Verification of a River Ice Process Model Using ...

Proceedings of 2013 IAHR Congress © 2013 Tsinghua University Press, Beijing

Verification of a River Ice Process Model Using Continuous Field Measurements Soheil Gharehaghaji Zare PhD student, Dept. of Civil Engineering, University of Ottawa, Ottawa, ON, CANADA. E-mail: [email protected] Stephanie A. Moore Post Doctorate fellow, Dept. of Civil Engineering, University of Ottawa, Ottawa, ON, CANADA. E-mail: [email protected] Colin D. Rennie Professor, Dept. of Civil Engineering, University of Ottawa, Ottawa, ON, CANADA. E-mail: [email protected] Ousmane Seidou Professor, Dept. of Civil Engineering, University of Ottawa, Ottawa, ON, CANADA. E-mail: [email protected] Joe Groeneveld Senior Hydrotechnical Engineer, Hatch Ltd., Calgary, AB, CANADA. E-mail: [email protected] Rajib Ahsan Senior Hydrotechnical Engineer, Hatch Ltd., Winnipeg,MB, CANADA. E-mail: [email protected] Habib Ahmari Sediment and Erosion Studies, Water Resources Engineering Department, Manitoba Hydro, Winnipeg, MB, CANADA. E-mail: [email protected]

ABSTRACT: This paper uses both numerical modeling and field measurements to describe river ice processes. River ice regime plays a significant role in river hydraulics and morphology in higher latitudes of the northern hemisphere, with important implications for hydro-electrical power generation operations. An ice-cover changes various river characteristics, including water stage and velocity distribution. These variations can influence sediment transport and channel morphodynamics, particularly during extreme conditions when ice-jamming and break-up can locally accelerate the flow, and ice can mechanically scour the river bed and banks. Although the influence of ice on rivers is tremendous and clear, it is not well studied largely due to the difficulty and danger of river ice field studies, especially during the unstable break-up period. In this paper we studied river ice conditions in the Lower Nelson River, Northern Manitoba, Canada, during winter 2012 using both numerical modeling and In-situ autonomous field measurements to validate the model predictions. The accuracy of numerical models is an intrinsic concern, especially in the analysis of dynamic processes such as river ice, and verification with field data is essential. . The ICESIM was originally developed by Acres International Limited (now Hatch) in 1973 for studies of the Nelson River hydroelectric plants; since then it has been continuously improved. The field data were collected from a bottom mounted instrument pod deployed below the ice-cover for a four-month period (March-June 2012), which included both the stable cover and break-up periods. Instrumentation included a 546 kHz ASL Multi-Functional Acoustic Water Column Profiler (MF-AWCP)

and 1200kHz TELEDYN RD Instrument Acoustic Doppler Current Profiler for measurements of water velocity. The MF-AWCP could measure frazil ice particles in the water column and more importantly, the variation in the condition and thickness of the ice cover. In this paper we demonstrate the model capability by comparing measured and model-calculated results, focusing specifically on the model's ability to predict ice thickness and timing of ice-cover stages i.e. freeze-up, stable ice-cover, and breakup. KEY WORDS: River ice, numerical modeling, acoustic techniques. 1 INTRODUCTION Hydroelectricity plays an important role in energy production around the globe. Canada generates 10.8% of the world's hydroelectric energy, which globally ranks Canada third in hydroelectricity generation (after China and Brazil) (BP Statistical Review of World Energy,2012). This production is equivalent to 85.2 megatones of oil, which demonstrates the importance of this source to Canada’s economy and environment. Hydroelectric production in Canada and other northern nations is complicated by the fact that rivers are completely covered by thick, stable ice cover in winter. This ice cover add a level of complexity to hydraulic processes in the river, and sometimes drastically influences river characteristics such as sediment transport and river morphology (Ettema 2008). Understanding River ice is therefore important for various aspects of river management, including flooding, power production, available habitat, reservoir sedimentation, and stability of in-stream structures. However, very little is known about river hydraulics and morphodynamics in winter compared to open-water conditions, largely due to the difficulty of obtaining in situ measurements during winter. This is particularly true for dynamic ice cover stages such as the freeze-up and break-up periods, when it is not safe to conduct river surveys. This paper describes and probes the performance of ICESIM, a river-ice process model originally developed by Acres International Limited (now Hatch) in 1973 for studies of the Nelson River hydroelectric plants, and identify possible improvements to the model routines; ICESIM predicts the progression and stabilization of a river ice-cover. A key component of model development is model calibration, which requires measurements of hydraulic and ice related terms of the river during winter that, as described above, are difficult to obtain. In order to achieve this objective, acoustic instrumentation recording autonomously from the river bed have been deployed to measure velocities, pressures and the elevation of the bottom of the ice cover conditions under both stable ice cover and the extremely dynamic break-up period. The remainder of the paper is organized as follow:

