Purpose: is to assess the actual water volumes in water objects (reservoirs, lakes, ponds) to identify the degree of maximum anthropogenic load, namely, the pollutant release.
Materials and methods. Lake Svyatoe, located in Kosino-Ukhtomsky administrative district of Moscow, was chosen as the research and experimental object for testing the automatized system. The programs Google Earth Pro, Surfer 22, local author's programs for data processing, author's unmanned surface vehicle (USV), echo sounder Garmin Striker Cast GPS were used in the study. Automated systems as an unmanned surface vehicle (unmanned boat – USV), sensors (including echo sounder) and local programs for data acceptance and processing can be applied to improve and organize continuous monitoring of water objects.
Results. The actual water volume assessment in the water object of 215 thousand cubic meters by means of the USV and its mounted echo sounder was demonstrated. The methodology of the unmanned boat operation for the water resources exploration was developed.
Conclusions. Taking Lake Svyatoe as an example of water resources volume survey, it can be concluded that the methodology is applicable to any other water body, including reservoirs. Through regular water resources survey using USV it is possible to record not only changes in the actual water resources volume, but also bottom relief changes, sedimentation or overgrowing processes at specific sites. It also allows calculating the maximum allowable anthropogenic impact on the given object, including the maximum allowable discharge and its intensity at a particular moment of time.
doi: 10.31774/2712-9357-2024-14-2-275-286
unmanned surface vehicle, unmanned boat, echo sounder, overgrowth, siltation, water reservoir, monitoring
Naumenko N. O., Shiryaeva M. A., Khanov N. V. Actual water volume assessment in a watershed via the surface drone. Land Reclamation and Hydraulic Engineering. 2024;14(2):275–286. (In Russ.). https://doi.org/10.31774/2712-9357-2024-14-2-275-286.
1. Naumenko N.O., Zhesmer V.B., Novikov A.V., Sumarukova O.V., 2019. Razrabotka avtomatizirovannoy sistemy monitoringa bezopasnosti gidrotekhnicheskikh sooruzheniy [Development of an automated system for monitoring the safety of hydraulic structures]. Potapovskie chteniya – 2019: sb. materialov [Potapov Readings – 2019: Proc.]. Moscow, MISI-MGSU Publ., pp. 214-218, EDN: PGAGVO. (In Russian).
2. Naumenko N.O., 2018. Vvedenie ratsional'nogo normirovaniya na ob"yemy sbrosov zagryaznyayushchikh veshchestv v vodnye ob"yekty s tsel'yu podderzhaniya ustoychivosti ekosistemy [Introduction of rational rationing on the volume of pollutant discharges into water bodies in order to maintain the stability of the ecosystem]. Sovremennye problemy i perspektivy razvitiya rybokhozyaystvennogo kompleksa: materialy VI Nauch.-prakt. konf. molodykh uchenykh [Modern Problems and Prospects of Development of Fishery Complex: Proceedings of the VI Scientific and Practical Conference of Young Scientists]. Moscow, VNIRO Publ., pp. 344-346, EDN: MZPRSR. (In Russian).
3. Hiller B., Yambaev H.K., 2016. Razrabotka i naturnye ispytaniya avtomatizirovannoy sistemy deformatsionnogo monitoringa [Development and field tests of the automated system of deformation monitoring]. Vestnik Sibirskogo gosudarstvennogo universiteta geosistem i tekhnologiy [Bull. of the Siberian State University of Geosystems and Technologies], no. 1(33), pp. 48-61, EDN: WDHJQH. (In Russian).
4. Semadeni I.V., 2023. Prostranstvennoe raspredelenie i fotosinteticheskaya aktivnost' fitoplanktona Rybinskogo vodokhranilishcha v letniy period 2018–2020 gg. [Spatial distribution and photosynthetic activity of phytoplankton of the Rybinsk reservoir in the summer period 2018–2020]. Transformatsiya ekosistem [Transformation of Ecosystems], vol. 6, no. 2(20), pp. 19-32, DOI: 10.23859/estr-220414, EDN: QROJFR. (In Russian).
