Spatial Modelling of Ice-Jam Susceptibility in Southern Quebec: An Enhanced Ice Jam Predisposition Index

Mirzaei, Elnaz; Chokmani, Karem; Lhissou, Rachid; Homayouni, Saeid

doi:10.5194/egusphere-2026-2457

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https://doi.org/10.5194/egusphere-2026-2457

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15 Jul 2026

| 15 Jul 2026

Spatial Modelling of Ice-Jam Susceptibility in Southern Quebec: An Enhanced Ice Jam Predisposition Index

Elnaz Mirzaei, Karem Chokmani, Rachid Lhissou, and Saeid Homayouni

Abstract. Ice jams represent a recurrent natural hazard in Quebec, where geomorphological and structural river characteristics play a key role in determining their spatial distribution. This study improves and evaluates the Ice Jam Predisposition Index (IJPI) using a dataset of 35 rivers across southern Quebec. The enhanced model integrates geomorphological parameters, including channel narrowing, sinuosity, and slope, along with structural (bridge) and hydrological (tributary) features to delineate reaches prone to ice-jam formation. Including rapids as an inverse indicator helps distinguish high-energy segments where jams are unlikely to occur. Model validation shows that over 90 % of observed ice jams occurred in reaches with medium to high predisposition, with an overall recall of 0.90, indicating promising performance as a screening tool. The results highlight the dominant influence of flat slopes, channel confinement, and curvature on jam formation, suggesting that the model can serve as a useful spatial tool for regional ice-jam risk management. These results demonstrate the potential of the proposed framework to support operational flood risk management by allowing proactive identification of vulnerable river sections. Decision-makers and emergency management agencies can use the model to prioritise monitoring efforts and improve preparation for ice-jam-related hazards.

Received: 28 Apr 2026 – Discussion started: 15 Jul 2026

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Elnaz Mirzaei, Karem Chokmani, Rachid Lhissou, and Saeid Homayouni

Status: final response (author comments only)

RC1:
'Comment on egusphere-2026-2457', Anonymous Referee #1, 24 Aug 2026

This is a well written paper and should be of wider interest to the journal’s readership. I recommend publication after the following comments have been addressed:
Line 36: "yet to be conducted"? On what? Fort McMurray? That flood was in 2020 but your citation is from 2009.
Line 50: “limited progress has been made in predicting floods associated with river ice break-up” – I don’t agree with this. There has been a lot of work forecast breakup using neural networks, fuzzy logic, etc.
Line 61: “ice-jam forecasting” or predicting breakup? In line 50 you focused on breakup predictions.
Line 231: “tributary-related reduction” – why is the main channel width reduced with the presence of a tributary?
Line 232: “bridge and tributary coefficients” – it would be interesting to see how sensitive these coefficients are to the outcome; if the values are too high, perhaps they overwhelm the end result, potentially setting too much of a bias towards bridges and tributaries?
Line 243: “0 < NI ≤ 1” – is a value of NI = 0 even possible?
Line 249: I don’t understand why you need to categorise NI into classes (1, 2 and 3) – makes it harder to carry out a sensitivity analysis; why not just use the NI value?
Line 286: {0, 1, 2, 3} – also for sinuosity, why categorise SI into classes and not use the actual sinuosity value instead; this hinders any sensitivity analysis to be made later.
Line 301: you’re assuming that the slope of the water surface is the same as the slope of the riverbed – can you justify that please? How do you know that steady state conditions prevailed during the time the surface elevations were acquired?
Line 305: |𝑍up,𝑖 −𝑍down,𝑖 | - why an absolute value? Is the effect of a slope increase in the flow direction the same as when there is a sudden flattening out of the slope?
Line 318: The min-max bounds of the SI class ranges are set logarithmically (max = 10 × min) whereas the NI class bounds are set linearly – won’t that incorporate a bias of how much influence each of these indices exhibit on the outcome?
Line 477: Can you please also define “recall” with more detail, like you did with “Precision” here?
Line 513: typo: “predictionsremained” should be “predictions remained”
Line 595: In the table, it would have been nice to see how much overlap there is in characteristics, i.e. how many of those counted segments had two or more factors. Were there segments that had both narrowing and low-radius meanders (high sinuosity), and what percentage? Also, which would be the stronger feature – NI or SI?

Citation: https://doi.org/10.5194/egusphere-2026-2457-RC1
- AC1: 'Reply on RC1', Elnaz Mirzaei, 26 Aug 2026
  
  We appreciate your careful reading of our manuscript and your constructive comments. Please find our responses to each comment below.
  
