the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 24 Oct 2024
CONCN: A high-resolution, integrated surface water-groundwater ParFlow modeling platform of continental China
Abstract. Large-scale hydrologic modeling at national scale is an increasing important effort worldwide to tackle ecohydrologic issues induced by global water scarcity. In this study, a surface water-groundwater integrated hydrologic modeling platform was built using ParFlow, covering the entire continental China with a resolution of 30 arcsec. This model, CONCN 1.0, has a full treatment of 3D variably saturated groundwater by solving Richards’ equation, along with the shallow water equation at the ground surface. The performance of CONCN 1.0 was rigorously evaluated using both global data products and observations. RSR values show good to excellent performance in streamflow, yet the streamflow is lower in the Endorheic, Hai, and Liao Rivers due to uncertainties in potential recharge. RSR values also indicate good performance in water table depth of the CONCN model. This is an intermediate performance compared to two global groundwater models, highlighting the uncertainties that persist in current large-scale groundwater modeling. Our modeling work is also a comprehensive evaluation of the current workflow for continental-scale hydrologic modeling using ParFlow and could be a good starting point for the modeling in other regions worldwide, even when using different modeling systems. More specifically, the vast arid and semi-arid regions in China with substantial sinks (i.e., the end points of endorheic rivers) and the large uncertainties in potential recharge pose challenges for the numerical solution and model performance, respectively. Incompatibilities between data and model, such as the mismatch of spatial resolutions between model and products and the shorter, less frequent observation records, require further refinement of the workflow to enable fast modeling. This work not only establishes the first integrated hydrologic modeling platform in China for efficient water resources management, but it will also benefit the improvement of next generation models worldwide.
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Status: open (until 19 Dec 2024)
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CC1: 'Comment on hess-2024-292', Nima Zafarmomen, 06 Nov 2024
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1) How did the authors handle uncertainty in datasets for potential recharge and soil properties in regions with sparse observational data, particularly in arid and semi-arid zones? Could more details on uncertainty quantification be provided?
2) The CONCN 1.0 model covers a vast area at high resolution, which demands substantial computational resources. Could the authors discuss any measures taken to optimize computational efficiency and how the model’s scalability could be extended to similar hydrologic regions?
3) Would the authors consider using coarser resolution or data assimilation techniques to make the model more computationally accessible, particularly for policy-making applications?
4) I recommend that the authors consider including a comparison with data assimilation approaches to enhance model accuracy and reduce uncertainties, especially in data-scarce regions. Data assimilation has been effectively applied in hydrologic modeling to integrate observed data with model predictions, often improving the alignment with real-world conditions. Techniques like Kalman filters or variational data assimilation could complement the current workflow, particularly for improving estimates of potential recharge and water table depth in arid and semi-arid regions where observational data is limited. A comparison with data assimilation methods may also highlight the strengths of the CONCN model and provide a pathway for future enhancements in large-scale hydrologic modeling.
5) To strengthen the contextual foundation of this study, I recommend the authors cite established integrated hydrologic models like SWAT-MODFLOW in the introduction. SWAT-MODFLOW, widely used for its integration of surface and subsurface processes, has significantly advanced our understanding of coupled surface-groundwater systems across various scales. Citing SWAT-MODFLOW alongside ParFlow and other large-scale models would provide readers with a broader perspective on the tools available for integrated hydrologic modeling. This comparison may also underscore the unique challenges and innovations of applying ParFlow within China’s hydrologic and geologic context, while highlighting the importance of diverse model approaches for managing complex water resources.
I strongly recommend to cite below paper:"Assessing regional‐scale spatio‐temporal patterns of groundwater–surface water interactions using a coupled SWAT‐MODFLOW"
"Assimilation of sentinel‐based leaf area index for modeling surface‐ground water interactions in irrigation districts"
"Development and application of the integrated SWAT–MODFLOW model."
Citation: https://doi.org/10.5194/hess-2024-292-CC1 -
RC1: 'Comment on hess-2024-292', Anonymous Referee #1, 19 Nov 2024
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General comments
The authors present a new hydrologic modeling platform over continental China, based on the ParFlow model, aimed at providing information for surface water and groundwater resources management. Their setup is adapted from the CONUS 2.0 modeling platform over the US. The authors discuss the parameters and input data used and they provide a comparison with modeled and observed data for groundwater table depth and river discharge.
The paper is well written and pleasant to read. The results are interesting and well presented via clear figures. My main concern is about the comparison with other datasets, which may be a little too simple, as detailed below.