5. Lagutina N.V., Naumenko N.O., Novikov A.V., Sumarukova O.V., 2019. Otsenka kachestva vod Rybinskogo vodokhranilishcha vsledstvie snizheniya urovnya vod [Estimation of water quality of Rybinsk reservoir due to water level decrease]. Prirodoobustroystvo [Environmental Engineering], no. 2, pp. 122-125, DOI: 10.34677/1997-6011/2019-2-122-126, EDN: YJRIVG. (In Russian).
6. Astakhov A.S., Dikolenko E.Ya., Kharchenko V.A., 2018. Ekologicheskaya bezopasnost' i effektivnost' prirodopol'zovaniya [Ecological Safety and Efficiency of Nature Management]. Vologda, Infra-Engineering Publ., 323 p. (In Russian).
7. Gryaznev D.Yu., Khlapov N.N., Zherebtsova T.O., Zaitseva A.D., Chernyshova D.M., 2021. Sistema avtomatizirovannogo monitoringа i kontrolya promyshlennoy bezopasnosti gidrotekhnicheskikh sooruzheniy [System of automated monitoring and control of industrial safety of hydraulic structures]. Nauchnye vesti [Scientific News], no. 11(40), pp. 73-87, EDN: YAEAGN. (In Russian).
8. Liu L., 2023. Drone-based photogrammetry for riverbed characteristics extraction and flood discharge modeling in Taiwan’s mountainous rivers. Measurement, vol. 220, 113386, https:doi.org/10.1016/j.measurement.2023.113386, EDN: XBDNDW.
9. Sudriani Y., Sebestyén V., Abonyi J., 2023. Surface water monitoring systems – The importance of integrating information sources for sustainable watershed management. IEEE Access, vol. 11, pp. 36421-36451, DOI: 10.1109/ACCESS.2023.3263802, EDN: RFOSHO.
10. Dralle D.N., Lapides D.A., Rempe D.M., Hahm W.J., 2023. Mapping surface water presence and hyporheic flow properties of headwater stream networks with multispectral satellite imagery. Water Resources Research, vol. 59, no. 9, e2022WR034169, https:doi.org/10.1029/2022WR034169, EDN: LQMZXZ.
11. Morgan B.J., Stocker M.D., Valdes-Abellan J., Kim M.S., Pachepsky Y., 2020. Drone-based imaging to assess the microbial water quality in an irrigation pond: A pilot study. Science of the Total Environment, vol. 716, 135757, DOI: https:doi.org/10.1016/j.scitotenv.2019.135757, EDN: LHKKZZ.
12. Rozanov V.B., Berezkin V.Yu., Chereshenko A.V., 2022. Vozmozhnye prichiny kolebaniya urovnya prirodnykh vod v Kosinskikh ozerakh v XX–XXI vekakh – prirodnye i antropogennye [Possible causes of natural water level variations in the Kosin Lakes in the 20th–21st century – natural and anthropogenic]. Vestnik Rossiyskogo universiteta druzhby narodov. Seriya: Ekologiya i bezopasnost' zhiznedeyatel'nosti [RUDN Journal of Ecology and Life Safety], no. 4, pp. 486-497, DOI: 10.22363/2313-2310-2022-30-4-486-497, EDN: MFQHJV. (In Russian).
13. Vélez-Nicolás M., García-López S., Barbero L., Ruiz-Ortiz V., Sánchez-Bellón Á., 2021. Applications of unmanned aerial systems (UASs) in hydrology: A review. Remote Sensing, vol. 13, no. 7, 1359, https:doi.org/10.3390/rs13071359.
14. Koparan C., Koc A.B., Privette C.V., Sawyer C.B., 2020. Adaptive water sampling device for aerial robots. Drones, vol. 4, no. 1, 5, https:doi.org/10.3390/drones4010005.
15. Yang B., Tong S.T.Y., Fan R., 2021. Using high resolution images from UAV and satellite remote sensing for best management practice analyses. Journal of Environmental Informatics, vol. 37, no. 1, pp. 79-92, DOI: 10.3808/jei.202000433.
16. Furlan L.M., Moreira C.A., de Alencar P.G., Rosolen V., 2021. Environmental monitoring and hydrological simulations of a natural wetland based on high-resolution unmanned aerial vehicle data (Paulista Peripheral Depression, Brazil). Environmental Challenges, vol. 4, 100146, https:doi.org/10.1016/j.envc.2021.100146, EDN: NHBJAI.
