Potential of RADARSAT-1 for the monitoring of river ice: Results of a case study on the Athabasca River at Fort McMurray, Canada

2009 ◽  
Vol 55 (2) ◽  
pp. 238-248 ◽  
Author(s):  
K.D. Unterschultz ◽  
J. van der Sanden ◽  
F.E. Hicks
Keyword(s):  
River Ice ◽  
Athabasca River ◽  
Fort Mcmurray

Observations of ice jam release waves on the Athabasca River near Fort McMurray, Alberta

2007 ◽  
Vol 34 (4) ◽  
pp. 473-484 ◽  
Author(s):  
T Kowalczyk Hutchison ◽  
F E Hicks
Keyword(s):  
Wave Propagation ◽  
Wave Speed ◽  
Flood Forecasting ◽  
River Ice ◽  
Historical Records ◽  
Athabasca River ◽  
Fort Mcmurray ◽  
Release Wave ◽  
Ice Jam ◽  
High Release

This paper presents an investigation of all documented ice jam release events for the Athabasca River at Fort McMurray, Alberta. A review of the historical records indicates that release waves in excess of 3 m and propagation speeds of 4–5 m/s are not uncommon. Numerous occurrences of increases in wave speed and magnitude suggest that temporary stalling of ice runs may be a significant factor in release event propagation. Detailed measurements of ice jam release events in 2001–2003, including most notably a 4.3 m high release wave measured in 2002, provide unprecedented data describing ice jam release wave propagation and suggest that continued propagation of a portion of the release wave downstream of a reformed jam could be a significant factor in immediate re-release.Key words: ice jam, floods, flood forecasting, river ice, ice jam release.

scholarly journals Integrating intensity and context for improved supervised river ice classification from dual-pol Sentinel-1 SAR data 

2021 ◽  
Author(s):  
Sophie de Roda Husman ◽  
Joost J. van der Sanden ◽  
Stef Lhermitte ◽  
Marieke A. Eleveld
Keyword(s):  
Texture Features ◽  
Open Water ◽  
Recursive Feature Elimination ◽  
River Ice ◽  
Athabasca River ◽  
Classifier Performance ◽  
Sar Data ◽  
Polarized Surface ◽  
Occurrence Matrix

<p>River ice is a major contributor to flood risk in cold regions due to the physical impediment of flow caused by ice jamming. Although a variety of classifiers have been developed to distinguish ice types using HH or VV intensity of SAR data, mostly based on data from RADARSAT-1 and -2, these classifiers still experience problems with breakup classification, because meltwater development causes overlap in co-polarization backscatter intensities of open water and sheet ice pixels.</p><p>In this study, we develop a Random Forest classifier based on multiple features of Sentinel-1 data for three main classes generally present during breakup: rubble ice, sheet ice and open water, in a case study over the Athabasca River in Canada. For each ice stage, intensity of the VV and VH backscatter, pseudo-polarimetric decomposition parameters and Grey Level Co-occurrence Matrix texture features were computed for 70 verified sample areas. Several classifiers were developed, based on i) solely intensity features or on ii) a combination of intensity, pseudo-polarimetric and texture features and each classifier was evaluated based on Recursive Feature Elimination with Cross-Validation and pair-wise correlation of the studied features.</p><p>Results show improved classifier performance when including GLCM mean of VV intensity, and VH intensity features instead of the conventional classifier based solely on intensity. This highlights the importance of texture and intensity features when classifying river ice. GLCM mean incorporates spatial patterns of the co-polarized intensity and sensitivity to context, while VH intensity introduces cross-polarized surface and volume scattering signals, in contrast to the commonly used co-polarized intensity.</p><p>We conclude that the proposed method based on the combination of texture and intensity features is suitable for and performs well in physically complex situations such as breakup, which are hard to classify otherwise. This method has a high potential for classifying river ice operationally, also for data from other SAR missions. Since it is a generic approach, it also has potential to classify river ice along other rivers globally.  </p>

scholarly journals Short-Term River Flow Forecasting Framework and Its Application in Cold Climatic Regions

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3049
Author(s):  
Chiara Belvederesi ◽  
John Albino Dominic ◽  
Quazi K. Hassan ◽  
Anil Gupta ◽  
Gopal Achari
Keyword(s):  
River Flow ◽  
Cold Weather ◽  
Difference Model ◽  
Short Term ◽  
Athabasca River ◽  
Explanatory Variables ◽  
Climatic Regions ◽  
River Flow Forecasting ◽  
Modelling Techniques

Catchments located in cold weather regions are highly influenced by the natural seasonality that dictates all hydrological processes. This represents a challenge in the development of river flow forecasting models, which often require complex software that use multiple explanatory variables and a large amount of data to forecast such seasonality. The Athabasca River Basin (ARB) in Alberta, Canada, receives no or very little rainfall and snowmelt during the winter and an abundant rainfall–runoff and snowmelt during the spring/summer. Using the ARB as a case study, this paper proposes a novel simplistic method for short-term (i.e., 6 days) river flow forecasting in cold regions and compares existing hydrological modelling techniques to demonstrate that it is possible to achieve a good level of accuracy using simple modelling. In particular, the performance of a regression model (RM), base difference model (BDM), and the newly developed flow difference model (FDM) were evaluated and compared. The results showed that the FDM could accurately forecast river flow (ENS = 0.95) using limited data inputs and calibration parameters. Moreover, the newly proposed FDM had similar performance to artificial intelligence (AI) techniques, demonstrating the capability of simplistic methods to forecast river flow while bypassing the fundamental processes that govern the natural annual river cycle.

