Skip to main content
SpringerLink
Log in

Modeling the Genetic Components of the Water and Chemical Runoff of Heavy Metals in the Basin of the Nizhnekamskoe Reservoir

  • MATHEMATICAL MODELS IN SOLVING PROBLEMS OF LAND HYDROLOGY
  • Published:
Water Resources Aims and scope Submit manuscript

Abstract

A semi-distributed physical-mathematical model ECOMAG-HM is used to simulate the genetic structure of water and chemical runoff of copper, zinc, and manganese in a large river basin of the Nizhnekamsk Reservoir. The model was tested against long-term data of hydrological and hydrochemical monitoring of water bodies. The contributions of the surface, soil, and subsoil components of the water and chemical runoff of metals are estimated for different segments of river network. It was found that, in the major portion of the catchment, river pollution by metals is mostly due to their diffuse washout from the soil layer. The effect of the genetic structure of water and chemical runoff on the year-to-year and seasonal variations of metal concentrations in the river network is demonstrated.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.
Fig. 3.
Fig. 4.
Fig. 5.
Fig. 6.
Fig. 7.
Fig. 8.
Fig. 9.
Fig. 10.
Fig. 11.
Fig. 12.
Fig. 13.

Similar content being viewed by others

REFERENCES

  1. Befani, A.N., Theoretical substantiation of the methods of studies and calculations of flood runoff in Far Eastern rivers, Tr. DVNIGMI, 1966, iss. 22, pp. 124–215.

  2. Blokov, I.P., Okruzhayushchaya sreda i ee okhrana v Rossii. Izmeneniya za 25 let (Environment and Its Protection in Russia. Changes over 25 Years), Moscow: Sovet Grinpis, 2018.

  3. Vinogradov, Yu.B. and Vinogradova, T.A., Matematicheskoe modelirovanie v gidrologii (Mathematical Modeling in Hydrology), Moscow: Akademiya, 2010.

  4. Gosudarstvennye doklady o sostoyanii prirodnykh resursov i okruzhayushchei sredy Respubliki Bashkortostan v 2006–2017 godakh (State Report on the State of the Natural Resources and Environment in the Republic of Bashkortostan in 2006–2017), Ufa, 2007–2018.

  5. Gubareva, T.C., Boldeskul, A.G., Gartsman, B.I., and Shamov, V.V., Analysis of natural tracers and genetic runoff components in mixing models: case study of small basins in Primor’e, Water Resour., 2016, vol. 43, no. 4, pp. 629–639.

    Article  Google Scholar 

  6. Datsenko, Yu.S., Specific features of the formation of manganese and iron river runoff during spring flood, Voda: Khim. Ekol., 2018, no. 4-6, pp. 3–6.

  7. Kondrat’ev, S.A. and Shmakova, M.V., Matematicheskoe modelirovanie massoperenosa v sisteme vodosbor-vodotok-vodoem (Mathematical Modeling of Mass Transport in a Drainage Basin–Watercourse–Water Body System), St. Petersburg: Nestor-Istoriya, 2019.

  8. Kudelin, B.I., Printsipy regional’noi otsenki estestvennykh resursov podzemnykh vod (Principles of Regional Estimation of the Natural Groundwater Resources), Moscow: Mosk. Gos. Univ., 1960.

  9. Kuchment, L.S., Rechnoi stok (genezis, modelirovanie, predvychislenie) (River Runoff. Genesis, Modeling, Precalculation), Moscow: IVP RAN, 2008.

  10. Motovilov, Yu.G. and Gel’fan, A.N., Modeli formirovaniya stoka v zadachakh gidrologii rechnykh basseinov (Runoff Formation Models in Problems of River Basin Hydrology), Moscow: Ross. Akad. Nauk, 2018.

  11. Motovilov, Yu.G. and Fashchevskaya, T.B., Spatially distributed model of heavy metal runoff in a river basin, Voda: Khim. Ekol., 2018, nos. 1–3, pp. 18–31.

  12. Pravila ispol’zovaniya vodnykh resursov Nizhnekamskogo vodokhranilishcha na r. Kame (Regulations on water resources development in the Nizhnekamskoe Reservoir on the Kama R.), Moscow: Rosvodresursy, 2014.

  13. Resursy poverkhnostnykh vod SSSR (USSR Surface Water Resources), vol. 11, Srednii Ural i Priural’e (Middle Urals and Ural Region), Leningrad: Gidrometeoizdat, 1973.

  14. Rets, E.P., Kireeva, M.B., Samsonov, T.E., Ezerova, N.N., Gorbarenko, A.V., and Frolova, N.L., Algorithm GRWAT for automated hydrograph separation by B.I. Kudelin’s method: problems and perspectives, Water Resour., 2022, vol. 49, no. 1, pp. 23–37.

    Article  Google Scholar 

  15. Selezneva, A.V., Anthropogenic load onto rivers due to point pollution sources, Izv. Samar. NTs RAN, 2003, vol. 5, no. 2, pp. 268–277.

    Google Scholar 

  16. SN 435-72. Ukazaniya po opredeleniyu raschetnykh gidrologicheskikh kharakteristik (Directions on the Estimation of Hydrological Characteristics), Leningrad: Gidrometeoizdat, 1972.

