Hydrosphere. Hazard processes and phenomena 2021-02-24T00:43:39+03:00 Editorial department / Редакционный отдел Open Journal Systems ICE IMPACT ON THE FLOW DYNAMICS THE NORTHERN DVINA RIVER MOUTH AREA 2021-02-24T00:43:38+03:00 Serafima V. Lebedeva Leonid S. Odoev <p>The authors consider case of the winter period 2019-2020 as example of ice cover and ice jam influence on the flow dynamics in the section upstream the delta of the Northern Dvina River mouth area.</p> <p>Hydrological monitoring, calculations and forecasts of the ice drift in the Northern Dvina River mouth area are actual practical tasks. The hydrological regime of a tidal river mouth is very complex phenomena and one of the most difficult for a quantitative description.</p> <p>The authors implemented an integrated approach to study ice jams, including field ice surveys, satellite data analysis, data from the state hydrological monitoring system and hydrodynamic modeling. STREAM_2D CUDA software is applied for two-dimensional numerical modeling. It takes into account the impact of ice cover.</p> <p>Ice phenomena usually make significant changes in the water level and velocity regime. In 2019/2020 hydrological year the ice cover provided and maintained a large backwater during all the autumn and the winter. When the spring ice drift came from the river upstream there was a 3-4 meter water level rise, which lasted for 5 days, creating a threat of flooding of the Kholmogory village by a breakthrough wave. There is no effective technique to forecast or assess probable scenarios for such situation.</p> <p>The article identified difficulties arising during ice conditions analysis and hydrodynamic modeling of the situation. Steps to optimize the monitoring of ice cover, flow discharges and water levels are also formulated</p> 2020-12-31T00:00:00+03:00 Copyright (c) POSSIBILITIES OF ASSESSING THE FLOOD RUNOFF PROBABILITY IN ANGARA REGION (IYA RIVER AS EXAMPLE) 2021-02-24T00:43:38+03:00 Boris I. Gartsman Tatiana S. Gubareva Natalia V. Kichigina <p>This work aim is analyzing the flood hazard in the Angara region with using stochastic hydrology methods. This region is characterized by greatest danger of summer rain floods, relatively rare but systematic, and are very dangerous. The possibility is studied to construct one-dimensional analytical distribution curves that reliably approximates a series of observed maximum annual discharges and allow to give a reasonable estimate of the extreme floods probability – using the example of extraordinary floods on the Iya River in 1984 and 2019. A number of three-parameter probability laws and methods of probability curves parameterization was used.</p> <p>Resulting, 14 variants of analytical distribution curves were constructed, compared by the statistical and rational criteria. Despite the several versions satisfy to almost all the requirements, it seems impossible to solve the problem qualitatively on their basis. All acceptable curve versions underestimate the probability for extraordinary floods of 1984 and 2019 years. Some of these versions go beyond the <br>5-95% confidence intervals of empirical probability curve, or are the results of direct mathematical fitting without the satisfactory conceptual reasons.</p> <p>Thus, it was not possible to construct adequate three-parameter&nbsp; maximum discharge distribution for the Iya River according to above requirements. The complexity of the runoff formation processes in the region, different generation mechanisms of ordinary and extraordinary floods, obviously, leads to statistical heterogeneity of the series, which cannot be correctly described by one-dimensional three-parameter distribution laws. To assess the extraordinary floods probability, it seems most appropriate to use dynamic-stochastic modeling based on a physically based runoff model</p> 2020-12-31T00:00:00+03:00 Copyright (c) ASSESSING PREDICTABILITY OF HYDROLOGICAL PROCESSES (ON THE EXAMPLE OF FROZEN SOIL WATER CONTENT DYNAMICS) 2021-02-24T00:43:38+03:00 Аlexander N. Gelfan <p><strong>Abstract. </strong>A method has been developed for assessing the limits of predictability of the frozen soil water content (according to observations at the Nizhnedevitskaya water balance station). The method is based on the analysis of the convergence of a given probabilistic measure (the variance of the calculated soil water content at a given date) to its stable value. The soil water content was simulated by the physically based model of heat and water transfer in a frozen soil column during a autumn-winter seasons. To assess variability of the modelled soil water content at a given date, the boundary meteorological conditions for the autumn-winter period were simulated by the Monte Carlo procedure using a stochastic&nbsp; weather generator. The initial conditions were assigned as the constant soil temperature and soil moisture values over the <br>1-meter soil column. The predictability of the soil water content in the one-meter layer of the studied soils has occurred to be about 1.5 months; it means that for the forest-steppe conditions, the soil water content before the beginning of soil freezing cannot serve as an indicator of soil water content before spring. Numerical experiments have shown that the soil water content predictability: (1) grows with an increase in the thickness of the considered soil layer and its depth; (2) decreases for coarser soils as compared to finely dispersed soils; (3) is more sensitive to changes in the soil texture than to changes in the climatic norms of precipitation and air temperature</p> 2020-12-31T00:00:00+03:00 Copyright (c) PROSPECTS FOR SHORT-TERM FORECASTING OF RIVER STREAMFLOW FROM SMALL WATERSHED RUNOFF USING MACHINE LEARNING METHODS 2021-02-24T00:43:38+03:00 Vsevolod M. Moreido Boris I. Gartsman Dimitri P. Solomatine Zoya A. Suchilina <p>The paper addresses