Hydroclimatological Drivers of Extreme Floods on Lake Ontario

Carter, Elizabeth

doi:10.1029/2018wr022908

Cited by 24 publications

(13 citation statements)

References 66 publications

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“…This coincides with the timing needed to prepare for and potentially reduce flood risk. While ENSO forecasts can be noisy over the Great Lakes, recent work suggests that nonlinearity in the underlying teleconnections could be used to improve forecast skill (Carter and Steinschneider 2018; Fu and Steinschneider 2019).…”

Section: Discussionmentioning

confidence: 99%

A Probabilistic, Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

Semmendinger

Foley

2020

J American Water Resour Assoc

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This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.

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Section: Discussionmentioning

confidence: 99%

A Probabilistic, Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

Semmendinger

Foley

2020

J American Water Resour Assoc

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“…For instance, Li et al (2012) demonstrated that variations in the western ridge of the North Atlantic subtropical high (NASH) control regional low-level moisture transport over the eastern United States, such that a southwest (northwest) shift in the western ridge position leads to wet (dry) conditions over much of the region. Building from these results, Carter and Steinschneider (2018) recently showed that west-east variations in the position of the NASH are related to late spring and early summer rainfall anomalies over the northeastern United States and Canada that can contribute to flooding in the Great Lakes.…”

Section: Introductionmentioning

confidence: 88%

Seasonal Predictability and Change of Large-Scale Summer Precipitation Patterns over the Northeast United States

Ahn

2019

Journal of Hydrometeorology

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This study examines space–time patterns of summer daily rainfall variability across the Northeast United States, with a focus on historical trends and the potential for long-lead predictability. A hidden Markov model based on daily data is used to define six weather states that represent distinct patterns of rainfall across the region, and composites are used to examine atmospheric circulation during each state. The states represent the occurrence of region-wide dry and wet conditions associated with a large-scale ridge and trough over the Northeast, respectively, as well as inland and coastal storm tracks. There is a positive trend in the frequency of the weather state associated with heavy, regionwide rainfall, which is mirrored by a decreasing trend in the frequency of stationary ridges and regionwide dry conditions. The frequency of state occurrences is also examined for historical Northeast droughts. Two primary drought types emerge that are characterized by region-wide dry conditions linked to a persistent ridge and an eastward-shifted storm track associated with light precipitation along the coastline. Finally, composites of May sea surface temperature anomalies (SSTAs) prior to summers with high and low frequencies of each weather state are used to assess long-lead predictability. These composites are compared against similar composites based on regional anomalies in low streamflow conditions [June–August 7-day low flows (SDLFs)]. Results indicate that springtime SSTs, particularly those in the Caribbean Sea and tropical North Atlantic Ocean, provide some predictability for summers with above-average precipitation and SDLFs, but SSTs provide little information on the occurrence of drought conditions across the Northeast.

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“…A major flood impacted the shoreline of Lake Ontario in the late spring and summer of 2017, driven primarily by an extended period of high static water levels. Flood levels, defined by static levels above 75.5 m, were reached in late April, and by late May, water levels peaked at 75.88 m, the highest in the 100‐year record (Carter and Steinschneider ). Levels remained elevated for several months afterward, causing widespread property damage linked to inundation and erosion.…”

Section: Introductionmentioning

confidence: 99%

A Rapid Response Survey to Characterize the Impacts of the 2017 High Water Event on Lake Ontario

Styler

Stedman

Austerman

2019

J American Water Resour Assoc

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In the spring and summer of 2017, communities along the Lake Ontario shoreline suffered from the worst flood event on record. In late May, daily water levels reached their highest point in over 100 years, and flooding continued throughout much of the summer as lake levels slowly declined, with inundation and erosion significantly impacting shoreline homes and businesses. In this work, we present results from a rapid response online survey of property owners along the New York Lake Ontario shoreline to quantify the perceived flood impacts of the 2017 extended high water event. The survey focused on the degree and spatial distribution of inundation and erosion; the duration and drivers of inundation; the associated damages to different property features, with an emphasis on shoreline protection; and the degree of disruption to business and other activities and services. Photographic documentation of inundation extent and property damage also was provided by survey respondents. We demonstrate the potential utility of this dataset by characterizing key features of inundation and erosion impacts across the shoreline, and by using classification and regression trees to explore the predictability of inundation and erosion based on property characteristics. This work is part of a larger effort to develop models of inundation and erosion that can support flood impact assessments across the shoreline and help communities better prepare for future extended high water events.

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Hydroclimatological Drivers of Extreme Floods on Lake Ontario

Cited by 24 publications

References 66 publications

A Probabilistic, Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

A Probabilistic, Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

Seasonal Predictability and Change of Large-Scale Summer Precipitation Patterns over the Northeast United States

A Rapid Response Survey to Characterize the Impacts of the 2017 High Water Event on Lake Ontario

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