the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 10 Jul 2026
Behavioral feedbacks reshape blue–green water scarcity and sustainability trade-offs in irrigated agriculture: A sociohydrological perspective
Abstract. Blue–green water scarcity in irrigation districts is influenced by both hydrological processes and farmer management, yet most studies treat agricultural decision-making as exogenous and static. We develop a spatially explicit, bidirectionally coupled framework integrating Soil and Water Assessment Tool (SWAT) with an agent-based model (ABM) of boundedly rational farmers, embedding crop choice and irrigation scheduling within basin-scale hydrology and crop growth. Applied to the Yaohekou Irrigation District in the Han River Basin, China, the model quantifies how behavioral heterogeneity and management portfolios affect blue–green water use, irrigation supply adequacy, ecological-flow pressure, and equitable water access in the water–ecology–food–society (WEFS) nexus. The district shows persistent supply–demand gaps and strong sensitivity to behavior. Profit-driven regimes concentrate cropping and synchronize irrigation during critical stages, increasing dry-year peak blue-water withdrawals, shrinking safety margins, and amplifying drought-time deficits and inequity. Conservative social-learning regimes maintain crop diversity and stagger demand, buffering drought impacts. Policy experiments show that supply augmentation alone is partly absorbed by demand catch-up (diminishing returns); uniform water-price increases raise efficiency but reduce equity via heterogeneous responses; combining efficiency improvements with moderate supply support lowers water use per unit output and dampens sensitivity during wet-to-dry transitions. Overall, sustainable management should target the chain of demand synchronization, peak extraction, and constraint triggering, supported by technology diffusion and protective mechanisms to build socially inclusive resilience.
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Status: open (until 30 Aug 2026)
- RC1: 'Comment on egusphere-2026-2016', Anonymous Referee #1, 19 Jul 2026 reply
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RC2: 'Comment on egusphere-2026-2016', Anonymous Referee #2, 13 Aug 2026
reply
This study couples SWAT with an agent-based model and applies the framework to Irrigation District. It addresses an important and relevant topic for the hydrology and earth system: the role of farmer behavioral feedbacks in shaping blue–green water scarcity, ecological-flow pressure, crop production, and equity in irrigated agriculture. Overall, I believe this paper will benefit the readers of HESS because it links hydrological processes, agricultural water management, and human decision-making in a coupled socio-hydrological system, through innovative modeling. Nevertheless, I have the following comments, which I hope will help the authors further improve the quality of the manuscript.
--A large number of existing studies have focused on water management in irrigation systems, including the impact of irrigation behaviors on the water environment and groundwater. I have noticed that blue-green water has received widespread attention in recent years and is of obvious significance to agricultural systems, as crops will definitely consume both blue and green water in extreme circumstances (such as sowing in indoor facilities). Therefore, I believe that the necessity of studying the issue of irrigation district resource management from the perspective of blue-green water should be expounded and/or discussed, which will also reflect the uniqueness of this paper in terms of the research perspective.
--The necessity and innovation of the SWAT-ABM coupling framework should be more fully interpreted. At present, numbers of studies have been conducted on modeling for the efficient and sustainable utilization of water resources in agricultural systems, such as modeling for the optimal distribution of irrigation water, modeling for the adjustment of crop planting structure, and modeling for the coordinated efficiency improvement of water-land-food-carbon. Are these modeling and empirical studies complementary to the Swat-ABM coupling framework in this paper? It should be mentioned in the introduction or other sections. I believe that readers, readers engaged in irrigation water management are very glad to see modeling and research results that go beyond the traditional path.
Specific suggestions:
- Lines 10–20: The abstract is informative but dense. The main finding could be made more prominent.
- The manuscript presents several possible contributions: a coupled SWAT–ABM modelling framework, a diagnostic mechanism linking farmer behavior to blue–green water scarcity, and policy insights from management scenarios in Lines 70–85.:. These are all valuable, but the main contribution should be stated more explicitly. At present, the manuscript sometimes reads as a model-development paper and sometimes as a policy-evaluation paper.
- Lines 460–470 and Lines 485–510: The distinction between case-specific results and general mechanisms should be clearer. The numerical results, such as the magnitude of BDI, BAEI, yield change, and ecological stress, are specific to the Yahekou Irrigation District. In contrast, the mechanism of behavioral convergence, demand concentration, and dry-year vulnerability may be transferable to other surface-water irrigation systems with similar seasonal supply constraints.
- Lines 380–395 and Lines 435–440: The discussion of S7 and S8, some wording may imply that the behavioral responses were directly observed from farmers. For example, expressions such as “farmers collectively shift” or “farmers remain in imitation” could be interpreted as empirical observations, while these are model-simulated behavioral outcomes under specified scenario assumptions.
- The management scenarios provide useful insights into different policy mechanisms. However, the policy-related conclusions should be framed as conditional insights derived from stylized scenario experiments, rather than as direct policy prescriptions (Lines 400–420, Lines 445–470, and Lines 500–510).