prospects for short-term (from 1 to 7 days) forecasting of river streamflow runoff based on several machine learning methods: multiple linear regression (LM) model, a multilayer perceptron (MLP) artificial neural network, and a recurrent artificial neural network with long short-term memory (LSTM). Methods for expanding the set of predictors for model construction are proposed, and the possibility of random shuffling of the time-series of predictors for model calibration and verification are assessed. The object of the study is the small river of Central Russia – river Protva (Spas-Zagorie gauge). Current and lagged values of streamflow discharge at the gauge and daily precipitation at local weather stations are used as predictors for the model, as well as moisture index and evaporation rate. The obtained results show the possibility of constructing an effective operational forecasting system for short-term runoff forecasting. The study revealed the applicability of artificial neural network models, acceptable for operational practice, using all available hydrometeorological information on the catchment, as they showed the most stable results at all lead times from 1 to 7 days. In contrast to the linear model, which efficiency decreases after lead time of more than 3 days, the artificial neural networks models have higher forecast efficiency up to 7 days. The results obtained are robust for all phases of the water regime, both spring floods and summer floods. The software implementation of the models is made on the basis of open software libraries in the Python language, which makes it possible to widely use the methods for scientific research and applied problems.</p> 2020-12-31T00:00:00+03:00 Copyright (c) ON CHANGE OF REDUCTION FACTOR OF MAXIMUM RUNOFF MODULUS WITH INCREASE OF CATCHMENT AREA 2021-02-24T00:43:39+03:00 Svetlana М. Tumanovskaya <p>When forming river runoff, as a rule, its maximum modulus decreases (reduction) as the catchment area increases. For catchments with an area of less than 5 km<sup>2</sup>, reduction of maximum drainage and runoff modules is not expressed, which confirms the accepted assumption of equal drainage and runoff for small catchment areas. This is the first stage of the process of forming flood water runoff on channel-free slopes. At the second stage, catchments with an area of 5 to 10 km<sup>2</sup> tend to decrease the maximum runoff modulus with an increase in catchment area. The reason for the decrease in drainage modules is a decrease in the effective catchment area as a result of a change in the snow cover area; for water flow modules - reduction of the effective catchment area and beginning of channel regulation. At the third stage, at catchments with an area of 10 to 100 km<sup>2</sup>, a decrease in the maximum water flow modulus depending on the catchment area has a stable tendency, determined by channel regulation, a decrease in the existing catchment area, and features of the increase in the catchment area along the length of the main watercourse. At the fourth stage, at catchments with an area of more than 100 km<sup>2</sup>, the reduction of the maximum module of water flow has a stable appearance and is determined by channel regulation and features of the increase in the catchment area along the length of the main watercourse. For small watercourses, the dependence of the shape coefficient of the catchment (K<sub>f</sub>) on its area reflects the process of development of the catchment from slopes in which the width of the catchment is significantly longer, to watercourses where the width of the catchment becomes less than the length of the watercourse. In general, K<sub>f</sub> is not sufficiently informative and does not reflect the features of the increase in catchment area along the length of the channel of the main watercourse. The main reason for using K<sub>f</sub> in analysis and calculations is the lack of cadastral data on the increase in catchment area along the length of the main watercourse bed for all hydrologically studied rivers</p> 2020-12-31T00:00:00+03:00 Copyright (c) STUDY OF THE VELOCITY OF ARTIFICIAL MUDFLOW ON A LABORATORY STAND 2021-02-24T00:43:39+03:00 Nikolay A. Kazakov Darya A. Bobrova Ekaterina N. Kazakova <p>In August 2020, an experiment was conducted to measure the velocity of an artificial coherent mudflow on a laboratory stand. The stand is a rectangular cross-section tray with a length of 3.0&nbsp;m, a width of 0.25 m, a depth of 0.25 m, slope – 29°. 4&nbsp;racks were installed in the tray to measure the hydrodynamic pressure of the mudflow. The movement of the mudflow was filmed by a high-speed video camera. Velocity of artificial mudflow were measured. Most of the mudflow mixture soil is made up of particles less than 0.25 mm in size (34%). The density of the prepared mudflow mixture was 1 880&nbsp;kg/m<sup>3</sup>. Density of mudflow deposits 2 040&nbsp;kg/m<sup>3</sup>. Despite the small values of the Reynolds number, the turbulent movement of the mudflow was observed. Comparison of the results of the measured mudflow velocities with the velocities calculated by different methods of calculating the mudflow velocity based on the structure of the Shezi formula (for Newtonian fluids) showed a strong variation of the calculated values. The methods either greatly overestimate or, on the contrary, greatly underestimate the measured values. This is probably due to the fact that the connected mudflow is not a Newtonian fluid, but a non-Newtonian fluid. The closest physical analogue of a connected mudflow is a pseudoplastic (non-Newtonian) fluid whose viscosity decreases with increasing shear stress. As a physical model of a connected mudflow, it is advisable to use the Bingham fluid model, the flow of which is similar to the flow of coherent debris-flows and mudflows</p> 2020-12-31T00:00:00+03:00 Copyright (c)