Streamflow of Russian Rivers under Current and Forecasted Climate Changes: A Review of Publications. 1. Assessment of Changes in the Water Regime of Russian Rivers by Observation Data

Frolova, Natalia; Magritskii, D. V.; Kireeva, Maria; Grigor’ev, V. Yu.; Gelfan, Alexander; Сазонов, А. А.; Shevchenko, A. I.

doi:10.1134/s0097807822030046

Cited by 19 publications

(24 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Although, Pettitt test was used for detecting a "change point" in water temperature and heat stick records. This test is used for detecting "change points" in water flow rows for Russian rivers (Frolova et al 2022), so it is an opportunity to compare changes in water and temperature regimes of rivers. This test is marked only one point in a row, so local changes were analyzing with trends and comparing statistical characteristics (mean, standard deviations) of two nearest periods.…”

Section: Heatmentioning

confidence: 99%

“…River heat flux is a function water flow and water temperatures. Annual water flow of Russian Arctic rivers had increased after 1970-1990-s for 7-10%, but statistically significant changes in Russian Arctic rivers are only in North-East of Russia (Frolova et al 2022). A great range of models as global, as regional ones show that water flow of these rivers will increase for 10-40% to the end of 21 century, first of all with a spring flood reduction and with an increase of summer and autumn water flow (Gelfan et al 2022).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Features Of A Long-Term Heat Flux Formation Of The Large Russian Arctic Rivers And Its Transformations In Estuaries Under The Influence Of Climate-Induced And Dam-Induced Effects

Василенко

Magritsky

Frolova

et al. 2023

GES

View full text Add to dashboard Cite

The heat flux of the large rivers flowing into the Arctic seas of Russia plays an essential role in the thermal and ice regime of the lower reaches of these rivers and the southern part of the Arctic seas. However, estimates of the total value of heat flux and its spatial-temporal distribution require clarification. In this research, we analyzed monthly, and yearly water temperature data from 55 gauges and water flow data from 35 gauges in the lower reaches of the rivers of the Russian Arctic northerner of 60 N. These rivers are: Onega, Northern Dvina, Mezen, Pechora, Ob, Nadym, Pur, Taz, Yenisei, Khatanga, Anabar, Olenek, Lena, Yana, Indigirka, Alazeya, Kolyma and their main tributaries. The collected data series covers 1930-2018, focusing more on 1960–2018. We used Spearman trend tests and Mann-Whitney U-test to clarify changes in the thermal regime of study rivers. Our estimations showed that heat flux did not significantly increase in the past three decades on most rivers except Yenisei and Yana lowlands. Water temperatures on rivers monotonically increased after 1960, especially in May and June, but without statistical significance. The role of dams in the water temperature regime is observed for nearly 500 km lower dams, but it is not observed in their lowlands and mouths. We also identified the decrease in water temperatures and heat flux in river lowlands, estuaries and deltas up to 25% for Yenisei lowlands.

show abstract

Section: Heatmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Features Of A Long-Term Heat Flux Formation Of The Large Russian Arctic Rivers And Its Transformations In Estuaries Under The Influence Of Climate-Induced And Dam-Induced Effects

Василенко

Magritsky

Frolova

et al. 2023

GES

View full text Add to dashboard Cite

show abstract

“…The selected parameters were calculated for each year and then averaged over two periods 1945-1977 and 1978-2019. The choice of periods is based on literary analysis, as according to the most modern studies (Long-term fluctuations... 2021; Frolova et al 2022) in the period from 1978 and up to today hydrological systems start to display different behavior compared to historical period in response to changes in climate.…”

Section: Hydrological Data and Observation Periodsmentioning

confidence: 99%

“…The hydrological regime of a river represents a specific pattern of changes in the state of the water body, unique for each territory (Frolova et al 2021(Frolova et al , 2022Gelfan et al 2021). The main characteristics of the hydrological regime of rivers are the character of inflow components, morphodynamic and climatic conditions (Blöschl et al 2017;Hall and Blöschl 2018;Frolova et al 2021;Frolova et al 2020;.…”

