the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Regionalization and its impact on global runoff simulations: A case study using the global hydrological model WaterGAP3 (v 1.0.0)
Abstract. Valid simulation results from global hydrological models (GHMs), such as WaterGAP3, are essential to detecting hotspots or studying patterns in climate change impacts. However, the lack of worldwide monitoring data makes it challenging to adapt GHMs' parameters to enable such valid simulations globally. Therefore, regionalization is necessary to estimate parameters in ungauged basins. This study presents new regionalization methods for WaterGAP3 and aims to provide insights into selecting a suitable regionalization method and evaluating its impact on the simulation. Our results suggest that machine learning-based methods may be too flexible for regionalizing WaterGAP3 due to a significant performance loss between training and testing. In contrast, the most basic regionalization method (using the concept of spatial proximity) outperforms most of the developed regionalization methods and a pre-defined benchmark-to-beat in an ensemble of split-sample tests. The method selection, whether spatial proximity-based or regression-based, has a greater impact on the regionalization than the specific details on how the method is applied. In particular, the descriptor selection plays a subsidiary role when at least a subset of selected descriptors contains relevant information. Additionally, our research has shown that regionalization causes spatially varying uncertainty for ungauged regions. For example, India and Indonesia are particularly affected by higher uncertainty. The impact of regionalization in ungauged areas propagates through the water system, e.g., one water balance component changed by approximately 2400 km3 yr-1 on a global scale, which is in the range of inter-model differences. The magnitude of the impact of regionalization depends on the variability in regionalized values and the region's sensitivity for the analysed component.
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Status: open (until 13 May 2024)
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CEC1: 'Comment on gmd-2024-47', Juan Antonio Añel, 28 Mar 2024
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Dear authors,
Unfortunately, after checking your manuscript, it has come to our attention that it does not comply with our "Code and Data Policy".
https://www.geoscientific-model-development.net/policies/code_and_data_policy.htmlFor your work you use the WaterGAP3 model, and in the "Code Availability" section of your manuscript you do not provide a repository for such model. Therefore, you must publish the WaterGAP3 model in one of the appropriate repositories according to our policy, and reply to this comment with the relevant information (link and DOI) , as it should be available before the Discussions stage.
In this way, if you do not fix promptly this problem, we will have to reject your manuscript for publication in our journal. I should note that, given this lack of compliance with our policy, your manuscript should not have been accepted in Discussions. Therefore, the current situation with your manuscript is irregular.
Also, note that you must include in any future version of your manuscript the modified 'Code Availability' section, with the link and the DOI of the WaterGAP3 code.
Juan A. Añel
Geosci. Model Dev. Executive EditorCitation: https://doi.org/10.5194/gmd-2024-47-CEC1 -
AC1: 'Reply on CEC1', Jenny Kupzig, 03 Apr 2024
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Dear Juan A. Añel,
thanks for your comment and the information that we need the model code to be open-source. We misunderstood your policy (we apologize!) and are currently working to provide a link and DOI for the model code.
Meanwhile, we want to highlight that there is already a smaller version of the model code which is open-source (WaterGAPlite). WaterGAPLite is a re-programmed version of the model in R that is easier to handle as the global model (in c/cpp) and runs the model code on single basins. The github repository is https://github.com/JKupzig/WaterGAPLite.
Thanks again for your contribution. We're working intensely to satisfy the journal's requirements as soon as possible.
Citation: https://doi.org/10.5194/gmd-2024-47-AC1 -
AC2: 'Reply on CEC1', Jenny Kupzig, 15 Apr 2024
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Dear Juan A. Añel,
We have now made the model code available through a Zenodo repository. The source code of WaterGAP3 (v1.0.0) is now accessible through https://zenodo.org/records/10940380. The corresponding DOI is 10.5281/zenodo.10940380.
We will update the code and data availability statement in our revised manuscript accordingly.
Thanks for your understanding!
