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INTEGRATION OF REAL-TIME MONITORING, MODELLING AND FORECASTING FOR THE DEVELOPMENT AND OPERATION OF THE GREAT LAKES STORM SURGE OPERATIONAL SYSTEM P. Delaney1, J.T. Sorensen2, J.S. Mariegaard2, G. Gallant3

1. DHI, Cambridge, ON, Canada 2. DHI, Horsholm, Denmark 3. Ontario Ministry of Natural Resources and Forestry, Peterborough, ON ABSTRACT: Due to the size of the Great Lakes, the communities located in low-lying regions along the shorelines are susceptible to short-term flooding events caused by storm surges. Severe storms with persistent high winds can dramatically change the water levels along the shoreline in a matter of hours, and this can be further exacerbated by large waves. Surges and shoreline waves can also create significant backwater effects on tributaries to the lakes. Communities along the shores of Lakes Erie and St. Clair are particularly susceptible to flooding and storm damage with storm surges of up to 2.5 m being measured at the eastern end of Lake Erie. In response to this problem, the Ontario Ministry of Natural Resources and Forestry’s Surface Water Monitoring Centre has developed the Great Lakes Storm Surge Operational System for generating accurate and reliable forecasts of water levels and wave heights for Ontario communities along the Great Lakes. The system automatically collects and displays real-time water levels and wave height measurements at selected locations on the Great Lakes along with wind and barometric pressure forecasts from Environment Canada. This information is then used to inform a series of hydrodynamic and wave models for Lake Ontario, Lake Erie, Lake St. Clair, Lake Huron and Lake Superior. The models and resulting forecasts are updated multiple times per day and the information is made available and disseminated to the Conservation Authorities responsible for flood warnings in these communities. Although there is nothing that can be done to prevent the storm surges from happening, the impacts to the communities have been mitigated by providing accurate forecasts and advanced warnings about storm surge events. Keywords: Great Lakes, Storm surge, flooding, early warning system, 1. INTRODUCTION The Ontario Ministry of Natural Resources and Forestry (MNRF) operates a Surface Water Monitoring Centre in Peterborough, ON. The centre observes water levels on lakes, rivers and streams in Ontario for the purpose of:

assessing the severity of drought

predicting where/when there is a risk of flooding

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limiting the impact of flooding and drought by enabling government and other agencies to put emergency response plans into operation

identifying sensitive and risk prone areas for flooding and drought throughout the province The Surface Water Monitoring Centre provides vital water management data and information specific to the Low Water Response Program, Flood Forecasting and Warning Program and the Canada/Ontario Hydrometric Stations Monitoring Partnership. Due to the size of the Great Lakes, the communities located in low-lying regions along the shorelines are susceptible to short-term flooding events caused by storm surges. Severe storms with persistent high winds can dramatically change the water levels along the shoreline in a matter of hours, and this can be further exacerbated by large waves. Surges and shoreline waves can also create significant backwater effects on tributaries to the lakes. Communities along the shores of Lakes Erie and St. Clair are particularly susceptible to flooding and storm damage with storm surges of up to 2.5 m being measured at the eastern end of Lake Erie. In order to provide these communities with more lead time to implement emergency response plans and warn residents and business about the potential for flooding, the MNRF entered into a contract with DHI to develop and implement the Great Lakes Storm Surge Operational System for generating accurate and reliable forecasts of water levels and wave heights for Ontario communities along the Great Lakes. This document provides a description of the system components, architecture and operational workflow, and operational performance. 2. OPERATIONAL SYSTEM COMPONENTS The Great Lakes Storm Surge Operational System (GLSSOS) is set up for Lake Superior, Lake Huron, Lake St. Clair, Lake Erie and Lake Ontario, and it consists of the following major components:

The existing WISKI database where the near real-time water level data for the lakes are stored

Meteorological forcings from Environment Canada

A calibrated hydrodynamic model and a wave model for each lake

An operational system that facilitates the automation of data retrieval, model execution, and result

publication on a regular schedule

A web-based management console to control and monitor the operational system performance