2 STUDY METHODS 2.1 FIELD INVESTIGATIONS; LOWER NELSON RIVER, CANADA The regulated semi-alluvial Lower Nelson River (LNR) in northern Manitoba, Canada is the case study for this research. The Nelson and the Churchill Rivers, which are two great Canadian rivers, drain most of mid-western Canada and some parts of the northern United States, emptying in Hudson’s Bay after passing the northern shields of Manitoba (Newbury and Malaher,1972). The Lower Nelson River is classified as the last 150 km reach before Hudson’s Bay. The study reach is between Limestone Generating Station (LGS) and Gillam Island near the estuary in Hudson's Bay. Between LGS and Gillam Island, the Nelson River is fast flowing without any lakes, and is characterized with high banks reaching to 50 m height in some locations. Water elevation drop is about 43 m over this 102 km reach, of which almost 30 m occurs over the first 28 km from the LGS by several sets of rapids (Figure 1). The remaining 13 m drop takes place over the lower portion. Thus, the slope of the upper portion is about S=0.0011 and for the lower part is S=0.00018. Previous observations demonstrate that in most years a continuous ice cover propagates approximately 100 km from the estuary to just downstream of LGS. Cooling of the water surface is the first initiation step for ice formation downstream of LGS.

Flow speed in LNR precludes growth of lateral border ice formation except at quiescent spots such as large bays along the river path. So, the majority of generated ice flows downstream until it accumulates at the leading edge of the ice cover, beginning approximately 10 km downstream of Gillam Island where the diminishing of water velocity permits the accumulation of ice and upstream progression of the ice cover front due to process of "juxtaposition". Ice front propagation takes place typically in mid to late December, although ice progression upstream of Gillam Island has been reported as early as mid November and as late as mid February (KGS Acres 2011). Different processes of ice thickening take place along the river and are controlled by topography and local hydraulic conditions such as steepness and water velocity. Along portions of the LNR, no woody vegetation exists within several meters of the river bank, presumably due to ice scour processes. This evidence suggests that ice processes have a major influence on the Lower Nelson River. 2.2 INSTRUMENTATION In this research, autonomously recording acoustic instruments including a 1200 kHz Acoustic Doppler current Profiler (ADCP) and 546 kHz Multi-Functional Acoustic Water Column Profiler (MF-AWCP) have been utilized to monitor ice cover condition and river hydraulic characteristics on the Lower Nelson River throughout the final stage of winter season, including the period of ice break-up. The instruments were deployed facing up from the river bottom in the thalweg just upstream of Jackfish Island (see Figure 1) between March 21st and May 20th, 2012.

Figure 1 Lower Nelson River profile: Lime stone Generating Station to Gillam Island.

2.2.1 ACOUSTIC DOPPLER CURRENT PROFILER (ADCP) The1200 kHz Sentinel ADCP has four transducers and each transducer operates independently in mono-static mode, which means that each transducer emits a sound pulse and receives the backscattered sound. Each transducer is orthogonal to the others, spaced at 90º around the circle, and angled at 20º from vertical. As a sound pulse propagates through the water its intensity attenuates due to both beam spreading and absorption of sound by water and suspended particles (see Section 2.2.2.1 below). Back scattered signal intensity is largely a function of scatterer size and concentration as well as beam attenuation. ADCP velocity measurements are based on the Doppler effect. The difference between the received frequency and the transmitted frequency is proportional to the velocity of the scattering particles (i.e water velocity). Calculation of the velocity component parallel to each beam can be done based on the Doppler shift of frequency of the back scattered sound (Fd) compared to the emitted sound (FS).

