Identifying changes in hydrological behaviour of Russian Plain rivers over the last 70 years by using clustering analysis

2021 ◽  
Author(s):  
Alexander Ivanov ◽  
Maria Kireeva
Keyword(s):  
Water Regime ◽  
Clustering Algorithm ◽  
Basic Research ◽  
Arctic Region ◽  
River Basins ◽  
Russian Plain ◽  
Russian Science ◽  
Water Regimes ◽  
Regime Types ◽  
And Shifts

<p>In the past two decades we see many signs of changing behaviour in hydrological regimes of Russian Plain rivers. River regimes classification was done in the early 1990s and it's possible that some rivers (especially in Don and Oka river basins) have already changed their behaviour. We believe that's the first time this was done by objective analysis and without reliance on experts opinion.</p><p>In this work we make an attempt at automatic and objective classification of water regime types for 220 rivers of Russian Plain and propose a method for automatic assesment of changes in hydrological behaviour of local rivers. We use monthly data and k-means clustering algorithm to classify each river water regime for every year with available data. Unlike most of other approaches we do not divide data by year but create clusters from all datapoints simultaniously. This allows us to use more datapoints and establish a more robust result. Next, when we have annual clusters for every datapoint we can assess the stability of water regime for each catchment over several decades and identify catchments with unstable and changing behaviour. </p><p>By using this method we're able to automatically identify 5 distinct water regimes for the rivers of Russian Plain: three with dominant peaks caused by spring freshets in March, April and Februaty with most discharge happening over the course of a single month and two types of water regimes with maximal discharges in April and June, but lacking a pronounced peak in these months. Unlike previous calssifications we can identify the closest water regime for every year and therefore make an attempt at quantifying stability of these regimes and changes over time. By using a very naive approach and calculating a standard deviation over a moving window of 10 years it's possible to detect unstable regions and therefore select periods of stability and shifts for each subregion of Russian Plain.</p><p>We're able to identify Don and Oka basins as regions with the most changes in water regimes and it corresponds with research data. In addition rivers in Kola peninsula and Ural regions peninsula demonstrate a slight shift in stability. In terms of hydrological behaviour we see siginificant changes in Don and Oka river basins that shifted from spring freshet peak in April into water regime type with a peak in March or a more southern water regime with less pronounced April peak having precedenig winter thaws.</p><p>We believe that this simple approach at identifying water regimes and changes in them can be successfuly used for other regions than Russian Plane.</p><p>The study was supported by the Russian Science Foundation (grant No.19-77-10032) in methods and Russian Foundation for Basic Research (grant No.18-05-60021) for analyses in Arctic region </p>

Objective classification of changes in water regime types of the Russian Plain rivers utilizing machine learning approaches

2020 ◽  
Author(s):  
Alexander Ivanov ◽  
Timophey Samsonov ◽  
Natalia Frolova ◽  
Maria Kireeva ◽  
Elena Povalishnikova
Keyword(s):  
Machine Learning ◽  
Water Regime ◽  
Cosine Similarity ◽  
Regime Type ◽  
Russian Plain ◽  
Regime Types ◽  
Objective Classification ◽  
Seasonal Runoff ◽  
Monthly Runoff

<p>Hydrological regime classification of Russian Plain rivers was always done by hand and by using subjective analysis of various characteristics of a seasonal runoff. Last update to this classification was made in the early 1990s. </p><p>In this work we make an attempt at using different machine learning methods for objective classification. Both clustering (DBSCAN, K-Means) and classification (XGBoost) methods were used to establish 1) if an established runoff types can be inferred from the data using supervised approach 2) similar clusters can be inferred from data (unsupervised approach). Monthly runoff data for 237 rivers of Russian Plain since 1945 and until 2016 were used as a dataset. </p><p>In a first attempt dataset was divided into periods of 1945-1977 and 1978-2016 in attempt to detect changes in river water regimes due to climate change. Monthly data were transformed into following features: annual and seasonal runoff, runoff levels for different seasons, minimum and maximum values of monthly runoff, ratios of the minimum and maximum runoff compared to yearly average and others. Supervised classification using XGBoost method resulted in 90% accuracy in water regime type identification for 1945-1977 period. Shifts in water regime types for southern rivers of Russian Plain rivers in a Don region were identified by this classifier.</p><p>DBSCAN algorithm for clustering was able to identify 6 major clusters corresponding to existing water regime types: Kola peninsula, North-East part of Russian Plain and polar Urals, Central Russia, Southern Russia, arid South-East, foothills and separately higher altitudes of the Caucasus. Nonetheless a better approach was sought due to intersections of a clusters because of the continuous nature of data. Cosine similarity metric was used as an alternative way to separate river runoff types, this time for each year. Yearly cutoff also allows us to make a timeline of water regime changes over the course of 70 years. By using it as an objective ground truth we plan to remake classification and clusterization made earlier and establish an automated way to classify changes in water regime over time.</p><p><strong>As a result, the following conclusions can be made</strong></p><ol><li>It’s possible to train an accurate classifier based on established water regime type and apply it to detect changes in water regime types over the course of time</li> <li>By applying the classifier to different periods of time we can detect a shift to “southern” type of water regime in the central area of Russian Plain</li> <li>Despite the highly continuous nature of data it seems possible to use cosine similarity metric to separate water regime types into zones corresponding to established ones</li> </ol><p><span><em>The study was supported by the Russian Science Foundation (grant No.19-77-10032) in methods </em><em>and Russian Foundation for Basic Research (grant No.18-05-60021</em>) </span><em><span>for analyses in Arctic region </span></em></p>