In particular:
- l. 278-293: Do I understand correctly that for the spinup, the authors used the 1981-2010 P-ET average as constant atmospheric forcing until a quasi-steady state was reached? Is this resulting state used for the evaluation in the next section or have the authors simulated a transient run over 1981-2010, starting from this quasi-steady state? This is not clearly stated, while it is very important for the evaluation and its analysis. For the following comments, I will assume that the authors evaluate the resulting quasi-steady state against other data sets.
- section 4.1. and 4.2.: While the main motivations for this modeling platform are (1) the impacts on water resources of the increased frequency, intensity, and duration of extreme weather events and (2) the management of these water resources, e.g., to prevent water scarcity, the authors limit their evaluation to a comparison of the steady state, which represents an idealized situation that never happens in the real world. In particular, the ability of the modeling platform to represent the dynamics (temporal evolution) on a yearly or better monthly or even daily time scale is not considered in this study, while this would be essential to assess whether the modeling platform is able to meet its primary aim (i.e., the aforementioned motivations).
- section 4.1.
- esp. l. 347-348: Do the authors use the longest available period for each gauge or the longest overlapping period (i.e., max 9 years between 2002 and 2010)? In any case, this relies on the hypothesis that an observed average over a few years (sometimes even only two years) as well as an observed average over two to several years covering another period (2002-2021) is representative for a steady state based on 1981-2010. I am not convinced that this hypothesis is true. I could agree that, the longer the observation period is, the closer the average gets to a steady state over the same period, even if this should still be verified. But in my opinion, there is no guarantee that the average over 2002-2021 is representative for the 1981-2010 steady state as this ignores potential shifts in the terrestrial water regime, e.g., due to climate change. One could think of the impact on streamflow of earlier snow melt, less snow accumulation in winter, more frequent extreme events, multi-year droughts, etc. Assuming that an average over a few years is representative for a steady state might be even more questionable, since these few years could be characterized by extreme events (droughts, floods). The resulting average would certainly not correspond to a steady-state, preventing a robust comparison. The authors already mention the potential impact of hydraulic engineering (e.g., dam operations) on the average streamflow (see l. 354), especially over short time periods.
- section 4.2.
- the structure of this section might be improved to make it easier for the reader to follow. For example, after the first paragraph (l. 364-371) describing Figure 6, one would expect a first analysis of these results. But this analysis only starts on l. 432.
- l. 364 and Figure 6: Are the steady states of the two global datasets over the same period as for CONCN (i.e., 1981-2010)? If not, is the hypothesis valid that these steady states, which might have been reached under different climatic conditions, are comparable? For example, if one region experiences less (or more) precipitation and/or higher evapotranspiration due to climate change, the resulting steady state will very likely be different.
- l. 397-416: In the same way as my comments above for the evaluation of streamflow, I do not see any reason why one could assume that the observed average over 2018 could be considered as representative or close to a steady state generated with data from 1981-2010. Especially for water table depth with a potentially huge impact of inherited conditions from previous years (memory effect), not only 2018, but also the previous years would need to be close to the 1981-2010 average hydrologic regime to – maybe – approach a steady-state-like state. I understand that this is the reason why the authors try to strengthen their evaluation with the analysis of the residuals in the context of the long-term trend from GRACE, thereby trying to make a link between the steady state based on 1981-2010 and 2018, but, in my opinion, the uncertainty of this whole evaluation remains high. The only way to provide a robust and representative evaluation is to do the comparison over a common period.
- l. 401-404: This might be easier to understand if the authors could briefly explain why this analysis integrating GRACE data is needed.
- l. 411: if the global models are calibrated, do they not implicitly account for human interaction, via the observational data used for calibration? This would then be contradictory with the statement in l. 406.
- l. 421-422: In l. 377, I understand that these grid cells are excluded from this analysis (precisely for the reason explained here). Please clarify this.
Specific comments
- l. 54-55: While I agree that it is pressing to develop a modeling platform accounting for it, this statement suggests that CONCN accounts for water quality control, which is not the case.
- l. 99: About the “unique dramatic topographic relief”. On one side, each part of the world has a unique relief, thus I could agree with this formulation. On the other side, many other regions (e.g., the US, South America, Africa, Europe, New Zealand, Japan, etc.) have transitions from mountains to coastal plains, thus facing similar challenges for hydrologic modeling.
