the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 27 Jun 2025
Satellite telemetry of surface ablation observations to inform spatial melt modelling, Place Glacier, British Columbia, Canada
Abstract. Four automated "smart stakes" equipped with ultrasonic sensors, Arduino microcontrollers, and Iridium satellite telemetry were deployed to monitor glacier surface elevation changes at Place Glacier, British Columbia, Canada during the 2024 ablation season. The smart stakes recorded air temperature, relative humidity, and distance to glacier surface every 15 minutes from May 14 to September 21, 2024, providing high-temporal resolution melt data across an elevation gradient. Integration with airborne lidar surveys and satellite snow cover observations enabled validation and spatial extrapolation of point measurements. Temperature-index modeling using smart stake data yielded ice melt factors of -4.26 to -5.63 mm w.e. °C⁻¹ d⁻¹ and snow melt factors of -3.74 to -4.42 mm w.e. °C⁻¹ d⁻¹, consistent with previous studies. The spatial melt model estimated a total seasonal melt volume of 11.61 × 10⁶ m³ water equivalent, representing a summer mass balance of -4.14 m w.e. for the glacier. Validation against manual ablation stakes showed reasonable agreement (R² = 0.58, RMSE = 0.45 m w.e.). Event-scale analysis revealed that three discrete heat events (July 5–22, August 1–12, and August 29–September 9) accounted for over half of the total seasonal melt despite comprising only one-third of the ablation season. Maximum daily melt rates reached -87 mm w.e. d⁻¹ during these extreme events, with higher elevation sites experiencing disproportionately greater melt rates. Non-linear temperature lapse rates were observed across the glacier, highlighting the importance of distributed temperature measurements for accurate melt modeling. The low-cost smart stake system demonstrates significant potential for automated glacier monitoring, providing near real-time data transmission and enabling event-scale melt attribution studies. This multi-scale monitoring approach combining in-situ sensors, airborne lidar, and satellite observations offers a comprehensive framework for understanding glacier melt dynamics in a changing climate, though challenges remain regarding sensor stability, power management, and accounting for glacier dynamics in melt estimates.
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Status: final response (author comments only)
- RC1: 'Comment on egusphere-2025-2702', Anonymous Referee #1, 21 Jul 2025
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CC1: 'Reviewer Comment on egusphere-2025-2702', Mauri Pelto, 27 Jul 2025
The authors have provided a detailed examination of the efficacy and operational approach to using smart stakes. Place Glacier, British Columbia is the location for this detailed field test. This location has a combination of long-term mass balance records, ongoing mass balance observations, automatic weather station, PlanetScope and recent annual Lidar observations. This makes for a perfect test site location. The authors emphasize repeatedly that smart stakes are low cost as a key part of this study but have not provided a cost range for the product or its operation. The cost is as essential as identifying their accuracy, for determining if they are best suited as a supplement to an existing stake network, adding high temporal resolution data or can be deployed in place of that network. There have been other detailed ablation surveys conducted during specific heat events that should be noted as part of this important growing data set. Smart stakes can be a valuable tool for expanding this data set. Melt models based on data with limited temporal resolution can be impaired by temporal and spatial variations in ice dynamics, albedo variations, and wind effects. The smart stakes can be effective in identifying the temporal variations that impact all ablation stake studies.
I applaud the authors for a thorough test of a new system and providing a best-case approach to utilization of extensive complimentary data sets for both analysis and validation. In future it would be wonderful to see how much melt context smart stake data can provide from a glacier that otherwise has limited field monitoring but has ongoing LIDAR or other geodetic observation.
Specific Comments
93: It is noted the upper part of the accumulation area was not instrumented due to logistical and safety reasons. However, earlier it was noted that the glacier has minimal crevassing. Is it worth being more specific on this constraint.
220: Other studies have utilized snow line migration across areas of previously measured snow depth to identify ablation. Is this what is being done with PlanetScope?
269: This is substantial tipping, did the manual stakes suffer this level of tilt due to near melt out? Are the smart stakes not emplaced as deeply or are they simply top heavy and prone to tilt earlier? The 4.88 m long stakes were drilled how deeply, it is noted that at least 0.8 m is exposed?
286: Figure 4A provides an excellent visual of snowpack variation. I recommend that the accumulation area ratio be reported for each. Given the difference in melt rates for snow surface vs ice surface this is important.
316: This similarity in ablation from stake to stake has been noted for other regional glaciers, which maybe worth noting that this is not unusual.
339: The number of melt days is a crucial variable to identify for a melt model to work accuartely, the mapping of this variable in Figure 9 is quite valuable as a an example of best practice
400: Important to note the increased specific melt rates observed during 24 recent heat wave event in the Nooksack Basin, North Cascades (Pelto et al. 2022) that supports observations provided here.