Midwinter breakup and jamming on the upper Saint John River: a case study

2003 ◽  
Vol 30 (1) ◽  
pp. 77-88 ◽  
Author(s):  
Spyros Beltaos ◽  
Sayed Ismail ◽  
Brian C Burrell
Keyword(s):  
Climate Change ◽  
Environmental Changes ◽  
Flood Damage ◽  
River Ice ◽  
Ice Thickness ◽  
Ice Jams ◽  
Damage Reduction ◽  
Saint John

Changing climates will likely result in more frequent midwinter ice jams along many Canadian rivers, thereby increasing the likelihood of flood damage and environmental changes. Therefore, the possibility of more frequent ice jams has to be considered during the planning of flood damage reduction measures, the design of waterway structures, and the enactment of measures to protect the environment. As a case study of midwinter jamming, four winter breakup and jamming events that occurred along an upper stretch of the Saint John River during the 1990s are described and the implications of similar midwinter jamming are discussed.Key words: breakup, river ice, climate change, ice jamming, ice thickness, winter, winter thaw.

scholarly journals Modeling River Ice Breakup Dates by k-Nearest Neighbor Ensemble

Water ◽  
2020 ◽  
Vol 12 (1) ◽  
pp. 220
Author(s):  
Wei Sun ◽  
Ying Lv ◽  
Gongchen Li ◽  
Yumin Chen
Keyword(s):  
Nearest Neighbor ◽  
Distance Functions ◽  
River Ice ◽  
K Nearest Neighbor ◽  
Athabasca River ◽  
Ice Breakup ◽  
Promising Tool ◽  
Improved Performance ◽  
Chebychev Distance ◽  
Leave One Out

Forecasting of river ice breakup timing is directly related to the local ice-caused flooding management. However, river ice forecasting using k-nearest neighbor (kNN) algorithms is limited. Thus, a kNN stacking ensemble learning (KSEL) method was developed and applied to forecasting breakup dates (BDs) for the Athabasca River at Fort McMurray in Canada. The kNN base models with diverse inputs and distance functions were developed and their outputs were further combined. The performance of these models was examined using the leave-one-out cross validation method based on the historical BDs and corresponding climate and river conditions in 1980–2015. The results indicated that the kNN with the Chebychev distance functions generally outperformed other kNN base models. Through the simple average methods, the ensemble kNN models using multiple-type (Mahalanobis and Chebychev) distance functions had the overall optimal performance among all models. The improved performance indicates that the kNN ensemble is a promising tool for river ice forecasting. The structure of optimal models also implies that the breakup timing is mainly linked with temperature and water flow conditions before breakup as well as during and just after freeze up.

River-ice blasting and explosive weight optimization for ice-flooding mitigation: A case study

2020 ◽  
Vol 177 ◽  
pp. 103103
Author(s):  
Hui Fu ◽  
Xinlei Guo ◽  
Peng Wu ◽  
Tao Wang ◽  
Yongxin Guo ◽  
...  
Keyword(s):  
River Ice ◽  
Weight Optimization

Forecasting breakup water levels at Fort McMurray, Alberta, using multiple linear regression

2006 ◽  
Vol 33 (9) ◽  
pp. 1227-1238 ◽  
Author(s):  
C Mahabir ◽  
F E Hicks ◽  
C Robichaud ◽  
A Robinson Fayek
Keyword(s):  
Linear Regression ◽  
Multiple Linear Regression ◽  
Meteorological Data ◽  
Linear Regression Analysis ◽  
Statistical Modelling ◽  
Water Levels ◽  
River Ice ◽  
Fort Mcmurray ◽  
Ice Jam ◽  
Ice Breakup

Spring breakup on northern rivers can result in ice jams that present severe flood risk to adjacent communities. Such events can occur extremely rapidly, leaving little or no advanced warning to residents. Fort McMurray, Alberta, is one such community, and at present no forecasting model exists for this site. Many of the previous studies regarding ice jam flood forecasting methods, in general, cite the lack of a comprehensive database as an obstacle to statistical modelling. This paper documents the development of an extensive database containing 106 variables, and covering the period from 1972 to 2004, that was created for ice jam forecasting on the Athabasca River. Through multiple linear regression analysis, equations were developed to model the maximum water level during spring breakup. The optimal model contained a combination of hydrological and meteorological data collected from early fall until the day before river ice breakup. The number of historical years of data, rather than the scope of variables, was found to be the major limitation in verifying the results presented in this study.Key words: river ice, breakup jam, multiple linear regression.

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