- Figure 1: What is the meaning of the white coloring within the model domain in Fig. 1f? Here, I would interpret it as “no data”, is that correct? If it is zero, it should be colored according to the color bar (i.e., dark blue). If it is “no data”, how do the authors deal with it as source-sink term for ParFlow? This should be clarified in the text.
- l. 167-169: The procedure is not clear to me. Did the authors generate D8 connectivity slopes in addition to the aforementioned D4 slopes? If yes, why was it needed? What do they mean by “vector networks”?
- l. 180: How are the sinks handled? Is the inflowing ponding water removed before/after each time step?
- l. 200: Why do the authors derive the soil texture from this global dataset instead of using directly the soil hydraulic properties?
- l. 208: What is meant by “flow barriers”? Is the permeability in these grid cells further reduced by a factor or set to a very small value?
- l. 250: “several locations” It might be useful to add some more details here: How many locations? Are they distributed over the country and/or hydroclimatic regions to ensure a representative analysis and selection of datasets?
- l. 259: Why did the authors use the period 1981-2010 and not, e.g., 1991-2020? This could have made the evaluation easier, as the authors state further below that more observations are available for the last years (esp. since the 2000s).
- l. 275: It might be useful to add a source or some additional explanation on how Manning’s coefficient is “set to vary by land cover type”.
- l. 284: What is the role of the seepage face boundary condition?
- l. 284-285 and 287: On which time scale does the total storage change have to be less than 1% (resp. 3%)? Is it e.g., between two consecutive time steps or on an inter-annual basis?
- l. 292: I understand that it is important to reach an equilibrium for groundwater and for river discharge, but does a quasi-steady state for discharge in arid and semi-arid regions really make sense? Is the resulting discharge not too far away from reality? I would guess that in reality, the discharge is highly variable in these regions, with very low flow, or even no flow at all, most of the time alternating with high discharge after precipitation events or snow melt.
- Figure 3: How can the authors explain that they have streamflow values everywhere and not just in the streambeds in the south-east and north-east of the model domain? Or is it just an impression due to the visualization of a dense hydrographic network?
- Figure 3: It would be useful to add in the caption which period is shown. Or is it the end of the spinup (i.e., resulting quasi-steady state)?
- Figure 5: It might be useful to add in the caption that the gauges are grouped per basin as shown on Fig. 1b.
- Figure 7 and in the text: Do the “residuals” correspond to the difference between CONCN and the observed values at the wells?
- l. 453: All regions in the world experience increasing extreme weather events such as droughts and floods. What may make China “one of the most significant ecohydrologic hotspots in the world” could be the intense water use in the highly populated areas of the country. However, this is not accounted for in the model platform presented in this paper.
Technical corrections
- l. 29: Meaning of RSR?
- l. 56: Correct “ with a 10 km resolution”.
- l. 67: Meaning of USGS?
- l. 112: Correct “key components of the ParFlow model”?
- Figure 1: The north-eastern edge of the domain is hidden behind the color bars.
- Figure 1: In the caption, what do “f.g.”, “sil.”, and “c.g.” stand for?
- l. 154-156: For clarity, it might be good to specify that this concerns each grid cell individually, e.g., something like “D4 connectivity means that, within each grid cell, streamflow is allowed…”.
- l. 182: Correct “with those in IHU”?
- l. 199: In l. 125, the thickness of the second layer (from the top) is 0.3 m. Here, it is indicated to be 0.4 m.
- l. 260: Correct “Tarim River Basin”?
- l. 267: It is important to expand the acronym of CLM to avoid any confusion, as nowadays CLM usually means “Community Land Model”, while the CLM integrated in ParFlow is the “Common Land Model”.
- l. 340-343: There is a mismatch in the number of gauges: 95 (total) – 6 (no location) – 1 (close to another) – 1 (outside of domain) = 87, not 88.
- Figure 7: Correct “The background shows the average decrease of groundwater storage”.
- l. 397: Correct “by the three models”?
- Figure 8: Indicate in the caption that you compare the steady state over 1981-2010 with observations from 2018.
- l. 433: Correct “and the two global models”?
- l. 435: Correct “across the three models”?
- l. 445: Correct “below – these require” or maybe “below. These require”?
- l. 515: Correct “have been cited”?
- l. 524: Correct “reported in this paper”?
- l. 525: Correct “which is a consortium”?
- l. 526: Correct “and the Office”?
Citation: https://doi.org/10.5194/hess-2024-292-RC1
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