| 27 Dec 2025
Diagnosing Dissolved Organic Carbon Simulation of SWAT-C model Using Machine Learning Approaches
Abstract. Dissolved organic carbon (DOC) plays a critical role in the terrestrial carbon cycle, and accurate simulation of its dynamics is essential for understanding carbon balance and climate change mitigation. However, DOC simulations still involve large uncertainty under complex environmental conditions. To address this challenge, we proposed a Module Diagnosis Framework (MDF) that quantitatively identifies the module-level sources of uncertainty in DOC modeling. The SWAT-MDF integrates the physically based SWAT-Carbon (SWAT-C) model with a data-driven module that employs machine learning algorithms and applies Shapley additive explanations (SHAP) and residual analysis to diagnose the uncertain source of DOC simulation in the Yalong River Basin.We found that the the data-driven module based on bidirectional long short-term memory (Bi-LSTM) networks achieved good performance for daily DOC predictions with an average NSE = 0.62 and R2 = 0.67 while the original SWAT-C model yielded average NSE = 0.51 and R2 = 0.61. Despite this improvement, the testing performance remains limited, suggesting that the main uncertainty arises from the structural limitations of SWAT-C and highlighting the need for further structural improvement and module-level diagnosis. The MDF results revealed that the carbon cycle module and pollutant transport module mainly regulated the magnitude and variation of DOC predictions in the original SWAT-C model, and the vegetation growth module and the carbon cycle module were major sources of DOC prediction uncertainties. We therefore proposed that further improvements in DOC prediction in the SWAT-C model should focus on the vegetation growth, carbon cycle modules. Our proposed SWAT-MDF framework significantly enhances the reliability of DOC simulations, and provides a quantitative basis for improving the SWAT-C model and offers a generalizable approach to module optimization in similar coupled modeling frameworks.
Review on „Diagnosing Dissolved Organic Carbon Simulation of SWAT-C model Using Machine Learning Approaches“ by Huang et al 2026.
The authors of the article by Huang et al aim to improve the simulation of dissolved organic carbon (DOC) in the Yalong River Basin using a coupled modeling framework called SWAT-MDF. The study integrates a process-based hydrological model (SWAT-C) with a data-driven learning module implemented through machine learning algorithms. The authors used SHAP and residual analysis to conduct a comprehensive diagnosis of model components and identify the structural sources of uncertainty in DOC simulation.
The authors find that the Bi-LSTM-based calibration showed the most reliable performance in simulating DOC dynamics with an average NSE of 0.67, which improved the original calibrated SWAT-C results slightly (NSE of 0.51). The SHAP-based global interpretation identified DOC_Simulate, TOT_P, and PRE as the most important predictors of DOC, and a residual analysis revealed that LAI, RH, and DOC_Simulate were the most significant contributors to prediction errors.
General comments
Overall, the authors present an impressive amount of analysis and go very deep into the technical implications of their research. They surely provide a good piece of work here. As nice as this reads, I have two main concerns:
Working on these general concerns would require going deeper into the rich literature body on DOC processes and how the export can be explained in general in this study catchment (before starting any of the modelling exercises). And it would require more data. As more data is always easy to ask for, but hard to get, why not focus on the observed discharge data as well? Or test the method in more catchments with DOC concentration observations?
At the current state, I can not recommend this manuscript for publication.
Specific comments
There is a lack of discussion of the limitations of this study. The article mentions some limitations, but it does not provide a comprehensive discussion of the potential limitations and biases of the research.
Overemphasis on technical details: The article focuses heavily on the technical details of the methodology and models used, which may be of interest to experts in the field but may not be as relevant to a broader audience. It would rather benefit from studying the processes being actually relevant for the DOC export. Where does it come from, why does it get exported, and is there a trend? If so, can the model reproduce the trend? There are not that many studies out there predicting DOC exports in streams, so why make it overly complicated with the additional layer of machine learning?
Limited discussion of practical implications: The article does not provide a clear discussion of the practical implications of the research findings, such as how they can be applied in real-world settings, how other researchers can benefit from the shown results?
Lack of transparency in data analysis: The article does not provide a clear description of the data analysis procedures used, which may make it difficult for readers to understand how the results were obtained. There is some information hidden in the supplement material, and the authors also provide their source code (which I highly appreciate), still it remains unclear at key points what has actually been done. There are no results shown of the sensitivity analysis and there is no information on the model calibration (e.g., which algorithm has been used, how many model runs were performed, which performance criterion has been used for the calibration, etc.).
