experimental protocol and preliminary results are presented.
Zhe Jin, Xiangjun Tian, Rui Han, Yu Fu, Xin Li, Huiqin Mao, and Cuihong Chen
Earth Syst. Sci. Data Discuss., https://doi.org/10.5194/essd-2021-210, https://doi.org/10.5194/essd-2021-210, 2021
Manuscript not accepted for further review
Short summary
Short summary
Here we present a global and regional resolved terrestrial ecosystem and ocean carbon flux dataset during 2015–2019. The dataset was generated using the Tan-Tracker inversion system by absorbing satellite CO2 observations. The posterior 5-year annual mean global net carbon emissions were 5.35 PgC yr−1; the terrestrial ecosystem and ocean sinks were −4.07 and −3.33 PgC yr−1, respectively. This dataset can help understand global and regional carbon cycle, and support climate policy formulation.
Cited articles
Andresen, C. G. and Lougheed, V. L.: Disappearing Arctic tundra ponds: Fine-scale analysis of surface hydrology in drained thaw lake basins over a 65 year period (1948–2013), J. Geophys. Res.-Biogeo., 120, 466–479, 2015.
Biskaborn, B. K., Smith, S. L., Noetzli, J., Matthes, H., Vieira, G., Streletskiy, D. A., Schoeneich, P., Romanovsky, V. E., Lewkowicz, A. G., and Abramov, A.: Permafrost is warming at a global scale, Nat. Commun., 10, 1–11, 2019.
Breiman, L.: Random forests, Mach. Learn., 45, 5–32, 2001.
Cael, B. B. and Seekell, D. A.: The size-distribution of Earth’s lakes, Sci. Rep., 6, 29633, https://doi.org/10.1038/srep29633, 2016.
Carlson, T. N. and Ripley, D. A.: On the relation between NDVI, fractional vegetation cover, and leaf area index, Remote Sens. Environ., 62, 241–252, 1997.
Carpenter, S. R.: Lake geometry: implications for production and sediment accretion rates, J. Theor. Biol., 105, 273–286, 1983.
Carroll, M. and Loboda, T.: Multi-Decadal Surface Water Dynamics in North American Tundra, Remote Sens.-Basel, 9, 497, https://doi.org/10.3390/rs9050497, 2017.
Carroll, M. L., Townshend, J. R. G., DiMiceli, C. M., Loboda, T., and Sohlberg, R. A.: Shrinking lakes of the Arctic: Spatial relationships and trajectory of change, Geophys. Res. Lett., 38, L20406, https://doi.org/10.1029/2011GL049427, 2011.
Cooley, S. W., Smith, L. C., Ryan, J. C., Pitcher, L. H., and Pavelsky, T. M.: Arctic-Boreal Lake Dynamics Revealed Using CubeSat Imagery, Geophys. Res. Lett., 46, 2111–2120, https://doi.org/10.1029/2018gl081584, 2019.
Danielson, J. J. and Gesch, D. B.: Global multi-resolution terrain elevation data 2010 (GMTED2010), US Department of the Interior, US Geological Survey Washington, DC, USA, 9781288703340, 2011.
Downing, J. A.: Global limnology: Up-scaling aquatic services and processes to planet Earth, Int. Ver. Theor. Angew. Limnol. Verhandlungen, 30, 1149–1166, 2009.
Downing, J. A.: Emerging global role of small lakes and ponds: little things mean a lot, Limnetica, 29, 9–24, 2010.
Downing, J. A., Prairie, Y., Cole, J., Duarte, C., Tranvik, L., Striegl, R. G., McDowell, W., Kortelainen, P., Caraco, N., and Melack, J.: The global abundance and size distribution of lakes, ponds, and impoundments, Limnol. Oceanogr., 51, 2388–2397, 2006.
Dranga, S. A., Hayles, S., and Gajewski, K.: Synthesis of limnological data from lakes and ponds across Arctic and Boreal Canada, Arct. Sci., 4, 167–185, https://doi.org/10.1139/as-2017-0039, 2017.
Du, J., Kimball, J. S., Jones, L. A., and Watts, J. D.: Implementation of satellite based fractional water cover indices in the pan-Arctic region using AMSR-E and MODIS, Remote Sens. Environ., 184, 469–481, https://doi.org/10.1016/j.rse.2016.07.029, 2016.
