ISSN 2070-7401 (Print), ISSN 2411-0280 (Online)
Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa


Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2022, Vol. 19, No. 5, pp. 40-52

Flood prediction on major rivers from radiometric microwave measurements from space. Is it possible?

V.V. Sterlyadkin 1 , D.M. Ermakov 2, 3 , А.V. Kuzmin 2 , E.V. Pashinov 2 
1 MIREA — Russian Technological University, Moscow, Russia
2 Space Research Institute RAS, Moscow, Russia
3 Kotelnikov Institute of Radioengineering and Electronics RAS, Fryazino Branch, Fryazino, Moscow Region, Russia
Accepted: 19.10.2022
DOI: 10.21046/2070-7401-2022-19-5-40-52
Heavy rainfall over catchment areas of large rivers leads to severe floods that pose serious threats to the population and infrastructure, cause significant socio-economic damage and hinder economic activities in the surrounding areas. In this work, based on a new method of satellite radiothermal imaging, which allows, based on the analysis of the reconstructed dynamics of precipitable water vapor (PWV) fields, calculating water vapor fluxes over the oceans, the accuracy of reconstructing PWV fields over land in the Amur River catchment area is estimated. The estimates make it possible to calculate the balance of atmospheric water over a selected area: the water vapor stored above the surface, the amount of water that entered or exited through the boundaries of the catchment area, and to determine the amount of precipitation for any selected time interval. The relative error of such measurements depends not only on the measurement errors of water vapor and the accuracy of radio thermal imaging methods in restoring the field of horizontal flows of atmospheric moisture, but also on the size of the river basin, the intensity and duration of precipitation. For example, for a territory that makes 20 % of the Amur basin with an average amount of precipitation of 60 mm for 10 days, the relative error of the proposed radiometric method for measuring precipitation, according to our estimates, will be about 22 %. Methods are proposed for checking the accuracy of the developed method by comparing it with data from ground-based radiosonde measurements, ground-based meteorological stations that measure the amount of precipitation, and data from ground-based meteorological radars. The analysis performed shows that it is quite possible to determine the amount of precipitation over the territory of the basin of large rivers based on multichannel radiometric microwave measurements from space.
Keywords: satellite monitoring of precipitation, flood forecast, Amur basin, water budget analysis, precipitable water vapor, PWV
Full text