  17. Suchkova, K.V., Motovilov, Yu.G., Edel’shtein, K.K., Puklakov, V.V., Erina, O.N., and Sokolov, D.I., Modeling the genetic components of river runoff with the use of hydrochemical method of water mass identification, Voda: Khim. Ekol., 2019, nos. 1–2, pp. 46–56.

  18. Fashchevskaya, T.B., Krasnogorskaya, N.N., and Rogozina, T.A., The impact of Ufavodokanal Enterprise on Water Quality in the Belaya River, Mater. mezhdunarod. nauch. konf. “Ekologicheskie i gidrometeorologicheskie problemy bol’shikh gorodov i promyshlennykh zon” (Trans. Intern. Sci. Conf. “Environmental and Hydrometeorol. Problems of Large Cities and Industrial Zones”), SPb.: Izd. RGGMU, 2006, pp. 80–82.

    Google Scholar 

  19. Fashchevskaya, T.B., Motovilov, Yu.G., and Shadiyanova, N.B., Natural and anthropogenic variations of the concentrations of iron, copper, and zinc in water streams of the Republic of Bashkortostan), Water Resour., 2018, vol. 45, no. 6, pp. 873–886.

    Article  Google Scholar 

  20. Shcherbakov, B.Ya., Chilikin, A.Ya., and Izhevskii, V.S., Volley discharges of industrial wastewaters and their effects, Ekologiya Promsst’ Rossii, 2002, no. 6, pp. 39–41.

  21. Edel’shtein, K.K., Strukturnaya gidrologiya sushi (Structural Land Hydrology), Moscow.: GEOS, 2005.

  22. Edel’shtein, K.K. and Smakhtina, O.Yu., Genetic structure of river flow and a chemical–statistical method for isolation of its elements, Vod. Resur., 1991, no. 5, pp. 5–20.

  23. Barlow, P.M., Cunningham, W.L., Zhai, T., and Gray, M.U.S., Geological Survey Groundwater Toolbox, a Graphical and Mapping Interface for Analysis of Hydrologic Data (version 1.0), User Guide for Estimation of Base Flow, Runoff, and Groundwater Recharge from Streamflow Data: Techniques and Methods, Book 3, Chapter B10, Reston, Virginia, U.S.: Geological Survey, 2015.

  24. Beven, K., How far can we go in distributed hydrological modelling?, Hydrol. Earth System Sci., 2001, vol. 5, iss. 1, pp. 1–12.

    Article  Google Scholar 

  25. Eckhardt, K., A comparison of baseflow indices, which were calculated with seven different baseflow separation methods, J. Hydrol., 2008, no. 1, pp. 168–173.

  26. Kirchner, J.W., Quantifying new water fractions and transit time distributions using ensemble hydrograph separation: theory and benchmark tests, Hydrol. Earth System Sci., 2019, no. 1, pp. 303–349.

  27. Klaus, J. and McDonnell, J.J., Hydrograph separation using stable isotopes: review and evaluation, J. Hydrol., 2013, vol. 505, pp. 47–64.

    Article  Google Scholar 

  28. McDonnell, J.J. and Beven, K., Debates on water resources: the future of hydrological sciences: a (common) path forward? A call to action aimed at understanding velocities, celerities and residence time distributions of the headwater hydrograph, Water Resour. Res., 2014, vol. 50, no. 6) pp. 5342–5350.

  29. Moriasi, D.N., Arnold, J.G., Van Liew, M.W., Bingner, R.L., Harmel, R.D., and Veith, T.L., Model evaluation guidelines for systematic quantification of accuracy in watershed simulations, Trans. ASABE, 2007, vol. 50, no. 3, pp. 885–900.

    Article  Google Scholar 

  30. Motovilov, Y. and Fashchevskaya, T., Modeling management and climate change impacts on water pollution by heavy metals in the Nizhnekamskoe Reservoir watershed, Water, 2021, vol. 13, no. 22.

  31. Motovilov, Y.G. and Fashchevskaya, T.B., Simulation of spatially-distributed copper pollution in a large river basin using the ECOMAG-HM model, Hydrol. Sci. J., 2019, pp. 739–756.

  32. Sivapalan, M. and Bloschl, G., The growth of hydrological understanding: technologies, ideas, and societal needs shape the field, Water Resour. Res., 2017, vol. 53, pp. 8137–8146.

    Article  Google Scholar 

Download references

Funding

This study was supported by the Russian Science Foundation, project no. 22-27-00598, https://rscf.ru/project/22-27-00598/.

Author information

Authors and Affiliations

  1. Water Problems Institute, Russian Academy of Sciences, 119333, Moscow, Russia

    T. B. Fashchevskaya, Yu. G. Motovilov & K. V. Kortunova

Authors
  1. T. B. Fashchevskaya
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Yu. G. Motovilov
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. K. V. Kortunova
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to T. B. Fashchevskaya.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fashchevskaya, T.B., Motovilov, Y.G. & Kortunova, K.V. Modeling the Genetic Components of the Water and Chemical Runoff of Heavy Metals in the Basin of the Nizhnekamskoe Reservoir. Water Resour 50, 583–599 (2023). https://doi.org/10.1134/S0097807823040073

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0097807823040073

Keywords:

Search

Navigation