  Line 36: The citation to Beltaos and Prowse (2009) refers to the general statement about the economic costs of ice-jam floods and the lack of a comprehensive economic assessment, rather than specifically to the 2020 Fort McMurray flood. We agree that the current wording may cause confusion and will clarify this in the revised manuscript.
  Line 50: The intention was to highlight the remaining challenges in reliably predicting ice-jam flooding, rather than to suggest that little work has been done on river ice break-up forecasting. We recognize that the original wording did not make this distinction clear, especially given the existing work using neural networks, fuzzy logic, and other approaches. We will revise the sentence to clarify this point.
  Line 61: The two terms refer to related but distinct prediction problems, and the wording in this section does not make that distinction clear. We will revise this part to distinguish more clearly between predicting river ice break-up and forecasting ice-jam occurrence.
  Line 231: The presence of a tributary does not represent a physical reduction in the width of the main channel. Rather, following De Munck et al. (2017), it is treated as an equivalent narrowing to represent the potential obstruction to ice transport caused by ice entering the main channel at a confluence. In this approach, the reduction is based on the tributary width at its outlet, giving greater influence to larger tributaries. We will clarify this point in the revised manuscript.
  Line 232: The bridge and tributary coefficients were adopted from De Munck et al. (2017) and were not independently calibrated in this study. We agree that their magnitude may influence the final IJPI and that a sensitivity analysis would be useful to quantify this effect. This will be acknowledged as a limitation of the current approach and considered in future work.
  Line 243: We checked the NI values across all reaches of the 35 rivers and confirmed that NI did not reach zero or take a negative value anywhere in the dataset. Thus, W_modified remained positive for all reaches considered in this study. The current implementation does not explicitly impose a lower bound on W_modified. Although zero or negative values did not occur in our dataset, we will add an explicit floor on W_modified in the revised implementation as a safeguard.
  Line 249: NI was classified to use the same ordinal scale as SI and slope in the IJPI calculation and to remain consistent with the original approach of De Munck et al. (2017). We acknowledge that this classification limits the detail available for sensitivity analysis. Using continuous values could be considered in future work.
  Line 286: SI was classified for the same reason as NI, to place it on a common ordinal scale for aggregation in the IJPI. We recognize that this classification limits sensitivity analysis using continuous SI values, which could be explored in future work.
  Line 301: Slope was calculated from the DEM elevations at the upstream and downstream endpoints of each reach, as described in Eq. (5). Examination of several cross-sections of the DEM showed that the elevation surface is nearly flat across the river channel, suggesting that it represents the water surface rather than the underlying riverbed. Therefore, we cannot confirm that steady-state flow conditions prevailed at the time of LiDAR acquisition. We will clarify in the revised manuscript that the calculated slope should be considered a proxy for the longitudinal channel gradient rather than a direct measurement of the riverbed slope.
  Line 305: The absolute value is used only to calculate the magnitude of the local slope. Flow direction is preserved by ordering the reaches from upstream to downstream. A downstream decrease in slope is treated separately in the slope-break calculation, where only a reduction in slope relative to the upstream reach is retained; an increase in slope is assigned a value of zero.
  Line 318: Both NI and SI are ultimately converted to the same ordinal scale {0, 1, 2, 3}, so the different spacing of the original class boundaries does not directly assign different weights to the two indices. However, the choice of thresholds can influence how reaches are distributed among the classes and may therefore indirectly affect the final IJPI.
  Line 477: Recall will be defined more explicitly as the proportion of observed ice-jam locations correctly identified by the model, consistent with the level of detail provided for Precision.
  Line 513: The typo will be corrected to “predictions remained”.
  Line 595: Of the 432 jam-intersecting segments, 61 (14.1%) exhibited both Narrowing ≥ 2 and Sinuosity ≥ 2. Regarding their relative influence, the frequency of occurrence within jam-intersecting segments alone does not establish which factor has greater discriminative strength, as this also requires comparison with their occurrence in non-jam reaches. The current weights (Section 3.4) reflect the observed spatial correspondence between predictor classes and documented ice-jam locations.
  
  We hope that our responses adequately address the points raised.
  
