Articles | Volume 14, issue 7
https://doi.org/10.5194/essd-14-3411-2022
© 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-14-3411-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
An integrated dataset of daily lake surface water temperature over the Tibetan Plateau
Linan Guo
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Hongxing Zheng
CSIRO Land and Water, Canberra, ACT 2601, Australia
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Lanxin Fan
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Mengxuan Wen
Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Junsheng Li
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
Fangfang Zhang
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
Liping Zhu
Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing 100101, China
Bing Zhang
CORRESPONDING AUTHOR
International Research Center of Big Data for Sustainable Development Goals, Beijing 100094, China
Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100094, China
University of the Chinese Academy of Sciences, Beijing 100049, China
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Cited articles
Andréassian, V., Le Moine, N., Perrin, C., Ramos, M.-H., Oudin, L.,
Mathevet, T., Lerat, J., and Berthet, L.: All that glitters is not gold: the
case of calibrating hydrological models, Hydrol. Process., 26, 2206–2210,
https://doi.org/10.1002/hyp.9264, 2012.
Austin, J. and Colman, S.: A century of temperature variability in Lake Superior, Limnol. Oceanogr., 53, 2724–2730, https://doi.org/10.4319/lo.2008.53.6.2724, 2008.
Bates, B., Kundzewicz, Z. W., Wu, S., and Palutikof, J.: Climate Change and
Water: Technical Paper VI, Environ. Policy Collect., 128, 343–355,
https://doi.org/10.1061/(ASCE)0733-9496(2002)128:5(343), 2008.
Bruce, L., Frassl, M., and Arhonditsis, G. B.: A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global
observatory network, Environ. Model. Softw., 102, 274–291,
https://doi.org/10.1016/j.envsoft.2017.11.016, 2018.
Crosman, E. T. and Horel, J. D.: MODIS-derived surface temperature of the Great Salt Lake, Remote Sens. Environ., 113, 73–81, https://doi.org/10.1016/j.rse.2008.08.013, 2009.
Czernecki, B. and Ptak, M.: The impact of global warming on lake surface water temperature in Poland – the application of empirical-statistical
downscaling, 1971–2100, J. Limnol., 77, 330–348, https://doi.org/10.4081/jlimnol.2018.1707, 2018.
Dokulil, M. T.: Predicting summer surface water temperatures for large Austrian lakes in 2050 under climate change scenarios, Hydrobiologia, 731,
19–29, https://doi.org/10.1007/s10750-013-1550-5, 2014.
Goudsmit, G. H., Burchard, H., Peeters, F., and Wuest, A.: Application of
k–ϵ turbulence models to enclosed basins: The role of internal
seiches, J. Geophys. Res.-Oceans, 107, 3230, https://doi.org/10.1029/2001jc000954, 2002.
Guo, L., and Zheng, H.: Modified_air2water_Model: v1.0.1, Zenodo [code], https://doi.org/10.5281/zenodo.6831947, 2022.
Guo, L., Wu, Y., Zheng, H., Zhang, B., and Wen., M.: An integrated dataset
of daily lake surface temperature over Tibetan Plateau, Zenodo [data set], https://doi.org/10.5281/zenodo.6637526, 2022.
Guo, Y. H., Zhang, Y. S., Ma, N., Song, H. T., and Gao, H. F.: Quantifying
Surface Energy Fluxes and Evaporation over a Significant Expanding Endorheic
Lake in the Central Tibetan Plateau, J. Meteorol. Soc. Jpn., 94, 453–465,
https://doi.org/10.2151/jmsj.2016-023, 2016.
Hondzo, M. and Stefan, H. G.: Regional Water Temperature Characteristics of
Lakes Subjected to Climate-Change, Climatic Change, 24, 187–211,
https://doi.org/10.1007/bf01091829, 1993.
