An Open-Source Python Framework for the Flexible Deployment and Advanced Applications of the Variable Infiltration Capacity (VIC) Model

Zheng, Xudong; Liu, Dengfeng; Tang, Qiuhong; Li, Qiang; Liao, Haoyu; Wang, Hao

doi:10.5194/egusphere-2026-4655

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https://doi.org/10.5194/egusphere-2026-4655

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12 Aug 2026

| 12 Aug 2026

Status: this preprint is open for discussion and under review for Geoscientific Model Development (GMD).

An Open-Source Python Framework for the Flexible Deployment and Advanced Applications of the Variable Infiltration Capacity (VIC) Model

Xudong Zheng, Dengfeng Liu, Qiuhong Tang, Qiang Li, Haoyu Liao, and Hao Wang

Abstract. The development of the Variable Infiltration Capacity (VIC) model to version 5 has endowed it with enhanced land-surface-modelling features, establishing it as a modern and cutting-edge tool for hydrological research. However, its adoption is limited by challenges in complex data processing and parameter preparation. To address this issue, this study proposes the Easy VIC Build (EVB) framework, an open-source Python package with an object-oriented, modular design, developed for efficient and flexible VIC deployment. Two real-world basin setups were selected for model application to validate the effectiveness of the EVB framework. The results indicate that, based on the EVB framework, reliable spatial parameters with robust transferability can be derived, while VIC models can be conveniently deployed and achieve satisfactory simulation performance. The EVB framework provides a well-suited workbench for advanced applications of the VIC model, promising to further leverage the value of VIC-5 in hydrology and related fields.

Received: 01 Aug 2026 – Discussion started: 12 Aug 2026

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Xudong Zheng, Dengfeng Liu, Qiuhong Tang, Qiang Li, Haoyu Liao, and Hao Wang

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'Comment on egusphere-2026-4655', Anonymous Referee #1, 03 Sep 2026 reply
This manuscript addresses practical challenges in deploying the VIC model, particularly the tedious preparation of input data and model parameters. The authors present Easy VIC Build (EVB), an open-source Python package that integrates these procedures into a unified workflow, and demonstrate its applicability in two real-world basins with different modeling settings. The integration of MPR further enhances the potential of EVB to support accessible spatially distributed parameterization. Overall, the manuscript is of high quality, fits well within the scope of the journal, and should be of interest to an international readership. In addition, the manuscript examines the relationship between the spatial distribution of model parameters and runoff generation from the perspective of physical plausibility, providing an indirect assessment of the rationality of the MPR approach. Although such an analysis is not commonly adopted, I find this aspect particularly interesting and believe that it deserves further discussion. I therefore minor revision, with several points regarding the presentation and wording to be addressed, as outlined below.
As noted above, the authors examine the physical plausibility of the spatial patterns of model parameters, which is an important advantage facilitated by the application of the MPR technique. However, the manuscript does not appear to provide sufficient discussion of the estimation of spatially distributed parameters, and thus this advantage is not fully highlighted. I therefore suggest that the authors expand the discussion of spatial parameter estimation, including (1) what alternative approaches, besides MPR, can be used to estimate spatially distributed parameters, (2) what are the major challenges and difficulties associated with spatial parameter estimation, and (3) what are the specific advantages of MPR in addressing these challenges. Such a discussion would help better contextualize the contribution of MPR and further highlight the significance of the spatial parameter patterns obtained in this study.

A key aspect that is currently missing from the manuscript is a clear description of the transfer functions, particularly which predictor variables are used to estimate each model parameter. I suggest that the authors provide a summary table in Section 3.1 specifying the predictor-parameter relationships. The corresponding transfer functions could also be presented explicitly, similar to those given in Eqs. (2)–(4). Providing this information would make the parameter estimation procedure more transparent and help readers better understand the implementation of the proposed approach.

Given the length of the manuscript, I suggest that the authors consider reducing or streamlining some of the less essential content to improve the overall readability and focus of the manuscript. The space saved through these revisions could instead be used to further strengthen several aspects that are more closely related to the scientific contribution of the study. Specifically:
Section 2.1.3: This subsection could be removed from the main text or reduced to a brief description and incorporated elsewhere, as the current level of detail does not appear essential to understanding the main contribution of the study.

Section 3.3: I suggest merging and substantially condensing the subsections under this section to provide a more concise description of the workflow and improve readability.

Section 4.1: This section could also be shortened, as the detailed description of the case-basin setup does not appear to be central to the main findings of the manuscript.

Section 5.3.2: I suggest expanding the discussion of spatially distributed parameter estimation as mentioned above, particularly the challenges associated with this process and the advantages of the MPR approach.

Abstract and Conclusion: I recommend further strengthening these sections by incorporating more quantitative results, such as specific model performance metrics and the accuracy achieved through cross-scale parameter transfer. Including these quantitative findings would allow readers to more readily assess the effectiveness and broader applicability of the proposed approach.

The manuscript also contains several minor formatting and writing issues that should be addressed. For example:
Line 417: A line break appears to be missing. Please check and correct the formatting accordingly.

Appendix C: The title appears to be identical to that of Appendix B. Please verify the correct title and revise it accordingly.

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Citation: https://doi.org/10.5194/egusphere-2026-4655-RC1

Xudong Zheng, Dengfeng Liu, Qiuhong Tang, Qiang Li, Haoyu Liao, and Hao Wang

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Short summary

Hydrological models help scientists understand water movement and predict changes in water resources. However, their increasing complexity makes them difficult to use. This study developed an open-source framework to simplify the setup and application of the Variable Infiltration Capacity model. Tests in multiple basins showed that the framework improves efficiency, provides reliable simulations, and supports wider use of hydrological models.


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