Gelfan, A. N., Millionshchikova, T. D. Validation of a Hydrological Model Intended for Impact Study: Problem Statement and Solution Example for Selenga River Basin. Water Resour., 2018. Vol. 45, Suppl. 1, pp. S90–S101
The study is aimed to evaluate a hydrological simulation model intended for assessing climate change impact. A new test was suggested and applied to evaluate the performance of a physically based model of Selenga River runoff generation. In this test, to calibrate the model, an enhanced Nash–and-Sutcliffe efficiency (NSE) criterion was used, including trend-oriented reference (benchmark) models instead of the simple reference model used in the original NSE criterion. Next, modifications were made in the Differential Split Sample test (DSS-test) of V. Klemeš (1986), focused on differences in the model performance criteria for climatically contrasting periods and a new statistical measure was proposed to estimate the significance of these differences. After that, model performance was evaluated for four sites within the catchment, three indicators of interest (daily, monthly, and annual discharge series), and the model ability to reproduce the observed trends in annual and seasonal discharge values was assessed. The model proved robust enough to be applied to assessing climate change impact on the annual and monthly runoff in different parts of the Selenga River basin.
2021Е.К. Сантьева, И.Л. Башмачников, М.А. Соколовский. Об устойчивости Лофотенского вихря Норвежского моря. Океанология, 2021, том 61, № 3, с. 353–365.
2021Moreido, Vsevolod; Gartsman, Boris; Solomatine, Dimitri P.; Suchilina, Zoya. 2021. How Well Can Machine Learning Models Perform without Hydrologists? Application of Rational Feature Selection to Improve Hydrological Forecasting. Water 13, no. 12: 1696.