Collecting data on the dynamic breakup of a river's ice cover is a notoriously difficult task. However, such data are necessary to reconstruct the events leading to the formation of ice jams and calibrate numerical ice jam models. Photogrammetry using images from remotely piloted aircraft (RPA) is a cost-effective and rapid technique to produce large-scale orthomosaics and digital elevation maps (DEMs) of an ice jam. Herein, we apply RPA photogrammetry to document an ice jam that formed on a river in southern Quebec in the winter of 2022. Composite orthomosaics of the 2-km ice jam provided evidence of overbanking flow, hinge cracks near the banks and lengthy longitudinal stress cracks in the ice jam caused by sagging as the flow abated. DEMs helped identify zones where the ice rubble was grounded to the bed, thus allowing ice jam thickness estimates to be made in these locations. The datasets were then used to calibrate a one-dimensional numerical model of the ice jam. The model will be used in subsequent work to assess the risk of ice interacting with the superstructure of a low-level bridge in the reach and assess the likelihood of ice jam flooding of nearby residences.
Photogrammetry datasets useful for calibrating 1D numerical river ice jam models.
Remotely sensed digital elevation models of grounded ice can provide estimates of ice jam thickness.
Remotely sensed large-scale orthomosaics provide insights into ice jam formation and evolution.
Ice rubble hitting the superstructure of a low-level bridge can compromise its structural integrity. For example, on January 12, 2018 an ice run near Sherbrooke, Quebec, Canada, destroyed a low-level railway bridge and a nearby footbridge. The run also caused a brief ice jam that resulted in the St Francois River unprecedentedly rising by 2 m in less than 45 min (Béland 2018). Selecting the elevation of low-level bridges to avoid, or at least reduce, the damage caused by rapidly forming ice runs and ice jams requires a full understanding of the hydrogeomorphologic and cryologic characteristics of the reach. Numerical fluvial ice models can help in this regard if validation measurements such as water and ice elevation profiles, discharges, and ice volume estimates of the event are available (Carson et al. 2011; Lindenschmidt 2017; Blackburn & She 2019). However, the unpredictability of most dynamic ice events usually makes gathering such data difficult. Recently, remotely piloted aircrafts (RPAs) have allowed geoscience researchers to obtain detailed digital elevation models (DEMs) of their subjects using aerial structure from motion (SfM) photogrammetry (Westoby et al. 2012; Ely et al. 2017; Albadra et al. 2020; David et al. 2021). The technique generates high-resolution DEMs of an area of interest, such as an ice jam, from overlapping aerial photographs taken during a short flight. Most commercial photogrammetry software also generate high-resolution coloured orthomosaics of the area, which provide a useful tool to assess ice jams at full scale and gain insights into how they formed.
At the spatial scale of an ice jam, aerial photogrammetry can provide advantages over other remote sensing techniques. For one, a team can be dispatched to fly at specified time intervals as the ice jam evolves. This is not possible for satellite-based radar and optical remote sensing systems, whose temporal resolutions are determined by the orbit of the satellite (Los & Pawlowski 2017; Zhang et al. 2019). Furthermore, a single RPA photogrammetry flight can produce a DEM and an orthomosaic of the ice jam with ground resolutions on the order of 1–5 cm (Alfredsen et al. 2018), thus providing far greater detail than satellite remote sensing systems, whose spatial resolutions are of the order 10–100 m (Los & Pawlowski 2017; Lindenschmidt & Li 2018). Also, the low altitude of the RPA (typically 50–100 m above the river's water level) means the camera is generally unaffected by cloud cover, which is often an issue with satellite-based optical sensors (Tracy & Daly 2003; Unterschultz et al. 2009; Chu & Lindenschmidt 2016). Exceptionally, light detection and ranging systems (LiDAR) can also provide high-resolution DEMs and orthomosaics (if equipped with a mapping camera), yet at 1- to 2-order-of-magnitude greater cost than popular photogrammetry RPAs. Finally, by using a light-weight RPA, such as DJI's Mavic 3 Mini, which weighs less than 250 g, many regulatory flight constraints imposed by transport agencies are not applicable thus simplifying flight logistics over ice jams in densely populated regions.