Fd = 2 FS (V�c)

(1)

Where V is the along-beam velocity of the scatterers which are assumed to have the velocity of the fluid, and c is the speed of sound (approximately 1500 m/s in water, depending on fluid density). The factor 2 is implemented because the Doppler shift occurs for both the forward sound propagation and the backscatter. The value of Fd is found using an autocovariance technique to find the mean frequency in the back scattered sound (Miller and Rochwarger 1972) which is equivalent to picking the peak frequency in the return spectrum. 2.2.2 MULTI-FUNCTIONAL ACOUSTIC WATER COLUMN PROFILER (MF-AWC P) MF-AWCP (previously, Shallow Water Ice Profiling Sonar, SWIPS) is a programmable instrument designed for autonomous long term deployment to measure and record information on objects in the water column, including ice in all forms. The operational deployment depth of MF-AWCP is up to 20 m. The instrument includes sensors for measuring the pressure, temperature and transducer tilt during the deployment. Similar to an individual ADCP beam, the MF-AWCP is a monostatic active sonar; it sends programmable sound pulses into the water column and records echoes scattered from objects in the water. A MF-AWCP does not employ Doppler processing to calculate velocities, thus pulse processing can be optimized to detect particles in the water based on backscatter intensity. As described above, the strength of back scattered echoes is dependent on both the range and reflectivity of the target(s). Based on the amplitude of reflected sound received by the transducer, suspended and/or floating objects are detectable and distinguishable using the sonar equation. 2.2.2.1 SONAR EQUATION The amplitude of returned sound, EL, can be calculated using the sonar equation as follows (Urick, 1983): EL = SL − 2. TL + TS

(2)

SL is the strength of transmitted signal (source level), TS is the target strength which means the reflectivity of scattering objects in water and TL is the transmission loss. All terms are values relative to a reference value in dB. The transmission loss term, TL, includes two main sub-terms as follows: TL = 20. logR + αR

(3)

The first term describes the geometrical spreading of a sound pulse propagating in the water column over the range (R) to the target. The second term accounts for absorption of sound by water and possibly suspended particles, characterized by an absorption coefficient (𝛼𝛼) in dB per meter, which varies with sound frequency, water temperature and salinity, and particle characteristics such as density, concentration, and grain size (Urick, 1983). 2.2.2.2 ICE THICKNESS CALCULATIONS Ice thickness can be calculated based on the data measured by MF-AWCP in a time series format of river ice draft. Ice draft, d, can be calculated as the difference between the simultaneous water level and elevation of the bottom of the ice, which is found from the peak in the vertical profile of backscatter intensity (Figure 2). Measured acoustic range to the underside of the cover is corrected for transducer tilt (θ). d = H − β. D. cos θ

(4)

Where 𝛽𝛽 is the correction for sound speed:

β = cact ⁄ cnom

(5)

Where cnom is the nominal sound speed (1450 m s-1) and cact is the actual sound speed that should be determined using empirical methods.

Figure 2 Representation of ice draft calculation using MF-AWCP data

Water depth, H, can be determined using the measured bottom pressure Pbtm, which should be adjusted according to the atmospheric pressure Patm. measured at a nearby weather station, 𝜌𝜌 is water density: (6) H = (Pbtm − Patm )/(ρ. g) For relatively flat ice, ice thickness can be calculated using the buoyancy equation as follows: Tice = d. (ρwater ⁄ ρice )

(7)

2.3 NUMERICAL MODELING; RIVER ICE SIMULATION MODEL (ICESIM) In 1973, Acres International Ltd. (now Hatch) developed a computer program to simulate the river ice formation process in order to facilitate the design of hydroelectric plants and to address ice issues on the Nelson River, Northern Manitoba, Canada. The program has evolved over the subsequent four decades, during which time the model has been improved in several stages with consideration of hydraulic and ice mechanics. The resulting ICESIM model is a steady state, one- dimensional program, able to simulate most of the ice processes during winter under a variety of conditions and types of rivers. Several technical papers have been published in the past describing the "ICESIM" model and its application such as Korbaylo and Carson (1992), Gerrard and Carson (1987), Carson (1982), Carson and Jonassen (1979), Breland and Carson (1995), Carson and Groeneveld (1997) and Judge et al. (1997). The model has been used very successfully as a tool to examine river ice mechanics and its effect on hydraulic and bearing capacity of the river and is still being used in situations where the steady state assumption does not lead to unrealistic results. Major applications are described briefly in Table 1. It should be noted that computer models do not provide precise prediction of hydraulic phenomena

such as like ice jam accumulation. Model predictions are considered to be approximate and engineering judgment is required in both formulating and interpreting the simulations of river ice processes.

