the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 02 Jun 2025
Exploring Diverse Modeling Schemes for Runoff Prediction: An Application to 544 Basins in China
Abstract. Hydrological modeling plays a key role in water resource management and flood forecasting. However, in China with diverse geography and complex climate types, a systematic evaluation of different modeling schemes for large-sample hydrological datasets is still lacking. This study preliminarily constructed a dataset of catchment attributes and meteorology covering 544 basins in China, and systematically evaluated the applicability of process-based models (PBMs), long short-term memory (LSTM) models, and hybrid modeling methods. The results demonstrated: (1) The accuracy of meteorological data critically impacts the prediction performance of hydrological models. High-quality precipitation data enables the model to better simulate the runoff generation process in the basin, thereby improving prediction accuracy. (2) The hybrid modeling method possesses regional modeling capabilities comparable to those of LSTM model. It also demonstrates strong generalization capabilities. In predicting ungauged basins, the hybrid model exhibits greater stability than the LSTM model. (3) Among the two hybrid modeling methods, the differentiable hybrid modeling scheme offers a deeper understanding and simulation of hydrological processes, along with the ability to output unobserved intermediate hydrological variables, compared to the alternative hybrid modeling schemes. Its prediction results are more consistent with the water balance of the basin. The research results provide a systematic analysis for evaluating the applicability of different hydrological modeling methods in 544 basins in China, offering important guidance for the selection and optimization of future hydrological models.
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CC1: 'Comment on egusphere-2025-1161', Junzhi Liu, 10 Jun 2025
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The authors constructed a watershed property and meteorological dataset covering 544 watersheds in China and systematically evaluated the applicability of different modeling schemes. The paper is well-structured and the experiments are comprehensive. I believe the following improvements will further enhance the quality of the article.
1. Is there any reference or rationale for the determination of the watershed boundaries?
2. The abbreviations of the models in the article are very confusing. Please explain them uniformly in the appropriate place.
3. Line 284 describes the PUB test method. Why are the remaining 9 clusters used for training?
4. The authors claim that the differentiable mixed hydrological model can output unobserved intermediate hydrological variables, but there is no data to support this.
5. What does the spatial distribution map in Figure 12 mean? A detailed explanation should be given in the image caption.
6. Why do we use the runoff predictions for water balance assessment? What is the purpose of calculating the water imbalance ratio? Please add an explanation.
7. There is an error in the labeling of Figure 11. Two sub-figures (b) appear. Please modify them.Citation: https://doi.org/10.5194/egusphere-2025-1161-CC1 -
CC2: 'Comment on egusphere-2025-1161', Zeqiang Chen, 11 Jun 2025
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This study conducted regional hydrological modeling work based on a large sample data set in China. This work can provide some reference for regions that currently do not have a large sample basin data set. However, I suggest the following modifications:
1. Why did the author only choose two sets of precipitation data, or did the temperature data also come from two sets of data products? A more detailed description of the data source is needed.
2. The description of the hybrid model structure in Section 3.4 is confusing. Please try to describe the operating logic of the two hybrid models separately.
3. Line251: Which of the 6 categories the 15 attributes belong to needs additional explanation, or should be added to Table 2.
4. Line455: The author mentioned here the accuracy of climate characteristics and rainfall data. Among the 15 attributes, which meteorological data product is used to calculate "p_mean", "pet_mean", etc., or did the author use other methods?
5. The author uses the Budyko curve to examine the watershed water balance in Section 4.1, while in Section 4.5, the water budget closure method is employed. Why are different methods used to verify the watershed's water balance situation?
6. There are still many available high-quality meteorological data products. I can understand the author's decision to limit the scope of the article to control its length. However, this needs to be clarified in the conclusion section of the article.Citation: https://doi.org/10.5194/egusphere-2025-1161-CC2 -
RC1: 'Comment on egusphere-2025-1161', Anonymous Referee #1, 18 Jun 2025
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In this work, the authors compare the performance of two process-based models, an LSTM and a couple of hybrid models on a rather large set of catchments in China. On the paper, this work is interesting, as Chinese hydrology is not so commonly studied, and insights on the use of hybrid methods is needed. However, the presentation of this work is of rather poor quality (Figures and figure captions), and some methodological features make it difficult to draw solid conclusions. In addition, discussions are very little and sparse over the results.
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Main remarks
Namely, the figures are most of them poorly described in the captions, and many small to larger mistakes or discrepancies are present. Some of them also show too small fonts to be read comfortably.
