Краткосрочное прогнозирование стока реки Уссури: методика и специфика

Nowadays, short-term river runoff forecasting remains one of the key and very important issues in terms of application in land hydrology. Obtaining a quality forecast remains a difficult problem despite the long history of developing methods for short-term hydrological forecasts and the availability of advanced modeling tools. The difficulties of solving this problem lie in the lack of a complete understanding of the runoff formation process, as well as the shortage of reliable and complete observation data. The article examines an example of developing a methodology for short-term forecasting of the Ussuri River runoff – the Kirovsky gauging station (catchment area 24400 km²). The main part of the basin is located in Primorsky Region, within the Sikhote-Alin mountain system. The East-Asian extratropical monsoon regularly develops in this region, due to which the most characteristic element of the hydrological regime is high rain floods, causing extensive destructive floods. Modern spatially distributed runoff models have already become a generally accepted tool for solving a wide variety of hydrological problems. However, experience of their application in Russia, specifically for short-term runoff forecasting, is still very limited, especially for rivers with a rain-fed regime. The aim of the study is to develop a methodology for creating effective methods for short-term forecasts of rain floods in the basins of rivers of the Far-Eastern type of regime based on modern modeling tools – a runoff formation model based on the ECOMAG information and computing complex and the WRF mesoscale meteorological model. The authors examined in detail the various stages of creating a short-term forecast method. First, this is the development of a quasi-distributed physically-based hydrological model of a river basin, its calibration and verification. Second, the development of a forecasting scheme and algorithms using forecast data from the WRF meteorological model, testing the forecast scheme on independent data. Third, the selection and justification of the technique for introducing operational corrections to the results of model runoff forecasting, and a comprehensive assessment of the quality of the forecasts obtained.