Phreatic Water Surface Profiles along Ice Jams – An Experimental Study

1996 ◽  
Vol 27 (3) ◽  
pp. 185-201 ◽  
Author(s):  
Raafat G. Saadé ◽  
Semaan Sarraf

In Northern Regions, the formation of ice jams along many rivers is a common phenomena. These ice jams may occur during the freeze-up and more importantly during the spring break-up period. Ice jams in general have considerable effects on the water levels because they alter the water surface profile for stretches of tens of kilometers along the rivers. As a consequence, water levels increase significantly upstream of the ice jam and result in the flooding of towns situated along the river banks. Knowledge of the water levels within an ice jam can be used to estimate many parameters that are difficult to measure and observe. Examples of such parameters are the local and global ice jam resistance to the flow, and forces acting within an ice jam. While ice jams are notorious causes of serious problems in hydraulic engineering, very little engineering methodology exists to deal with such problems. In this paper, the results of a laboratory study aimed at investigating the development of the water surface profile along an ice jam that is lodged in place, are analyzed and presented. A rectangular flume with a horizontal bed was used for the experiments. Twelve experiments carried out under different geometrical, hydrodynamic and ice conditions, were analysed. A simulated floating ice cover was used to arrest the downstream transport of the ice floes, forming the ice jams. The experiments indicate two types of ice jams, those that are floating and others that are lodged at one or more locations along their length. The phreatic water level along a floating ice jam is up to 0.92 the ice jam thickness. This is not true when an ice jam is lodged in place. Different experiments have shown that the water surface profile along a lodged ice jam follows similar tendencies regardless of the geometry, ice floe size distribution and hydrodynamic conditions. It was found that the phreatic water level varies linearly from the trailing edge of the ice jam up to approximately 90% of its length downstream. Towards the remaining part of the jam's length the water level follows a cubic polynomial line.


2021 ◽  
Vol 1116 (1) ◽  
pp. 012112
Author(s):  
Avdhesh Kumar Sharma ◽  
Prashant Kumar Dixit ◽  
Shashank Srivastava


2021 ◽  
Author(s):  
Radosław Szostak ◽  
Przemysław Wachniew ◽  
Mirosław Zimnoch ◽  
Paweł Ćwiąkała ◽  
Edyta Puniach ◽  
...  

<p>Unmanned Aerial Vehicles (UAVs) can be an excellent tool for environmental measurements due to their ability to reach inaccessible places and fast data acquisition over large areas. In particular drones may have a potential application in hydrology, as they can be used to create photogrammetric digital elevation models (DEM) of the terrain allowing to obtain high resolution spatial distribution of water level in the river to be fed into hydrological models. Nevertheless, photogrammetric algorithms generate distortions on the DEM at the water bodies. This is due to light penetration below the water surface and the lack of static characteristic points on water surface that can be distinguished by the photogrammetric algorithm. The correction of these disturbances could be achieved by applying deep learning methods. For this purpose, it is necessary to build a training dataset containing DEMs before and after water surfaces denoising. A method has been developed to prepare such a dataset. It is divided into several stages. In the first step a photogrammetric surveys and geodetic water level measurements are performed. The second one includes generation of DEMs and orthomosaics using photogrammetric software. Finally in the last one the interpolation of the measured water levels is done to obtain a plane of the water surface and apply it to the DEMs to correct the distortion. The resulting dataset was used to train deep learning model based on convolutional neural networks. The proposed method has been validated on observation data representing part of Kocinka river catchment located in the central Poland.</p><p>This research has been partly supported by the Ministry of Science and Higher Education Project “Initiative for Excellence – Research University” and Ministry of Science and Higher Education subsidy, project no. 16.16.220.842-B02 / 16.16.150.545.</p>



1990 ◽  
Vol 17 (5) ◽  
pp. 675-685 ◽  
Author(s):  
Harold S. Belore ◽  
Brian C. Burrell ◽  
Spyros Beltaos

In Canada, flooding due to the rise in water levels upstream of an ice jam, or the temporary exceedance of the flow and ice-carrying capacity of a channel upon release of an ice jam, has resulted in the loss of human life and extensive economic losses. Ice jam mitigation is a component of river ice management which includes all activities carried out to prevent or remove ice jams, or to reduce the damages that may result from an ice jam event. This paper presents a brief overview of measures to mitigate the damaging effects of ice jams and contains a discussion on their application to Canadian rivers. Key words: controlled ice breakup, flood control, ice jams, ice management, river ice.



2021 ◽  
Author(s):  
Fatemehalsadat Madaeni ◽  
Karem Chokmani ◽  
Rachid Lhissou ◽  
Saeid Homayuni ◽  
Yves Gauthier ◽  
...  

Abstract. In cold regions, ice-jam events result in severe flooding due to a rapid rise in water levels upstream of the jam. These floods threaten human safety and damage properties and infrastructures as the floods resulting from ice-jams are sudden. Hence, the ice-jam prediction tools can give an early warning to increase response time and minimize the possible corresponding damages. However, the ice-jam prediction has always been a challenging problem as there is no analytical method available for this purpose. Nonetheless, ice jams form when some hydro-meteorological conditions happen, a few hours to a few days before the event. The ice-jam prediction problem can be considered as a binary multivariate time-series classification. Deep learning techniques have been successfully applied for time-series classification in many fields such as finance, engineering, weather forecasting, and medicine. In this research, we successfully applied CNN, LSTM, and combined CN-LSTM networks for ice-jam prediction for all the rivers in Quebec. The results show that the CN-LSTM model yields the best results in the validation and generalization with F1 scores of 0.82 and 0.91, respectively. This demonstrates that CNN and LSTM models are complementary, and a combination of them further improves classification.



2021 ◽  
Vol 147 (5) ◽  
pp. 04021011
Author(s):  
Mohammadreza Jalili Ghazizadeh ◽  
Hamideh Fallahi ◽  
Ebrahim Jabbari


1980 ◽  
Vol 106 (2) ◽  
pp. 133-139
Author(s):  
Walter W. Hu






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