the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Quantifying uncertainty in flood predictions due to river bathymetry estimation
Abstract. River bathymetry is important for accurate flood modelling but often unavailable due to the time-intensive and expensive nature of its acquisition. This leads to several proposed and implemented approaches for its estimation. However, the errors in measurements and estimations inherent in these methods, affecting the accuracy of the flood modelling outputs, are not extensively researched. We investigate the sensitivity of flood predictions to these errors in two formulas: the Uniform Flow and the Conceptual Multivariate Regression. Given channel slope, width and bank-full discharge, these formulas can be used to estimate bathymetry. However, errors in estimated bathymetry will affect the flood results. We employed a Monte Carlo framework to introduce random errors into these parameters drawn from a normal distribution with zero mean and a standard deviation set to 10 % of their best estimates. Using this process, we generated 50 simulated river bathymetries for each parameter along with an additional 50 where the errors were applied to all parameters simultaneously. The riverbeds generated from these bathymetries were combined with topographic LiDAR data to create model grids. Each grid was used in the hydrodynamic model LISFLOOD-FP to simulate the 2005 flood event in the Waikanae River area of New Zealand. We assessed the resulting flood predictions for their variability and sensitivity. The results indicate that, between the two methods, the combined errors in the parameters using the Uniform Flow formula are associated with greater uncertainty in flood depths (median error: 3.89 m, quartile range: 2.36 to 7.78 m) and extents (208.72 ha, 206.59 to 209.58 ha), compared to Conceptual Multivariate Regression (depth: 3.61 m, 2.32 to 7.37 m; extent: 207.82 ha, 206.42 to 208.48 ha). Among the parameters, the width errors correspond to the highest uncertainty, while the slope errors correspond to the lowest.
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Status: open (until 20 Feb 2025)
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RC1: 'Comment on hess-2024-356', Anonymous Referee #1, 17 Jan 2025
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This study analyzes the uncertainty in flood predictions due to river bathymetry estimation using a Monte Carlo framework. Three important parameters are considered, and the influence of the uncertainties in these parameters on the bathymetry estimation are analyzed. Although this is an important question in flood predictions, I find some important issues not described clearly in the paper. My major concerns are as followed.
- The relation between flood predictions and river bathymetry estimation is not clear for me. When talking about flood prediction, I would expect the simulation or prediction of streamflow, the peak flow volume and peak time. However, reading the paper, I only find results of bathymetry and flood extents. The authors didn’t provide many details about the calculation of flood extents, but it seems that the flood extents can be determined by the river bathymetry directly. Consequently, in my understanding, it seems that “flood prediction” and “bathymetry estimation” in this study is one thing to some extent.
- Related to the first comment, I am also confused about the flood prediction model used in this study. The authors provide little descriptions on the LISFLOOD-FP model, missing some important issues. For example: what is the input and output of the model? What’s the relationship between this model and the two formulas for river bathymetry estimation? Is the estimated river bathymetry used in this model?
- The uncertainties in flood predictions come from several sources, including the accuracy of three parameters (S, Q and w), the accuracy of two formulas for bathymetry estimation, and the influence of bathymetry uncertainties on flood prediction. Unfortunately, the analysis conducted in this study is more likely a sensitivity analysis, without addressing these uncertainty sources clearly. The authors analyzed the uncertainty brought by a standard deviation of 10%, but the question should be what is the actual uncertainty in S, Q and w estimation themselves. Besides, the study didn’t use any measurement data to validate the estimated bathymetry, so the analysis actually only shows the range of estimated bathymetry caused by a 10% variation in S/Q/w, which, in my opinion, is a rather direct procedure from the viewpoint of mathematic, since the formulas for bathymetry (Eq.2) is a very simple equation.
- Some questions about UF and CMR formulas. 1) According to section 2.2 the only difference in these two formulas is the different value of α and β, am I right? 2) Table 1: In my understanding, Manning’s n should be a parameter reflecting the characteristics of riverbed. Why is it different in different formulas?
In the end, as I am not an expert in river bathymetry estimation, my concerns are primarily raised from the perspective of hydrological modeling. I’m not sure whether I may have misunderstood or overlooked some critical aspects of this study. Therefore, I recommend a major revision and encourage the authors to address the issues mentioned above and to highlight any inaccuracies in my understanding.
Citation: https://doi.org/10.5194/hess-2024-356-RC1
Data sets
Quantifying uncertainty in flood predictions due to river bathymetry estimation - Results and Github Martin Nguyen, Matthew Wilson, Emily Lane, James Brasington, and Rose Pearson https://doi.org/10.26021/canterburynz.27644997.v1
Model code and software
Quantifying uncertainty in flood predictions due to river bathymetry estimation - Results and Github Martin Nguyen, Matthew Wilson, Emily Lane, James Brasington, and Rose Pearson https://doi.org/10.26021/canterburynz.27644997.v1
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