Abstract
The summertime hydrothermal regime of the Yenisei River downstream of the Krasnoyarsk hydroelectric power plant is modeled using a remote sensing and deterministic approach. The Fourier equation is used, and the following physical processes contributing to the heat exchange between the water and the surroundings are taken into consideration: the absorption of direct and scattered solar radiation by water, the absorption of downwelling thermal infrared radiation (TIR) from the atmosphere by water surface, TIR back from the water surface, the convection of heat and the heat loss due to evaporation of water. A clear-skies river thermal regime under no wind condition is studied at 32-km downstream the power plant, and the obtained results are compared against remote sensing data.
Highlights
The Yenisei River in terms of runoff is the largest in Russia (599 km3/year) and the seventh largest in the world (1.5% of the global runoff) [1]
The Fourier equation is used, and the following physical processes contributing to the heat exchange between the water and the surroundings are taken into consideration: the absorption of direct and scattered solar radiation by water, the absorption of downwelling thermal infrared radiation (TIR) from the atmosphere by water surface, TIR back from the water surface, the convection of heat and the heat loss due to evaporation of water
We have proposed a simple model for simulating the summertime hydrothermal regime of a river based on the calculation of water temperature in a coordinate system moving with the water
Summary
The Yenisei River in terms of runoff is the largest in Russia (599 km3/year) and the seventh largest in the world (1.5% of the global runoff) [1]. Temperature is a key indicator of the natural environment It can be measured by ground based methods or satellite data. One of the most interesting satellites which regularly surveys the earth's surface in the thermal infrared (IR) range is Landsat 8. Landsat 8 is not the only thermal infrared satellite, but because of a combination of data availability and high spatial resolution, it is considered to be the leader in the subject area [14]. We switch to a coordinate system moving with water and end up with a simple differential equation for water temperature With this equation we can predict the longitudinal water temperature fluctuation along the river at various times considering real morphometric characteristics.
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