Dyke, A. and Prest, V.: Late Wisconsinan and Holocene history of the Laurentide ice sheet, Geogr. Phys. Quatern., 41, 237–263, 1987.
Fayne, J. V., Smith, L. C., Pitcher, L. H., Kyzivat, E. D., Cooley, S. W., Cooper, M. G., Denbina, M. W., Chen, A. C., Chen, C. W., and Pavelsky, T. M.: Airborne observations of arctic-boreal water surface elevations from AirSWOT Ka-Band InSAR and LVIS LiDAR, Environ. Res. Lett., 15, 105005, https://doi.org/10.1088/1748-9326/abadcc, 2020.
Feng, M. and Sui, Y.: High resolution inland surface water dataset for the tundra and boreal in North America, National Tibetan Plateau Data Center [data set], https://doi.org/10.11888/Hydro.tpdc.271021, 2020.
Feng, M., Sexton, J. O., Channan, S., and Townshend, J. R.: A global, high-resolution (30 m) inland water body dataset for 2000: first results of a topographic–spectral classification algorithm, Int. J. Digit. Earth, 9, 113–133, https://doi.org/10.1080/17538947.2015.1026420, 2015.
Forkel, M., Carvalhais, N., Rödenbeck, C., Keeling, R., Heimann, M., Thonicke, K., Zaehle, S., and Reichstein, M.: Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems, Science, 351, 696–699, 2016.
Glińska-Lewczuk, K.: Water quality dynamics of oxbow lakes in young glacial landscape of NE Poland in relation to their hydrological connectivity, Ecol. Eng., 35, 25–37, https://doi.org/10.1016/j.ecoleng.2008.08.012, 2009.
Graversen, R. G., Mauritsen, T., Tjernström, M., Källén, E., and Svensson, G.: Vertical structure of recent Arctic warming, Nature, 451, 53–56, 2008.
Grosse, G., Jones, B., and Arp, C.: 8.21 Thermokarst Lakes, Drainage, and Drained Basins, in: Treatise on Geomorphology, edited by: Shroder, J. F., Academic Press, San Diego, 325–353, https://doi.org/10.1016/B978-0-12-374739-6.00216-5, 2013.
Heathcote, A. J., del Giorgio, P. A., and Prairie, Y. T.: Predicting bathymetric features of lakes from the topography of their surrounding landscape, Can. J. Fish. Aquat. Sci., 72, 643–650, 2015.
Higgins, S., Desjardins, C., Drouin, H., Hrenchuk, L., and Van der Sanden, J.: The role of climate and lake size in regulating the ice phenology of boreal lakes, J. Geophys. Res.-Biogeo., 126, e2020JG005898, https://doi.org/10.1029/2020JG005898, 2021.
Holgerson, M. A. and Raymond, P. A.: Large contribution to inland water CO2 and CH4 emissions from very small ponds, Nat. Geosci., 9, 222–226, https://doi.org/10.1038/ngeo2654, 2016.
Huffman, G. J., Stocker, E. F., Bolvin, D. T., Nelkin, E. J., Jackson Tan: GPM IMERG Final Precipitation L3 1 month 0.1 degree x 0.1 degree V06, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) [data set], https://doi.org/10.5067/GPM/IMERG/3B-MONTH/06, 2019.
IPCC: Climate Change 2007: Synthesis Report. Contribution of Working Groups I, II and III to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pachauri, R. K and Reisinger, A., IPCC, Geneva, Switzerland, 104 pp., 2007.
IPCC: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Pachauri, R. K. and Meyer, L. A., IPCC, Geneva, Switzerland, 151 pp., 2014.
Isikdogan, F., Bovik, A. C., and Passalacqua, P.: Surface Water Mapping by Deep Learning, IEEE J. Sel. Top. Appl., 10, 4909–4918, https://doi.org/10.1109/JSTARS.2017.2735443, 2017.
Jiang, X., Zheng, P., Cao, L., and Pan, B.: Effects of long-term floodplain disconnection on multiple facets of lake fish biodiversity: Decline of alpha diversity leads to a regional differentiation through time, Sci. Total Environ., 763, 144177, https://doi.org/10.1016/j.scitotenv.2020.144177, 2021.
