Dynamic-stochastic modeling of snow cover formation on the European territory of Russia

A dynamic-stochastic model, which combines a deterministic model of snow cover formation with a stochastic weather generator, has been developed. The deterministic snow model describes temporal change of the snow depth, content of ice and liquid water, snow density, snowmelt, sublimation, re-freezing of melt water, and snow metamorphism. The model has been calibrated and validated against the long-term data of snow measurements over the territory of the European Russia. The model showed good performance in simulating time series of the snow water equivalent and snow depth. The developed weather generator (NEsted Weather Generator, NewGen) includes nested generators of annual, monthly and daily time series of weather variables (namely, precipitation, air temperature, and air humidity). The parameters of the NewGen have been adjusted through calibration against the long-term meteorological data in the European Russia. A disaggregation procedure has been proposed for transforming parameters of the annual weather generator into the parameters of the monthly one and, subsequently, into the parameters of the daily generator. Multi-year time series of the simulated daily weather variables have been used as an input to the snow model. Probability properties of the snow cover, such as snow water equivalent and snow depth for return periods of 25 and 100 years, have been estimated against the observed data, showing good correlation coefficients. The described model has been applied to different landscapes of European Russia, from steppe to taiga regions, to show the robustness of the proposed technique

Динамико-стохастическое моделирование формирования снежного покрова на Европейской территории России

A dynamic-stochastic model, which combines a deterministic model of snow cover formation with a stochastic weather generator, has been developed. The deterministic snow model describes temporal change of the snow depth, content of ice and liquid water, snow density, snowmelt, sublimation, re-freezing of melt water, and snow metamorphism. The model has been calibrated and validated against the long-term data of snow measurements over the territory of the European Russia. The model showed good performance in simulating time series of the snow water equivalent and snow depth. The developed weather generator (NEsted Weather Generator, NewGen) includes nested generators of annual, monthly and daily time series of weather variables (namely, precipitation, air temperature, and air humidity). The parameters of the NewGen have been adjusted through calibration against the long-term meteorological data in the European Russia. A disaggregation procedure has been proposed for transforming parameters of the annual weather generator into the parameters of the monthly one and, subsequently, into the parameters of the daily generator. Multi-year time series of the simulated daily weather variables have been used as an input to the snow model. Probability properties of the snow cover, such as snow water equivalent and snow depth for return periods of 25 and 100 years, have been estimated against the observed data, showing good correlation coefficients. The described model has been applied to different landscapes of European Russia, from steppe to taiga regions, to show the robustness of the proposed technique. Разработана динамико-стохастическая модель, компоненты которой – детерминистическая модель формирования снежного покрова и стохастические модели временны́х рядов входных метеорологических величин (стохастический генератор погоды). Детерминистическая модель формирования снежного покрова описывает изменения во времени толщины снега, содержания льда и талой воды в нём, процессы снеготаяния, сублимации и замерзания талой воды в толще снега. Калибровка модели по данным многолетних наблюдений за снежным покровом на 36 метеорологических станциях на Европейской территории России позволила получить адекватные результаты по воспроизведению запасов воды в снежном покрове и толщины снега. Разработан стохастический генератор погоды (NEsted Weather Generator, NEWGen) для моделирования методом Монте-Карло многолетних временны́х рядов среднесуточных значений температуры воздуха, осадков и влажности воздуха, которые представляют собой входные переменные детерминистической модели. Для оценки параметров и проверки стохастического генератора погоды использовали данные многолетних наблюдений на метеорологических станциях Европейской территории России. Тысячелетние ряды метеорологических величин, сгенерированные методом Монте-Карло, задавались в качестве «входов» в модель формирования снежного покрова, с помощью которой рассчитывались ряды толщины снега и снегозапасов и оценивались их вероятностные характеристики. Получено удовлетворительное соответствие статистических параметров, определённых по фактическим и рассчитанным рядам характеристик снежного покрова

Ensemble seasonal forecast of extreme water inflow into a large reservoir

An approach to seasonal ensemble forecast of unregulated water inflow into a large reservoir was developed. The approach is founded on a physically-based semi-distributed hydrological model ECOMAG driven by Monte-Carlo generated ensembles of weather scenarios for a specified lead-time of the forecast (3 months ahead in this study). Case study was carried out for the Cheboksary reservoir (catchment area is 374 000 km2) located on the middle Volga River. Initial watershed conditions on the forecast date (1 March for spring freshet and 1 June for summer low-water period) were simulated by the hydrological model forced by daily meteorological observations several months prior to the forecast date. A spatially distributed stochastic weather generator was used to produce time-series of daily weather scenarios for the forecast lead-time. Ensemble of daily water inflow into the reservoir was obtained by driving the ECOMAG model with the generated weather time-series. The proposed ensemble forecast technique was verified on the basis of the hindcast simulations for 29 spring and summer seasons beginning from 1982 (the year of the reservoir filling to capacity) to 2010. The verification criteria were used in order to evaluate an ability of the proposed technique to forecast freshet/low-water events of the pre-assigned severity categories