Despite these advantages, RPA photogrammetry has received little attention in the study of fluvial ice processes. Alfredsen et al. (2018) used the technique to extract 2D elevations and characterise ice type information of an ice jam and estimate the thickness of the ice by subtracting a measured bathymetry from the photogrammetrically derived DEM of grounded ice sections. They also used the orthomosaics to identify key features of the ice jam's formation. Rødtang et al. (2021) used DEMs derived from RPA photogrammetry to estimate ice thicknesses and volumes on a steep reach of river in Norway prone to forming complex successions of anchor ice jams. The technique proved adequate for measuring ice thicknesses and elevations over hard, or impossible, to access alpine reaches in difficult terrain. Ehrman et al. (2021) quantified the roughness of various intact ice covers using DEMs generated by RPA photogrammetry and used statistical techniques to distinguish between smooth and rough ice types. Ehrman et al. (2021)'s roughness estimates were used to predict the Manning's roughness coefficient of the underside of the ice cover. These studies have shown the promise of RPA photogrammetry for documenting fluvial ice processes such as ice jams, yet its potential to assist in numerical modelling of ice jams has yet to be assessed.
Numerical fluvial ice models have been used most notably to predict ice jam flood hazards and risks (Das et al. 2020; Das & Lindenschmidt 2021), select the height of dikes (Lindenschmidt & Rokaya 2019), forecast ice jam water levels (Williams et al. 2021; Das et al. 2022), and assess how in-stream infrastructure such as low-level bridges are affected by ice jams (Daly & Vuyovich 2003; Zufelt 2005). High water levels are frequently used to calibrate numerical ice models. In many cases, such data can be derived from photographs (Lindenschmidt et al. 2012), pressure transducers placed preemptively in locations prone to dynamic breakups (Beltaos 2019), or surveys along the bank of an ice jam after it has set in place (Beltaos 2018; Rokaya et al. 2019). However, these methods generally provide sparse information on ice jam elevations and are prone to miss relevant topological information. For example, as discharge recedes after the formation of an ice jam, rubble ice may ground on gravel bars near the banks (Alfredsen et al. 2018), while sinking lower into the thalweg in others, causing an unequal lateral ice elevation profile to develop. Also, as rubble ice enters bends and meanders, it can force the outer sections of the ice jam higher, resulting in an elevated shear wall compared to the surrounding ice (Beltaos & Burrell 2010). Furthermore, large boulders and obstructions in the river may force ice rubble to flow around them, causing one side of the ice to heave upwards resulting in significant lateral elevation variations. Accounting for spatial variability is good practice to accurately calibrate a numerical ice jam model or reconstruct the events leading to the jam's formation. Despite its importance, this variability has frequently been overlooked, likely in part due to a lack of a low-cost and easily deployable technique to obtain high-resolution elevation maps of the ice jam.
In this study, we use DEMs and orthomosaics obtained from RPA photogrammetry to study an ice jam that formed on the Aux Saumons River in the province of Quebec, Canada in 2022. The datasets are also used to calibrate a 1D numerical fluvial ice model, specifically by using the ice elevation DEM to calibrate the ice elevations predicted in the numerical model. The model will be used in subsequent work to assess the likelihood of ice interacting with a low-level railway bridge on the reach and develop an ice jam flood hazard map for nearby residences. The strengths and weaknesses of the RPA photogrammetry datasets are examined, and practices are suggested to increase the quality of RPA photogrammetry datasets for calibrating and validating numerical ice models. Our findings are of interest to those involved in documenting and numerically assessing the hazards posed by ice jams to riverine communities in northern climates.
