Modeling the Genetic Components of River Runoff for the Mozhaisk Reservoir Watershed
The physically-based ECOMAG model of river runoff formation has been adapted to simulate the processes in the Mozhaisk Reservoir watershed. The main goal of the study was to correctly simulate the genetic components of the runoff considering the hydrochemical methods of identifying the water masses in calibrating the model parameters. To break down the runoff hydrograph by genetic components, a technique was applied, based on the chemical–statistical analysis of the composition of the water mass mixture. The many years’ runoff hydrographs from 3 gauging stations and hydrochemical data from which the genetic components of the river runoff have been determined were used to calibrate model parameters. A satisfactory agreement has been obtained between the runoff hydrographs from gauge stations and the hydrographs simulated by the model and obtained by analyzing hydrochemical data of the genetic components of the river water. The regularities of the annual distribution of the genetic runoff components have been analyzed and the genetic types of waters prevailing in different phases of water regime have been demonstrated. The proposed method of determining model parameters by hydrometric and hydrogeochemical data allows simulation of the behavior of the water sources and description of the spatial-temporal genetic structure of the river runoff.
Keywordssimulation river runoff formation hydrograph hydrogeochemical data genetic components
Unable to display preview. Download preview PDF.
- 1.Antokhina, E.N. and Zhuk, V.A. Application of the ECOMAG model to simulation of river runoff from watersheds differing in their area, Vodn. Khoz. Rossii: Probl., Tekhnol., Upravl., 2011, no. 4, pp. 17–32.Google Scholar
- 5.Edelstein, K.K. and Smakhtina, O.Yu. Genetic river runoff structure and the chemical-statistical method for separation of its elements. Water Resour., 1991, vol. 18, no. 5, pp. 5–20.Google Scholar
- 6.Gelfan, A., Gustafsson, D., Motovilov, Yu, Arheimer, B., Kalugin, A., Krylenko, I., and Lavrenov, A. Climate change impact on the water regime of two great Arctic rivers: modeling and uncertainty issues, Clim. Change, 2016, doi 10.1007/s10584-016-1710-5Google Scholar
- 10.Hooper, R.P. Diagnostic tools for mixing models of stream water chemistry. Water Resour. Res., 2003, vol. 39, no. 3, doi 10.1029/2002WR001528Google Scholar
- 11.Kichigina, N.V., Gubareva, T.S., Shamov, V.V., and Gartsman, B.I. Tracer studies of river runoff formation in the watershed of Lake Baikal, Geografiya i Prirodnye Resursy, 2016, no. 5, pp. 60–69.Google Scholar
- 12.Kuchment, L.S. Rechnoj stok (genezis, modelirovanie, predvychislenie) (River Runoff: Genesis, Simulation, Preliminary Calculation), Moscow: Inst. Vodn. Probl., 2008.Google Scholar
- 13.Liu, F., Williams, M. W., and Caine, N. Source waters and flow paths in an alpine catchment, Colorado Front Range, United States. Water Resour. Res., 2004, vol. 40, no. 9, W09401.Google Scholar
- 15.Mamaev, O.I. Termohalinnyj analiz vod Mirovogo okeana (The Thermohaline Analysis of the Waters of the World Ocean), Leningrad: Gidrometizdat, 1987.Google Scholar
- 20.Motovilov, Yu.G., and Fashchevskaia, T.B. Spatially distributed model of the heavy metals flow formation in the river watershed, Voda: Khim. Ekol., 2018, vols. 1−3, pp. 18–31.Google Scholar
- 24.Voronkov, P.P. Gidrohimiya mestnogo stoka evropejskoj territorii SSSR (Hydrochemistry of the local runoff in the European part of the USSR), Leningrad: Gidrometeoizdat, 1970.Google Scholar