Table 1. Summary of main applications of "ICESIM"

Location

River

Problem Addressed

Calibration

Years of Application

Limestone

Nelson

Ogdensberg

St.Lawrence

Fort McMurray

Ice effect on dam construction site

Positive

1974,1985,2003-2009

Winter navigation

Positive

1978

Athabasca

Spring ice jam flood

Positive

1982

Nipawin

Saskatchewan

Spring ice jams

Positive

1984

Woodstock- Perth Andover

St. John

Spring ice jams

Positive

1987-92

Whitehorse

Yukon

Ice jamming due to hydro plant peaking

Positive

1993

Churchill

Churchill

Construction of control weir

Positive

1993

2.3.1 ICESIM THEORY ICESIM has been formulated to simulate, in discrete time steps, major river ice processes as well as parameters that affect the water stage profile along the river, namely a. Ice generation over the river's available open water surface area b. Ice cover advancement by frontal progression using a Froude number criterion (juxtaposition process) c. Ice deposition and transport d. Ice erosion e. Border ice growth f. Ice retreat and mechanical thickening due to forces acting on the leading edge (shoving process) g. Ice cover progression due to back water effect (packing process) Generally, ICESIM re-computes the water surface profile in every time step in order to update river hydraulic conditions. Daily volume of ice entering to the simulation reach can be directly entered as an input or can be calculated by the model using an approximate engineering formula based on total heat loss, upstream area of open water and latent heat of fusion of ice. Frontal progression of ice cover is governed by the water stage. Under-turning of incoming ice floes and their deposition under the existing cover, which causes the water stage to rise, makes the ice frontal progression possible. The point at which the ice cover starts to propagate upstream is defined by a critical Froude number. ICESIM looks for possible locations (cross sections) for floes travelling under ice to deposit where the velocity is less than the critical ice eroding velocity. Ice floes are deposited at these sections until the ice at the section thickens such that velocity at the section increases to the limit velocity of erosion. Border ice growth calculation is performed in two ways in ICESIM. One option uses the Newbury(1968) empirical formula for border ice calculation, while the second option uses a `border ice factor` which is the fraction of open water area as a function of freezing degree-days based on field observations or judgments. Stresses within the cover increase as the ice cover progresses upstream, mainly due to shear flow under the cover, the down slope component of the cover weight and the water thrust on the leading edge of the cover. ICESIM numerically integrates the external forces and internal stresses along the cover, which permits the model to depart from the equilibrium condition assumption. In other words, the model

considers non-uniform ice thickness in the longitudinal direction. In this way, accuracy will increase in calculation of the stress of the ice cover. If the stress exceeds the internal resistance, then `shoving` occurs, meaning the section thickens mechanically until it can resist against the new external forces. Calculation of ice thickness due to shoving goes from upstream to downstream along the study reach so the required volume of ice to thicken all the weak points can be calculated, and the recession of the ice cover front location can be implemented accordingly. The one dimensional Bernoulli energy equation is used in ICESIM for water surface profile calculations, yielding water surface elevations for gradually varied subcritical flow. A known elevation is used at the downstream cross section with known hydraulic conditions, and water surface elevation and all related hydraulic information, i.e., bearing capacity with/without ice, widths, areas, etc., is calculated for each section in the upstream direction using an iterative standard step procedure. Finally, it is notable that ICESIM is a modular group of several subroutines. Some subroutines are used only for specific model applications. , Figure 2 shows a simplified ICESIM algorithm of river ice calculation including the various subroutines.

