the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impacts of spatio-temporal resolutions of precipitation on flood events simulation based on multi-model structures — A case study over Xiang River Basin in China
Xiaodong Qin
Dongyang Zhou
Tiantian Yang
Xinyi Song
Abstract. Accurate flood events simulation and prediction, enabled by effective models and reliable data, are critical for mitigating the potential risk of flood disaster. This study aims to investigate the impacts of spatio-temporal resolutions of precipitation on flood events simulation in a large-scale catchment of China. We use the high spatio-temporal resolutions Integrated Multi-satellite Retrievals for Global Precipitation Measurement (IMERG) products and a gauge-based product as precipitation forcings for hydrologic simulation. Three hydrological models (HBV, SWAT, and DHSVM) and a data-driven model (Long Short-Term Memory (LSTM) network) are utilized for flood events simulation. Two calibration strategies are carried out, one of which targets at matching the flood events and the other one is the conventional strategy to match continuous streamflow. The results indicate that the event-based calibration strategy improves the performance of flood events simulation, compared with conventional calibration strategy, except for DHSVM. Both hydrological models and LSTM yield better flood events simulation at finer temporal resolution, especially in flood peaks simulation. Furthermore, SWAT and DHSVM are less sensitive to the spatial resolutions of IMERG, while the performance of LSTM obtains improvement when degrading the spatial resolution of IMERG-L. Generally, the LSTM outperforms the hydrological models in most flood events, which implies the usefulness of the deep learning algorithms for flood events simulation.
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Qian Zhu et al.
Status: final response (author comments only)
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CC1: 'Comment on hess-2022-301', Xichao Gao, 20 Jan 2023
Meaningful study! could the authors distinguish the sensitivities of these different models to the spatio-temporal resolutions of precipitation? and explain the reasons?
Citation: https://doi.org/10.5194/hess-2022-301-CC1 -
AC1: 'Reply on CC1', Qian Zhu, 14 Mar 2023
Thank you for your comment. In our study, the hydrological models are generally more sensitive than the machine learning model, but the sensitivity of the model is also related to the precipitation input. As illustrated in Fig.7 and Fig.8, when the spatiotemporal resolution of CMA changes, DHSVM is the most sensitive one, for the mean NSE of flood events declines from 0.68 to 0.45 when the spatial resolution of precipitation changes from 0.1° to 0.5°. Maybe for CMA-driven DHSVM, the impact of spatial resolution on the capture of precipitation variability during flood event periods can propagate to the flood events simulation.
But when the spatiotemporal resolution of IMERG changes, SWAT and DHSVM model perform similarly, which is consistent with previous research results (Lobligeois et al. 2014, Huang et al. 2019), where insignificant improvement was reported with higher spatial resolution of observed rainfall. It probably dues to the large catchment area and only the outlet station is used for calibration. Liang et al. (2004) found a critical resolution (1/8° for the VIC model) for a watershed with 1,233 km2, beyond which the spatial resolution shows limited impact on model performance. For our study area (82,375 km2), when the spatial resolution of precipitation changed from 0.1° to 0.5°, small variety is shown in the performance of flood events simulation, which indicates the critical resolution may be larger for large watershed. For HBV, it is not sensitive to changes in temporal resolutions because its simple hydrological model structure.
For LSTM, higher resolution shows better performance, and similar conclusion is drawn from previous study conducted by Sun et al. (2017), which found that deep learning model performs better with larger datasets.
Reference:
Huang, Y., Bárdossy, A. and Zhang, K. 2019. Sensitivity of hydrological models to temporal and spatial resolutions of rainfall data. Hydrology and Earth System Sciences, 23(6), 2647-2663.
Lobligeois, F., et al. 2014. When does higher spatial resolution rainfall information improve streamflow simulation? An evaluation using 3620 flood events. Hydrology and Earth System Sciences, 18(2), 575-594.
Liang, X., Guo, J. and Leung, L. R. 2004. Assessment of the effects of spatial resolutions on daily water flux simulations. Journal of Hydrology, 298(1-4), 287-310.
Sun, C., et al. 2017. Revisiting Unreasonable Effectiveness of Data in Deep Learning Era. 2017 Ieee International Conference on Computer Vision (Iccv), 843-852.
Citation: https://doi.org/10.5194/hess-2022-301-AC1
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AC1: 'Reply on CC1', Qian Zhu, 14 Mar 2023
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RC1: 'Comment on hess-2022-301', Anonymous Referee #1, 12 Feb 2023
The authors have chosen multiple hydrological models (HBV, SWAT, DHSVM, and LSTM) and applied multiple calibration strategies, separately to all flows and flood flows. Moreover, the models have been applied at multiple spatial and temporal resolutions. The main finding is that calibration with respect to flood flows resulted in better flood flow simulation. LSTM is relatively better in simulating flood flows.
