the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jun 2025
A new approach for joint assimilation of cosmic-ray neutron soil moisture and groundwater level data into an integrated terrestrial model
Abstract. Uncertainties in hydrological simulations can be quantified and reduced through data assimilation (DA). This study explores strategies for assimilating soil moisture (SM) data from Cosmic-Ray Neutron Sensors (CRNS) and groundwater level (GWL) data into the Terrestrial System Modeling Platform (TSMP), which integrates both land surface and subsurface processes. DA experiments incorporating both state and parameter estimation were performed using the localized Ensemble Kalman Filter (LEnKF) within a representative catchment in Germany over the period 2016 to 2018, with cross-validation conducted on non-overlapping years. Univariate assimilation of SM reduced the unbiased root mean square error (ubRMSE) by approximately 50 %, while univariate assimilation of GWL achieved up to a 70 % reduction in ubRMSE at assimilation sites. Improvements in GWL estimates extended up to 5 km from the assimilation points, with ubRMSE reductions ranging between 2 % and 50 %. However, assimilating GWL independently had a negative effect on SM representation, and similarly, assimilating SM alone degraded GWL predictions. To address these issues, a novel multivariate DA framework was developed, enabling SM and GWL to be assimilated independently through separate modules. Groundwater data were used to constrain the water table position, thereby improving the estimation of the boundary between unsaturated and saturated zones and allowing updates to hydraulic conditions within the saturated zone. Meanwhile, SM data improved the representation of hydrological processes in the unsaturated zone. The multivariate assimilation approach resulted in comparable improvements in GWL, SM, and evapotranspiration (ET) at the assimilation sites. Moreover, including parameter estimation alongside state updating further reduced the ubRMSE by up to 17 %.
Competing interests: One of the authors (Harrie-Jan Hendricks Franssen ) is a member of the editorial board of this journal. The authors have no other competing interests to declare.
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 paper. While Copernicus Publications makes every effort to include appropriate place names, the final responsibility lies with the authors. Views expressed in the text are those of the authors and do not necessarily reflect the views of the publisher.- Preprint
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CC1: 'Comment on egusphere-2025-2124', Nima Zafarmomen, 13 Jun 2025
This is a carefully done study and the findings are of considerable interest. And the submission is worth of publication. Following are some minor comments:
- The paper presents a novel multivariate data assimilation (DA) framework for integrating cosmic-ray neutron sensor (CRNS) soil moisture (SM) and groundwater level (GWL) data into the Terrestrial System Modeling Platform (TSMP). The approach is innovative in its use of a weakly coupled DA scheme to independently update saturated and unsaturated zones, addressing limitations of fully coupled DA systems. However, the manuscript could strengthen its claim of novelty by explicitly comparing the proposed method to existing multivariate DA frameworks in other coupled models (e.g., CATHY, Flux-PIHM, MIKE-SHE, and SWAT-MODLFOW) beyond the brief mentions in the introduction and discussion. For example, "Assimilation of Sentinel-Based Leaf Area Index for Modeling Surface-Ground Water Interactions in Irrigation Districts" used the integrated model fully coupled DA systems and you can cite it to make a stronge fundation for you research. Moreover, the introduction effectively highlights the importance of SM and GWL in terrestrial hydrology and the role of DA in reducing model uncertainties. However, it could better contextualize the study by discussing recent advancements in CRNS technology and its adoption in DA frameworks. For instance, referencing studies like Bogena et al. (2022) or Schrön et al. (2017) earlier in the introduction would clarify why CRNS is a superior choice compared to traditional in-situ or RS-based SM data.
- he description of the Rur catchment and data sources (Section 2.1 and 2.3) is thorough, but the rationale for selecting specific CRNS sites and GWL wells is not fully explained. For example, why were only 78 out of 616 wells used for DA, and how was the median GWL selection criterion determined? A brief justification of these choices would enhance transparency.
- The results section provides a comprehensive analysis of ubRMSE, RMSE, and correlation coefficients across multiple experiments. However, the focus on ubRMSE as the primary metric is not fully justified. While ubRMSE accounts for bias, a discussion of why it is prioritized over RMSE or other metrics (e.g., mean absolute error) would clarify its relevance.