  Sincerely, The Authors
  
  Citation: https://doi.org/10.5194/egusphere-2026-2457-AC1
RC2: 'Comment on egusphere-2026-2457', Anonymous Referee #2, 25 Sep 2026

This paper presents an extension/refinement of a pre-existing geomorphically-based tool for predicting ice jam locations. The new tool presented reports an improvement of recall from 77% to 90%. However, I cannot recommend this paper for publication, there are too many problems. First, in the introduction (and later) the previous literature has been misrepresented, often citations are given to support comments, where the cited paper does not mention the comments. Second, many aspects of the methods are not described in enough detail for the reader to understand what was done and why. Third, the choice to ignore slope changes in preference for absolute slope is not supported by the literature (in general, and as presented in the paper). The tool appears to have been built without a separation of “validation” data; that is, all the data were consulted when the tool was built. Therefore, the results appear to represent “calibration” results and not how the tool will perform when applied to regions it has not seen. Fourth, the paper does not quantify false positives, except for 1 out of the 35 rivers. I accept that “false positives” do not necessarily take on their traditional meaning here because of the potential incompleteness of the observation data, but omitting a discussion on the topic is not rigorous. Overall, there are many small problems and the study seems incomplete: I would like to see validation or evidence of transferability (even if an example) before a paper can be published. My specific comments are below.
Review Notes by Line:
Line 13: What is “the enhanced model”? What has been enhanced? Did you enhance it? Sentence unclear - reader needs more context (even in the abstract).
Line 17: “channel confinement” are you using this to mean the same thing as “channel narrowing” . “Confinement” has a specific geomorphological meaning that describes a channel’s relation to the valley walls. Be specific and scientifically precise.
Line 17: “curvature” are you using this to mean “sinuosity”? These are not the same things, curvature is a local property, sinuosity is reach-based. Be specific and scientifically precise.
Line 24: First sentence needs a citation.
Line 25: Sentence starting “The annual cycle…”: Sentence vague and scientifically imprecise: do you need a "respectively" in there, or something else?
Line 28: “Existing ice blocks” - by “blocks” do you mean “pieces of ice” or “an ice blockage”? If you mean “pieces of ice” - does the ice need to be in “blocks” to cause an ice jam? Wouldn’t an intact ice cover also cause an ice jam? This is inaccurate, revise.
Line 30: Do ice jams always “gradually” extend in length. They do not always.
Line 30: “which contributes”: what is contributing? The sentence is not clear or it is inaccurate, or both.
Line 31: “Tends to be extensive”. This implies that ice jams always cause flooding and always cause damage. This is not the case, and the sentence is scientifically imprecise.
Line 32: There is no reference for U.S. Army Corps of Engineers (2004) in the reference list.
Line 33: Is this in 2004 dollars?
Line 34-35 - $500 million CAD damage - is this insured or uninsured damage? Is this 2025 dollars?
Line 36 - 38: One sentence says that a comprehensive damage assessment has not been done, next sentence gives an estimate of damages. This appears contradictory, revise to explain.
Line 56: “fluid-dynamic” - does White (1996) actually talk about “fluid dynamic” models or “hydraulic” models. Use the correct terminology.
Line 57: “chaotic”. Ice jams can be described as "chaotic" in the vernacular definition of the word. “Difficult to predict” is more technically correct here.
Line 63: “ice maturity” what is “ice maturity” what does it have to do with the rest of this paragraph? Suggest deletion or clarification?
Line 65: “Channel morphology plays a vital role in the occurrence and formation of ice jams along rivers (Lindenschmidt and Das, 2015). This was not a conclusion of the paper Lindenschmidt and Das (2015), but was a clearly-stated assumption. Further, this paper was a case study for a single river, it does not support the sentence at Line 65, which seems to purport that this assumption applies to rivers in general. The underlying literature has been mis-characterized.
Line 88-89: “delivering valuable insights….” seems like marketing talk. Doesn’t have a place in a scientific article. Revise or delete.
Line 94 and 98: “rapids”: Rapids have not so far been discussed in the introduction. There has been no case made for including them in any improved model.
Line 106-108: “Extensive studies” and “primarily governed by channel morphology”: Beltaos (2008a) and Das et al. (2017) are not “extensive”, nor do they conclude that ice jams are primarily governed by channel morphology in a general sense. Das et al. (2017) focuses on one location only (Fort McMurray). Beltaos (2008a) is a review paper that mentions channel morphology only once, in a general sense. The underlying literature has been mis-characterized.
Line 125 (Figure 1): “Slope variation” and “Longitudinal slope changes”: these phases in the figure imply the method uses “changes” in slope in the model. The authors state at lines 311-318 (and later lines) that only “slope” is used in the model, that changes in slope are not used. Tributaries, bridges, rapids should be explicitly included in this figure.