Each of these components is described in the following sections of this document. 2.1. Water Level Monitoring The province of Ontario Ministry of Natural Resources and Forestry (MNRF) has been collecting and managing near-real time information from surface water bodies throughout the province for many years. This information includes, but is not limited to, water levels, flow rates, current speeds and direction, wave heights and water quality. Some of the monitoring stations collecting and transmitting this information are maintained by the province while others are maintained by Environment Canada and, via data sharing agreements, the information is collected, processed and stored in a central hydrometric database that is maintained and operated by the MNRF. The availability and accessibility of near real-time water level data at multiple locations for each of the lakes was a key component to the success of the operational forecasting system. The database of historical water levels and wave heights were used to calibrate the hydrodynamic and wave models for each lake, while the near real-time water level data was used for data assimilation on the hydrodynamic models. The number of available water level monitoring stations and wave height monitoring stations for each lake is listed in Table 1.

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Table 1: Water Level and Wave Height Monitoring Stations

Lake Water Level Stations

Wave Height Stations

Lake Superior 5 2

Lake Huron 9 4

Lake St. Clair 3 2

Lake Erie 9 2

Lake Ontario 7 2

3. METEOROLOGICAL DATA AND FORECASTS For a storm surge and wave forecasting system, the wind speed and direction are the main drivers influencing water level changes and wave generation. As such, the availability and accessibility of accurate and reliable wind data and forecasts is an essential component for the system. For this system Environment Canada provided access to the meteorological forecasts including wind, barometric pressure, and Albedo. The information is provided as two-dimensional, time series data in a GRIB2 binary file format. Once this data is retrieved it is converted to a .DFS2 format in order to be compatible with the required input formats for MIKE 21 models. 4. THE LAKE MODELS The hydrodynamic and wave models were prepared using DHI’s MIKE 21 modelling system utilizing a flexible mesh approach for both the hydrodynamic and the wave model. The flexible mesh approach was selected because it allowed for a variable refinement of the model whereby the mesh size along the shoreline could be small enough to providing a meaningful representation of water levels and wave heights in the areas of interest, and larger in the middle of the lake where there is less data and generally less concern about water levels and wave heights. The following sections provide a general description of the setup and calibration for the hydrodynamic and wave models. 4.1. Hydrodynamic Models The hydrodynamic models were setup with a mesh spacing of approximately 1 km along the shoreline with a gradual transition to a mesh spacing of 2-3 km in the middle of the lake. The models were required to be relatively coarse in order to be able to incorporate data assimilation processes with multiple forecasting cycles per day. The basic data required to run and calibration the model includes the following:

Bathymetry: The bathymetry of the lakes was obtained using a bathymetric data sources called

C-MAP that represents a compilation of available data from Canadian Hydrographic Service

(CHS) and National Oceanic and Atmospheric Association (NOAA).

Bed resistance: The bed resistance described the roughness of the bathymetry and the amount

of resistance it provides for flow. This parameter was described as constant throughout each lake

and the value used varied from 25 to 50 m1/3/s depending on the lake (typically higher values for

deeper lakes)

Wind Forcing: In this model the inflows and outflow to the lakes were ignored, so the wind is the

only driving force to initiate the movement of water in the lakes. The model utilizes the two-

dimensional time series of wind forecast data provided by Environment Canada for the calibration

period as well as for the operational forecast period.

Ice Coverage: Ice coverage is an important consideration for the hydrodynamic model because it

eliminates the drag of the wind on the water and thus reduces the setup of lake water levels. The

ice coverage data for the calibration period was obtained from 2D raster files of Albedo readings

provided by Environment Canada, while the ice coverage for the forecast period was obtained

from 2D raster files of Albedo forecasts, also provided by Environment Canada. An Albedo value

greater than 0.4 was considered to represent ice coverage.

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Initial Conditions: The initial water level and current conditions at the start of the calibration

period simulations were obtained from monitoring data but in general the start of the calibration

periods were chosen at a time when the water levels were relatively unaffected by wind. The

initial conditions for the forecast period were taken from the simulated conditions at the end of the

previous forecast period.

