Changes in sediment transport as a key factor in the transformation of branches structure and braided pattern channel types: case study of unregulated (Northern Dvina) and regulated (Vistula) rivers

<p>The paper presents a new approach to the evaluation of braided rivers structure, mainly with the use of quantitative methods proposed by Alexeevsky and Chalov. Comparative study was conducted in relation to lowland braided reaches of large rivers, the Vistula (in Poland) and Northern Dvina (in Russia). The authors point at a directe relationship between braided channel types and sediment supply.</p

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R2, RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions

A Stacking Ensemble Learning Model for Monthly Rainfall Prediction in the Taihu Basin, China

The prediction of monthly rainfall is greatly beneficial for water resources management and flood control projects. Machine learning (ML) techniques, as an increasingly popular approach, have been applied in diverse climatic regions, showing their respective superiority. On top of that, the ensemble learning model that synthesizes the advantages of different ML models deserves more attention. In this study, an ensemble learning model based on stacking approach was proposed. Four prevalent ML models, namely k-nearest neighbors (KNN), extreme gradient boosting (XGB), support vector regression (SVR), and artificial neural networks (ANN) are taken as base models. To combine the outputs from the base models, the weighting algorithm is used as second-layer learner to generate predictions. Large-scale climate indices, large-scale atmospheric variables, and local meteorological variables were used as predictors. R2, RMSE and MAE, were used as evaluation metrics. The results show that the performance of base models varied among the nine stations in the Taihu Basin, while the stacking approach generally performed better than the four base models. The stacking model showed better performance in spring and winter than in summer and autumn. During wet months, the accuracy of model prediction varied more significantly. On the whole, based on performance evaluation measures, it is concluded that the proposed stacking ensemble multi-ML model can provide a flexible and reasonable prediction framework applicable to other regions

Dissolved organic carbon and total dissolved nitrogen concentration of Lena River water from 26.08.2021 (#488) to 16.08.2022 (#612)

We collected water samples from the river surface in the center of the Olenekskaya Channel near Samoylov Island using a pre-rinsed HDPE 1 L bottle. During the open water period (June to October), water was sampled from a small boat, and during ice-covered period (November to May), through a hole drilled through the river ice. Some samples during the river ice break-up (between May and June) as well as some samples during the ice freeze-up in October were taken from the shore due to the inaccessibility of a more centered location on the river channel. For DOC and TDN, the sample was filtered through a 0.45 μm cellulose acetate filter which had been rinsed with 20 mL sample water. Samples were filled into a pre-rinsed 20 mL glass vial and acidified with 25 μL HCl Suprapur (10 M) and stored in the dark at 4°C. After transport, samples were analyzed at Lomonosov Moscow State University in Moscow, Russia (MSU). DOC and TDN concentrations were analyzed using a TOPAZ NC manufactured by Informanalitika LLC (Russia). For analysis, ISO 11905-2:1997 for nitrogen and ISO 8245:1999 for carbon was followed. Three replicate measurements of each sample were averaged and three standards (5,15, and 100 mg L-1) as well as blanks (Milli-Q water) were used to ensure high accuracy of the measurements

INTEGRATING MULTI-SCALE DATA FOR THE ASSESSMENT OF WATER AVAILABILITY AND QUALITY IN THE KHARAA - ORKHON - SELENGA RIVER SYSTEM

The environmental and socio-enonomic impacts of water pollution are particularly severe in regions with relatively limited water resources [WWAP, 2012]. Water quantity and quality are closely interlinked aspects which are relevant for surface water ecology, water use, and integrated management approaches. However, an intensive monitoring of both is usually prohibitive for very large areas, particularly if it includes the investigation of underlying processes and causes. For the Kharaa - Orkhon - Selenga River system, this paper combines results from the micro (experimental plots, individual point data), meso (Kharaa River Basin) and macro (Selenge River Basin) scales. On the one hand, this integration allows an interpretation of existing data on surface water quantity and quality in a wider context. On the other hand, it empirically underpins the complimentary character of intensive monitoring in selected model regions with more extensive monitoring in larger areas

Glaciological, hydrological, meteorological observations and isotopes sampling results during 2007-2017 at Djankuat Glacier Station in the North Caucasus, Russia

The study presents a dataset on the long-term complex glaciological, hydrological, meteorological observations and isotopes sampling in an extremely underreported alpine zone of the North Caucasus. The Djankuat research basin is of 9.1 km2, situated on elevations between 2500 – 4000 m, by 30% covered with glaciers. The biggest in the basin Djankuat glacier was chosen as representative of the central North Caucasus during the International Hydrological Decade and is one of 30 'reference' glaciers in the world that have annual mass-balance series longer than 50 years (Zemp et al, 2009). The dataset covers 2007-2017 and contains the result of yearly measurements of snow thickness and density; dynamics of snow and ice melting; measurements of water runoff, conductivity, turbidity, temperature, δ18O, δ2H on the main gauging station (774 samples in sum) with a one-hour or several-hours step depending on the parameter; data on δ18O and δ2H sampling of liquid precipitation, snow, ice, firn, groundwater in different parts of the watershed regularly in time during the melting season (485 samples in sum); precipitation amount, air temperature, relative humidity, shortwave incoming and reflected radiation, longwave downward and upward radiation, atmospheric pressure, wind speed and direction – measured on several automatic weather stations within the basin with 15 min – one-hour step; gradient meteorological measurements to estimate turbulent fluxes of heat and moisture, measuring three components of wind speed at a frequency of 10 hertz to estimate the turbulent impulse heat fluxes over the glacier surface by the eddy covariance method. The observations were held during ablation period June-October and were interrupted in winter. The dataset will be further updated. The dataset can be useful for developing and verifying hydrological, glaciological and meteorological models for high elevation territories, to study impact of climate change on hydrology of mountain regions, using isotopic and hydrochemical approaches to study mountain territories. -- This work was supported by the Russian Foundation for Basic Research (project No. 16-35-60042 - in part of hydrological observations, project No. 18-05-00420 - in part of glaciological observations, 17-05-00771 - in part of meteorological observations, project No. 18-05-60272 - in part of isotope analysis)