Line 130: “35 rivers” Please include a list of the rivers used in supplementary data. Which 12 are particularly vulnerable?
Line 130: “were selected”. What was your selection criteria? Are these all the rivers in the MSP database that have ice jams? If not, why these 35?
Line 33: “corroborated through expert consultations”: Who were the experts? If you don’t want to name them, where do they work? What are their credentials? Most importantly: what were their criteria for choosing the 35 rivers?
Line 135: “Lindenschmidt and Das, 2015”: the cited doesn’t mention the climate of your study area, or prolonged freeze-thaw cycles, or frequent mid-winter thaws or high freshets. That paper is a case study in a subarctic river with a totally different climate. Why are you citing this paper here?
Line 138 (Figure 2): Part a of this figure makes me think ice jams happen everywhere on the rivers.
Line 138 (Figure 2): It would be helpful to show on this figure how this reach was discretized into 250 m sections.
Line 139 (Figure 2): Caption should be clearer that all 35 study rivers are shown.
Line 141: “alignment”: do you mean "location"?
Line 149: “River centrelines, islands…” was river width also derived from this dataset?
Line 154: “morphologically complex”: vague, what does this mean?
Line 156: “bridges or intersecting” Do you mean “bridges and intersecting”?
Line 160: “manually delineate and verify rapids” how did you manually delineate the rapids? Do you mean you visually identified them and then delineated them? How did you distinguish from wind-perturbed water surface and a perturbed water surface that represents rapids?
Line 160: “other hydraulic features”: vague, like what?
Line 162: “250 m” how does this length compare to the length of the ice jams observed in these rivers? Is it longer, shorter, about the same?
Line 163: "effectively capture”. Vague. What does it mean to “effectively capture”, do you mean it was a long enough to capture several riffles and pools? Capture at least 2? What would be effective? Why?
Line 164: “robust spatial analysis”. Vague: what do you mean? Be more specific about what was important for you to capture?
Line 167: “enforced”. Do you mean “used”?
Line 176: “constricted reaches along the Slave River… accelerated breakup by concentrating flow energy, whereas wider sections persistently retain ice”. I interpret this sentence to mean that narrow sections broke up early and wider sections broke up later. That is not the same thing as “narrow sections had ice jams”.
Line 177: “their findings show that local reduction in channel width fundamentally govern…spatial propensity for ice jam formation.” This was not a conclusion of Lindenschmit and Das (2015), the underlying literature has been mis-characterized.
Line 190: “islands…introduce a transition in slope from steep to gentle.” I have never seen evidence that, in general, islands exist at a transition from steep to gentle slopes, or more, that they somehow “introduce” one. What are you trying to say?
Line 214: “50% reduction in channel width”. This is an arbitrary number. What evidence leads you to use such a large width reduction? What would be the implication of using 20%, 40%, or another factor?
Line 215: “this parameter may be adjusted”: was it adjusted? One could adjust any number in future work? Why say this? You are implying you are unsure about your 50% reduction number (Line 214).
Line 219: “steeper gradients”. Do tributaries always have steeper gradients? This seems very site-specific.
Line 219: “faster runoff response” Do tributaries always have faster runoff response? This seems very site-specific. If tributaries are substantially smaller, perhaps there are thermal components to their earlier breakup.
Line 221-223: Any discussion of tributary influence on ice jam formation must necessarily cite foundational work by Ettema et al. (1999) and Ettema and Must (2001).
Line 222-224: Wording around how the effective width reduction calculation works: This is not clear: please provide the equation used (I think I need the equation used for alpha t).
Line 228: “Dominant local constraint”: vague: please be clearer: I think you mean that you only applied one width reduction factor per reach (the largest one), and didn’t apply more than one? It is unclear how this works with the description on Lines 230 -233. How these coefficients were applied is not clear. Revise this section.
Line 234: “No formal sensitivity analysis was conducted…. Classification should be interpreted within a screening-level context” This sentence makes me doubt all the results presented in the paper. All results in this paper then need to be interpreted skeptically?
Line 284, 288, 289: “curvature”. Use “sinuosity”. This distinguishes between a local versus reach-based measure.
Line 311-315: The authors have calculated a slope break indicator but have not implemented it, choosing to have “slope” (simpliciter) as the slope-based metric. This is an indefensible choice. All research cited in the introduction have discussed how changes in slope from steeper to flatter is where ice jams form. The authors say this allows for a “parsimonious model structure”. I agree, it is simpler, but it is not supported by existing literature.