Kind regards,
Jenny KupzigCitation: https://doi.org/10.5194/gmd-2024-47-AC2 -
CEC2: 'Reply on AC2', Juan Antonio Añel, 16 Apr 2024
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Dear authors,
Many thanks for addressing this issue and publishing the code.
Regards,
Juan A. Añel
Geosci. Mode Dev. Executive Editor
Citation: https://doi.org/10.5194/gmd-2024-47-CEC2
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CEC2: 'Reply on AC2', Juan Antonio Añel, 16 Apr 2024
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AC1: 'Reply on CEC1', Jenny Kupzig, 03 Apr 2024
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RC1: 'Comment on gmd-2024-47', Anonymous Referee #1, 10 Apr 2024
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The article presents a comparison of regionalization methods to estimate the calibration parameter of the WaterGAP3 GHM model in ungauged catchments worldwide.
The article presents relevant work, and it is well written. Although, in my opinion, the fact that the methodology relies completely for data and evaluation in the model rest validity to analysis and findings of the results. Nevertheless, if these constraints and their implications are better analyzed and addressed, the paper is publishable.
General comments:
In several parts of the manuscript, the authors claim they are using new regionalization methods, and that is misleading, they are applying regionalization methods (that are not new) for the first time in the context of the WaterGap model. Please clarify throughout the text.
The implementation of regionalization methods is not very well explained, many assumptions and choices are made without proper justification (see specific comments). These may have important impacts in the performance of each regionalization method, unfairly favoring one over another.
I have some reservations with the fact that all regionalization is made within the WaterGap model, e.g., the use of model input data as basin descriptors, for example “soil storage” coming from a look-up table, does not seem to be a proper descriptor of a basin; the selection of these descriptors based on the correlation with the calibrated parameter; the evaluation of each method by comparing regionalized gamma versus calibrated gamma (if I understood correctly); and also, to use as benchmark the resulting parameter distribution of a previous version of the model at a different resolution and regionalization method.
Specific comments:
Line 131, explain the standard calibration approach and sufficient performance indicators referred here.
Line 148-149, explain what do you mean with the need to define sensitive parameters for a better calibration of WaterGAP3 if the model only has one calibration parameter.
Line 162-163: I would say you are minimizing redundant information, but not avoiding it, some of the descriptors are correlated.
Line 173 to 177: To use the calibrated parameter to select descriptors comes with many drawbacks, as you explain here, why then not to choose another more independent variable to define meaningful basin descriptors?
Line 177: More than a complex relationship, a very uncertain one, if there is one at all.
Table 1: Define IG(&) in the Table
Line 210 to 214: Please clarify, the evaluation is done by comparing regionalized gamma with calibrated gamma? Or model predictions? Why a MAE value of 2.1?
Line 220 to 222: Please explain better the “tunning approach”, what are gamma 1 and gamma 2?
Line 252, why three clusters?
Line 259, explain why the “highly flexible version” uses 162 clusters?
Line 305, re-write to clarify.
Line 306 to 308, re-write to clarify the meaning of the sentence. I believe that it has to do more with the relevance of the descriptors included in explaining catchment behavior, or in this case calibrated parameter gamma.
Line 317-317: Change “However” for “Therefore”?, if I understand the intention of the sentence correctly.
Line 356-357, the “high flexible” k-means results are not in Figure 4? They need to be added.
Line 358 to 361: I do not understand this statement, why and how a regionalization method will “extract” information form the descriptors. Pagliero et al. (2019) is not correctly cited in the context of this statement.
Figure 5d: I have reservations to the validity of comparing global distribution of gamma for WG2 and WG3 given they have different resolutions. Please comment whether this is an issue for this comparison.
Figure 6, what was the reason to choose the year 1989 for this comparison? Seems arbitrary. Same for year 2010 in Figure 7. Why not use an average year, or the driest/wetter year of the period.
Line 453-454. This statement contradicts itself. Basin descriptors selected for regionalization are of crucial importance because they need to be selected due to the amount of information they contain.
Line 459: use a more academic term instead of “blurring”.
Citation: https://doi.org/10.5194/gmd-2024-47-RC1
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