Using large-scale tracer-aided models to constrain ecohydrological partitioning in complex, heavily managed lowland catchments

Zheng, Hanwu; Tetzlaff, Doerthe; Birkel, Christian; Wu, Songjun; Sauter, Tobias; Soulsby, Chris

doi:https://doi.org/10.5194/egusphere-2025-2166

Preprints

https://doi.org/10.5194/egusphere-2025-2166

Preprints

05 Jun 2025

| 05 Jun 2025

Status: this preprint is open for discussion and under review for Hydrology and Earth System Sciences (HESS).

Using large-scale tracer-aided models to constrain ecohydrological partitioning in complex, heavily managed lowland catchments

Hanwu Zheng, Doerthe Tetzlaff, Christian Birkel, Songjun Wu, Tobias Sauter, and Chris Soulsby

Abstract. Tracer-aided modelling (TAM) enhances ecohydrological process understanding, as stable water isotopes (ẟ¹⁸O and ẟ²H) can help constrain equifinality and provide complementary information beyond streamflow. Despite being primarily applied in rural (<100 km²) catchments with minimal disturbance, TAM may assess epistemic uncertainties from unrecorded human activities affecting streamflow, improving model reliability. This study investigated four sub-catchments (Berste, Wudritz, Vetschauer, and Dobra) in the heavily-managed Middle Spree River basin (ca. 2800 km²), in NE Germany, a strategically vital water resource supplying drinking water to Berlin, Germany’s capital, and sustaining agricultural and industrial demands. Detailed evaluation of ecohydrological water partitioning in this evapotranspiration (ET)-dominated region is complicated by heterogeneous land use, extensive hydraulic infrastructure and overall intensive management. We used the spatially distributed tracer-aided model STARR to simulate the effects of natural water storage-flux dynamics and management interventions on streamflow over a 6-year period. Seasonal isotope data used for calibration additionally to streamflow effectively captured subsurface runoff, with isotope fractionation intensity strongly linked to ET apportionment. This multi-criteria calibration helped reduce equifinality in complex systems with human-induced epistemic challenges. Epistemic errors were manifested as strong trade-offs between the information content of the different calibration constraints (i.e., streamflow and isotopes). Although compromised solutions occasionally failed to meet acceptable performance thresholds for both calibrated variables, such conflicts highlight potentially important mismatches in process representation. Our modelling framework shows the potential for informative insights from wider use of (even sparse) isotope data sets in tracer-aided modelling of complex, heavily managed catchments.

Received: 08 May 2025 – Discussion started: 05 Jun 2025

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.

Download & links

Preprint (PDF, 2101 KB)

Supplement (570 KB)

Download & links

Hanwu Zheng, Doerthe Tetzlaff, Christian Birkel, Songjun Wu, Tobias Sauter, and Chris Soulsby

Status: open (until 23 Sep 2025)

Post a comment Subscribe to comment alert

RC1:
'Comment on egusphere-2025-2166', Anonymous Referee #1, 24 Jul 2025 reply
General comments:
The study falls within the scope of HESS and is well written, with clear structure and fluent language. The quality of the figures is mixed and the methods used were insufficiently robust to provide any confidence in the generalizability of the results or conclusions. The study is broadly similar to several previous publications on multi-objective optimization using isotope tracers, and the new contribution, beyond replication of previous findings in a new location, is not yet clear. With revisions, this could be an excellent publication for HESS.
Specific comments:
I see three areas in need of substantial revision: study differentiation, calibration methodology and presentation of results.
The study looks quite similar to previous studies in other areas, some of which have not yet been referenced in the introduction or discussion; multi-objective optimizations using flow and isotopes have been coming out for many years, e.g.: (He et al., 2019; Holmes et al., 2023; Nan & Tian, 2024; Tafvizi et al., 2024; Tunaley et al., 2017). The novelty is currently unclear, and the authors should revise to highlight the specific aspects that are new (this will likely involve only minor changes to the text). Is it the study site (agricultural with substantial groundwater pumping) or the spatial discretization of the model? Or something else, perhaps relating to the analysis of the results?
A more fundamental issue with the present version is the methodology applied. Given the central importance of calibration to the study, the methods applied are not as robust and defensible as they ought to be for a publication. In particular:
The model was calibrated to optimize NSE. This metric has lost support as a calibration objective because as a squared error metric, it overemphasises peak flow timing, and leads to erroneously damped simulation variability (Gupta et al., 2009). Unsurprisingly, the presented model results had erroneously damped variability (low flows too high, high flows too low). Further, for sparse datasets (like the isotope series here) it is highly sensitive to individual points, as noted in the text. Why was this metric used in spite of its well-known deficiencies?

There was no validation or clear evaluation of the model. Shen et al. (2022) was referenced to justify this omission, but this does not excuse the absence of some other method than split-sample validation to test the calibrated models. There is currently no clear evidence that the final models are at all reliable and not just overfit to the calibration data. This might be corrected by using satellite or other data to justify the ‘trustworthiness’ of the models but it should be an explicit evaluation.