Line 322-325: Further to the discussion for lines 311-315 - The authors acknowledge that slope changes are recognized as critical controls on ice jams and state they use absolute magnitude only. They state this is consistent with the “index’s overall structure”. This statement is indefensible: the authors have other indexes that are related to longitudinal changes (i.e. channel narrowing) and have created thresholds within that. The authors have made no attempt to explain why this cannot be done with a slope break parameter. Further, using absolute slope (simpliciter) as an index parameter makes no physical sense in a general sense: do all flat rivers have ice jams everywhere? They do not: because there is no upstream capacity to move ice. Ice jams form where there is upstream capacity to move ice and less capacity downstream. Perhaps there is something about the local geomorphological setting that makes the absolute slope a helpful parameter; if so, this needs to be expressed. It has implications for a broader application of this model.
Line 331: what does “consolidate” or “interlock”-ing have to do with the formation of “stable ice covers”. The mechanics of ice cover formation are misconstrued in this description.
Line 355: “approximate geographic location” - how approximate do you think the ice jam locations are? Does this impact the choice of a 250 m discretization of the river?
Line 372-373: “long river reaches”: how long are ice jams typically in these rivers? Even if the MSP database can’t show that, would the experts consulted be able to tell you? Is there additional information that can tell you?
Line 375: “anchor ice or frazil accumulations... (Madaeni et al, 2022)”. The cited paper does not mention anchor ice or fazil. Why is it being cited here?
Line 375-379: This section is a description of the data cleaning done to the MSP database. Madaeni et al (2022) describes in more detail their data cleaning of the same dataset, including more problems cleaned than is presented in this paper. Did you use their clean version of the database? If so, the cleaning should be presented in the same manner (i.e. more completely), and/or that paper should be cited in this section.
Line 399: “rapid” should be “rapids”
Line 406: “dynamic” typically means changing in time. Suggest deletion.
Line 410-429 - Section regarding index thresholds: the author’s description of the method to determine the thresholds is insufficient. A graph of the clusters would be helpful so the reader can verify that the thresholds make sense.
Line 430-452 - Section describing the relative weighting of indices: the authors describe their method for determining the weights of the different indices. The description is not detailed enough for the reader to fully understand how the weights were determined. Based on the description supplied, I understand that the authors determined the weighting by inspection of the entire dataset and then chose weights that added to 1.0 and were bigger for narrowing, medium for sinuosity, and small for slope.
Line 461-462: “These thresholds were selected to balance sensitivity and interpretability in identifying reaches prone to ice jam formation”. The thresholds appear arbitrary because the authors do not provide any detail on how they were determined. Were they determined by looking at the entire dataset? Was there an objective function that was calibrated?
Line 474: “evaluation focused primarily on recall…(a false negative) is a more critical outcome”. I agree. However, you can get good recall if almost everything is identified as ice-jam prone because false positives are not taken into account. In general, both metrics must be calculated, and all components need to be presented: TP, FP, FN. I see this is difficult for a dataset like this, but some quantification of false negatives must be presented.
Lines 486-566: The model shows good recall statistics of 90%. This is not surprising because the authors appear to have chosen the final index thresholds and parameter weighting based on the entire dataset. Therefore, this is the same as presenting the calibration run of a model as the model performance. It does not appear that any data was left out for comparison as a validation step. False positives are presented as “low”, but no numbers are given anywhere in the text. Mapped results of how much of a river is “high”, “medium”, “low” is not provided except for the Chaudiere River. Mapped results and quantification of the false positives would help the reader understand model performance better.
Lines 567-619 - Dominant geomorphological drivers: It is not clear what the 85%, 49%, 16%, and 3% mean. Why does an ice jam get classified as “flat slope”? Why is “flat slope” dominant here but “narrowing” was weighted as more important in the development of the index? Are they not taken from the same dataset?
Lines 620-692 - Chaudiere River case study - The authors state this case study is a good benchmark for “validating” the model. If the data for the Chaudiere was used in model development, it cannot be characterized as "validation" only as an illustration of something more like calibration. Figure 8 is very difficult to interpret because the “baloon-shaped” icons significantly overlap the ice jam formation index results. This must be corrected.

Citation: https://doi.org/10.5194/egusphere-2026-2457-RC2

Elnaz Mirzaei, Karem Chokmani, Rachid Lhissou, and Saeid Homayouni

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Short summary

Ice-jam flooding poses a significant risk in cold regions, particularly for vulnerable communities. This study maps river sections most prone to ice jams based on their physical characteristics. The findings offer a practical tool for improving flood preparedness and risk management by helping decision-makers prioritize high-risk areas, especially as climate change increases the frequency of extreme winter conditions.


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