Establishment of Three Emergent Macrophytes Under Different Water Regimes

1999 ◽  
Vol 40 (3) ◽  
pp. 233-240 ◽  
Author(s):  
S. G. T. Giovannini ◽  
D. M. L. da Motta Marques
Keyword(s):  
Water Level ◽  
Aquatic Macrophytes ◽  
Water Regime ◽  
Emergent Macrophytes ◽  
Morphological Response ◽  
Water Regimes ◽  
Wetland Construction ◽  
Scirpus Californicus ◽  
Level 3 ◽  
Plot Design

The behavior of three emergent aquatic macrophytes under different water regimes was studied with the aim of achieving reconvertion of degraded wetlands and wetland construction for water quality improvement. Scirpus californicus, Typha subulata and Zizaniopsis bonariensis establishment was evaluated under a split plot design, in a factorial experiment with three levels of a water regime factor over a subsoil substratum. The stagnant 10±2 cm water level was best suited to T. subulata and Z. bonariensis development and S. californicus developed better at oscillating water level (3±2 cm) with flooding at 48 hour intervals. The morphological response variables (thickness and width at half length of the tallest leaf or stem per plant, height of tallest leaf or stem per plant, number of green leaves or stems and number of shoots per plant, and survival of propagules' original leaves or stems) were satisfactory descriptors to differentiate (p<0.1%) growth of above ground parts as related to water regimes and species. The three species did survive satisfactory in subsoil-like substratum under the tested water regimes. Mortality was in the worse case, 17.2%, 36.7%, and 9.4% for S. californicus, T. subulata, and Z. bonariensis, respectively. Although Z. bonariensis growth was very poor, only S. californicus and T. subulata could be indicated for planting under similar limiting conditions.

Waterlogging Tolerance of Lowland Tree Species of the South¹

1984 ◽  
Vol 8 (3) ◽  
pp. 136-149 ◽  
Author(s):  
Donal D. Hook
Keyword(s):  
Natural Water ◽  
Tree Species ◽  
Water Regime ◽  
Classification Systems ◽  
The South ◽  
Waterlogging Tolerance ◽  
Pertinent Literature ◽  
Flood Water ◽  
Water Regimes ◽  
Forest Managers

Abstract Many tree species in the South are adapted to periodic and/or prolonged soil waterlogging. However, artificial disturbances of natural water regimes sometimes cause flooding to occur at abnormal times or the flood water to be deeper and waterlogging longer in duration than is normal. As a consequence, it is difficult for forest managers to predict how a species will respond to such disturbances or to decide how to manage an area where the water regime has been significantly altered. This paper discusses some factors which influence the waterlogging tolerance of tree species, compiles several classification systems, indicates the pertinent literature, and offers a new relative waterlogging-tolerance rating for southern lowland tree species.

Occasional floods on the Russian Plain: types, frequency and conditions for the origin in the face of changing climate

2020 ◽  
Author(s):  
Maria Kireeva ◽  
Ekaterina Rets ◽  
Frolova Natalia ◽  
Gorbarenko Artem
Keyword(s):  
Discharge Rate ◽  
Geographic Location ◽  
Low Flow ◽  
Russian Plain ◽  
Amur River Basin ◽  
Russian Science ◽  
Unit Discharge ◽  
Flood Peak ◽  
Natural Zones ◽  
Flood Peaks

