Research Laboratory for Reservoir Hydrology and Ecology
Main research areas: study of physical processes in reservoirs and in the atmosphere above the reservoirs
Research objectives:
Development of systems for monitoring water quality and occurring processes for remote control of the environmental status of reservoirs
Key results:
2022
1. An archive of high-resolution satellite images from 2013 onwards was compiled, allowing the identification of processes at scales of 10 metres or more. Cases of ship-generated pollution, significant coastal erosion, abundant suspended sediment transported by inflowing rivers, areas of catastrophic blue-green algae blooms, etc. were detected.
- For the first time, upstream and downstream flow profiles and actual bottom profiles with high spatial resolution have been recorded under different operating regimes of the hydropower plant. Based on the results of bottom sediment analysis, it was found that the hydropower plant has an impact on the hydro-ecological characteristics of the reservoir: the main impact is observed within a few kilometres. The presence of pronounced run-off prevents silt accumulation not only in the channel trough near the dam, but also contributes to the flushing of fresh sediment on the nearby flood bed.
- Since April 2022, regular measurements of the main optical characteristics of the atmosphere for the purpose of atmospheric correction of satellite images have been implemented. For the dates when high values of aerosol optical thickness were observed, the type of aerosol and its possible source were determined from satellite data. Cases of dust aerosol from the Karakum desert and smoke aerosol from forest fires in the Urals were identified for the Nizhny Novgorod region. Ecological assessments of the atmosphere were carried out according to the AQI index, reaching values of 185 µg/m³ and corresponding to "unhealthy air quality".
- Based on shipboard measurements of water colour in the Gorky Reservoir and atmospheric transparency above it, a reliable approach to eliminate the atmospheric contribution to water brightness on Sentintel-3/OLCI colour scanner satellite images is proposed with an accuracy of 2-35%. This is an extremely high accuracy, given that the atmospheric contribution to the total signal recorded by the satellite is as high as 90%. This result opens the way to the development of reliable regional seasonal algorithms for reconstructing water impurity concentrations from remote sensing (satellite) data.
