Draft:Effects of Lakes on Floods in Canada

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Effects of Lakes on Floods in Canada[edit]

The abundance of lakes in Canada is unique in the world, with nearly 900,000 lakes covering more than 10 hectares. This unique abundance is due to Canada’s glacial history, with the vast majority of the country covered by a massive ice sheet during the last ice age. Canadian lakes represent approximately 62% of the world's 1.42 million lakes[1]. Lake levels influence many aspects of our lives, such as water resource management, and environmental sustainability. Water levels in lakes are highly susceptible to climatic fluctuations, which have a significant impact on both the volume and purity of available water resources, as well as the ecological health of the watershed. Accurate lake level predictions have therefore become critical for effective water resource management in an era of increasing climate variability and changing hydrological patterns. Indeed, water levels in lakes are highly susceptible to climatic fluctuations, which have a significant impact on both the volume and purity of available water resources, as well as the ecological health of the watershed. The expected increase in both the frequency and intensity of extreme weather events may threaten the natural quality of water, emphasising the critical need for well-planned strategies for managing water resources and maintaining water quality[2] [3].

(a) Lake area density (limnicity) calculated as percent area covered by lakes within a 25 km radius. (b) Average depth of all lakes within a 25 km radius, weighted by their partial area within that radius. Both maps include reservoirs from the Global Reservoir and Dam (GRanD) database.[4][1].
First ten countries with the most lakes[5]
Rank Country Number of Lakes
1  Canada 879,800
2  Russia 201,200
3  United States 102,500
4  China 23,800
5  Sweden 22,600
6  Brazil 20,900
7  Norway 20,000
8  Argentina 13,600
9  Kazakhstan 12,400
10  Australia 11,400

Role of Lakes in Hydrological Cycle[edit]

Saguenay flood

Lakes and reservoirs influence the timing and magnitude of river flow, acting as buffers that attenuate and delay flow, and are therefore essential components of hydrological models, particularly in the context of large-scale flood modelling[6], which can be used to produce flood maps at the watershed scale[7]. Floods are the most costly natural hazard throughout the world[8][9] and it is therefore critically important to be able to accurately predict their impact with flood maps. For example, in Quebec, there were severe consequences of the spring floods in 2017 and 2019, which caused widespread damage and evacuations and therefore had severe societal and economic consequences. The 1996 Saguenay flood is also one of Quebec's most notable floods, encompassing several exceptional occurrences, including lake breaching[10]. The Saguenay flood killed ten people, forced thousands of people to evacuate, and caused $1.5 billion in damage[11]. Heavy precipitation also inundated the Richelieu River and Lake Champlain regions in 2011, resulting in widespread evacuations and millions of dollars in damage[12]. Understanding and predicting lake levels is thus not just an academic exercise, but a necessity for protecting people, property, and ecosystems.

Large-Scale Flood Modeling[edit]

It is critical to understand the concept of Large-Scale Flood Modeling (LSFM), as lake levels have a significant impact on such models and the overall flood mapping process. LSFM is important in hydrological studies and disaster risk management because it can predict and analyze flood patterns and their potential consequences over large geographic areas such as entire drainage basins, regions, or even countries. This type of modeling is critical for understanding how various water bodies, such as lakes, rivers, and reservoirs, interact during flood events[13][14][7].

Lakes are natural moderators in watersheds, and their levels have a significant impact on downstream flow dynamics. As a result, accurate predictions of lake water levels are critical to LSFM, serving as critical boundary conditions that influence flood extents and behaviors[6][7]. A thorough understanding of LSFM enables a more comprehensive understanding of the significance of lake level dynamics in the larger context of hydrological modeling and flood risk assessment[9].

Understanding lake level dynamics is critical for making accurate predictions about flooding scenarios. Predicting lake levels is important in flood modeling because they serve as upstream or downstream boundary conditions in hydrological models[7][14]. Accurate lake level prediction can improve understanding of potential flood extents, depths, and durations, particularly in complex watershed systems.

Lidar (high-resolution topographic data)

In hydrological modeling, particularly for flood prediction, accurate lake level data is important. This integration, however, faces challenges, particularly the frequently limited availability of detailed river bathymetry data, which is critical for precise flood hazard mapping. Choné [7] fills this void by investigating the use of Light Detection and Ranging (LiDAR) technology as a substitute method. In situations where traditional bathymetric data is unavailable, LiDAR's high-resolution topographic data provides a viable solution for flood hazard mapping. Additionally, Neal[13] contribute to this field, emphasizing the importance of estimating river channel bathymetry in large-scale flood models, particularly in data-scarce areas. They argue that gradually varied flow theory outperforms conventional methods, demonstrating how this approach significantly improves flood modeling accuracy and reduces biases in a large-scale study in Mozambique. Such advancements are critical for determining the impact of lake levels on flood models, allowing for the creation of more accurate flood hazard maps even in the absence of traditional data sources. Choné's research[7] emphasizes the importance of leveraging advanced technologies such as LiDAR to improve the predictive capabilities of hydrological models, particularly in complex terrains where data scarcity is a significant challenge. Incorporating such innovative methodologies is critical for developing more reliable and comprehensive flood risk assessments, resulting in more informed water resource management and disaster preparedness strategies.

