1Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Chongqing 400715, China
2Karst Dynamic Laboratory, Ministry of Land and Resources, Guilin 541004, China
3College of Engineering Science and Technology, Shanghai Ocean University; Shanghai Engineering Research Center of Marine Renewable Energy 201306, China
4Chongqing municipal hydrological monitoring station, Chongqing 401120, China
1Chongqing Jinfo Mountain Karst Ecosystem National Observation and Research Station, Chongqing Key Laboratory of Karst Environment, School of Geographical Sciences, Southwest University, Chongqing 400715, China
2Karst Dynamic Laboratory, Ministry of Land and Resources, Guilin 541004, China
3College of Engineering Science and Technology, Shanghai Ocean University; Shanghai Engineering Research Center of Marine Renewable Energy 201306, China
4Chongqing municipal hydrological monitoring station, Chongqing 401120, China
Received: 14 Apr 2021 – Accepted for review: 31 Aug 2021 – Discussion started: 31 Aug 2021
Abstract. Karst trough valleys are prone to flooding, primarily because of the unique hydrogeological features of karst landform, which are conducive to the spread of rapid runoff. Hydrological models that represent the complicated hydrological processes in karst regions are effective for predicting karst flooding, but their application has been hampered by their complex model structures and associated parameter set, especially so for distributed hydrological models, which require large amounts of hydrogeological data. Distributed hydrological models for predicting the Karst flooding is highly dependent on distributed structrues modeling, complicated boundary parameters setting, and tremendous hydrogeological data processing that is both time and computational power consuming. Proposed here is a distributed physically-based karst hydrological model, known as the QMG (Qingmuguan) model. The structural design of this model is relatively simple, and it is generally divided into surface and underground double-layered structures. The parameters that represent the structural functions of each layer have clear physical meanings, and the parameters are less than those of the current distributed models. This allows modeling in karst areas with only a small amount of necessary hydrogeological data. 18 flood processes across the karst underground river in the Qingmuguan karst trough valley are simulated by the QMG model, and the simulated values agree well with observations, for which the average value of Nash–Sutcliffe coefficient was 0.92. A sensitivity analysis shows that the infiltration coefficient, permeability coefficient, and rock porosity are the parameters that require the most attention in model calibration and optimization. The improved predictability of karst flooding by the proposed QMG model promotes a better mechanistic depicting of runoff generation and confluence in karst trough valleys.
The paper concerns a topic consistent with the aim of the GMD journal, and I really appreciate the huge work made by the authors. The presented analysis and model application could be potentially useful in karst basins. In this study, a karst hydrological model, i.e., the QMG model-V1.0 was developed for karst floods simulation and forecasting. The model itself is a valuable improvement, and what interested me was the applicability of the model in karst areas, so I went through the entire process of modeling and validating the model myself (https://zenodo.org/deposit?page=1&size=20), and the model simulation results were satisfactory. I think the subsequent research should focus on the validation study of the model in more karst areas to prove its general applicability in karst hydrological forecasting. However, there are few drawbacks affect the manuscript and have to be addressed before the paper can be published in GMD.
Specific comments
1) English needs modification
I found several incorrect words, grammar and unclear sentences, make it very difficult to understand the analysis carried out and the results obtained. The authors need to carefully correct the language errors in the whole text.
2) More information about the potential of this new model, ie.e., the QMG model-V1.0 for application in karst areas needs to be added in the Introduction part, especially the advantages and disadvantages compared to current numerical karst groundwater models.
3) In the Methodology part, the section 3.1 Hydrological model, this title is inappropriate here, as it obviously also includes the Parameter Optimization in Section 3.2 and Model Setting in 3.4. Suggest changing it to a model framework and algorithm.
4) In section 3.3 Uncertainty Analysis, it is not clear how to analyze uncertainty in input data and model structure for this new QMG model-V1.0.
Other minor comments
1) All tables should be set to three-line tables.
2) The right side of Figure 3 seems to be a photograph, please explain the necessity of its existence.
3) Each variable in Figure 5 needs to be clearly labeled as to which parameter it refers to.
4) The horizontal axis in Figure 7 represents the date, but the interval is not one-to-one with the marked time, please check that.
1. A physically-based distributed QMG model is effectively devoloped. 2. This model requires only a small amount of data for modelling in karst areas. 3. Eighteen satisfactory flood simulation results indicate this QMG model is feasible. 4. An improved chaotic particle swarm optimization algorithm is effective for parameter optimization.
1. A physically-based distributed QMG model is effectively devoloped. 2. This model requires...