Figure 3 Schematic of ICESIM algorithm including the Subroutines

2.3.2 ICECIM MODEL CALIBRATION Majority of ICESIM processes use empirical equations with coefficients that should be calibrated to get the best simulation of observed conditions. Modified coefficient help model to reproduce observed historical data such as water level and more importantly ice propagation rate and thickness. The first model calibration is open water condition modifications by varying the Manning’s n- value of the bed to match the water level data then the calibration of complicated winter condition takes place. ICESIM calibration has been performed for 16 years of available data (1993-2009). Winter 2010-11 is selected as validation year and winter 2012 is selected for verification year. Adopted main factors for ICESIM calculation have been shown in Table 2.

Table 2. "ICESIM" calibrated ice formation parameters.

Parameter Maximum water velocity for under cover ice deposition Minimum water velocity for undercover ice erosion Maximum Froude number for Juxtaposition K2- Coefficient of internal strength of ice ( equal to Rankin Passive coefficient for soils) K1- Ratio of lateral or longitudinal stress in ice cover Φ -Internal angel of friction of ice mass Manning’s “n” value for ice cover

Value 1.0 m/s 2.5 m/s 0.12 7.2 K1.tanΦ= 0.18 0.015- 0.13

3 RESULTS AND DISCUSSION 3.1 ICE OBSERVATIONS Figure 4 shows a time series of ice cover data based on backscatter intensity data recorded with the MF-AWCP. In Figure 4, the lower surface is the elevation where backscatter intensity increased significantly, and is indicative of the location of the lower surface of the ice cover. In the open water period after ice break-up, Figure 4 shows the location of the free water surface. Oscillations in ice cover elevation and river stage are readily apparent in Figure 4 and are due to fluctuations in river flow due to hydropeaking operations at LGS. Ice stage dropped substantially by about 2 m in mid-April (from an

Figure 4 Ice cover and open water condition over deployment site during March 21 to May 21, 2012.

average of 12 m to 10 m), which may indicate the beginning of the break-up period. The ice cover also appears to have thinned in late April and early May. Ultimately, the ice/water surface dropped again on May 10, probably corresponding to full release of the ice cover, followed by open water condition. Although spring break-up typically occurs in May on the LNR, it started to occur in mid April in the study year (2012) and the process continued until mid May. Figure 5 shows the ice thickness values calculated for the measurment period using the method outlined in section 2.2.2.2. Results show fluctuations in ice thickness, which are due to thermal and mechanical effects of water flowing under the ice cover. As the ice cover spreads over the entire surface, stabilizes, and thickens, the thermal conductivity of the ice decreases, which decreases heat transfer from water to the cold air above. Under a stable ice cover condition, water flow melts, erodes, and smoothes the undersurface of the ice cover. The ice erosion process decreases the ice thickness, which in turn increases the cross section area of flow, thus water velocity drops. Lower water velocities render the cross

Figure 5 Ice cover thickness condition over deployment site during March 21 to May 21, 2012.

-section suitable for deposition of eroded ice from upstream sections, which locally thickens the ice cover. The sequence of erosion and deposition processes, which is dictated by the flow velocity condition, causes the oscillations in ice thickness as can be seen in Figure 5. As shown in figure 5, there are peaks in ice thickness values during early May which can be identified as thick ice floes passing the deployment site. Passing ice floes are formed as a result of fragmentation of thermally decayed ice cover or ice jam release remnants. The thickness of passing floes decreases in time illustrating the end of the break up period and beginning of the open water condition. 3.2 HYDRAULIC OBSERVATIONS Formation and release of ice jams, and their spatiotemporal re-jamming, causes drastic changes in water level and flow velocity. Figure 6 shows ADCP data for water velocity magnitude under stable ice cover condition and also during the break-up period.

Figure 6 Velocity magnitude and profile during different ice stages, March 21 to May 21, 2012.