Section: Introductionmentioning

confidence: 99%

“…One of the most important tasks of modern society is to develop resistance mechanisms and adapt to these changes (Frolova et al 2021(Frolova et al , 2022Djamalov et al 2014Djamalov et al , 2015. In many regions, climate change has a negative impact on the quality and quantity of water resources, water temperature and the state of related ecosystems, leading to an increase in the scale and frequency of extreme natural events such as floods and droughts (Georgievsky and Shalygin 2012;Longterm fluctuations… 2021).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms

Ivanov

Gorbarenko

Kireeva

et al. 2022

GES

View full text Add to dashboard Cite

The article presents the results of study of the application of machine learning methods to the problem of classification and identification of different river water regimes in a large region – the European territory of Russia. An accumulation of hydrological observation data for the 60 – 80 years makes it possible to create an information basis for such studies. The article uses information on the average monthly runoff at 351 hydrological gauges during the period from 1945 to 2018. The most widely used data clustering approaches were used as analysis methods – K-means, EM-method, agglomerative hierarchical clustering, DBSCAN algorithms and the application of gradient boosting methods (CATBUST). Clustering and classification algorithms were given eight parameters as a basis for prediction. It was found that the most distinct and stable clusters are formed with three parameters, and the highest silhouette coefficient (SS = 0,3-0,5) is obtained using the numbers for months of the maximum and minimum runoff and the ratio of the maximum to the minimum water flow. The best result gives DBSCAN (SS = 0,6 – 0,7). Supervised classification models also show high correspondence with the reference classification, with an accuracy of 87%. Both clustering methods and classification methods showed a shift of clusters representing southern water regimes. In the central region these regimes expanded by a 1000 km to the north. Furthermore, results demonstrate that currently available data already makes it possible to apply machine learning methods to the analysis of hydrological data. Clusters corresponding to different types of water regime can be obtained by utilizing contemporary clustering algorithms. The study shows that over the past 40 years, the southern types of water regimes have noticeably shifted to the north.

show abstract

Changes of the streamflow of northern river basins of Siberia and their teleconnections to climate patterns

Zhang,

Gan,

Bush

et al. 2023

Intl Journal of Climatology

View full text Add to dashboard Cite

The Arctic rivers contribute more than one‐third of the total freshwater streamflow into the Arctic Ocean and play an essential role in the heat and mass circulation of the Arctic atmosphere/ocean system. As the Arctic is warming faster than the global average, the streamflow from Arctic basins increases. This study analyzed the streamflow of the three largest Siberian rivers: the Lena, Yenisei, and Ob', at multiple temporal scales. Results show that the annual streamflow of each river basin exhibits statistically significant increasing trends, while the seasonal streamflow of sub‐basins generally decreases in the summer but increases in the winter. Both autocorrelation and long‐term persistency are often found in the streamflow time series, which indicates significant changes in the large‐scale climatological environment. Therefore, wavelet coherence between the streamflow and large‐scale climate patterns, including the El Niño–Southern Oscillation (ENSO), North Pacific pattern (NP), Arctic Oscillation (AO), and the Pacific/North America Pattern (PNA), have been conducted. NP and ENSO are found to have positive relationships with the precipitation and the ratio of potential evapotranspiration over the precipitation. AO and the PNA are found to have positive relationships with the streamflow of the Ob' and Yenisei rivers at decadal and multidecadal scales. This study demonstrates that the existence of nonstationarities within the Siberian streamflow as the combined impact of climate change alters the hydroclimatological and terrestrial environment of Siberia. These findings provide new insights into the mechanisms underlying the hydrologic changes to warming trends and oscillations of climate patterns, which contribute to our understanding and the prediction of streamflow of these northern rivers.

show abstract

Streamflow of Russian Rivers under Current and Forecasted Climate Changes: A Review of Publications. 1. Assessment of Changes in the Water Regime of Russian Rivers by Observation Data

Cited by 19 publications

References 39 publications

Features Of A Long-Term Heat Flux Formation Of The Large Russian Arctic Rivers And Its Transformations In Estuaries Under The Influence Of Climate-Induced And Dam-Induced Effects

Features Of A Long-Term Heat Flux Formation Of The Large Russian Arctic Rivers And Its Transformations In Estuaries Under The Influence Of Climate-Induced And Dam-Induced Effects

Identifying Climate Change Impacts On Hydrological Behavior On Large-Scale With Machine Learning Algorithms

Changes of the streamflow of northern river basins of Siberia and their teleconnections to climate patterns

Contact Info

Product

Resources

About