Following the devastating large floods in Quebec in 2017 and 2019, the Quebec government launched the "Info-Crue" project, which aims to revise and update flood maps throughout the province. This project highlights the importance of taking a comprehensive and systematic approach to flood risk management, especially given the region's extensive river networks. With over 25,000 km of rivers to consider, as well as several ungauged lakes, LSFM is playing a central role in the Info-Crue initiative[7].

The Importance of Lake Levels in Hydraulic Modelling and Flood Prediction[edit]

Accurate lake level predictions are critical for hydraulic modeling and flood prediction because they serve as the boundary conditions that define the starting and ending points in hydraulic simulations. These conditions are critical for determining how water flows and accumulates, which affects the accuracy of flood models. Lake levels, in particular, are important because they determine the volume of water entering or leaving a system, influencing downstream water flow and flood risk[15][16].

Recent improvements in terrain data quality and computational capabilities have resulted in large-scale flood models that are more detailed and accurate than ever before[17][18][19]. However, the incorporation of lakes and reservoirs into these models is frequently constrained due to data availability and the complexity of the processes involved. As a result, lake and reservoir modeling remains an area that requires additional research, particularly in understanding how their levels influence flood dynamics at various scales[18][16].

Lane[20] identified global non-floodplain wetlands, emphasizing the importance of these ecosystems in hydrological modeling and flood prediction. Their findings not only broaden our understanding of wetland distribution, but also highlight the importance of incorporating various water bodies, such as lakes and wetlands, into hydraulic models to improve flood risk assessment and management strategies.

The importance of accurately predicting lake levels is becoming more widely recognized, particularly in light of flood events that affect lakes and reservoirs across Canada and beyond. The serious consequences of lake level fluctuations highlight the need for accurate forecasting methods. The complex relationship between lake levels and river flood dynamics is an important factor in hydrological modeling. According to Choné[7], knowing lake levels, whether as downstream or upstream boundary conditions, has a significant impact on predicting river flood levels. In their assessment of large-scale flood modeling using high-resolution LiDAR data, the researchers emphasize the importance of accurately measuring lake levels in river catchment areas. This approach, which employs advanced LiDAR-based Digital Elevation Models (DEMs), not only helps to achieve a more realistic representation of river bathymetry, but also emphasizes the interdependence of lake and river systems in flood risk analysis.


References[edit]