Hu, Q., Jiang, D., and Fan, G.: Evaluation of CMIP5 models over the Qinghai-Tibetan Plateau, Chin. J. Atmos. Sci., 38, 924–938,
https://doi.org/10.3878/j.issn.1006-9895, 2014.
Hulley, G. C., Hook, S. J., and Schneider, P.: Optimized split-window
coefficients for deriving surface temperatures from inland water bodies,
Remote Sens. Environ., 115, 3758–3769, https://doi.org/10.1016/j.rse.2011.09.014, 2011.
Ke, L. H., and Song, C. Q.: Remotely sensed surface temperature variation of an inland saline lake over the central Qinghai-Tibet Plateau, ISPRS J.
Photogram. Remote Sens., 98, 157–167, https://doi.org/10.1016/j.isprsjprs.2014.09.007,
2014.
Kendall, M. G.: Rank correlation methods, Econ. J., 59, 575–577, https://doi.org/10.2307/2226580, 1949.
Kennedy, J. and Eberhart, R.: Particle swarm optimization, in: Proceedings of
ICNN'95-International Conference on Neural Networks, 27 November–1 December 1995, Perth, WA, Australia, 1942–1948, https://doi.org/10.1109/ICNN.1995.488968, 1995.
Kirillin, G., Wen, L. J., and Shatwell, T.: Seasonal thermal regime and climatic trends in lakes of the Tibetan highlands, Hydrol. Earth Syst. Sci., 21, 1895–1909, https://doi.org/10.5194/hess-21-1895-2017, 2017.
Launiainen, J. and Cheng, B.: Modelling of ice thermodynamics in natural water bodies, Cold Reg. Sci. Tech., 27, 153–178, https://doi.org/10.1016/s0165-232x(98)00009-3, 1998.
Layden, A., Merchant, C., and MacCallum, S.: Global climatology of surface
water temperatures of large lakes by remote sensing, Int. J. Climatol., 35,
4464–4479, https://doi.org/10.1002/joc.4299, 2015.
Layden, A., MacCallum, S. N., and Merchant, C. J.: Determining lake surface
water temperatures worldwide using a tuned one-dimensional lake model (FLake, v1), Geosci. Model Dev., 9, 2167–2189, https://doi.org/10.5194/gmd-9-2167-2016, 2016.
Li, Z. G., Lyu, S. H., Ao, Y. H., Wen, L. J., Zhao, L., and Wang, S. Y.:
Long-term energy flux and radiation balance observations over Lake Ngoring,
Tibetan Plateau, Atmos. Res., 155, 13–25, https://doi.org/10.1016/j.atmosres.2014.11.019, 2015.
Liu, B. J., Wan, W., Xie, H. J., Li, H., Zhu, S. Y., Zhang, G. Q., Wen, L.
J., and Hong, Y.: A long-term dataset of lake surface water temperature over
the Tibetan Plateau derived from AVHRR 1981–2015, Scient. Data, 6, 48, https://doi.org/10.1038/s41597-019-0040-7, 2019.
Liu, C., Zhu, L., Wang, J., Ju, J., Ma, Q., Qiao, B., Wang, Y., Xu, T., Chen, H., Kou, Q., Zhang, R., and Kai, J.: In-situ water quality investigation of the lakes on the tibetan plateau, Sci. Bull., 66, 1727–1730, https://doi.org/10.1016/j.scib.2021.04.024, 2021.
Livingstone, D. M.: Break-up dates of Alpine lakes as proxy data for local
and regional mean surface air temperatures, Climatic Change, 37, 407–439,
https://doi.org/10.1023/a:1005371925924, 1997.
Livingstone, D. M.: Impact of Secular Climate Change on the Thermal Structure of a Large Temperate Central European Lake, Climatic Change, 57, 205–225, https://doi.org/10.1023/A:1022119503144, 2003.
Mann, H. B.: Nonparametric Tests Against Trend, Econometrica, 13, 245–259,
https://doi.org/10.2307/1907187, 1945.