Johannessen, O. M., Bengtsson, L., Miles, M. W., Kuzmina, S. I., Semenov, V. A., Alekseev, G. V., Nagurnyi, A. P., Zakharov, V. F., Bobylev, L. P., and Pettersson, L. H.: Arctic climate change: observed and modelled temperature and sea-ice variability, Tellus A, 56, 328–341, 2004.
Johnson, L.: The Great Bear Lake: its place in history, Arctic, 28, 231–244, 1975.
Jones, B. M., Grosse, G., Arp, C. D., Jones, M. C., Anthony, K. W., and Romanovsky, V. E.: Modern thermokarst lake dynamics in the continuous permafrost zone, northern Seward Peninsula, Alaska, J. Geophys. Res.-Biogeo., 116, G00M03, https://doi.org/10.1029/2011JG001666, 2011.
Karlsson, J., Lyon, S., and Destouni, G.: Temporal Behavior of Lake Size-Distribution in a Thawing Permafrost Landscape in Northwestern Siberia, Remote Sens.-Basel, 6, 621–636, https://doi.org/10.3390/rs6010621, 2014.
Karlsson, J. M., Lyon, S. W., and Destouni, G.: Thermokarst lake, hydrological flow and water balance indicators of permafrost change in Western Siberia, J. Hydrol., 464–465, 459–466, https://doi.org/10.1016/j.jhydrol.2012.07.037, 2012.
King, K. B., Bremigan, M. T., Infante, D., and Cheruvelil, K. S.: Surface water connectivity affects lake and stream fish species richness and composition, Can. J. Fish. Aquat. Sci., 78, 433–443, 2021.
Kuhn, C. and Butman, D.: Declining greenness in Arctic-boreal lakes, P. Natl. Acad. Sci. USA, 118, e2021219118, https://doi.org/10.1073/pnas.2021219118, 2021.
Kuhn, M., Lundin, E. J., Giesler, R., Johansson, M., and Karlsson, J.: Emissions from thaw ponds largely offset the carbon sink of northern permafrost wetlands, Sci. Rep., 8, 9535, https://doi.org/10.1038/s41598-018-27770-x, 2018.
Laird, N. F., Walsh, J. E., and Kristovich, D. A.: Model simulations examining the relationship of lake-effect morphology to lake shape, wind direction, and wind speed, Mon. Weather Rev., 131, 2102–2111, 2003.
Langer, M., Westermann, S., Boike, J., Kirillin, G., Grosse, G., Peng, S., and Krinner, G.: Rapid degradation of permafrost underneath waterbodies in tundra landscapes – Toward a representation of thermokarst in land surface models, J. Geophys. Res.-Earth, 121, 2446–2470, https://doi.org/10.1002/2016jf003956, 2016.
Laske, S. M., Rosenberger, A. E., Wipfli, M. S., and Zimmerman, C. E.: Surface water connectivity controls fish food web structure and complexity across local- and meta-food webs in Arctic Coastal Plain lakes, Food Webs, 21, e00123, https://doi.org/10.1016/j.fooweb.2019.e00123, 2019.
Lehner, B. and Döll, P.: Development and validation of a global database of lakes, reservoirs and wetlands, J. Hydrol., 296, 1–22, https://doi.org/10.1016/j.jhydrol.2004.03.028, 2004.
Li, X., Che, T., Li, X., Wang, L., Duan, A., Shangguan, D., Pan, X., Fang, M., and Bao, Q.: CASEarth poles: big data for the three poles, B. Am. Meteorol. Soc., 101, E1475–E1491, 2020.
MacIntyre, S., Fram, J. P., Kushner, P. J., Bettez, N. D., O'brien, W., Hobbie, J., and Kling, G. W.: Climate-related variations in mixing dynamics in an Alaskan arctic lake, Limnol. Oceanogr., 54, 2401–2417, 2009.
Markon, C. J. and Derksen, D. V.: Identification of tundra land cover near Teshekpuk Lake, Alaska using SPOT satellite data, Arctic, 47, 222–231, 1994.
McCullough, I. M., King, K. B. S., Stachelek, J., Diaz, J., Soranno, P. A., and Cheruvelil, K. S.: Applying the patch-matrix model to lakes: a connectivity-based conservation framework, Landscape Ecol., 34, 2703–2718, https://doi.org/10.1007/s10980-019-00915-7, 2019.