The models were calibrated to measured water levels in each of the lakes for 4 separate calibration periods (depending on availability of monitoring data during these periods):

June 1-June 30, 2002

June 1-July 2, 2002

Nov 15 – Dec 15, 2006

Feb 1 – Feb 28, 2007

The calibration process used Bed Resistance and Wind Friction as the main calibration adjustment parameters. Table 2 provides a summary of the parameter values used for each lake. Table 2: Hydrodynamic models calibration parameter values

Parameter Lake Superior

Lake Huron

Lake St. Clair

Lake Erie

Lake Ontario

Bed Resistance (m1/3/s) 32 40 50 50 40

Wind Friction Coefficient (speed (m/s) / friction coeff)

5 / 0.00106 20 / 0.00311

7 / 0.001255 20 / 0.002425

5 / 0.00106 20 / 0.00311

5 / 0.00106 20 / 0.00311

5 / 0.00106 20 / 0.00311

4.1.1 Data Assimilation Data assimilation is a process whereby an algorithm is used to automatically adjust the forecasted model results to account for the observed deviation in the model at the time of the forecast. Even though the hydrodynamic models and wave models for each lake have been calibrated against several different periods of measurement, the calibration is never perfect and skill is intrinsically limited by the accuracy of the wind forcing and ice coverage. However, point observations of water levels at level gauges provide a measure of the present water level. Data assimilation is a technique for interpreting the deviation of the model forecast to observations and creating a consistent estimate of wind correction, water level and currents throughout each lake. This estimate of model parameters and wind speed is subsequently used to initiate the forecast. Data assimilation techniques can thus be used to more accurately reflect the observed conditions and improve predictions. The data assimilation is both applied in the hindcast and the forecast period. The hindcast period is run for the past 12 hours to ensure that all available observations are assimilated. The hindcast period further allows an online optimization of the data assimilation methodology to accommodate for present ice conditions during the winter months. The model is then run for the forecast period, starting at the present time and ending at a desired time interval in the future (e.g. the model is run for the next 72 hours using forecasted meteorological data). The data assimilation process is then applied via the initial conditions of wind correction, lake water levels and currents using the calibrated model to provide the hydrodynamic lake forecast while wind corrections are slowly phased out over 3-6 hours. For GLSSOS, the data assimilation runs by applying a smoothed Ensemble Kalman filter using five ensembles members for the hindcast period. The forecast period is based on using Steady Kalman filter. Both methodologies are described in detail in Sørensen et. al (2004). The methodology has the inherent property that slow long term changes in overall lake level are maintained. Figure 1 provides an example of the clear skill improvements in model predictions that are achieved using the data assimilation technique described above.

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Figure 1: Comparison of forecasted water levels at the Barpoint monitoring station (with and without data assimilation)

4.2. Wave Models The wave models were setup with exactly the same mesh as the hydrodynamic models and each model used essentially the same model setup description with very few differences between them. The main required inputs for a wave model are as follows:

Water Level Conditions: The water level conditions describe the changes in the water level in

space and time during the simulation period. This information is read from the results of the

hydrodynamic model for the same simulation period.

Current Conditions: The current conditions describe the changes in the current in space and

time during the simulation period. This information is read from the results of the hydrodynamic

model for the same simulation period.

Wind Forcing: The wind forcing are obtained from the Environment Canada meteorological

forecasts for the study area.

Ice Coverage: Ice coverage is an important consideration for the wave model because it

essentially eliminates the development of waves. The ice coverage data for the calibration period

was obtained from 2D raster files of Albedo readings provided by Environment Canada, while the

ice coverage for the forecast period was obtained from 2D raster files of Albedo forecasts, also

provided by Environment Canada. An Albedo value greater than 0.4 was considered to represent

ice coverage.

Bottom Friction: The bottom friction describes the wave bottom interaction and acts to dissipate

the waves in shallow water. All of the models used a Nikuradse roughness, kn value of 0.04 m

except for Lake Erie where a value of 0.01 m was used because it is relatively shallow compared

to the other lakes.

The wave models were calibrated against the same calibration periods as the hydrodynamic models using wave breaking coefficients, bottom friction coefficients, and white capping dissipation coefficients as the main calibration adjustment parameters. An example of the wave model calibration against wave height and wave period for Lake Superior is provided in Figure 2.