Mccombie, A. M.: Some Relations Between Air Temperatures and the Surface
Water Temperatures of Lakes, Limnol. Oceanogr., 4, 252–258, https://doi.org/10.4319/lo.1959.4.3.0252, 1959.
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, 11, https://doi.org/10.1038/ncomms13603, 2016.
Moukomla, S. and Blanken, P. D.: Estimating the Great Lakes net radiation
using satellite remote sensing and MERRA reanalysis, Int. J. Digit. Earth, 10, 764–784, https://doi.org/10.1080/17538947.2016.1252432, 2017.
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual
models part I – A discussion of principles – ScienceDirect, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970.
Naumenko, M. A., Guzivaty, V. V., and Karetnikov, S. G.: Climatic trends of
the water surface temperature in Lake Ladoga during ice-free periods, Dokl.
Earth Sci., 409, 750–753, https://doi.org/10.1134/s1028334x06050163, 2006.
Ngai, K. L. C., Shuter, B. J., Jackson, D. A., and Chandra, S.: Projecting
impacts of climate change on surface water temperatures of a large subalpine
lake: Lake Tahoe, USA, Climatic Change, 118, 841–855,
https://doi.org/10.1007/s10584-013-0695-6, 2013.
Peeters, F., Livingstone, D. M., Goudsmit, G. H., Kipfer, R., and Forster, R.: Modeling 50 years of historical temperature profiles in a large central
European lake, Limnol. Oceanogr., 47, 186–197, https://doi.org/10.4319/lo.2002.47.1.0186, 2002.
Piccolroaz, S.: Prediction of lake surface temperature using the air2water
model: guidelines, challenges, and future perspectives, Adv. Oceanogr. Limnol., 7, 36–50, https://doi.org/10.4081/aiol.2016.5791, 2016.
Piccolroaz, S., Toffolon, M., and Majone, B.: A simple lumped model to convert air temperature into surface water temperature in lakes, Hydrol.
Earth Syst. Sci., 17, 3323–3338, https://doi.org/10.5194/hess-17-3323-2013, 2013.
Piccolroaz, S., Toffolon, M., and Majone, B.: The role of stratification on
lakes' thermal response: The case of Lake Superior, Water Resour. Res., 51, 7878–7894, https://doi.org/10.1002/2014wr016555, 2015.
Piccolroaz, S., Woolway, R. I., and Merchant, C. J.: Global reconstruction
of twentieth century lake surface water temperature reveals different
warming trends depending on the climatic zone, Climatic Change, 160, 427–442, https://doi.org/10.1007/s10584-020-02663-z, 2020.
Prats, J., and Danis, P. A.: An epilimnion and hypolimnion temperature model
based on air temperature and lake characteristics, Knowl. Manage. Aquat. Ecosyst., 8, 420, https://doi.org/10.1051/kmae/2019001, 2019.
Prats, J., Reynaud, N., Rebiere, D., Peroux, T., Tormos, T., and Danis, P. A.: LakeSST: Lake Skin Surface Temperature in French inland water bodies for
1999–2016 from Landsat archives, Earth Syst. Sci. Data, 10, 727–743,
https://doi.org/10.5194/essd-10-727-2018, 2018.
Prowse, T., Alfredsen, K., Beltaos, S., Bonsal, B. R., Bowden, W. B., Duguay, C. R., Korhola, A., Mcnamara, J., Vincent, W. F., and Vuglinsky, V.: Effects of Changes in Arctic Lake and River Ice, Ambio, 40, 63–74,
https://doi.org/10.1007/s13280-011-0217-6, 2011.
Rahel, F. J. and Olden, J. D.: Assessing the effects of climate change on
aquatic invasive species, Conserv. Biol., 22, 521–533,
https://doi.org/10.1111/j.1523-1739.2008.00950.x, 2008.