McFeeters, S. K.: The use of the Normalized Difference Water Index (NDWI) in the delineation of open water features, Int. J. Remote Sens., 17, 1425–1432, 1996.
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, 1–11, 2016.
Meyer, M. F., Labou, S. G., Cramer, A. N., Brousil, M. R., and Luff, B. T.: The global lake area, climate, and population dataset, Sci. Data, 7, 174, https://doi.org/10.1038/s41597-020-0517-4, 2020.
Muster, S., Heim, B., Abnizova, A., and Boike, J.: Water body distributions across scales: A remote sensing based comparison of three arctic tundra wetlands, Remote Sens.-Basel, 5, 1498–1523, 2013.
Muster, S., Roth, K., Langer, M., Lange, S., Cresto Aleina, F., Bartsch, A., Morgenstern, A., Grosse, G., Jones, B., Sannel, A. B. K., Sjöberg, Y., Günther, F., Andresen, C., Veremeeva, A., Lindgren, P. R., Bouchard, F., Lara, M. J., Fortier, D., Charbonneau, S., Virtanen, T. A., Hugelius, G., Palmtag, J., Siewert, M. B., Riley, W. J., Koven, C. D., and Boike, J.: PeRL: a circum-Arctic Permafrost Region Pond and Lake database, Earth Syst. Sci. Data, 9, 317–348, https://doi.org/10.5194/essd-9-317-2017, 2017.
Napiórkowski, P., Bąkowska, M., Mrozińska, N., Szymańska, M., Kolarova, N., and Obolewski, K.: The Effect of Hydrological Connectivity on the Zooplankton Structure in Floodplain Lakes of a Regulated Large River (the Lower Vistula, Poland), Water, 11, 1924, https://doi.org/10.3390/w11091924, 2019.
Nitze, I., Cooley, S. W., Duguay, C. R., Jones, B. M., and Grosse, G.: The catastrophic thermokarst lake drainage events of 2018 in northwestern Alaska: fast-forward into the future, The Cryosphere, 14, 4279–4297, https://doi.org/10.5194/tc-14-4279-2020, 2020.
Olefeldt, D., Goswami, S., Grosse, G., Hayes, D., Hugelius, G., Kuhry, P., McGuire, A. D., Romanovsky, V. E., Sannel, A. B. K., Schuur, E. a. G., and Turetsky, M. R.: Circumpolar distribution and carbon storage of thermokarst landscapes, Nat. Commun., 7, 13043, https://doi.org/10.1038/ncomms13043, 2016.
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.
Pickens, A. H., Hansen, M. C., Hancher, M., Stehman, S. V., Tyukavina, A., Potapov, P., Marroquin, B., and Sherani, Z.: Mapping and sampling to characterize global inland water dynamics from 1999 to 2018 with full Landsat time-series, Remote Sens. Environ., 243, 111792, https://doi.org/10.1016/j.rse.2020.111792, 2020.
Ritter, M. E.: The physical environment: An introduction
to physical geography, http://www.uwsp.edu/geo/faculty/ritter/geog101/textbook/title_page.html, (last access: 25 July 2008), 2006.
Sandlund, O. T., Eloranta, A. P., Borgstrøm, R., Hesthagen, T., Johnsen, S. I., Museth, J., and Rognerud, S.: The trophic niche of Arctic charr in large southern Scandinavian lakes is determined by fish community and lake morphometry, Hydrobiologia, 783, 117–130, https://doi.org/10.1007/s10750-016-2646-5, 2016.
Schilder, J., Bastviken, D., van Hardenbroek, M., Kankaala, P., Rinta, P., Stötter, T., and Heiri, O.: Spatial heterogeneity and lake morphology affect diffusive greenhouse gas emission estimates of lakes, Geophys. Res. Lett., 40, 5752–5756, 2013.
Serikova, S., Pokrovsky, O. S., Laudon, H., Krickov, I., Lim, A. G., Manasypov, R. M., and Karlsson, J.: High carbon emissions from thermokarst lakes of Western Siberia, Nat. Commun., 10, 1–7, 2019.
