Remote Estimation of Water Body Depth Based on the Date of the Beginning of Ice Formation Using a Hydrophysical Model
https://doi.org/10.59887/2073-6673.2025.18(2)-10
EDN: ONRDHI
Abstract
The aim of the study is to develop a methodology for estimating the average lake depth based on remote sensing data of ice conditions using methods of modelling thermohydrodynamic processes in a freezing water body. The primary tool f or accomplishing this goal is the FLake — lake hydrophysical model. Using meteorological data from the ERA5 reanalysis for the coordinates of the selected water body, the model calculates the time of ice formation on the water body at different values of its average depth. Based on remote sensing data, the date or time interval of water body freezing estimated. If data for several years are available, the depth of the water body specified by averaging the values for each year. At discreteness of satellite images with an interval of several days, the range of average lake depths corresponding to the time interval between satellite passes over the water body is determined. Information on the onset of ice phenomena was obtained based on the results of thematic interpretation of Sentinel‑2, Landsat‑7, 8, 9 satellite images for the period from 2016 to 2023. The methodology tested on four groups of morphometrically- studied lakes located in permafrost zone of Eastern Siberia in the Republic of Buryatia and Transbaikal Territory. The results of approbation showed a satisfactory correspondence between the calculated and measured values of the average depth of the lakes under consideration. The quality and quantity of satellite images in the study region limited the accuracy of the proposed methodology. The prospects of the methodology lie in the possibility of fully remote assessment of water resources of poorly studied regions of the country.
About the Authors
S. A. KondratyevRussian Federation
9 Sevastyanova Str., St. Petersburg, 196105
S. D. Golosov
Russian Federation
9 Sevastyanova Str., St. Petersburg, 196105
I. S. Zverev
Russian Federation
9 Sevastyanova Str., St. Petersburg, 196105
A. M. Rasulova
Russian Federation
9 Sevastyanova Str., St. Petersburg, 196105
References
1. Izmailova AV. Water resources of lakes in the Russian Federation. Geografiya i prirodnyye resursy. 2016, 4, 5–14. (in Russian).
2. Ryanzhin SV. New estimates for global surface area and volume of natural world lakes. Doklady Earth Sciences. 2005, 401(2), 253–257.
3. Salo Yu A, Potakhin M.S., Tolstikov A.V. Calculation of the average depth of lakes in Karelia in the absence of bathymetric data. Izvestiya Russkogo geograficheskogo obshchestva. 2010, 142(3), 47–52. (in Russian)
4. Messager ML, Lehner B, Grill G, Nedeva I, Schmitt O. Estimating the volume and age of water stored in global lakes using a geo-statistical approach. Nature Communications. 2016, 7: 13603. doi: 10.1038/ncomms13603
5. Khazaei B, Read LK, Casali M. et al. GLOBathy, the global lakes bathymetry dataset. Scientific Data. 2022, 9: 36. doi: 10.1038/s41597-022-01132-9
6. Håkanson L. On lake form, lake volume and lake hypsographic survey. Geografiska Annaler: Series A, Physical Geography. 1977, 59(1–2), 1–29.
7. Kochkov NV, Ryanzhin SV. Methodology of estimation of morphometric characteristics of lakes using satellite information. Water Resources. 2016, 43(1), 15–20. doi 10.7868/S0321059616010107.
8. Stumpf RP, Holderied K, Sinclair M. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnology and Oceanography. 2003, 48(2), 547–556. doi: 10.4319/lo.2003.48.1_part_2.0547
9. Wen K. et al. Satellite-Based Water Depth Estimation: A Review. Geoinformatics in Sustainable Ecosystem and Society. GSES GeoAI 2019. Communications in Computer and Information Science. 2020, 1228, 177–195. doi: 10.1007/978-981-15-6106-1_14
10. Johansson H, Brolin AA, Håkanson L. New Approaches to the Modelling of Lake Basin Morphometry. Environ Model Assess. 2007, 12, 213–228. doi: 10.1007/s10666-006-9069-z
11. Bazarova VV. Floristic composition and spatial structure of aquatic vegetation of lakes Yeravno-Khargin system (Buryatia). Ekosistemy. 2018, 13(43), 3–12. (in Russian).
12. Nestereva MI. Morphometric indicators of the largest and most significant lakes of Buryatia. Molodoy uchenyy. 2014, 14, 81–83. (in Russian).
13. Sheveleva NG. Zooplankton of water bodies of the Jerginsky Reserve (Eastern Siberia). Nauchnyye trudy Gosudarstvennogo prirodnogo zapovednika “Prisurskiy”. 2015, 30(1), 279–283. (in Russian).
14. Prosekin KA, Prosekina AA. Hydrological characterisation of water bodies and watercourses of the Djerginsky Reserve as a habitat for hydrobionts. Samarskaya Luka: problemy regional’noy i global’noy ekologii. 2009, 18(2), 149–154. (in Russian)
15. Matafonov PV, Shoidokov AB, Matyugina EB. et al. Influence of Ivano-Arachleysk lakes openness on the formation of bottom landscapes in them during the extreme low-water period. Uspekhi sovremennogo yestestvoznaniya. 2023, 12, 115–120. (in Russian). doi: 10.17513/use.38180.