STUDY SITE AND METHODS
Study site and ice jam description
Discharges and water levels
Meteorological events causing the ice jam
A rain on snow event on February 17, 2022 caused excessive snow-melt run-off and a rapid rise in the Aux Saumons River's water level. Between 28 and 35 mm of rain were recorded at numerous weather stations in the region around the Aux Saumons' watershed on February 17, a day prior to the formation of the ice jam on February 18. This initiated the dynamic breakup of the ice cover and ultimately the ice jam downstream of the dam on the Aux Saumons River. Because the precipitation event happened mid-winter, the ice cover on the Aux Saumons was intact and structurally competent with no thermal degradation prior to the formation of the ice jam. After the formation of the ice jam, the air temperature dropped down to between −5 and −15 °C until February 22, when air temperatures rose above zero again to 6.5 °C. A total of 15 mm of rain was recorded on February 22 and was responsible for a rapid rise in the water level observed at the gauging station between 21 and 22 February (see Figure 2(b)).
Photogrammetry acquisition and processing
On February 21, 2022, two photogrammetry flights were performed using a remotely piloted DJI Mavic Pro 2 aerial RPA. The RPA is equipped with a global navigation satellite system (GNSS) using GPS/GLONASS for positioning and a colour camera with a resolution of 3,840 × 2,160 pixels. The RPA was flown on a preplanned flight grid with the camera pointing downward in the nadir position. The first flight extended 650 m upstream of the railway bridge to the dam and collected 638 images, whereas the second flight went 1,500 m downstream of the railway bridge to the confluence and collected 829 images. The flight upstream was composed of five flight lines while the downstream flight had four. The flights were programmed in the flight planning software DroneLink to have a sufficient overlap between the images for accurate post-processing (a least 75% lateral and longitudinal overlap).
For the upstream flight, six ground control points (GCPs) were placed on, or near the railway bridge, and another GCP was placed near the road bridge upstream. The downstream flight only had four GCPs on the foot bridge and two GCPs near the railway bridge. The coordinates of the GCPs were taken using a GNSS-RTK receiver with a precision of ±2 cm. On March 18, 2022 a single flight was performed over the section between the railway bridge prior to the jam's release on March 20, 2022. No GCPs were placed during this flight as it was originally intended to obtain an orthomosaic to visually assess the degradation of the ice jam.
The photos from both dates were processed in the commercial photogrammetry software Pix4D version 4.6.3. The software uses a Structure from Motion (SfM) algorithm to create a three-dimensional point cloud of the ground's elevation and notable landmarks such as bridges or dams. The point cloud was used to derive a DEM with a spatial resolution of 5 cm. A coloured high-resolution (0.7 cm per pixel) orthomosaic of the entire flight zone was also output from Pix4D.
During processing, the GCPs were used to georeference the DEMs and orthomosaics of the flights of February 21, 2022. The root-mean-squared error (RMSE) values of the targets visible in the orthomosaics from their measured locations using the GNSS were both 0.02 and 0.02 m in the lateral and longitudinal directions, respectively. The RMSE between the elevations obtained from the DEM and the measured GNSS elevations was 0.02 m in the upstream portion. The RMSE values were similar for the downstream flight, however the DEM was found to be artificially bowed which introduced important errors in the elevations of the flood plains compared to a provincial LiDAR database. For this reason, only the upstream DEM is considered to have valid elevations to be used in any further analysis. The upstream DEM of February 21, 2022 was validated against the provincial LiDAR dataset in addition to the GNSS measurements of the GCPs. The vertical offsets between DEM and LiDAR were less than 30 cm on the flood plains and often less than 10 cm on hard features such as the adjacent road and bridges.
