the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 15 Jan 2026
A coupled surface water–groundwater multi-objective optimization framework for coordinated water–ecosystem–agriculture management in arid inland river basin
Abstract. In arid regions, water is the key link sustaining both production and ecosystems, and its sustainable management is essential for regional security. This study constructs a coupled surface water–groundwater hydrology–agriculture multi-objective optimization model for the mainstream area of the Tarim River Basin, in which the NSGA-III algorithm is applied to optimize four objectives, including agricultural economic benefit per unit of irrigation water (fAB), groundwater level rise (fGL), terminal lake area (fLA), and total agricultural nitrogen load (fTN). Based on the optimized solutions, the trade-offs and synergistic pathways among multiple objectives within the water–ecosystem–agriculture system are systematically evaluated under different hydrological year conditions. The results indicate that significant trade-offs exist among objectives, with fAB showing negative relationships with fTN and fLA, and solutions with higher economic benefits generally accompanied by reduced ecological water supply and increased nitrogen loads. The spatial heterogeneity of the basin necessitates the adoption of differentiated management strategies, whereby upstream areas with relatively stable water availability can sustain higher levels of agricultural production, while midstream and downstream areas are highly sensitive to ecological water constraints and therefore require priority allocation to ecological water use. The optimization results show that cultivated land area should be dynamically adjusted under different hydrological conditions, ranging from 11.3–14.3 × 10⁴ hm2 in wet years, 10.1–13.1 × 10⁴ hm2 in normal years, and contracting to 9.5–11.9 × 10⁴ ha in dry years. The cropping structure is dominated by cotton (69.7 %–75.8 %), with the proportion of high-benefit crops such as vegetables and fruit crops moderately increased in wet years, whereas in dry years the structure shifts toward water-saving crops and high water-consuming crops are appropriately restricted. This study demonstrates that combining multi-objective optimization with spatially differentiated regulation can achieve coordinated management of water resources, ecosystems, and agriculture, and provides an operational decision-making basis for managing water–ecosystem–agriculture systems in arid inland river basins.
- Preprint
(1783 KB) - Metadata XML
-
Supplement
(984 KB) - BibTeX
- EndNote
Status: open (until 26 Feb 2026)
- CC1: 'Comment on egusphere-2026-55', Nima Zafarmomen, 26 Jan 2026 reply
-
RC1: 'Comment on egusphere-2026-55', Anonymous Referee #1, 30 Jan 2026
reply
Comments for Manuscript egusphere-2026-55
General Comments
This study develops a coupled surface water–groundwater multi‑objective optimization framework to coordinate water‑ecology‑agriculture management in the Tarim River Basin. The work integrates the GSFLOW hydrological model, the SRM snowmelt module, surrogate modeling, and the NSGA‑III algorithm. The methodology is advanced and reasonable, and the research holds clear scientific value and application potential for synergistic water resources management in arid inland river basins. The paper is highly readable, well‑structured, and provides in‑depth analysis. However, the following aspects require further clarification and strengthening. It is recommended for publication after the authors address these points.
Specific Comments
Comment 1. Regarding the construction of the surrogate model, the current description of its input variables (e.g., which key GSFLOW parameters or optimization decision variables are included), the size and dimensionality of the training sample set generated via Latin Hypercube Sampling, as well as the method and specific proportion for dividing the training and test sets, remains insufficient. Additional explanation is needed.
Comment 2. Although the paper mentions that model results still require validation with field data, the discussion of potential real‑world constraints during the implementation of the proposed solutions is not yet sufficient. It is recommended to supplement the analysis by discussing the potential impacts of the Pareto‑optimal solutions-such as the suggested dynamic adjustments to cropland scale and planting structure-on local livelihoods (e.g., residents and the economy), regional agricultural production stability, and food security. This would strengthen the linkage between the research findings and actual management decision‑making.
Comment 3. The paper notes that the estimation of agricultural non‑point source pollution relies on a simplified model, but the explanation of the method's applicability assumptions and limitations could be further deepened. It is suggested to elaborate on the context in which the simplified method is applied in this study (e.g., its computational efficiency advantages in strategic, large‑scale multi‑objective optimization analyses) and to specify its main limitations more clearly, such as the inability to depict the transport and transformation processes of nitrogen in the "soil-groundwater-surface water" system. Building on this, a more targeted discussion could be provided regarding potential future improvements, such as coupling the hydrological model with a biogeochemical process model (e.g., MODFLOW‑MT3DMS) to enhance the precision and reliability of pollutant simulation.
Comment 4. The justification for selecting the compromise solutions (S5, S10, S15) in Section 4.2.2 is currently quite brief. It is recommended to supplement the explanation with the specific criteria and rationale used for choosing these compromise solutions, thereby increasing the feasibility and transparency of the proposed method.
Comment 5. It is suggested to add a technical workflow diagram in the Methods section to clearly illustrate the overall process and data flow among the key steps (e.g., SRM, GSFLOW, surrogate model, NSGA‑III optimization). This would enhance the intuitive understanding of the framework's logic.
Minor Comments
- It is recommended to unify the formatting for numerical ranges throughout the text, for example, consistently using the format "11.3×10⁴ - 14.3×10⁴ hm²."