16. Materials of the complex ecological and economic survey of the territory, justifying the need to ensure the status of a specially protected natural area of federal significance — the national park ‘Kodar’ in the Kalar district of the Zabaikalsky Krai. Volume 1: Ecological and economic substantiation of the Kodar National Park. Chita: IPREC SB RAS, 2016. 479 p. (in Russian).
17. Sofronov AP, Vladimirov IN, Kobylkin DV. Structure of the vegetation cover of the Amutskaya Basin (Djerginsky Reserve, Republic of Buryatia). Geobotanicheskoye kartografirovaniye. 2023, 2023, 48–57. (in Russian). doi: 10.31111/geobotmap/2023.48
18. Sukhovnina VO, Sheveleva NG. Cyclops scutifer Sars, 1863 in the water bodies of southern Eastern Siberia (morphology and distribution) // Ecological Collection 6: Proceedings of young scientists of the Volga region: International Youth Scientific Conference. Togliatti: Cassandra, 2017, 366–369. (in Russian).
19. Solotchina EP, Sklyarov EV, Solotchin PA, et al. Authigenic carbonate formation in lakes of the Yeravninskaya group (Western Transbaikalia): response to Holocene climate changes. Geologiya i geofizika. 2017, 58(11), 1749–1763. (in Russian). doi: 10.15372/GiG20171109
20. Plusnin AM, Peryazeva EG. Hydrological and hydrochemical features of lakes in the Yeravninskaya Basin. Geography and Natural Resources. 2012, 2, 67–73. (in Russian).
21. Borzenko SV, Zamana LV. Hydrogeochemistry of the Ivano-Arachleyskoye lakes. Geosfernye Issledovaniya. 2020, 3, 69–79. (in Russian). doi: 10.17223/25421379/16/6.
22. Golyatina MA. Estimation of the dynamics of water surface areas of Ivano-Arachleyskiy lakes using space monitoring. Water resources and water use: collection of works. Volume Issue 8. Chita: Transbaikal State University, 2017, 107–114. (in Russian).
23. Obyazov VA. Hydrological regime of lakes in Transbaikalia under the changing climate (on the example of Ivano-Arachleysk lakes). Vodnoye khozyaystvo Rossii: problemy, tekhnologii, upravleniye. 2011, 3, 4–14. (in Russian).
24. Krivenkova IF. Zooplankton in lakes Maloye and Bolshoye Leprindo. Uchenyye zapiski Zabaykal’skogo gosudarstvennogo universiteta. 2016, 11(1), 81–85. (in Russian).
25. Golosov S, Kirillin G. A parameterized model of heat storage by lake sediments. Environmental Modelling & Software. 2010, Vol. 25(6), 793–801. doi: 10.1016/j.envsoft.2010.01.002
26. Kirillin G, Hochschild J, Mironov D. et al. FLake-Global: Online lake model with worldwide coverage. Environmental Modelling & Software. 2011, 26(5), 683–684.
27. Mironov D, Heise E, Kourzeneva E. et al. Implementation of the lake parameterization scheme Flake into the numerical weather prediction model COSMO. Boreal environ. Res. 2010, 15, 218–230.
28. Mironov DV. Parameterization of Lakes in Numerical Weather Prediction. Description of a Lake Model / COSMO Technical Report. No. 11. Offenbach am Main: German Weather Service, 2008. 44 p.
29. Zdorovennov R, Golosov S, Zverev I. et al. Arctic climate variability and ice regime of the Lena River delta lakes. E3S Web of Conferences (IV Vinogradov Conference). 2020, 163, 04008. doi: 10.1051/e3sconf/202016304008
30. Google Earth Engine. URL: https://code.earthengine.google.com/ (Accessed 19.09.2024).
31. Brown CF, Brumby SP, Guzder-Williams B. et al. Dynamic World, Near real-time global 10 m land use land cover mapping. Scientific Data. 2022, 9, 251. doi: 10.1038/s41597-022-01307-4
32. Riggs G, Hall D, Salomonson V. A snow index for the Landsat Thematic Mapper and Moderate Resolution Imaging Spectrometer. Proceeding of the International Geoscience and Remote Sensing Symposium, IGARSS ‘94, Vol. 4: Surface and Atmospheric Remote Sensing: Technologies, Data Analysis, and Interpretation. Pasadena, 1994, 1942–1944.
33. Adamovich TA, Ashikhmina TY, Kantor GY. Use of different combinations of spectral channels of Landsat 8 satellite images for assessment of natural environments and objects (review). Teoreticheskaya i prikladnaya ekologiya. 2017, 2, 9–18. (in Russian).
34. Stepanov SYu, Petrov YA, Vagizov MR, Sidorenko AYu. Monitoring of Earth remote sensing data using Landsat satellite data. Informatsionnyye tekhnologii i sistemy: upravleniye, ekonomika, transport, pravo. 2020, 1(37), 206–216. (in Russian).
Review
For citations:
Kondratyev S.A., Golosov S.D., Zverev I.S., Rasulova A.M. Remote Estimation of Water Body Depth Based on the Date of the Beginning of Ice Formation Using a Hydrophysical Model. Fundamental and Applied Hydrophysics. 2025;18(2):137-150. https://doi.org/10.59887/2073-6673.2025.18(2)-10. EDN: ONRDHI