412: Providing a better reference to more specific regional studies where melt factors were derived demonstrates contrast and context. Bidlake et al (2010) noted melt factors for South Cascade Glacier of 0.0039+ 0.0006 for snow and 0.0056 + 0.0008 for ice. On Mount Baker in the North Cascades, Pelto et al (2022) reported under overall weather conditions DDFs for snow is 0.0035 m w.e. °C−1d−1. For ice, the DDFi is 0.0053 m w.e. °C−1d−1 During heat waves this rose to a DDFs snow of 0.0043 m w.e. °C−1d−1. For ice, the DDFi is 0.0067 m w.e. °C−1d−1.
425: The four smart stakes did provide high resolution temporal data but at a low spatial resolution raising again the cost issue.
442: Good description of the challenges posed by the vertical component of velocity. Make sure to note that this poses the same challenge for any ablation stake system . Smart stakes may in fact allow for better understanding of this.
References
Bidlake, W.R., Josberger, E.G. and Savoca, M.E.: Modelled and Measured Glacier Change and Related Glacioloical, Hydrological and Meterorological conditions at South Cascade Galcier, WA, Balance and water years 2006-2007. USGS Science Investigation Report 2010-5143, US Geological Survey, Reston VA USA, 2010.
Pelto, M. S., Dryak, M., Pelto, J., Matthews, T., & Perry, L. B.: Contribution of Glacier Runoff during Heat Waves in the Nooksack River Basin USA. Water (7), 1145. https://doi.org/10.3390/w14071145, 2022.
Citation: https://doi.org/10.5194/egusphere-2025-2702-CC1 -
RC2: 'Comment on egusphere-2025-2702', Anonymous Referee #2, 21 Aug 2025
This manuscript describes the design and deployment of a novel “smart stake” system that monitors surface melt and air temperature in near real time. The authors apply the data collected to a simple spatial melt modelling framework and use them to evaluate the influence of heat waves on glacier melt at Place Glacier, British Columbia.
The manuscript presents a clear and detailed description of the smart stake design and deployment. The use of low-cost sensors and satellite telemetry is interesting and has the potential to make glaciological monitoring more accessible. The real-time transmission capability is a valuable feature, and the overall approach contributes to ongoing conversations about alternatives to traditional on-ice AWS.
While the smart stake concept is promising, I was not fully convinced of its added value relative to a conventional on-ice AWS setup, and I found the melt modelling and event attribution to be fairly simplistic. The manuscript covers many topics but could benefit from more depth in each of them. Addressing some of these limitations would be important before publication.
In summary, I enjoyed reading about the smart stake setup and seeing its performance and data outputs. However, I found the presentation of the subsequent analyses too simplistic. I detail these comments below.
Major comments
Cost argument: The main stated benefit of the smart stakes is their low cost, but no cost assessment is provided in the manuscript. Including such an assessment would be very useful for evaluating this setup. Without an explicit comparison, the argument for “low-cost” deployment remains difficult to evaluate. Furthermore, several suggested improvements (e.g., adding sensors to address tipping or solar heating) could make the smart stakes nearly as complex as an on-ice AWS, which would further weaken the cost advantage.
Increased spatial resolution: While the smart stakes did improve temporal resolution compared to seasonal mass balance surveys, the gain over a single on-ice AWS is less clear, particularly when combined with mass balance stake measurements. The poor performance of the upper site further reduces the usefulness of deploying four sites. Would a single smart stake or on-ice AWS at mid-elevation, combined with the off-glacier stations for lapse rates, provide comparable results? A stronger case for the sensors’ value could be made by explicitly testing the added benefit relative to existing approaches, especially given the availability of two off-ice AWS at this site.
Spatial melt modelling: The modelling approach is quite rudimentary, applying uniform melt factors across snow and ice despite calculating melt factors at four individual stakes. As a result, it is not clear how the smart stake data meaningfully enhance the analysis. The model performs reasonably, but not particularly well, and the value added by the smart stakes is not evident.
Heat wave analysis: Much of the main ablation season is classified as “heat waves,” making it unsurprising that a large fraction of melt occurred during those periods. More detail on how heat waves were defined, and how these events compare with other years, would strengthen this section. As currently framed, the analysis feels shallow, particularly since it relies heavily on site 4, which performed poorly compared to the lidar. The paper might benefit from focusing more deeply on either the melt modelling or the heat wave analysis, rather than presenting both at a fairly simplified level.
Minor comments
It was not clear to me why the ECCC station is included in the lapse rate calculation. This choice made the glacier sites appear bundled together and harder to interpret. Clarification would be helpful.
In several places, the writing alternates between very detailed and overly casual phrasing, which occasionally disrupted the flow. For example, line 99: “some 400 m away” . Could this be made more precise?
When justifying sensor or method choices, referencing prior use is not always sufficient. For example, line 140 states that a method was “used in other glaciological studies.” It would be stronger to explain whether it worked well in those studies and what was gained by its use.
If the issue with using GOES is that the station might move and lose connection, could an option such as transmitting data by radio signal to the main off-ice station, and then using GOES, be feasible?
Citation: https://doi.org/10.5194/egusphere-2025-2702-RC2
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Review in attached PDF