Preprints
https://doi.org/10.5194/hess-2024-230
https://doi.org/10.5194/hess-2024-230
05 Aug 2024
 | 05 Aug 2024
Status: a revised version of this preprint is currently under review for the journal HESS.

Achieving water budget closure through physical hydrological processes modelling: insights from a large-sample study

Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng

Abstract. Modern hydrology is embracing a data-intensive new era, information from diverse sources is currently providing support for hydrological inferences at broader scales. This results in a plethora of data reliability-related challenges that remain unsolved. The water budget non-closure is a widely reported phenomenon in hydrological and atmospheric systems. Many existing methods aim to enforce water budget closure constraints through data fusion and bias correction approaches, often neglecting the physical interconnections between water budget components. To solve this problem, this study proposes a Multisource Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called PHPM-MDCF. The concept of decomposing the total water budget residuals into inconsistency and omission residuals is embedded in this framework to account for different residual sources. We examined the efficiency of PHPM-MDCF and the residuals distribution across 475 CONUS basins selected by hydrological simulation reliability. The results indicate that the inconsistency residuals dominate the total water budget residuals, exhibiting highly consistent spatiotemporal patterns. This portion of residuals can be significantly reduced through PHPM-MDCF correction and achieved satisfactory efficiency. The total water budget residuals have decreased by 49 % on average across all basins, with reductions exceeding 80 % in certain basins. The credibility of the correction framework was further verified through several noise experiments. In the end, we explored the potential factors influencing the distribution of residuals and found notable scale effects where residuals decrease with increasing basin area. This emphasizes the importance of carefully evaluating the water balance assumption when employing multisource datasets for hydrological inference in small basins.

Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims made in the text, published maps, institutional affiliations, or any other geographical representation in this preprint. The responsibility to include appropriate place names lies with the authors.
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng

Status: final response (author comments only)

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on hess-2024-230', Anonymous Referee #1, 21 Aug 2024
    • AC1: 'Reply on RC1', Dengfeng Liu, 25 Aug 2024
  • RC2: 'Comment on hess-2024-230', Anonymous Referee #2, 25 Aug 2024
    • AC2: 'Reply on RC2', Dengfeng Liu, 05 Sep 2024
      • RC3: 'Reply on AC2', Anonymous Referee #2, 21 Sep 2024
        • AC5: 'Reply on RC3', Dengfeng Liu, 25 Sep 2024
  • CC1: 'Comment on hess-2024-230', Hui LIU, 02 Sep 2024
    • AC3: 'Reply on CC1', Dengfeng Liu, 05 Sep 2024
    • AC4: 'Reply on CC1', Dengfeng Liu, 05 Sep 2024
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng
Xudong Zheng, Dengfeng Liu, Shengzhi Huang, Hao Wang, and Xianmeng Meng

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Short summary
Water budget non-closure is a widespread phenomenon among multisource datasets, which undermines the robustness of hydrological inferences. This study proposes a Multisource Datasets Correction Framework grounded in Physical Hydrological Processes Modelling to enhance water budget closure, called PHPM-MDCF. We examined the efficiency and robustness of the framework using the CAMELS dataset, and achieved an average reduction of 49 % in total water budget residuals across 475 CONUS basins.