- The discussion compares the proposed method to Hung et al. (2022) and Zhang et al. (2016), but it could be expanded to include other multivariate DA studies (e.g., Botto et al., 2018; Shi et al., 2015) to highlight how the weakly coupled approach addresses their limitations. For instance, how does the proposed method mitigate the issue of spurious correlations noted in Zhang et al. (2016)?
- Terms like “weakly coupled DA” and “fully coupled DA” are used consistently but may confuse readers unfamiliar with DA jargon.
Citation: https://doi.org/10.5194/egusphere-2025-2124-CC1 -
RC1: 'Comment on egusphere-2025-2124', Anonymous Referee #1, 11 Aug 2025
This manuscript develops and presents a novel multivariate data assimilation framework and implements it in an integrated hydrology model. This approach is tested in a catchment in Germany using groundwater and soil moisture observations. It is interesting that this approach resulted in improved state variables and also in some minor improvement to related fluxes such as ET. The manuscript is clearly written and organized. I recommend this manuscript be published pending some minor revisions and / or responses to my comments below.
1. Are (or how are) the _PAR runs spun up to account for potential impact of the parameters (e.g. ln(Ksat)) on model equilibrium states? The methods section does a nice job describing this process in general and it appears that great care was taken here to ensure a good model spin up, but it is still a bit unclear how this interacted with the adjustment of model inputs for these cases.
2. Table 3 would benefit from some narrative text to describe the different experiments, in words, to help better describe them.
3. In the experiments that assimilate model derived outputs (such as WTD) and those that also adjust ln(Ksat) there does not appear to be a difference in temporal behavior. That is, often if there is bias in say soil moisture it will drift back to a value different than the observation after the assimilation period over time. Adjusting ln(Ksat) should help correct this model "bias" but I'm not seeing this behavior in the results (but agree with the overall improvement the authors point out). I'm wondering if they can comment on this more?
Citation: https://doi.org/10.5194/egusphere-2025-2124-RC1 -
RC2: 'Comment on egusphere-2025-2124', Anonymous Referee #2, 12 Aug 2025
This is a well written and well executed technical study on multivariate DA for a catchment in Germany. The authors have a history of such studies, and this is another nice example. My main suggestion, to improve the potential impact of the paper, is that the authors expand on some of their comments because – at least to me – they are central to the motivation and implications of the study. However, they are currently not given appropriate attention.
Main comments:
[1] Why is there a trade-off between groundwater levels and soil moisture? How strongly are SM and GWL connected? How does this connection depend on the location within the catchment? Is it larger when GWL are shallower, and does it (for CNRS estimates) disconnect for deeper GWL?
[2] You state that you propose a “novel multivariate assimilation method*. Can you be more explicit about its novelty, given that you cite references with previous multivariate DA examples. What is novel about the algorithm introduced in this study? I am not doubting its novelty, but you do not elaborate on this issue when introducing the algorithm, only in the discussion section.
[3] Lines 268-269. The authors state that: “With a localization radius of ~100 km, exceeding the domain size, assimilation effects covered the entire area.” Can you please elaborate on the meaning of this statement?
[4] The model improves through the assimilation, but what have you learned about the model? And especially its limitations? What does the updating reveal about problems within the model (that make the updating necessary)?
[5] (section 4.1) When assimilating SM only, ET and GWL changed by just a few percent. How relevant is this change? How does this change compare to – say – making slightly different assumptions about the noise and noise structure? Or is this small change the equivalent to essentially no change? You state in your abstract that: “However, assimilating GWL independently had a negative effect on SM representation, and similarly, assimilating SM alone degraded GWL predictions.” This effect seems very minimal, and I do not really see a significant decline in SM performance when GWL is assimilated.
[6] (section 4.2) How would you reduce the problem that performance declines with distance from assimilation wells?
[7] (discussion section) Can the independent updating of different parts of the model lead to water balance issues?
[8] In the conclusion you again state that: “However, assimilating GWL data alone negatively affected SM prediction accuracy, and similarly, assimilating SM data alone reduced the accuracy of GWL estimates.” However, the improvement during univariate assimilation is more than a factor 10 than the reduction in the other variables. I think it would be good to discuss this aspect a bit more transparently.
Citation: https://doi.org/10.5194/egusphere-2025-2124-RC2
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