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Figure 2: Example of wave model calibration for Lake Superior (black is observed, red is modelled)

4.3. Computational Demands Due to the relatively coarse mesh resolution for each of the models they all run a 12 hour simulation period in less than 3 minutes on a relatively standard modern computer (Quad Core Xeon, 3.33 GHz). The main outputs from the model include water levels (scalar maps and time series at a point), current speed (scalar maps) and current direction (vector maps). The data assimilation process for the hydrodynamic models requires 5 times longer since it is running an ensemble of 5 models for the 12 hour simulation for the hindcast period. Therefore, for each lake it takes approximately 15 minutes to run a 12 hour hindcast and approximately 12 minutes to run a 48 hour forecast. The wave models are more computationally demanding than the hydrodynamic models and result in an increase in run times by a factor of approximately 5 over the corresponding hydrodynamic model. Therefore it takes approximately 60 minutes to run a 48 hour forecast for each lake. Since all of the models are running in sequence on the same computer it takes a little more than 7 hours to run through a 12 hour hindcast (including data assimilation) and a 48 hour forecast of water levels and wave heights for all five of the lakes. 4.4. Operational System Configuration Once the hydrodynamic and wave models were developed and calibrated for each of the lakes, the Great Lakes Storm Surge Operational System was developed such that it could automate the process of generating twice-daily 48 hour forecast, including a data assimilation ensemble run, and then publishing the results. Figure 3 shows a schematic representation of the Operational Forecast System including all of the main components and how they interact.

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Figure 3: Schematic representation of the Great Lakes Storm Surge Operational System

The automation of the system is accomplished using Windows Scripting Host while the management of all of the data that is collected, processed, and generated by the system is handled by a central MySQL database. The database basically consists of three types of data:

Model definitions: Contains information about each model and their relations (i.e. that the wave

model for one lake depends on the output from the hydrodynamic model from the same lake,

which in turn depends on the meteorological forcing).

Progress: Contains information about the start, end and publish time for each 12-package for

each model (see Figure 5 for an example).

Published results: Holding the actual produced data from the models.

The initiation of a forecast cycle is triggered by Environment Canada making the meteorology files available on their web site. The Meteorological Data Retrieval Script continuously polls the Environment Canada web site for new data and downloads it to the database when it is available. The script converts the meteorological data from the native GRIB2 format provided by Environment Canada to a DFS2 format that is compatible with MIKE 21. The HD Model Run Script continuously polls the database to check when the meteorological forcings are ready for a new model run. When the new forcings are available the data assimilation ensemble run is launched for all 5 hydrodynamic models. The script will look for available water level monitoring data in the WISKI database and include this data in the ensemble run. The ensemble run covers the previous 12 hour period (i.e. from 24:00 to 12:00 or from 12:00 to 24:00). The ensemble run creates the adjustment (correction) matrices that are used for the forecast period and must be completed successfully before the forecast cycle can begin.

MNRF Expert

WISKI Soda

Database

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The HD Model Run Script will then run hydrodynamic models for each of the lakes for a 48 hour forecast period. The 48 hour forecast period is run in 12 hour ‘packages’ whereby the end period of one 12 hour package is used as the initial conditions for the next 12 hour package. The running of the model in 12 hour packages is done for redundancy and recovery purposes to make the system more robust. If the 48 hour forecast was run in just one simulation period and it failed for some reason, then there would be no forecast available. By running the forecast in 12 hour packages the system provides some assurance that a forecast will be available for at least some of the intended period. In addition, the system has built-in error trapping such that if one of the 12 hour forecast packages fails, it will look to the previous 12 hour package to retrieve the initial conditions for the next forecast period. When the first hydrodynamic model is completed the SW Model Run Script will look in the progress database to check that all necessary forcings (hydrodynamic and meteorological) are available then it will execute the wave model simulation for the 48 hour forecast period. The Publish Script continuously polls the progress database to check when result files are ready to be published. Once the results are ready they are published to the system database and from there they are available to the relevant system dashboards and web sites. 4.5. Management Dashboard While GLSSOS was designed to be as robust as possible, it is important that the processes can be monitored, managed and customized on-site by experts at the Surface Water Monitoring Centre. The operational system management is handled using a decision support system component called the Dashboard Manager. The Dashboard Manager provides a fast and flexible platform to develop custom, web-based applications that facilitate system configuration, performance monitoring, information management, and scenario management. Since the GLSSOS Management Dashboard has been implemented as a web-based solution, it can be opened from any computer on the intranet with access to the web server where the dashboard is installed. Due to the relatively complex nature of the system and the importance of having it available and running at all times, the GLSSOS Management Dashboard has been designed to provide different levels of access for different login IDs. This allows system experts to modify current system settings when necessary while other users can simply view and monitoring the current system settings. The GLSSOS Management Dashboard consists of 4 different views:

Main: The Main view (see Figure 4) is designed to provide a high-level overview of the system

operation and results at selected locations. The indicators shown on the left-hand side of the view

provide an indication of the state of the system processes. A green symbol indicates the process

has been successfully completed while a red symbol indicates the process failed. In this case

there is one indicator for acquiring the meteorological data and one for each model. The time

series plots on the right-hand side of the view provide a summary of the modelling results (wind

speed, water level and wave height) for the period from -24 hours to + 48 hours based on the

most recent successfully completed forecast. Each plot contains a time-series from each of the

five lakes.

Scripts: The Scripts view provides an overview of the Status of each script that is required to run

the system and allows the user to either stop or start a script.

Database: The Database view is used to modify the system progress tables for the purpose of

adding new processes / scripts.

Models: The Models view (see Figure 5) is used to monitoring the historical progress of each

individual model (the progress database) where the top rows of the table reflect the most recently

completed models runs. This view also allows the user to rerun the models beginning from a

selected start time interval. This is typically done if some of the model runs unexpectedly fail due

to a power failure or a discontinuity in the automation processes.

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Figure 4: GLSSOS Dashboard - Main view Figure 5: GLSSOS Dashboard - Model View

4.6. Publishing Forecasting Results The results from the modelling forecasts are published to the Ontario Ministry of Natural Resources Surface Water Monitoring Centre’s web site. The forecasts are made available as pdf format reports for each lake containing time series plots of 48 hour forecasts for water level, wave height, mean wave direction, and peak wave period at selected locations as follows:

Lake Superior: Schrieber, Thunder Bay and Gros Cap

Lake Huron: Sarnia, Tobermory, Parry Sound

Lake St. Clair: Bell River, Mitchell Bay

Lake Erie: Port Colbourne, Long Point, Port Stanley, Hillman Marsh

Lake Ontario: Kingston, Coburg, Burlington

An example report is presented in Figure 6.

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Page 1

Page 2

Page 3

Page 4

Figure 6: Sample 48 hour forecast report for Lake Erie

In addition, the web site also provides map format animations of the key results (water level, wave height and wave period) for hindcast and forecast period (see example shown in Figure 7).

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Figure 7: Example animation of forecasted wave period for Lake Ontario

The web site requires a secure login but it is made accessible to Conservation Authorities and potentially affected communities and organizations on an as-requested basis. The last significant storm surge event happened on Lake Erie on Christmas Day 2014. GLSSOS was able to provide an accurate 48 hour forecast and a very accurate 36 hour forecast as shown in Figure 8.

Figure 8: Forecasted and measured lake level at Port Colborne (red = measured, blue = forecast 48 hours prior to event, green = forecast 36 hours prior to event.

The threshold level for significant flooding in this section of Lake Erie is 2 m, so GLSSOS was able to provide 48 hours’ notice to local authorities about the potential for flooding and allowed sufficient time to notify residents and businesses in the affected areas and ensure a proper level or preparedness and response to the threat.

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5. SUMMARY GLSSOS has been in operation since 2007 and has proven to be a robust operational forecasting system capable of delivering accurate and reliable 48 hour forecasts of water levels and wave heights for the Great Lakes communities in Ontario. The level of accuracy demonstrated by the historical performance of the forecasts has developed a strong sense of confidence in the system and has proven to be a valuable tool for facilitating emergency response preparations in potentially affected communities. 6. REFERENCES DHI 2011, Great Lakes Storm Surge Operational System: Background and Operational Cycle, Edition

2.01, Prepared by DHI for Ministry of Natural Resources and Forestry Ontario Sorensen et al. 2004 Efficient Kalman filter techniques for the assimilation of tide gauge data in three-

dimensional modeling of the North Sea and Baltic Sea system. Journal of Geophysical Research - Part C - Ocean 01/2004; 109:14. DOI: 10.1029/2003JC002144