Serreze, M. C. and Francis, J. A.: The Arctic amplification debate, Clim. Change, 76, 241–264, 2006.
Sharma, S., Blagrave, K., Magnuson, J. J., O'Reilly, C. M., Oliver, S., Batt, R. D., Magee, M. R., Straile, D., Weyhenmeyer, G. A., Winslow, L., and Woolway, R. I.: Widespread loss of lake ice around the Northern Hemisphere in a warming world, Nat. Clim. Change, 9, 227–231, https://doi.org/10.1038/s41558-018-0393-5, 2019.
Smith, L. C., Sheng, Y., MacDonald, G. M., and Hinzman, L. D.: Disappearing arctic lakes, Science, 308, 1429–1429, 2005.
Smith, L. C., Sheng, Y., and MacDonald, G. M.: A first pan-Arctic assessment of the influence of glaciation, permafrost, topography and peatlands on northern hemisphere lake distribution, Permafr. Periglac., 18, 201–208, https://doi.org/10.1002/ppp.581, 2007.
Sun, J., Wang, G., He, G., Pu, D., Jiang, W., Li, T., and Niu, X.: Study on the Water Body Extraction Using GF-1 Data Based on Adaboost Integrated Learning Algorithm, Int. Arch. Photogramm., 42, 641–648, 2020.
Vaideliene, A. and Michailov, N.: Dam influence on the river self-purification, Proc. of the 7th International Conference Environmental Engineering, ISBN 978-981-10-1926-5, 748–757, 2008.
van Huissteden, J., Berrittella, C., Parmentier, F. J. W., Mi, Y., Maximov, T. C., and Dolman, A. J.: Methane emissions from permafrost thaw lakes limited by lake drainage, Nat. Clim. Change, 1, 119–123, https://doi.org/10.1038/nclimate1101, 2011.
van Zyll de Jong, M., Adams, B., Cote, D., and Cowx, I.: The effects of population density and lake characteristics on growth and size structure of brook trout Salvelinus fontinalis (Mitchill, 1815) in boreal forest lakes in Canada, J. Appl. Ichthyol., 33, 957–965, https://doi.org/10.1111/jai.13407, 2017.
Walter Anthony, K., Daanen, R., Anthony, P., Schneider von Deimling, T., Ping, C.-L., Chanton, J. P., and Grosse, G.: Methane emissions proportional to permafrost carbon thawed in Arctic lakes since the 1950s, Nat. Geosci., 9, 679–682, https://doi.org/10.1038/ngeo2795, 2016.
Watson, C. S., Quincey, D. J., Carrivick, J. L., and Smith, M. W.: The dynamics of supraglacial ponds in the Everest region, central Himalaya, Global Planet. Change, 142, 14–27, 2016.
Winslow, L. A., Read, J. S., Hanson, P. C., and Stanley, E. H.: Lake shoreline in the contiguous United States: quantity, distribution and sensitivity to observation resolution, Freshwater Biol., 59, 213–223, 2014.
Xiong, G., Wang, G., Wang, D., Yang, W., Chen, Y., and Chen, Z.: Spatio-temporal distribution of total nitrogen and phosphorus in Dianshan lake, China: The external loading and self-purification capability, Sustainability, 9, 500, https://doi.org/10.3390/su9040500, 2017.
Xu, H.: Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery, Int. J. Remote Sens., 27, 3025–3033, 2006.
Yvon-Durocher, G., Hulatt, C. J., Woodward, G., and Trimmer, M.: Long-term warming amplifies shifts in the carbon cycle of experimental ponds, Nat. Clim. Change, 7, 209–213, https://doi.org/10.1038/nclimate3229, 2017.
Zupanc, A.: Improving cloud detection with machine learning, https://medium.com/sentinel-hub/improving-cloud-detection-with-machine-learning-c09dc5d7cf13, last access: 10 October 2020.
Short summary
High-latitude water bodies differ greatly in their morphological and topological characteristics related to their formation, type, and vulnerability. In this paper, we present a water body dataset for the North American high latitudes (WBD-NAHL). Nearly 6.5 million water bodies were identified, with approximately 6 million (~90 %) of them smaller than 0.1 km2.
High-latitude water bodies differ greatly in their morphological and topological characteristics...
Altmetrics
Final-revised paper
Preprint