It seems only a single calibration trial was performed for each objective type. The final calibrated models will vary depending on the initial population for the genetic algorithm, and on the random seed used in mutating new solutions. It is therefore important to run several independent calibration trials for each objective, as a single trial may be an outlier or fail to generate solutions near the ‘true’ Pareto front (i.e., solutions that are actually as good as the model can do). Without multiple independent calibration trials, it remains possible and plausible that the poor quality solutions for Berste were simply a fluke.

The presentation of the results would benefit greatly from revision in a few areas. In no particular order:
The presented time-series results have only the extreme end points of the pareto front, not the ‘compromise’ solutions, basically throwing out the ‘multi-objectiveness’ in favor of one simulation or the other. Why show only outliers?

Figure 4 is mislabeled as showing the Pareto fronts, but it actual has both dominated and non-dominated solutions from the calibration. Either the figure or label needs to change.

Labeling can be challenging to decipher. For example, subfigure 7 c2 is apparently ‘BSI in schemes 2-5 for wet year of 2023’ while figure 8 c2 is ‘Vetschauer compromised solution in scheme 2-5’ (I don’t know which compromise solution, just that it is one). Some figures are quite reader-friendly (Figure 5 and 6 for example can be followed without taxing decoding). However, I was quite unable to read the alphabet soup of Table 4 even after writing out a ‘key’ on scrap paper to track the 4 item deep ‘respectively’ label linking processes to letters (I think at least one comma is missing from the list).

Returning to the mysterious compromised solution, the actual solution is not defined, only that it comes from the ‘middle part of the Pareto front’. Is it the optimal solution when equal weight is given to the flow and isotope KGE or was it just sort of eyeballed?

A final, minor, point: it was frustrating to be told about finicky model details like roughness coefficient values without knowing any of the model basics, which were relegated to the supplement. Certainly, detailed model descriptions are out of scope but it would be lovely to at least have a couple sentences so the reader knows how many soil layers there are or if there is lateral groundwater flow between cells without hunting down a separate document.
Technical corrections:
The precipitation isotope input is referenced as coming from Bowen et al. (2003) which covers annual averages, but the inputs seem to be the monthly average estimates. The monthly estimation method comes from the subsequent 2005 paper (Bowen G. J., Wassenaar L. I. and Hobson K. A. (2005) Global application of stable hydrogen and oxygen isotopes to wildlife forensics. Oecologia 143, 337-348, doi:10.1007/s00442-004-1813-y.).
References:
Gupta, H. V., Kling, H., Yilmaz, K. K., & Martinez, G. F. (2009). Decomposition of the mean squared error and NSE performance criteria: Implications for improving hydrological modelling. Journal of Hydrology, 377(1–2), 80–91. https://doi.org/10.1016/j.jhydrol.2009.08.003
He, Z., Unger-Shayesteh, K., Vorogushyn, S., Weise, S. M., Kalashnikova, O., Gafurov, A., Duethmann, D., Barandun, M., & Merz, B. (2019). Constraining hydrological model parameters using water isotopic compositions in a glacierized basin, Central Asia. Journal of Hydrology, 571, 332–348. https://doi.org/10.1016/j.jhydrol.2019.01.048
Holmes, T. L., Stadnyk, T. A., Asadzadeh, M., & Gibson, J. J. (2023). Guidance on large scale hydrologic model calibration with isotope tracers. Journal of Hydrology, 621. https://doi.org/10.1016/j.jhydrol.2023.129604
Nan, Y., & Tian, F. (2024). Isotope data-constrained hydrological model improves soil moisture simulation and runoff source apportionment. Journal of Hydrology, 633. https://doi.org/10.1016/j.jhydrol.2024.131006
Tafvizi, A., James, A. L., Holmes, T., Stadnyk, T., Yao, H., & Ramcharan, C. (2024). Evaluating the significance of wetland representation in isotope-enabled distributed hydrologic modeling in mesoscale Precambrian shield watersheds. Journal of Hydrology, 637, 131377. https://doi.org/10.1016/j.jhydrol.2024.131377
Tunaley, C., Tetzlaff, D., Birkel, C., & Soulsby, C. (2017). Using high-resolution isotope data and alternative calibration strategies for a tracer-aided runoff model in a nested catchment. Hydrological Processes, 31(22), 3962–3978. https://doi.org/10.1002/hyp.11313

Reply
Citation: https://doi.org/10.5194/egusphere-2025-2166-RC1
- AC1: 'Reply on RC1', Hanwu Zheng, 26 Aug 2025 reply
  
  The comment was uploaded in the form of a supplement: https://egusphere.copernicus.org/preprints/2025/egusphere-2025-2166/egusphere-2025-2166-AC1-supplement.pdf
  Reply
  