<p>In the last decade, floods on the rivers of Russia have become one of the most terrifying natural disasters. Among the catastrophic events, historical flood in Krymsk (2012), Amur River basin (2013), Veliky Ustyug (2016), floods in the Voronezh and Volgograd Region (2018) and Irkutsk and Novgorod Region (2019) can be called.</p><p>Floods on the rivers of the Russian Plain are divided into three main genetic types: rain, snowmelt and mixed. There is also a classification by seasons in which they can be observed. The seasonality of the flood peaks passage depends on the geographic location of the catchment and it’s local features. For most of the rivers of Central Russia, it was traditionally believed that occasional floods are mainly observed in the summer-autumn low flow period. In the summer, they are most often associated with intensive rainfall, and in the fall, with prolonged and drizzling rains. The influence of climate change on the processes of runoff formation has led to a transformation of the conditions for the occurrence of flood peaks and the need to rethink traditional ideas.</p><p>In this work, we analyzed the daily discharge time-series and highlighted flood peaks at 60 hydrological stations located in different natural zones of the European territory of Russia. Occasional flood peaks were divided into 5 classes, taking into account the time of their formation and genesis: a) thaw peaks during the winter low flow period, b) mixed peaks during the winter low flow period, c) mixed peaks during the rise of the main seasonal (snowmelt) wave, d) rain peaks during the decline of the main seasonal (snowmelt) wave, e) rain peaks during the summer-autumn low flow period.</p><p>The total number of peaks, the maximum peak discharge and its unit discharge rate, the beginning, end and duration of the flood peak, the total runoff volume of the flood, the relative stability of the low-flow period were estimated.</p><p>On average, the number of flood peaks in the rivers of the study area varies from 1 to 8 events per year. The greatest number of flood peaks is characteristic of the foothills of the Caucasus and the rivers of the Kola Peninsula, as well as the most western regions - the upper reaches of the Seversky Donets, Dnieper, and Western Dvina. The maximum unit discharges of rain floods on average is from 5 to 50 and more and thaw from 2 to 20 l/s*km<sup>2</sup>. The spatial pattern shows that higher unit discharges are typical for the windward western slopes of the hills, and relatively low ones are observed on the leeward, eastern slopes. In general, unit discharge rats increase from southwest to east, northeast.</p><p>In recent decades, the seasonality of flood peaks has changed significantly, they began to be observed in almost any period of the year, the number of events in the pre-flood period increased, as well as in the autumn period, at the time of transition to negative air temperatures.</p><p>The study was supported by the Russian Science Foundation grant No.19-77-10032</p>

Correlation between wave activities in different layers of the atmosphere

2020 ◽  
Author(s):  
Konstantin Ratovsky ◽  
Irina Medvedeva ◽  
Anna Yasyukevich ◽  
Boris Shpynev ◽  
Denis Khabituev
Keyword(s):  
Correlation Coefficients ◽  
Internal Gravity Wave ◽  
Basic Research ◽  
Temporal Variations ◽  
Wave Activity ◽  
Electron Content ◽  
Russian Science ◽  
Period Range ◽  
Total Electron ◽  
Mesopause Temperature

<p>We study the correlation between wave activities in different layers of the atmosphere. The variability of the measured characteristic in the range of internal gravity wave periods is used as a proxy of wave activity. In the case of ground-based measurements, we consider temporal variations with periods less than ~ 6 hours; while in the case of satellite measurements we take into account spatial variations with periods less than ~ 1000 km. The wave activity is calculated as the standard deviation of variations in the indicated period range with averaging over one day. The aim of the study is to detect a correlation between day-to-day variations of wave activity in different layers of the atmosphere. Correlation coefficients are calculated for various intervals from one month to one year. Correlation analysis reveals the potential relationship between wave phenomena in the stratosphere, mesosphere and ionosphere. The study uses the following characteristics. The ionospheric characteristics are the peak electron density from the Irkutsk ionosonde (52.3 N, 104.3 E) and the total electron content from the Irkutsk GPS receiver. The characteristic of the mesosphere is the mesopause temperature from spectrometric measurements of the OH emission (834.0 nm, band (6-2)) near Irkutsk (51.8 N, 103.1 E, Tory). The stratospheric characteristic is the vertical gas velocity at 1 hPa from the ERA-Interim reanalysis (apps.ecmwf.int/datasets/data/).</p><p>This study was supported by the Grant of the Russian Science Foundation (Project N 18-17-00042). The observational results were obtained using the equipment of Center for Common Use «Angara» http: //ckp-rf.ru/ckp/3056/ within budgetary funding of Basic Research program II.12.</p>

Effects of organic horizon removal, ash, watering regime, and shading on red pine seedling emergence

1996 ◽  
Vol 26 (3) ◽  
pp. 422-427 ◽  
Author(s):  
David G. Herr ◽  
Luc C. Duchesne
Keyword(s):  
Water Regime ◽  
Prescribed Burning ◽  
Seedling Emergence ◽  
High Water ◽  
Jack Pine ◽  
Red Pine ◽  
The Other ◽  
Organic Horizon ◽  
Water Regimes