  1. Abshaev M. T., Abshaev A. M., Malkarova A. M., Mizieva Z. Y., Radar estimation of water content in cumulonimbus clouds, Izvestiya, Atmospheric and Oceanic Physics, 2009, Vol. 45, No. 6, pp. 731–736, DOI: 10.1134/S0001433809060061.
  2. Bolgov M. V., Trubetskova M. D., Filippova I. A., Kharlamov M. A., Characteristics of extreme precipitation events within the amur river basin in summer 2013, Geography and Natural Resources, 2017, Vol. 38, No. 2, pp. 139–146, DOI: 10.1134/S1875372817020044.
  3. Bolelov E. A., Meteorological service for civil aviation: problems and ways of their solution, Nauchnyi vestnik Moskovskogo gosudarstvennogo tekhnicheskogo universiteta grazhdanskoi aviatsii, 2018, Vol. 21, No. 5, pp. 117–129 (in Russian),
  4. Gartsman B. I., Dozhdevye navodneniya na rekakh yuga Dal’nego Vostoka: metody raschetov, prognozov, otsenok riska (Rain floods on rivers methods of calculation, forecasts, risk assessments), Vladivostok: Dalnauka, 2008, 223 p. (in Russian).
  5. Ermakov D. M., Zrazhevsky A. Yu., Chernushich A. P., Automatic analysis of radio images for radio vision systems: modeling and numerical experiment, Jurnal Radioelektroniki, 2013, No. 7, 16 p. (in Russian),
  6. Ermakov D. M., Kuzmin A. V., Mazurov A. A., Pashinov E. V., Sadovsky I. N., Sazonov D. S., Sterlyadkin V. V., Chernushich A. P., Cherny I. V., Streltsov A. M., Sharkov E. A., Ekimov N. S., The concept of streaming data processing of Russian satellite microwave radiometers of the MTVZA series based on IKI-Monitoring Center for Collective Use Center), Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2021, Vol. 18, No. 4, pp. 298–303 (in Russian), DOI: 10.21046/2070-7401-2021-18-4-298-303.
  7. Kryzhov V. N., Vilfand R. M., Macrometeorological conditions for the formation of heavy precipitation in the Amur River basin in June – September 2013 and the accuracy of its forecast, Ekstremal’nye pavodki v basseine r. Amur: prichiny, prognozy, rekomendatsii, Moscow: Roshydromet, 2014, pp. 40–53 (in Russian).
  8. Lisina I. A., Vasilevskaya L. N., Vasilevsky D. N., Podverbnaya E. N., Ageeva S. V., The lower Amur River hydrological regime and relations between the summer-autumn runoff and circulation indices, Geographical Bull., 2020, No. 3(54), pp. 98–112 (in Russian), DOI: 10.17072/2079-7877-2020-3-98-112.
  9. Makhinov A. N., Kim V. I., Voronov B. A., Floods in the Amur basin in 2013: causes and consequences, Vestnik Dal’nevostochnogo otdeleniya Rossiiskoi akademii nauk, 2014, No. 2(174), pp. 5–14 (in Russian).
  10. Pashinov E. V., Retrieval of integrated water vapor content of the atmosphere over the ocean using MTVZA-GY (Meteor-M No. 2) data, Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa, 2018, Vol. 15, No. 4, pp. 225–235 (in Russian), DOI: 10.21046/2070-7401-2018-15-4-225-235.
  11. Pashinov E. V., Ermakov D. M., Reconstruction of the integral steam content of the atmosphere over land according to SSMIS data, Materialy 19-i Mezhdunarodnoi konferentsii “Sovremennye problemy distantsionnogo zondirovaniya Zemli iz kosmosa” (Proc. 19th Intern. Conf. “Current Problems in Remote Sensing of the Earth from Space”), 2021, p. 187 (in Russian), DOI: 10.21046/19DZZconf-2021a.
  12. Semenov E. K., Sokolikhina N. N., Tatarinovich E. V., Tudry K. O., Synoptic conditions for the formation of a catastrophic flood on the Amur in 2013, Russian Meteorology and Hydrology, 2014, Vol. 39, No. 8, pp. 521–527, DOI: 10.3103/S1068373914080032.
  13. Stepanenko V. D., Radiolokatsiya v meteorologii (Radar in meteorology), Leningrad: Gidrometeoizdat, 1966, 350 p. (in Russian).
  14. Deeter M. N., A new satellite retrieval method for precipitable water vapor over land and ocean, Geophysical Research Letters, 2007, Vol. 34, Issue 2, Art. No. L02815, 5 p., DOI: 10.3103/S1068373914080032.
  15. Du J., Jones L. A., Kimball J. S., Daily Global Land Surface Parameters Derived from AMSR-E and AMSR2, Version 2 (NSIDC-0451), NASA National Snow and Ice Data Center Distributed Active Archive, Boulder, 2017.
  16. Ermakov D., Satellite radiothermovision of atmospheric processes: method and applications, Ser.: Springer Praxis Books, Cham: Springer, 2021, 199 p.,
  17. Ermakov D., Kuzmin A., Pashinov E., Sterlyadkin V., Chernushich A., Sharkov E., Comparison of Vertically Integrated Fluxes of Atmospheric Water Vapor According to Satellite Radiothermovision, Radiosondes, and Reanalysis, Remote Sensing, 2021, Vol. 13, No. 1639,
  18. Hollinger J. P., Peirce J. L., Poe G. A., SSM/I instrument evaluation, IEEE Trans. Geoscience and Remote Sensing, 1990, Vol. 28, No. 5, pp. 781–790.
  19. Kalugin A. S., Motovilov Yu. G., Runoff formation model for the Amur River basin, Water Resources, 2018, Vol. 45, No. 2, pp. 149–159, DOI: 10.1134/S0097807818020082.
  20. Zhang Q., Ye J., Zhang S., Han F., Precipitable Water Vapor Retrieval and Analysis by Multiple Data Sources: Ground-Based GNSS, Radio Occultation, Radiosonde, Microwave Satellite, and NWP Reanalysis Data, J. Sensors, 2018. Vol. 2018, Art. ID 3428303, 13 p. ,