Reinart, A. and Reinhold, M.: Mapping surface temperature in large lakes with MODIS data, Remote Sens. Environ., 112, 603–611, https://doi.org/10.1016/j.rse.2007.05.015, 2008.
Schindler, D. W.: The cumulative effects of climate warming and other human
stresses on Canadian freshwaters in the new millennium, Can. J. Fish. Aquat.
Sci., 58, 18–29, https://doi.org/10.1139/cjfas-58-1-18, 2001.
Schmid, M. and Koster, O.: Excess warming of a Central European lake driven by solar brightening, Water Resour. Res., 52, 8103–8116, https://doi.org/10.1002/2016wr018651, 2016.
Schneider, P. and Hook, S. J.: Space observations of inland water bodies show rapid surface warming since 1985, Geophys. Res. Lett., 37, L22405, https://doi.org/10.1029/2010gl045059, 2010.
Sharma, S., Walker, S. C., and Jackson, D. A.: Empirical modelling of lake
water-temperature relationships: a comparison of approaches, Freshwater Biol., 53, 897–911, https://doi.org/10.1111/j.1365-2427.2008.01943.x, 2008.
Sharma, S., Gray, D. K., Read, J. S., O'Reilly, C. M., Schneider, P., Qudrat, A., Gries, C., Stefanoff, S., Hampton, S. E., Hook, S., Lenters, J. D., Livingstone, D. M., McIntyre, P. B., Adrian, R., Allan, M. G., Anneville, O., Arvola, L., Austin, J., Bailey, J., Baron, J. S., Brookes, J., Chen, Y., Daly, R., Dokulil, M., Dong, B., Ewing, K., de Eyto, E., Hamilton, D., Havens, K., Haydon, S., Hetzenauer, H., Heneberry, J., Hetherington, A. L., Higgins, S. N., Hixson, E., Izmest'eva, L. R., Jones, B. M., Kangur, K., Kasprzak, P., Koster, O., Kraemer, B. M., Kumagai, M., Kuusisto, E., Leshkevich, G., May, L., MacIntyre, S., Mueller-Navarra, D., Naumenko, M., Noges, P., Noges, T., Niederhauser, P., North, R. P., Paterson, A. M., Plisnier, P.-D., Rigosi, A., Rimmer, A., Rogora, M., Rudstam, L., Rusak, J. A., Salmaso, N., Samal, N. R., Schindler, D. E., Schladow, G., Schmidt, S. R., Schultz, T., Silow, E. A., Straile, D., Teubner, K., Verburg, P., Voutilainen, A., Watkinson, A., Weyhenmeyer, G. A., Williamson, C. E., and Woo, K. H.: A global database of lake surface temperatures collected by in situ and satellite methods from 1985–2009, Sci. Data, 2, 150008, https://doi.org/10.1038/sdata.2015.8, 2015.
Song, K., Wang, M., Du, J., Yuan, Y., Ma, J., Wang, M., and Mu, G.:
Spatiotemporal Variations of Lake Surface Temperature across the Tibetan
Plateau Using MODIS LST Product, Remote Sens., 8, 854, https://doi.org/10.3390/rs8100854, 2016.
Stepanenko, V., Mammarella, I., Ojala, A., Miettinen, H., Lykosov, V., and
Vesala, T.: LAKE 2.0: a model for temperature, methane, carbon dioxide and
oxygen dynamics in lakes, Geosci. Model Dev., 9, 1977–2006,
https://doi.org/10.5194/gmd-9-1977-2016, 2016.
Takacs, K., Kern, Z., and Pasztor, L.: Long-term ice phenology records from
eastern-central Europe, Earth Syst. Sci. Data, 10, 391–404,
https://doi.org/10.5194/essd-10-391-2018, 2018.