HEC-RAS ice modelling description
HEC-RAS is a popular 1D–2D numerical model developed by the United State's Army Corps of Engineers to model free surface channel flows. However, it can also model the effect of static ice covers and wide-river ice jams (equilibrium ice jams) on a channel's hydraulics in 1D only. For reaches of gradually varied open-channel flow, the energy equation is used at each cross-section to obtain an estimate of the river's water surface level. At sections containing rapidly varied flow (e.g. hydraulic jumps, contractions, expansions, ice jams) HEC-RAS instead solves the momentum equation to estimate the water surface level. The momentum equation accounts for how forces acting on a control volume between two cross-sections alter the momentum of the fluid within it. The forces acting on the control volume are the hydrostatic pressure forces, the gravitational force and the frictional forces acting on the control volume (i.e. shear stress on the bed). Both the energy and momentum equations are modified to account for the effects that a static ice cover or an equilibrium ice jam have on water surface levels.
For ice jam simulations, HEC-RAS assumes steady-flow conditions, which is a limitation of the model. In addition, the model is sensitive to the distance between sections, which can lead to instabilities (Beltaos & Tang 2013). The latest versions of HEC-RAS can interpolate bathymetric sections to create a 2D surface, making it easier to add interpolated sections to ensure model stability.
When simulating a wide-river ice jam, in addition to discharge and boundary conditions, the user must specify (1) the location and ice thickness of the toe and head of the ice jam, (2) Manning coefficient of the ice, which can be estimated according to depth and the ice thickness by HEC-RAS using the equation of Nezhikhovsky (1964), (3) ice parameters including the porosity of the rubble comprising the jam, usually taken as 0.4 and the ratio of the lateral-to-longitudinal internal stresses within the rubble mass () usually taken as 0.33, (4) the maximum allowable flow velocity underneath the jam () with default value of 1.5 m/s, yet recommended to be higher by Beltaos & Tang 2013, and (5) the internal friction angle of the rubble comprising the jam () with default value of 45°, but can be varied to calibrate the simulation (Beltaos & Tang 2013; Beltaos 2018, 2019).
Proposed method to calibrate a fluvial ice model with photogrammetry DEMs
Conventionally, the internal friction angle and the ice thickness at the toe and head of an ice jam can be adjusted according to observed elevations, usually taken at hydrometric stations at a few locations within the ice jam. The sparsity of observations limits the quality of the calibration. However, elevations extracted from a photogrammetry DEM provide much greater resolution, but are generally acquired after the ice jam formed when the discharge was significantly lower. This highlights an important drawback with using photogrammetric DEMs to assist in the calibration of a numerical ice jam model – it is very difficult to perform an RPA flight at the time the ice jam forms, and because of decreasing discharges in the falling limb of the hydrograph, the resulting DEM will nearly always be an underestimate of the ice jam's top of ice profile during its formation. Thus, a method for calibrating ice parameters is proposed to use photogrammetry DEMs most effectively according to the following five steps:
Perform a wide-river ice jam model using the flow at the time of ice jam formation to obtain simulated ice thickness and elevation profiles.
Perform a static ice model using the flow at the time of photogrammetry acquisition using the ice thicknesses simulated in step 1.
Compare the static ice model's top of ice elevation profile with the photogrammetry DEM.
Redo step 1 after modifying and the thicknesses at the toe and the head of the ice jam and then repeat steps 2 and 3.
Iterate steps (1) through (4) until a satisfactory profile is obtained.
Ice jam description using orthomosaic images
Ice thickness estimates
As the discharge receded in the days after the ice jam formed on February 18 (from 38 to 11 m3/s), the water surface elevation at the gauging station dropped by ≈1.4 m from a peak of 149.8 m recorded on February 18 to the elevation of 148.4 m recorded on February 21 (see Figure 2). When this happened, the ice to the right slumped lower into the deeper thalweg, and the ice to the left grounded on the bed at cross-section II. This created sufficient vertical shear and bending stresses in the ice jam to form the longitudinal crack. Further evidence that the ice jam descended is noted by the elevated regions of ice left higher along the banks in the DEMs at numerous locations after the central portion of the ice jam descended lower. Long hinge cracks along the banks visible in the orthomosaic of February 21 also provide evidence of the central section of the ice jam slumping lower as the discharge receded. Also, the same blocks of ice and woody debris to the left of the crack in the February 21 orthophoto are still visible in the March 18 orthophoto (Figure 4(d)). However, a preferential flow channel through the ice near the right bank thermally and mechanically degraded the ice there, explaining why open water is visible in this location of the March 18 orthomosaic. The fact that the same pieces of ice rubble and woody debris are present in the orthophotos on both dates provides evidence that the ice jam was grounded on the bed in these locations, which prevented it from being degraded by active flow.
