Abstract
This chapter describes a new methodology for forecasting ice-jam flooding. Detailed steps of the development of the methodology is provided first followed by spreadsheet exercises, which are continuations of the spreadsheet exercises from previous chapters. The exercises serve to reinforce the concepts underlying the forecasting approach by presenting simplified components of the method in a piecewise manner to progressively build understanding of the method. These exercises can also be a basis for the reader’s own ice-jam flood forecasting case studies. Modelling exercises conclude the chapter which build on the previous chapter’s modelling exercise and provide additional practise in the use of the RIVICE model, embedded now in the stochastic modelling framework.
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References
Berry, P. L. J. (2016). An economic assessment of on-farm surface water retention systems. MES (Master in Environment and Sustainability) thesis submitted to the School of Environment and Sustainability, University of Saskatchewan. https://harvest.usask.ca/handle/10388/7646
Gringorten, I. I. (1963). A plotting rule for extreme probability paper. Journal of Geophysical Research, 68(3), 813–814.
Gumbel, E. J. (1941). Probability-interpretation of the observed return-periods of floods. Transactions of the American Geophysical Union, 22(3), 836–850.
Lindenschmidt, K.-E., & Li, Z. (2019). Radar scatter decomposition to differentiate between running ice accumulations and intact ice covers along rivers. Remote Sensing, 11, 307. https://doi.org/10.3390/rs11030307.
Lindenschmidt, K.-E., Rokaya, P., Das, A., Li, Z., & Richard, D. (2019). A novel stochastic modelling approach for operational real-time ice-jam flood forecasting. Journal of Hydrology, 575, 381–394. https://doi.org/10.1016/j.jhydrol.2019.05.048.
Rokaya, P. (2018). Impacts of climate and regulation on ice-jam flooding of northern rivers and their inland deltas. Ph.D. thesis submitted to the School of Environment and Sustainability, University of Saskatchewan. https://harvest.usask.ca/handle/10388/9207
Rokaya, P., Peters, D., Bonsal, B. Wheater, H. & Lindenschmidt, K.-E. (2019). Modelling the effects of flow regulation on ice-affected backwater staging in a large northern river. River Research and Applications, 35, 587–600. https://dx.doi.org/10.1002/rra.3436
Turkann, N. (2014a). MathCad script “GEV-Fit.xmcd”: Generalized extreme value (GEV) parameter estimation using method of moments. https://community.ptc.com/t5/PTC-Mathcad/GEV-Fit-xmcd/td-p/449867. Accessed 18 October 2018.
Turkann, N. (2014b). MathCad script “GEV-MLE-Fit.xmcd”: Generalized extreme value (GEV) parameter estimation using maximum likelihood (MLE). https://community.ptc.com/t5/PTC-Mathcad/GEV-MLE-Fit-xmcd/m-p/449888. Accessed 18 October 2018.
Warkentin, A. A. (1999). Hydrometeorologic parameter generated floods for design purposes. Winnipeg: Water Resources Branch, Manitoba Department of Natural Resources, Manitoba Water Resources. http://www.ijc.org/rel/pdf/alfsflood.pdf.
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Lindenschmidt, KE. (2020). Stochastic Modelling Framework. In: River Ice Processes and Ice Flood Forecasting. Springer, Cham. https://doi.org/10.1007/978-3-030-28679-8_8
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DOI: https://doi.org/10.1007/978-3-030-28679-8_8
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