the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A mathematical model to improve water storage of glacial lakes prediction towards addressing glacial lake outburst floods
Abstract. Moraine-dammed glacial lakes are vital sources of freshwater but also pose a hazard to mountain communities if they drain in sudden glacial lake outburst floods. Accurately measuring the water storage of these lakes is crucial to ensure sustainable use and safeguard mountain communities downstream. However, thousands of glacial lakes still lack a robust estimate of their water storages because bathymetric surveys in remote regions are difficult and expensive. Here we geometrically approximate the shape and depths of moraine-dammed lakes and provide a cost-effective model to improve lake water storage estimation. Our model uses the outline and the terrain surrounding a glacier lake as input data, assuming a parabolic lake bottom and constant hillslope angles. We validate our model using ten new bathymetrically surveyed glacial lakes on the Qinghai-Tibet Plateau, and compiled data from 34 recently measured lakes. Our model overcomes the autocorrelation issue inherent in earlier area/depth-water storage relationships and incorporates an automated calculation process based on the topography and geometrical parameters specific to moraine-dammed lakes. Compared to other models, our model achieved the lowest average relative error of approximately 14 % when analyzing 44 observed data, surpassing the >44 % average relative error from alternative models. Finally, the model is used to calculate the water storage change of moraine-dammed lakes in the past 30 years in High Mountain Asia. The model has been proven to be robust and can be utilized to update the water storage of lake water for conducting further management of glacial lakes with the potential for outburst floods in the world.
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RC1: 'Comment on hess-2024-24', Adam Emmer, 25 Mar 2024
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This study approximates glacial lake volume from simplified geometric representations of 4 main sub-types of moraine-dammed lakes, considering glacier-lake relationship (connected vs. unconnected) and lake width to length ratio. The performance of this new approach is reportedly better than the performance of other methods (comparison in Table 5).
However, this is not surprising if the authors used the dataset of 44 Himalayan lakes with measured bathymetries to determine their parameters (section 3.3), and then use the same data to compare the performance of various methods (section 4.2). I hope I understood this correctly since the validation procedure is not described clearly in methods section. If I get it correctly, such performance evaluation is weak. A proper validation would require two independent datasets (training and testing).
And the whole validation procedure is even more confusing since only 4 bathymetries are mentioned as input data for model validation in section 4.1. This is statistically not convincing, considering 4 sub-types of moraine-dammed lakes and number of parameters that are used. Further, a subset of 12 lakes is used in section 5.1 while 4 and 10 lakes are mentioned in Conclusions. This needs to be clarified.
The application section 4.3 is not linked to the methodology. It is not clear what was done and whether (and how?) all 13,166 lakes mapped by Wang et al. (2020) were classified according to the classification scheme used in this study and whether all these are moraine-dammed lakes?
At the end, the importance of this improvement in lake volume estimation for GLOF studies (the main justification throughout the study) is unclear unless other (and much larger) sources of uncertainties in GLOF studies (e,g, coming up with realistic scenarios of GLOF triggers and GLOF mechanism, plausible breach development and dimensions, associated shape of the outburst hydrograph curve, % of lake volume release, etc.) are addressed.
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L39-40: this definition is artificial; moraine-dammed lakes not only trap meltwater (how about water from liquid precipitation?); debris at or near the termini of glacier doesn’t necessarily need to be a moraine
L53: ice- or landslide-dammed lakes may be unstable too
L72-74: the peak discharge is rather linked to the magnitude of triggering event than lake volume
L77: how much was that?
L126-127: this indication is not clear since the ratio is dimensionless (really a width of 1 m?)
Fig. 2: please only display parameters that are further use (remove slope beta, points f and g)
Table 2: please clarify whether alpha is mean or median slope (as mentioned in Table 3); what is the influence of DEM acquisition date on alpha estimation?
Table 4: what is simulated lake depth – a mean? And what do the two values in error column refer to?
Table 5: some of the lakes (e.g. Imja Tsho or Jialong Co) are represented more than once. This may influence performance evaluation; the areas of Jialong Co do not match between Table 4 and 5)
L313-315: R^2 will always be very high (>0.95) for most of the methods
Figure 8: I don’t understand what is the meaning of these box plots unless it is connected to measured data? The XY graph type (inset) is way more meaningful and the authors may consider showing a panel with performance of all methods in XY graphs.
L339: the scaling up of the lake volume estimation procedure to the whole HMA is not properly described in methods.
L367-376: this seems bit out of the context. Clearly, large lakes are frequently considered risky since lake area / volume is commonly used as GLOF susceptibility criteria.
L375: the annual expansion rate +5.6% a^-1 over 32 years does not correspond to a reported growth of 178% over this period
L395: they are not flat (as documented in your Fig. 10)
L433: what is MDLVL?
L453: the term “outburst water storage” is not appropriate. What is estimated here is a lake volume / lake water storage. It doesn’t have much to do with outburst / outburst volume.
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To sum up, this study can help to improve moraine-dammed lake volume estimates in HMA. However, especially the validation process needs to be clarified and treated in statistically convincing way. I recommend major revisions.
Citation: https://doi.org/10.5194/hess-2024-24-RC1
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