The orthomosaic from March 18 for section IV shows evidence of longitudinal and lateral cracking consistent with ice grounded on the rocky riffle section downstream of the dam (see Figures 4(d) and 6). The lateral elevation profile from the March 18 DEM in Figure 5(d) shows an erroneous elevation profile between 22 and 40 m from the left bank, followed by an irregular 20 m wide profile corresponding to the cracked grounded ice, before another section of mixed ice and open water near the right bank.
We have used information available in the orthomosaic (i.e. cracking) and in the DEM (i.e. locally raised sections near the banks and raised sections on shallow bathymetry) of February 21, 2022 as compelling evidence of grounded sections of ice along the ice jam. However, due to a number of practical constraints, including safety considerations, lack of trained personal and difficulties measuring the thickness of a nearly 4-m thick ice jam, it was not possible for us to go out on the ice jam to measure its thickness. Future efforts might benefit from ground penetrating radar to measure the ice thickness or potentially using specialty drilling equipment capable of attaining depths on the order of 3–5 m. The effectiveness of such methods needs to be addressed in future work.
Photogrammetry to calibrate a 1D HEC-RAS model of the ice jam
Model setup and open water calibration
The model was first calibrated for open water conditions using flow data measured with an acoustic Doppler current profiler (ADCP) upstream of the dam and water levels measured along the reach downstream of the dam with a GNSS. Data were acquired on March 21, 2020 (38.1 m3/s) and March 26, 2021 (37.8 m3/s) in the context of an entirely different project. The March 21, 2020 water level measurement was done with a GNSS on a kayak and provided a water level profile from the dam to the confluence. In contrast, only three water surface level points were taken on March 26, 2021.
A normal depth boundary condition was defined at the downstream end of the HEC-RAS model. The slope at the downstream boundary was adjusted to correspond to that of the normal depth in this location. Afterwards, a Manning's coefficient of 0.028 was determined to accurately reproduce the measured water surface profile over the first 600 m upstream of the confluence and a Manning's coefficient of 0.038 was used for the remainder of the reach. The slope set at the downstream boundary condition was 0.0001. Figure 7 shows the calibrated and observed levels for March 21, 2020.
Ice jam modelling
The model was calibrated according to the procedure described in subsection 2.6. The ice thickness estimate of 3.4 m was used as the first ice thickness estimate for the head of the ice jam. The model showed little sensitivity to ice thickness at the toe of the jam, and this value was estimated at 0.7 m. A value of 42° gave the best results. This value is lower than the HEC-RAS's default value. Beltaos & Tang 2013 obtained higher values for this parameter. However, the rivers studied were much wider than the Aux Saumons River, which could explain this difference.
The simulated thicknesses at cross-sections I, II, III, and IV are, respectively, 4.18, 2.39, 2.83, and 1.0 m, respectively. The thicknesses measured from the photogrammetry DEM correspond at station I (4.0 m) but are higher at stations II (4.2 m), III (4.2 m), and IV (3.4 m). Higher simulated elevations are obtained at nearby sections. Uncertainties related to the bathymetry may also help explain the differences between the thicknesses estimated from the photogrammetry DEM and the simulated ice thicknesses using HEC-RAS. In general, the top of ice elevation profile extracted from the photogrammetric DEM aligns well with the predicted top of ice elevations of HEC-RAS (Figure 8(b)).