  Citation: https://doi.org/10.5194/egusphere-2025-2166-AC1
RC2:
'Comment on egusphere-2025-2166', Anonymous Referee #2, 15 Sep 2025 reply
This manuscript applies a large-scale tracer-aided modeling (TAM) approach to disentangle ecohydrological processes in the heavily managed Middle Spree catchment (MSC), Germany, an evapotranspiration-dominated region facing strong anthropogenic pressures. By integrating stable water isotopes (δ¹⁸O and δ²H) with streamflow into the distributed STARR model and calibrating with a multi-objective NSGA-II algorithm, the study evaluates runoff generation, groundwater contributions, and evapotranspiration (ET) partitioning across four sub-catchments (Berste, Wudritz, Vetschauer, Dobra). The key contribution lies in showing how streamflow–isotope trade-offs emerge as diagnostic signals of epistemic errors from unrecorded human impacts, such as irrigation or mining legacies. While isotope inclusion sometimes reduced discharge simulation performance, it significantly improved process representation such as subsurface mixing. Overall, the study demonstrates that even sparse seasonal isotope datasets can provide critical constraints in TAM for complex, human-altered hydrological systems, offering new insights into ecohydrological partitioning and informing future water management under anthropogenic and climatic pressures. From a reader’s perspective not deeply familiar with isotope tracer methods, I have several comments and suggestions for clarification.

Points for the Authors to Consider
1.Clarifying the added value of isotopes
The added value of incorporating isotopes over other hydrological variables remains somewhat unclear. For instance, while the introduction emphasizes human influences, isotope integration did not appear to improve the model’s ability to capture these anthropogenic effects, which raises questions about the practical contribution of isotopes in this context. How would the results compare if ET data were used in a multi-objective calibration of the STARR model? Could the process descriptions be refined to more clearly illustrate the unique role isotopes play relative to other potential data sources?
2.Improving figure clarity and linkage to discussion

Figures 5–8 combine multiple dimensions (temporal, spatial, and calibration metrics), making them information-rich but sometimes challenging to interpret. The figure captions and related explanations in the text could more directly highlight the core message of each figure. Including a short statement of motivation or the specific hypothesis addressed by each figure would help guide readers and improve accessibility. Moreover, because the figures are complex and the key messages are not always clearly highlighted, the subsequent discussion section becomes less convincing. Readers may find it difficult to fully trust the discussion, as the results and the interpretations are not always tightly aligned. Strengthening the clarity of figures and explicitly linking their core findings to the corresponding discussion points would improve the manuscript’s overall persuasiveness.

Specific Comments
Lines 127 and 140: Please clarify the meaning of SE and m.a.s.l.

Lines 240–243: Rainfall inputs are provided at daily resolution, whereas precipitation isotope inputs are monthly. How does this temporal inconsistency affect the results, and is this assumption reasonable?

Lines 249–251: Although a citation is provided, the manuscript would benefit from more detail on the isotope observations. Were these instantaneous grab samples, or integrated/accumulated values?

Table 3 (Scheme 1): Please clarify whether the calibration was performed jointly across all basins, or if each basin was calibrated independently.

Figure 3: Why are only δ²H time series presented, while δ¹⁸O observations and simulations are not shown? It would also help readers unfamiliar with isotope applications if key concepts such as LMWL and VSMOW were briefly explained.

Figure 4: KGE is used for isotopes and NSE for streamflow. Why not use the same performance metric for both, to improve comparability?

Table 4: The description of Table 4 appears in the first paragraph of the Results, though the table is first referenced in Section 3.2.2. Consider relocating the description for consistency.

Reply
Citation: https://doi.org/10.5194/egusphere-2025-2166-RC2

Hanwu Zheng, Doerthe Tetzlaff, Christian Birkel, Songjun Wu, Tobias Sauter, and Chris Soulsby

Supplement

https://doi.org/10.5194/egusphere-2025-2166-supplement

Hanwu Zheng, Doerthe Tetzlaff, Christian Birkel, Songjun Wu, Tobias Sauter, and Chris Soulsby

Viewed

Total article views: 482 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	Supplement	BibTeX	EndNote
425	44	13	482	22	9	24

HTML: 425
PDF: 44
XML: 13
Total: 482
Supplement: 22
BibTeX: 9
EndNote: 24

Views and downloads (calculated since 05 Jun 2025)

Month	HTML	PDF	XML	Total
Jun 2025	103	20	7	130
Jul 2025	61	10	2	73
Aug 2025	118	10	3	131
Sep 2025	143	4	1	148

Cumulative views and downloads (calculated since 05 Jun 2025)

Month	HTML	PDF	XML	Total
Jun 2025	103	20	7	130
Jul 2025	61	10	2	73
Aug 2025	118	10	3	131
Sep 2025	143	4	1	148

Viewed (geographical distribution)

Total article views: 482 (including HTML, PDF, and XML) Thereof 482 with geography defined and 0 with unknown origin.

Country	#	Views	%

Latest update: 17 Sep 2025

Short summary

Ecohydrological processes in heavily managed catchments are often incorrectly represented in models. We applied a tracer-aided model STARR in an ET-dominated region (the Middle Spree, NE Germany) with major management impacts. Water isotopes were useful in identifying runoff contributions and partitioning ET even at sparse resolution. Trade-offs between discharge- and isotope-based calibrations could be partially mitigated by integrating more process-based conceptualizations into the model.


Total:	0
HTML:	0
PDF:	0
XML:	0