Soil monoliths were used to determine the effects of organic horizon removal, ash, water regime, and shading on red pine (Pinusresinosa Ait.) seedling emergence. Soil monoliths were collected from a jack pine (Pinusbanksiana Lamb.) stand and taken to the laboratory for prescribed burning, leading to 25%, 50%, 75%, and 100% organic horizon removal. One half of each monolith contained ash generated from burning, while the other half was kept ash-free. Each half of every monolith was sown with red pine seeds. The monoliths were then placed in a greenhouse and, in separate experiments, were exposed to different water regimes and shade regimes. Red pine seedling emergence was highest under high water regimes, increased shade regimes, and increased organic horizon removal. Seedling emergence was reduced by the presence of ash.

Understanding Water Regime Formation—A Research Framework with Lessons from Europe

2008 ◽  
Vol 8 (4) ◽  
pp. 117-140 ◽  
Author(s):  
Stefan Lindemann
Keyword(s):  
Water Regime ◽  
River Basins ◽  
Regime Theory ◽  
Regime Formation ◽  
International Rivers ◽  
Research Framework ◽  
High Conflict ◽  
Water Cooperation ◽  
International Water ◽  
International River

International river basins are mostly characterized by upstream-downstream externalities that involve asymmetric incentives to cooperate and, therefore, suggest a high conflict potential between riparian states. However, with more than 400 river basin treaties, cooperation along international rivers by far outweighs water-related conflicts. The abundance of international water cooperation despite the odds is puzzling and has so far received little systematic attention. Against this background, I develop a research framework that draws on international regime theory and combines power, interest, knowledge and contextbased approaches to water regime formation. In a second step, I probe the plausibility of my framework in two case studies on international water cooperation in the Rhine and Elbe river basins. The empirical findings suggest that there is no “one-answer-fits-all” in trying to explain water regime formation. While power-based approaches are of limited explanatory value, a thorough understanding of cooperation along the two international rivers requires the combination of interest, knowledge and context-based arguments.

scholarly journals Effect of Moisture Regimes on Water use Efficiency of Rabi / Summer Groundnut Genotypes in Northern Transition Zone of Karnataka

2021 ◽  
Author(s):  
R. Naveen Kumar ◽  
R.H. Patil ◽  
B.S. Yenagi ◽  
S. Sagar Dhage
Keyword(s):  
Water Use Efficiency ◽  
Transition Zone ◽  
Water Use ◽  
Irrigation Water ◽  
Water Regime ◽  
Moisture Stress ◽  
Irrigation Regime ◽  
Water Regimes ◽  
Pod Yield ◽  
Use Efficiency

Background: During rabi / summer irrigation water is a scarce resource, but crop needs more water due to non-rainy season and warmer climate. Hence, a field experiment was conducted during rabi / summer season of 2016-17 to study the effect of irrigation water regimes on water use efficiency (WUE) of groundnut genotypes in Northern Transition Zone of Karnataka. Methods: This field study comprised of four main plots as water regimes viz. I1: (control) seven irrigations at 15 days interval from sowing to 105 DAS, I2: Stress at pegging stage; withdrawal of one irrigation between 45 - 60 DAS, I3: Stress at pegging and pod filling stage; withdrawal of two irrigations between 45-75 DAS, I4: Stress at pegging, pod filling and kernel development stage; withdrawal of four irrigations from 45-105 DAS and four genotypes as sub plots viz. G1: Dh-86, G2: Dh-101, G3: K-9 and G4: G2-52. Treatments were replicated thrice and laid out in split plot design. Result: Among the water regime, I2 recorded significantly higher WUE (6.2 kg ha-1 mm-1) followed by I1 (control; 5.5 kg ha-1 mm-1). Water regime I2 also recorded significantly higher pod yield and haulm yield (2,857 kg ha-1 4,648 kg ha-1, respectively) along with other yield attributes, but was found at par with control (I1). This study showed that WUE as well as yield of rabi / summer groundnut can be enhanced if crop was exposed only to mild stress by skipping an irrigation at pegging stage out of total seven irrigations. Among the genotypes, Dh-86 with 2,375 kg ha-1 of pod yield performed significantly better over others like Dh-101 (2,215 kg ha-1), K-9 (2,048 kg ha-1) and G2-52 (1,880kg ha-1) suggesting differential response to moisture stress, thus choice of moisture stress tolerant genotypes is equally important to enhance WUE. Interaction between irrigation regime and genotypes showed that Dh-86 (G1) with I2 irrigation regime recorded significantly higher WUE (6.9 kg ha-1 mm-1), pod yield (3,168 kg ha-1) and net return (Rs. 95,655 ha-1) and was found at par with full irrigation regime (I1). 

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