  1. ^ a b Messager, Mathis Loïc; Lehner, Bernhard; Grill, Günther; Nedeva, Irena; Schmitt, Oliver (2016-12-15). "Estimating the volume and age of water stored in global lakes using a geo-statistical approach". Nature Communications. 7 (1): 13603. Bibcode:2016NatCo...713603M. doi:10.1038/ncomms13603. ISSN 2041-1723. PMC 5171767. PMID 27976671.
  2. ^ Valizadeh, Nariman; El-Shafie, Ahmed; Mirzaei, Majid; Galavi, Hadi; Mukhlisin, Muhammad; Jaafar, Othman (2014). "Accuracy Enhancement for Forecasting Water Levels of Reservoirs and River Streams Using a Multiple-Input-Pattern Fuzzification Approach". The Scientific World Journal. 2014: 1–9. doi:10.1155/2014/432976. ISSN 2356-6140. PMC 3982474. PMID 24790567.
  3. ^ "Assessment of potential hydrological climate change impacts in Kastoria basin (Western Macedonia, Greece) using EUROCORDEX regional climate models". Global NEST Journal. 2021-02-03. doi:10.30955/gnj.003444. ISSN 1790-7632.
  4. ^ Lehner, Bernhard; Liermann, Catherine Reidy; Revenga, Carmen; Vörösmarty, Charles; Fekete, Balazs; Crouzet, Philippe; Döll, Petra; Endejan, Marcel; Frenken, Karen; Magome, Jun; Nilsson, Christer; Robertson, James C; Rödel, Raimund; Sindorf, Nikolai; Wisser, Dominik (2011-05-31). "High-resolution mapping of the world's reservoirs and dams for sustainable river-flow management". Frontiers in Ecology and the Environment. 9 (9): 494–502. Bibcode:2011FrEE....9..494L. doi:10.1890/100125. ISSN 1540-9295.
  5. ^ T, Victor (October 25, 2023). "The 10 Countries With the Most Lakes in 2024. A-Z Animals".
  6. ^ a b Zajac, Zuzanna; Revilla-Romero, Beatriz; Salamon, Peter; Burek, Peter; Hirpa, Feyera A.; Beck, Hylke (May 2017). "The impact of lake and reservoir parameterization on global streamflow simulation". Journal of Hydrology. 548: 552–568. Bibcode:2017JHyd..548..552Z. doi:10.1016/j.jhydrol.2017.03.022. PMC 5473175. PMID 28649141.
  7. ^ a b c d e f g h Choné, Guénolé; Biron, Pascale M.; Buffin-Bélanger, Thomas; Mazgareanu, Iulia; Neal, Jeff C.; Sampson, Christopher C. (August 2021). "An assessment of large-scale flood modelling based on LiDAR data". Hydrological Processes. 35 (8). Bibcode:2021HyPr...35E4333C. doi:10.1002/hyp.14333. ISSN 0885-6087.
  8. ^ "FloodList – Floods and flooding news from around the world". floodlist.com. Retrieved 2024-04-03.
  9. ^ a b Trigg, Mark A.; Bernhofen, Mark; Marechal, David; Alfieri, Lorenzo; Dottori, Francesco; Hoch, Jannis; Horritt, Matt; Sampson, Chris; Smith, Andy (2021-08-04), Wu, Huan; Lettenmaier, Dennis P.; Tang, Qiuhong; Ward, Philip J. (eds.), "Global Flood Models", Geophysical Monograph Series (1 ed.), Wiley, pp. 181–200, doi:10.1002/9781119427339.ch10, ISBN 978-1-119-42730-8, retrieved 2024-04-03
  10. ^ Capart, H.; Spinewine, B.; Young, D.L.; Zech, Y.; Brooks, G.R.; Leclerc, M.; Secretan, Y. (December 2007). "The 1996 Lake Ha! Ha! breakout flood, Québec: Test data for geomorphic flood routing methods". Journal of Hydraulic Research. 45 (sup1): 97–109. Bibcode:2007JHydR..45S..97C. doi:10.1080/00221686.2007.9521836. ISSN 0022-1686.
  11. ^ Canada, Environment and Climate Change (2009-04-02). "Flooding events in Canada: Quebec". www.canada.ca. Retrieved 2024-04-03.
  12. ^ "Décret 405-2011". www.environnement.gouv.qc.ca. Retrieved 2024-04-15.
  13. ^ a b Neal, Jeffrey; Hawker, Laurence; Savage, James; Durand, Michael; Bates, Paul; Sampson, Christopher (May 2021). "Estimating River Channel Bathymetry in Large Scale Flood Inundation Models". Water Resources Research. 57 (5). Bibcode:2021WRR....5728301N. doi:10.1029/2020WR028301. ISSN 0043-1397.
  14. ^ a b Fleischmann, Ayan Santos; Fialho Brêda, João Paulo; Rudorff, Conrado; Dias de Paiva, Rodrigo Cauduro; Collischonn, Walter; Papa, Fabrice; Ravanello, Mariane Moreira (2021), "River Flood Modeling and Remote Sensing Across Scales: Lessons from Brazil", Earth Observation for Flood Applications, Elsevier, pp. 61–103, doi:10.1016/b978-0-12-819412-6.00004-3, ISBN 978-0-12-819412-6, retrieved 2024-04-06
  15. ^ Dottori, Francesco; Salamon, Peter; Bianchi, Alessandra; Alfieri, Lorenzo; Hirpa, Feyera Aga; Feyen, Luc (August 2016). "Development and evaluation of a framework for global flood hazard mapping". Advances in Water Resources. 94: 87–102. Bibcode:2016AdWR...94...87D. doi:10.1016/j.advwatres.2016.05.002.
  16. ^ a b Schumann, Guy J.-P. (March 2014). "Fight floods on a global scale". Nature. 507 (7491): 169. doi:10.1038/507169e. ISSN 0028-0836. PMID 24622194.
  17. ^ Alfieri, Lorenzo; Salamon, Peter; Bianchi, Alessandra; Neal, Jeffrey; Bates, Paul; Feyen, Luc (2014-06-30). "Advances in pan-European flood hazard mapping". Hydrological Processes. 28 (13): 4067–4077. Bibcode:2014HyPr...28.4067A. doi:10.1002/hyp.9947. ISSN 0885-6087.
  18. ^ a b Christopher C., Sampson (2015). "A high-resolution global flood hazard model". Water Resources Research. 51 (9): 7358–7381. Bibcode:2015WRR....51.7358S. doi:10.1002/2015WR016954. PMC 4989447. PMID 27594719.
  19. ^ Wing, Oliver E. J.; Bates, Paul D.; Sampson, Christopher C.; Smith, Andrew M.; Johnson, Kris A.; Erickson, Tyler A. (September 2017). "Validation of a 30 m resolution flood hazard model of the conterminous U nited S tates". Water Resources Research. 53 (9): 7968–7986. Bibcode:2017WRR....53.7968W. doi:10.1002/2017WR020917. ISSN 0043-1397.
  20. ^ Lane, Charles R.; D'Amico, Ellen; Christensen, Jay R.; Golden, Heather E.; Wu, Qiusheng; Rajib, Adnan (2023-07-11). "Mapping global non-floodplain wetlands". Earth System Science Data. 15 (7): 2927–2955. Bibcode:2023ESSD...15.2927L. doi:10.5194/essd-15-2927-2023. ISSN 1866-3516. PMC 10569017. PMID 37841644.
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