- The thresholds used in Table 4 for classifying hydrological year types based on the runoff anomaly percentage (P) are not rigorously defined. It is recommended to use symbols such as "≤" to clearly specify the interval boundaries.
- The terminology for describing hydrological year types is inconsistent in the text. It is suggested to standardize the terms, for example, consistently using "wet/normal/dry year" throughout the main text, avoiding mixed usage with terms like "High/Low Flow Year."
- There are errors in the sub‑figure labels in Figure 9. The letters (f)-(l) overlap with (i)-(q), and the number of labeled sub‑figures does not match the number of representative schemes described in the text. Please correct the labels according to the actual content.
Citation: https://doi.org/10.5194/egusphere-2026-55-RC1 -
RC2: 'Comment on egusphere-2026-55', Anonymous Referee #2, 03 Feb 2026
reply
Comments for "A coupled surface water–groundwater multi-objective optimization framework for coordinated water–ecosystem–agriculture management in arid inland river basin" by Chen et al.
General Comments
Chen et al. (2026) develops a coupled surface water-groundwater multi objective optimization framework to coordinate water ecology agriculture management in the Tarim River Basin. The research gap is clear, the methods are novel, and the manuscript is mostly well written. However, several aspects require further clarification to improve its coherence and readability before publication.
Specific Comments
1. Methods
This study adopts multiple methods, but the methodology appears somewhat overloaded and fragmented. It is recommended to briefly introduce the Data in this section and present the detailed information in the form of supplementary materials or appendices. Also, a flowchart is suggested here to clearly illustrate the integration of the adopted methods. Furthermore, the descriptions of the design of the GSFLOW model and the surrogate model are insufficient, and detailed information (e.g. parameters and variables for the surrogate model) is needed.
2. Discussion
The findings of this study need to be further discussed to enhance the value of the manuscript. It is recommended to discuss the advantages and limitations of the multi-objective optimization framework used in this study compared with previous research (e.g. for single methods or combinations of two methods). It would greatly increase the significance of the study, if quantitative comparison can be presented. The discussion could also be strengthened by addressing how the findings of this study could be applied to other regions. This may involve identifying which of the datasets used has the greatest impact on the results, as well as considering how errors from different datasets could limit the applicability of the methodology in other areas.
Specific Comments
Line 23 Please unify the units used in the manuscript, such as hm, km, etc., throughout the text.
Line 51 References should be added here. Also, references are lacked in some parts of the remain Introduction.
Line 83-87 This paragraph is unnecessary.
Line 158 This is the first time GSFLOW appears, and it is recommended to provide a brief introduction. However, since this section is actually about the Data, please briefly introduce GSFLOW in the appropriate place.
Line 261 Please unify the use of these symbols
Line 503 The order of the subfigures in the figure captions does not match their actual order.Citation: https://doi.org/10.5194/egusphere-2026-55-RC2
Viewed
| HTML | XML | Total | Supplement | BibTeX | EndNote | |
|---|---|---|---|---|---|---|
| 162 | 58 | 20 | 240 | 28 | 19 | 15 |
- HTML: 162
- PDF: 58
- XML: 20
- Total: 240
- Supplement: 28
- BibTeX: 19
- EndNote: 15
Viewed (geographical distribution)
| Country | # | Views | % |
|---|
| Total: | 0 |
| HTML: | 0 |
| PDF: | 0 |
| XML: | 0 |
- 1
This paper presents a coupled surface water–groundwater–agriculture multi-objective optimization framework for coordinated management of water resources, ecosystems, and agricultural production in the Tarim River mainstream, an arid inland river basin in China. The authors integrate a basin-scale SRM–GSFLOW hydrological model with a multi-objective optimization model solved using NSGA-III, targeting four competing objectives: maximizing agricultural economic benefit per unit irrigation water (fAB), maximizing groundwater level recovery (fGL), maximizing terminal lake area (fLA), and minimizing agricultural nitrogen load (fTN). Well written and fitted for the publication.
1) Several figures (e.g., Figures 4–7) contain dense scatter plots and parallel coordinate lines that are difficult to interpret at first glance. Adding brief quantitative annotations (e.g., correlation coefficients or key threshold markers) would improve readability and interpretability.
2) The manuscript alternates between the terms WEA and WAE when referring to the water–ecosystem–agriculture system. Please standardize terminology throughout the text for consistency.
3) Although the surrogate model performance is strong (R² =0.98), the paper would benefit from explicitly stating how surrogate prediction errors may influence decision-making, especially near Pareto front extremes where trade-offs are most sensitive.
4) While the study carefully calibrates the coupled SRM–GSFLOW model and validates the surrogate RBF-NN, uncertainty is not explicitly propagated through the optimization results. Key sources of uncertainty, such as climate forcing, groundwater parameters, crop water requirements, and fertilizer coefficients, may significantly affect Pareto fronts and identified “compromise solutions.” I recommend to add this limitation in the conclusion part.
5) I do strongly suggest that the authors consider relevant studies that have explored the "assimilation of Sentinel-derived leaf area index to improve the representation of surface–groundwater interactions in irrigation districts". Citing and briefly discussing such work would strengthen the linkage between the proposed framework and existing literature.