Two features are apparent in Figure 6. First, there was a significant increase in water velocity during break up, with water velocities during break up a factor of two larger than those observed under stable ice cover. This occurs for two reasons: 1) Release of water which was stored behind ice jams along the reach which forms longitudinal surges moving downstream, and 2) Increased discharge due to melting of the ice cover and introduction of freshet flows from the watershed. Second, there is a nearly diurnal variation

in water velocity magnitude, both under stable ice-cover and during break up. This diurnal fluctuation corresponds to increases and decreases in flow stage associated with hydro-peaking. 3.3 ICESIM MODEL SIMULATION

Results of calibrated ICESIM simulation for a day in the measurement period are shown in Figure 7, which compares the predicted to in-situ measurements of ice bottom location and water surface elevation at one location in the study reach on March 25 2012 to represent model ability in river ice simulation. As shown, the model simulated the water surface elevation and ice bottom location reasonably accurately, although ICESIM slightly over-predicted the water surface elevation and ice bottom location over the deployment site. Given the one dimensional steady-state nature of the model, and considering that the model employs an “equilibrium condition” assumption for uniform river ice thickness across the cross section, model presents acceptable results in comparison to real measurements at the deployment site which is located almost at the flat part of the reach.

Figure 7 ICESIM simulation model results and real measurements at day 177 of simulation (25/03/2012)

250

Days from October 1st

200 150 100 50 0 0

10

20

30 40 50 60 70 Distance upstream of Gillam Island (Km)

80

90

Figure 8 Ice front locations for ICESIM simulation period (2012)- "Red Dots" are ice front locations according to satellite Images

River ice can be expected to vary across a section. For a symmetrical cross section thicker ice is found at river banks, due to presence of border ice and the more quiescent environment of river banks, which allows for more vertical growth. Thinner ice is usually present in the middle of the section, due to more turbulent water flow and higher velocities that preclude thermal thickening, although mechanical thickening (“shoving”) can occur, particularly in steep reaches. On the other hand, the in situ acoustic measurement is local in nature, thus does not represent the river ice and river hydraulic conditions all across the river cross section. The spatial domain of the ice thickness measurement is limited to some meters. For the vertical beam MF-AWCP this is a function of beam spreading angle. For the ADCP, this is a function of beam spreading angle and the beam angle from vertical, thus the spatial domain is a circle with the diameter almost equal to the deployment depth. Given the potential for spatial variation in ice thickness across a section, utilization of a localized in situ measurement may lead to some discrepancy between predicted and observed ice thickness as the model results provide an estimated average cross sectional ice thickness. It is also notable that the estimation of starting point of ice progression, i.e the time that ice front reaches the Gillam Island from Nelson River estuary, is a key point in model simulation. Therefore, wrong estimation of the time may leads to significant difference in model results in ice thickness aspect as well as propagation distance and rate. In the other word, model is highly sensitive to this factor. Over estimation in ice thickness makes the model to be slightly not precise in propagation rate, especially at the end of the ice period when the thermal weakening starts. Thermal decay weakens the thinner parts of cover as much as they can’t resist against the weight of upstream thicker ice cover and start to fragment. This process makes the ice front to recede as shown in figure 8. Therefore, over estimation in ice thickness affects model accuracy in final ice stages even though the model shows almost accurate simulation during propagation and stabilization periods.

4 CONCLUSION In conclusion, given the model limitations and assumptions, the model performance and response is acceptable and ICESIM implementation is advisable for future applications. However, more investigations in steeper portions of the reach are needed to evaluate the accuracy of the model to predict the mechanical thickening process. Accordingly, we are currently measuring and monitoring ice and river

condition using the same techniques in two separate Nelson River locations. The ICESIM program has and will continue to evolve, as it is a “working” model. The model has been adapted to incorporate new knowledge in river ice engineering as well as to satisfy site specific requirements. As an example of recent developments with respect to the ICESIM model capabilities, it has become apparent that the ICESIM model has been limited in some applications by its inability to consider varying river flows during a simulation. To eliminate this shortcoming, an unsteady version of the “ICESIM” model, which is called “ICEDYN”, has been developed. Future developments being considered by the authors include addition of new subroutines for more comprehensive simulation of river and ice interactions, such as break-up and ice jam simulations, as well as incorporation of sediment transport predictions. 5 ACKNOWLEDGEMENT Authors would like to thank Nathan Lambkin and his crew from Manitoba Hydro for all their help and contributions during field measurements, including deployment and retrieval of instruments. We also acknowledge Mark Lapointe at University of Ottawa Civil Engineering Hydraulic Laboratory for building the deployment mount. This work was funded by Hatch corporation Ltd., Manitoba Hydro (R&D grant) and the Natural Sciences and Engineering Research Council (CRD grant).

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