STUDY LIMITATIONS AND AREAS FOR IMPROVEMENT
Tree branches obscuring direct views of the ice jam limit the quality of the photogrammetrically derived DEM. Generally, a higher level of confidence can be placed in the elevations estimated in the center of the channel, yet much less so near the banks where the views of the ice are obstructed by tree branches. If elevations near the banks of the river are of special interest, then a RPA equipped with a LiDAR payload and a camera may be more appropriate. The high frequency LiDAR scan can provide detailed ground elevations even through thick vegetation (Bigdeli et al. 2018) and the photographs can be used to produce an orthomosaic and add colour to the LiDAR point cloud. However, a LiDAR-equipped enterprise-grade RPA is currently at least an order of magnitude more expensive than a consumer-grade RPA equipped with an optical camera suitable for performing photogrammetry.
Placing ground reference points at regular spacings along the entire flight area is recommended to obtain the highest quality DEMs (Alfredsen et al. 2018; Kalacska et al. 2020). However, various constraints such as restricted access to private property, limited sunlight hours, large spatial scales involved, and limited access to the opposing shore, due either to the river itself, or the absence of a bridge can make placing sufficient ground reference points difficult. An RPA equipped with a real-time kinematic module (RTK) can be used to provide centimeter accurate geotagged locations of each image (Kalacska et al. 2020). An RTK-equipped RPA will likely considerably reduce the dependency on ground reference points (Kalacska et al. 2020), thus freeing time for personnel to perform flights over a larger area of the ice jam.
An improved DEM could have been attained for the purposes of calibrating a 1D numerical ice model if the RPA flight was performed on the February 18, 2022 immediately after the ice jam formed. This might have allowed the ice jam to be modelled in a single step using the wide-river ice jam module of HEC-RAS, instead of the iterative process described in subsection 2.6. A single step approach would reduce much of the uncertainty in the predicted ice jam surface profile, as the ice jam would not have to be subjected to excessive grounding, slumping and ice erosion. However, this was not possible due to various practical constraints such as climatic conditions and a lack of available personnel trained in the operation of the RPA.
Even though the measured and modelled elevations and ice thicknesses are similar, some deviations are noted that result from difficulties modelling the 2022 Aux Saumons River ice jam. For one, HEC-RAS assumes that water always flows under the ice jam in Figure 8(b), where in reality there is clear evidence of grounded ice in the DEM (see section 3.1). Thus, the modelled underflow is not completely realistic and ultimately introduces errors into the predicted ice surface profile and ice jam thicknesses; however, this assumption is unavoidable for the model to maintain continuity. Another possible explanation for the small deviations could be that HEC-RAS does model narrow ice jams. It is possible that the 2022 Aux Saumons River ice jam behaved more like a narrow ice jam rather than a wide-river ice jam as modelled. Narrow jams occur when, in addition to ice juxtaposing upstream of an ice cover, frazil flocs and pans also submerge under the ice cover and deposit on its underside (Lindenschmidt 2020). This phenomenon is not modelled in the equilibrium ice jam equation. Additionally, the fact that the HEC-RAS models were run in steady-state, whereas in reality the ice jam was highly transient, as noted by the fluctuations of discharge and water level in Figure 2, is bound to bring some uncertainty into the modelled results. Finally, HEC-RAS cannot account for ice erosion processes inducing changes to the predicted ice thickness profiles. Exploring other one-dimensional models (e.g. RIVICE and River1D) capable of modelling narrow ice jams, transient ice jam formation and ice erosion processes may provide more accurate predictions of the ice surface elevation and thickness profiles of the Aux Saumons River ice jam in 2022. Despite its limitations, HEC-RAS remains a simple to implement and widely used hydraulic model, and consequently exploring the potential of photogrammetry to calibrate HEC-RAS ice jam models is a useful exercise for researchers and practicing hydraulic engineers.