Tian, B. S., Li, Z., Engram, M. J., Niu, F. J., Tang, P. P., Zou, P. F., and
Xu, J.: Characterizing C-band backscattering from thermokarst lake ice on
the Qinghai-Tibet Plateau, ISPRS J. Photogram. Remote Sens., 104, 63–76,
https://doi.org/10.1016/j.isprsjprs.2015.02.014, 2015.
Toffolon, M., Piccolroaz, S., Majone, B., Soja, A. M., Peeters, F., Schmid, M., and Wuest, A.: Prediction of surface temperature in lakes with different
morphology using air temperature, Limnol. Oceanogr., 59, 2185–2202,
https://doi.org/10.4319/lo.2014.59.6.2185, 2014.
Torbick, N., Ziniti, B., Wu, S., and Linder, E.: Spatiotemporal Lake Skin
Summer Temperature Trends in the Northeast United States, Earth Interact., 20, 1–21, https://doi.org/10.1175/ei-d-16-0015.1, 2016.
Wan, W., Li, H., Xie, H., Hong, Y., Long, D., Zhao, L., Han, Z., Cui, Y., Liu, B., and Wang, C.: A comprehensive data set of lake surface water temperature over the Tibetan Plateau derived from MODIS LST products 2001–2015, Scient. Data, 4, 170095, https://doi.org/10.1038/sdata.2017.95, 2017.
Wan, Z.: Collection-6 MODIS land surface temperature products users' guide,
ICESS, University of California, Santa Barbara, https://lpdaac.usgs.gov/documents/118/MOD11_User_Guide_V6.pdf (last access: 13 July 2022), 2013.
Wang, M. and Hou, J.: Monitoring data on lake water temperature in Bangong Co and Dagze Co (2012–2013), National Tibetan Plateau Data Center,
https://doi.org/10.11888/Hydrology.tpe.249431.db, 2018.
Wang, S. M. and Dou, H. S.: Records of lakes in China, Science Press, Beijing, 342–483, ISBN 7-03-006706-1, 1998.
Webb, M. S.: Surface temperatures of Lake Erie, Water Resour. Res., 10, 199–210, https://doi.org/10.1029/WR010i002p00199, 1974.
Williamson, C. E., Saros, J. E., and Schindler, D. W.: Climate Change Sentinels of Change, Science, 323, 887–888, https://doi.org/10.1126/science.1169443,
2009.
Woolway, R. I. and Merchant, C. J.: Amplified surface temperature response
of cold, deep lakes to inter-annual air temperature variability, Sci. Rep., 7, 4130, https://doi.org/10.1038/s41598-017-04058-0, 2017.
Wu, Y. and Lei, L.: Water level variation of Inland lakes on the southeasten of Tibetan Plateau in 1972–2012, Acta Geogr. Sin., 69, 993–1001, https://doi.org/10.11821/dlxb201407011, 2014.
Yao, T., Wu, F., Ding, L., Sun, J., Zhu, L., Piao, S., Deng, T., Ni, X., Zheng, H., and Ouyang, H.: Multispherical interactions and their effects on
the Tibetan Plateau's earth system: a review of the recent researches, Natl. Sci. Rev., 2, 468–488, https://doi.org/10.1093/nsr/nwv070, 2015.
Zhang, G. Q., Yao, T. D., Xie, H. J., Qin, J., Ye, Q. H., Dai, Y. F., and Guo, R. F.: Estimating surface temperature changes of lakes in the Tibetan
Plateau using MODIS LST data, J. Geophys. Res.-Atmos., 119, 8552–8567,
https://doi.org/10.1002/2014jd021615, 2014.
Short summary
Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem and an indicator of climate change. By combining the strengths of satellites and models, we produced an integrated dataset on daily LSWT of 160 large lakes across the Tibetan Plateau (TP) for the period 1978–2017. LSWT increased significantly at a rate of 0.01–0.47° per 10 years. The dataset can contribute to research on water and heat balance changes and their ecological effects in the TP.
Lake surface water temperature (LSWT) is a critical physical property of the aquatic ecosystem...
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