For assessing the performance of the different experiments, the authors compare the simulated streamflow to streamflow from VIC-CCN5.1… which is also simulated streamflow. This choice is justified, although I guess that VIC-CCN5.1 had to be evaluated against observed streamflow, so why not using it. To add to the confusion, several of the experiments come from models forced by CCN5.1. That induces a bias in the conclusions that can be drawn.
Finally, there is no discussions section. Some discussions arise in the results section, but those are rather rare and sparse. The effect is that it is difficult to understand the added value of this work for the hydrological community, and we do not have recommendations.
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Miscellaneaous
Abstract:
- The abstract mentions the use of PBMs, but not what they are used for, neither what we can conclude about them.
- Line 36: conclusions about the two hybrid models are drawn, but those are not detailed before
- L 40-42: This is not a concluding sentence for an abstract, this is the rationale of the study. Here we need you to give us the major guidance resulting from your work.
Line 52: Why is the complexity of hydrological processes increasing? It seems to me that all this discussion is about natural processes, which do not complexify in time.
L 86: I do agree for physically-based models, but conceptual/empirical ones only need from 3 variables, namely precipitation, temperature and streamflow. This is not a substantial amount of high-quality data! For example, the EXP-HYDRO used by the authors exactly need these data, plus the day length, and the Xin'an jiang model only needs these data.
L 91: I do not agree, see previous comment
L 168: From now onwards, I wonder if most elements should rather appear in the material and methods section of the manuscript
L 190: Why do accurate daily runoff observation data often need to be kept confidential?
Figure 1:Â Please make the different maps more uniform. Panel b uses a different color for foreign countries. In addition, please do not use the same color for China and seas (panel a). I also suggest removing the bottom right islands, as there as no basins there and they are originally not on the map. Imagine if French researchers put all French territories on all maps!!
Caption of Figure 1: In a I see the areas, in b the DEM, in c the catchments and in d the climates (only this one s correct). Please modify
L 234: I was completely lost here. There must be a nuance between the different terms (observation, runoff, runoff hydrograph), but I initially didn't get it. Only later on, while reading the results, I understood that the VIC-CN05.1 dataset is simulations from the VIC model forced by CN05.1. That was not clear at all.
L 284: Do you mean 4? There are 5 clusters
Figure 4: While a is understandable, I do not get b at all. What is FCNN? It is never defined in the text. Please improve or develop the caption.
Figure 5: Please use the same range for the distribution of P values for the two products over the diverse basins. Also make sure to use the same categories, it seems that there are many more categories for CN05.1 than for ERA5. I guess this is basin-averaged P and T? Please specify.
Figure 5: The scale indicates a gradual color scale for P and T, but the maps only display categorical values, with only 5 colors. Please correct. What is the period? Is it the total period or the evaluation period (1995-2015)? These two comments are valid for most figures that follow
L 407: How is the drought index calculated?
L 415: That definitely induces a bias! It is easier to reproduce streamflow obtained from a model forced by a dataset, when you use the same dataset…
L 416: This is methods, not results
L 420-425: This is discussions, not results
Figure 6: what is the blue shaded area?
L 433: This is a somehow unfair comparison, as the reference data used to calculate NSE comes from VIC forced by CN05.1. Then, when you compare models forced by ERA5 to these data, you include the error coming from the PBM and the error coming from the input data set.
Figure 7, left: what is this scale? It does not include regular intervals between values
Figure 7, caption: the authors state that the colormap include vales from 0 to 1. That would be great, to compare the four maps together. Unfortunately, the left maps do not use the same range as the right maps
L 411 and following: The differences should be discussed in terms of what processes are important for these basins and what is the link with the processes present in the PBMs. We need interpretation!
L 469: The fact that the LSTM performs very well with CN05.1 comes from the fact that the authors do not try to reproduce observed streamflow but simulated streamflow. This means that LSTM does not excels in reproducing the processes leading to streamflow from meteorological input, but rather excels in mimicking the behavior of the VIC model. This is highly different and is caused by the experiment setup. In addition, this might indicate that the LSTM cannot cope with input errors
L 488-491: these are discussions, not results
Figure 9, 10: random scales prevent from comparing the different parts of the figure
L 528-537: these are discussions, not results
Figure 12, 13: fonts are too small, we cannot read
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Citation: https://doi.org/10.5194/egusphere-2025-1161-RC1
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