Limited discussion of uncertainty: The article does not provide a clear discussion of the uncertainty associated with the research findings, there are too many places confusions between uncertainty and a lack of model performance. After reading the article, I have the feeling that the authors interpret uncertainty as some sort of reason for decreasing model performance. However, one should be specific here. There are several sources of uncertainty and they should always be clearly stated. Are we talking about input data uncertainty, model process uncertainty, model parameter uncertainty, model structure uncertainty, etc.?
Lack of clear conclusions and recommendations: The article does not provide clear conclusions and recommendations based on the research findings. What is the main benefit of applying this additional layer of machine learning? What new knowledge can be gained here and in general? There are some elements of this in the manuscript already, but I think a separate section in the discussion and putting the results in the context of available literature would help the readers.
Technical corrections
L46: I do not see the argument why the lack of observation data justifies the need for DOC modelling.
L60: What is meant by “incomplete module designs and vague parameter representations”? Please provide examples.
L63: Please provide references to this statement.
L66: Please state again what is meant by the “coupled modelling approach”.
L68: Which “variables” are meant here?
L75: Provide reference
Line 107: Please provide the runoff amount also as an annual average sum in mm to allow for comparison with other catchments.
Line 108: What exactly is meant by particulate matter? How was it measured? Please provide a reference.
Line 110: Please provide an overview of the temperature in the study area as well, to give the reader an impression of the local climate.
L127: State the temporal resolution of the measured DOC data.
L129: Sentence out of context.
Table 1: Please state the year for the sources of Runoff and Sediment data. Please also try to provide a link where information about this source can be found.
Table 1: The reference of Xu et al 2024 for DOC data does not provide any daily observations, as the table implies here. Xu et al measured monthly, and increased the sampling to weekly in the monsoon season. Additionally, I have my doubts that there are daily sediment observations, which can not be double checked however, as there is no reference.
Table 2: There are no parameters shown here, even though the table title suggests it. I think those are model in and outputs? And Table S4 is incomplete as it shows “etc.” at some points, e.g. at the core of the study, the DOC simulation. As such it is not possible to judge whether the selection of the parameters is meaningful. I have my doubts, e.g. because why is the meteorological forcing part of the calibration? I do not see any justification for this in the manuscript.
Table S3: What is YLR? Wouldn’t it be interesting to study here which model processes are relevant for DOC export?
L162: What is meant with “Bayesian optimization was applied”? Which algorithm was applied and on what? Keep in mind that Bayesian optimization is not only used in Machine Learning, it can also be used for model calibration, so it is important to state specifically what has been done.
Structure: I think it would be more intuitive to explain SWAT-C first (currently chapter 2.4) and then the additional layer of SWAT-MDF (currently chapter 2.3)
Chapter 2.3: This chapter needs to be better explained and better linked with Figure 2. Why are there so many transfers necessary? As this method is not a standard approach in hydrology (yet), try to keep it understandable for readers who are not familiar with it.
Line 227: There is no information on the sensitivity analysis in the supplement material. It is only stated that it was performed, without providing any details. Please provide those details.
Figure 3: What is meant by the unit DOC/kg? A kilogram of what? Sediment or discharge, or? Is this supposed to quantify the DOC load? Why not work with DOC concentration? I doubt that any DOC-related process can be fitted by working with DOC loads, as it seems mainly discharge driven (Figure 3e).
Figure 5 and 6: Why is the LAI the least important in Figure 5 and the most important in Figure 6b?
References
Kratzert, F., Gauch, M., Klotz, D., and Nearing, G.: HESS Opinions: Never train a Long Short-Term Memory (LSTM) network on a single basin, Hydrology and Earth System Sciences, 28, 4187–4201, https://doi.org/10.5194/hess-28-4187-2024, 2024.
Xu, S., Li, S.-L., Bufe, A., Klaus, M., Zhong, J., Wen, H., Chen, S., and Li, L.: Escalating Carbon Export from High-Elevation Rivers in a Warming Climate, Environ. Sci. Technol., 58, 7032–7044, https://doi.org/10.1021/acs.est.3c06777, 2024.