The requirement of 1D models to often force flow through a narrow area above the bed limits their ability to model ice jams containing grounded sections. Evidence of flow moving around grounded sections in the Aux Saumons ice jam is apparent by the contiguous region of lower elevations in the DEM of Figure 4(a). Also, large parts of this region turn to open water in the March 18 orthomosaic (Figure 4(d)), providing further support that the flow there caused thermal degradation of the ice jam in these locations as it flowed around the grounded sections between the time it set in place on February 21 to the March 18. Two-dimensional models such as CRISSP2D or the KHIONE module of Telemac-Mascaret may help overcome the limitations inherent to 1D ice modelling approaches when attempting to model flow through ice jams with grounded sections. However, to the authors' knowledge, modelling heavily grounded ice jams using a 2D numerical model has not yet been attempted and future research on this topic should prove beneficial.
RPA photogrammetry has been demonstrated to be a low-cost, rapidly deployable technique for gathering high-resolution spatial data of an ice jam for calibrating and validating a 1D numerical ice jam model. Inspecting the large-scale orthomosaics can reveal information about the ice jam's condition and how it formed, information that would otherwise go unnoticed on the ground. Longitudinal hinge fractures near the banks, showing where the ice jam sagged lower as the flood waters receded, or central cracks, indicating where the ice jam grounded near one bank, but sagged lower into the thalweg near the other bank, are two examples. Furthermore, frozen puddles of water visible in the flood plain provide useful information on the extents of overbank flooding during the formation of the ice jam. Moreover, photogrammetry DEMs can be used to extract continuous longitudinal elevation profiles of the ice jam's surface, allowing a numerical model's simulated ice profile to be validated. Finally, if grounded locations in the DEM can be identified and bathymetric measurements are available, estimates of ice jam thickness in these locations can possibly be obtained (using a similar approach to Alfredsen et al. 2018), thus providing additional calibration data to improve numerical modelling results.
The modelling of the 2022 Aux Saumons River ice jam highlighted an important limitation of one-dimensional numerical models such as HEC-RAS: they are unable to model grounded sections of the ice jam or the 2D channels that the flow follows to move around them. Rather, as the underside of the ice jam approaches the bed, the models maintain continuity by preventing the underside of the ice jam from descending all the way to the bed. This is unrealistic. However, because of the many advantages of 1D models (e.g. ease of use and speed), future development efforts to accommodate the effects of ice grounding would be beneficial to improve the versatility of these handy one-dimensional ice models. Alternatively, investigating the potential of two-dimensional fluvial ice models such as CRISSP2D or Telemac-Mascaret's KHIONE ice module to account for 2D flow around grounded sections of ice may produce a more accurate representation of the ice jam, yet at the cost of considerably greater computational requirements. Finally, regular RPA flights over the ice jam could provide temporal information on the ice jam's state and elevations, which could be used to validate transient simulations of the ice jam as it responds to recorded discharge measurements and its eventual release. In summary, our study has shown RPA photogrammetry to be a convenient remote sensing technique to supplement traditional in situ measurements to acquire data to development and calibrate numerical ice jam models.
The authors would like to thank Nicolas Simard and Daniel Breton for lending a hand during the collection of the field data. We also extend our gratitude to the MinistÃ¨re des Affaires municipales et de l'Habitation of Quebec for providing financial support to carry out this work. Partial funding was also provided by the Global Water Futures programme at the University of Saskatchewan and NSERC's Discovery Grant funding programme. We would also like to thank the MinistÃ¨re des ForÃªts, de la Faune et des Parcs of Quebec for providing access to their LiDAR database and the MinistÃ¨re de l'Environnement, de la Lutte contre les changements climatiques, de la Faune et des Parcs of Quebec for providing the gauging station data.
DATA AVAILABILITY STATEMENT
All relevant data are included in the paper or its Supplementary Information.
CONFLICT OF INTEREST
The authors declare there is no conflict.