Although the authors have done a lot work and results look interesting, I feel depth is missing and there is not much novelty. It is not surprising that models calibrated using flood flow data more accurately predict flood flows. The authors have considered mean values for comparision, which I think is problematic. Means are unreliable when the distributions are skewed.
There are a lot of results, but the discussions are not doing proper justice -- more in-depth discussions need to be added. For example, it is not sufficiently clear why spatial resolution should matter. The authors can provide more convincing explanations by referring to the concept of time-area diagram.
Model calibration considering parts of discharge time series is not a new idea.
Specific comments:
Lines 20: It is not clear what you mean by "flood event." Also, I am not comfortable with the term "to match continuous streamflow." May be you can write "to match the entire streamflow time series."
How did you select the flood events?
295: Mean NSE may not be a reliable indicator. You should consider median, 75th and 25th percentile NSE.I see 75th NSE falling in case of CMA. The authors need to discuss it.
NSEs in Figure 6: I don’t see any consistent pattern. The results are not discussed properly.
NSEs in Figure 7: Again, I dot see a consistent pattern.
Results and discussions should be put together. It is difficult to follow discussion when results are not immediately available.
Citation: https://doi.org/10.5194/hess-2022-301-RC1 - AC2: 'Reply on RC1', Qian Zhu, 16 Mar 2023
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RC2: 'Comment on hess-2022-301', Anonymous Referee #2, 19 Apr 2023
The authors used the high spatio-temporal resolutions IMERG precipitation products and in-situ observations to force three hydrological modes and a data-driven model to investigate the impacts of spatio-temporal resolutions of precipitation on flood events simulation in a large-scale catchment. The study determines the compatibility between various models and precipitation datasets, which is critical for selecting the appropriate model for precipitation sources in hydrological simulation and prediction, and vice versa.
In general, the manuscript is well organized and well written. The scope of the study fits the journal’s focuses and audience community. The presentations are clear, and the novelty of the work is pointed out. The current manuscript has good potential to be published subject to some minor revisions and a few editing issues. The following suggestions are identified, and the authors shall revise them in their next version towards publication:
- Line 66: “the study”-> “they”.
- Line 124-127: The structure of these two sentences is suggested to be revised. The conjunction "so" in the beginning of the second sentence may be unclear.
- Line 139: “(hereafter CMA)” needs to be put behind “China Meteorological Administration”.
- Line 185: the reference “(AghaKouchak et al. 2013)” should be located behind the “HBV model”.
- Line 230-233: please pay attention to the format of the variables, such as xt, and t.
- Line 268-269: please explain how the eleven historical flood events are selected.
- Line 338: “as the resolution get coarser”-> as the resolution is coarser or as the resolution gets coarser.
- Line 350-352: “However, the uncertainty of NSE, KGE and BIAS-P values of flood events simulated with IMERG is decreasing as the spatial resolution.” As the spatial resolution what? finer or coarser?
- Line 365: in most instances -> in most cases.
- Line 407-408: “the same results” means the results are exactly the same, does that what the authors indicate? Otherwise, the same results -> the comparable/similar results or the results are almost the same.
- Line 417-418: the calibration strategy II is an effective way for training the LSTM model to obtain the best flood events simulation results -> the calibration strategy II is an effective way to train the LSTM model to obtain the best flood events simulation.
- Line 430: performs -> perform.
- Line 431: please delete the “results”. And please check the whole manuscript for this issue.
- Line 440: larger data set -> larger dataset.
- Isn’t the “Fig. 9” shall be colored red to be consistent with other figures?
- The colors used in Fig.10 are not so easy to distinguish.
- Same issue of Appendix C , and please refer to the comment #15
In summary, I personally think the work is solid and the manuscript has a good potential to be published with some minor editing and polish to be done by the authors.
Citation: https://doi.org/10.5194/hess-2022-301-RC2 - AC3: 'Reply on RC2', Qian Zhu, 24 Apr 2023
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EC1: 'Comment on hess-2022-301 (Editor's comment)', Dimitri Solomatine, 17 May 2023
The comments of referees are very valid. The authors have shown that they appreciate these and have a plan for the paper revision. I would suggest to give special attention to the genelra comments of Referee 1, more clearly highlighting the novelty of this work.
Citation: https://doi.org/10.5194/hess-2022-301-EC1
Qian Zhu et al.
Qian Zhu et al.
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