- Lines 355–365: Explain the reason of ecological stress reduction can coexist with worse social outcomes. Please clarify that lower ecological stress does not necessarily indicate an overall improvement in system sustainability if it occurs through reduced irrigation satisfaction, lower agricultural production, or worsening social-water security. This would strengthen the trade-off interpretation and prevent readers from interpreting lower ecological stress as an unambiguously positive outcome.
- Lines 475–480: The limitations section already discusses behavioral representation, data uncertainty, and static policy assumptions. However, the uncertainty discussion could be expanded slightly to cover the main uncertainty sources in the coupled framework.
- Lines 370–395, Lines 415–420, and Lines 485–510: These indicators, BDI, ecological stress, yield, and BAEI, represent different normative dimensions and should not be treated as if they can be directly ranked without specifying a value criterion.
- Lines 380–395 and Lines 425–455: Reduce interpretive repetition between Results and Discussion. The Results section already provides substantial interpretation of crop convergence, demand synchronization, peak withdrawals, and dry-year vulnerability. The Discussion then repeats and expands the same mechanism. The mechanism is important, but the manuscript would read more clearly if the Results section were more descriptive and the broader interpretation were concentrated in the Discussion.
- Lines 230 and 525–530: The manuscript refers to the Supplement for detailed crop management practices, spatial and time-series datasets, and model-related information. The Code and data availability section also states that the coupling scripts and analysis scripts are available from the corresponding author upon reasonable request. Given the complexity of the coupled SWAT–ABM framework, the Supplement should provide enough detail for traceability.
- More attentions should be paid to the use of Spaces. Spaces should be used between punctuation marks and words, such as “Spatial(DEM,LULC,SOIL) in Figure 1”. The whole text should be checked carefully.
- Regarding the names of the study areas, both Yahekou (e.g. Figure 1) and Yaohekou (e.g. line 13) appear. Please verify and keep them consistent.
- The second author (B. Shengqian Zhang) in the main text is not written in the same way as on the web page (Shengqian Zhang). Please check. I suggest that authors conduct a detailed review of the entire text to avoid errors in writing, format, expression, charts and other aspects.
Citation: https://doi.org/10.5194/egusphere-2026-2016-RC2
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- 1
The reciprocal feedbacks between human activities and natural systems profoundly shape regional water resource utilization and sustainable development trajectories. The manuscript entitled “Behavioral feedbacks reshape blue–green water scarcity and sustainability trade-offs in irrigated agriculture: A sociohydrological perspective” establishes, through cross-scale modeling, a novel linkage between agricultural blue–green water scarcity and sustainability trade-offs. This work is innovative, timely, and of significant importance, given that the coupled human–natural dynamics and their evolutionary pathways across geographic units remain poorly understood from a systemic scientific perspective.
Overall, this manuscript is generally well structured and addresses an important limitation in conventional hydrological and crop modelling. I appreciate the author's efforts in the modeling of natural-social water systems in the irrigation district, and recommend the manuscript potentially publishable after minor revisions.
1. Clarify the main novelty more explicitly. The manuscript presents several contributions: SWAT–ABM coupling, blue–green water accounting, ecological-flow assessment, and equity analysis. These are all valuable, but the central novelty could be stated more sharply.
2. Explain the HRU-agent assumption more carefully. The model assumes that agricultural HRUs correspond to farmer agents. This is a reasonable simplification for spatially explicit modelling, but it should be acknowledged more explicitly because HRUs are hydrological units rather than real socioeconomic units.The manuscript already states that agricultural HRUs are assumed to correspond to individual farmer agents in the ABM. This assumption is understandable, but readers may want to know why it is acceptable for the purpose of this study.
3. Slightly strengthen the behavioral-model justification. The use of CONSUMAT is appropriate for representing bounded rationality, because the manuscript distinguishes repetition, imitation, social comparison, and deliberation. The current description is understandable, but the connection to farmer behavior in the study area could be made more convincing.
4. Present behavioral calibration more cautiously. The behavioral calibration uses observed crop planting area and canal-head diversion to constrain the ABM parameters. This is a reasonable strategy given the lack of micro-level farmer data, and the manuscript is transparent about using aggregate indicators. However, the text could be more cautious when interpreting the calibrated behavioral parameters. Aggregate crop area and canal diversion can support the plausibility of the behavioral module, but they do not directly measure risk perception, forecast trust, or psychological switching thresholds.
5. Add a simple sensitivity check if feasible, but do not overburden the paper. The behavioral parameters α, β, γ, and δ are important because they drive risk perception, forecast trust, and state switching. The current scenario design already changes these parameters, which partly functions as a behavioral sensitivity analysis.
6. The proportional mixing method used to partition green and blue water is transparent and appropriate for basin-scale modelling. The manuscript states that effective infiltration is allocated according to the precipitation-irrigation ratio and that outflows are removed proportionally from the previous green-blue soil water mixture. The blue–green water accounting assumptions should be considered for elaboration.
7. Ecological-flow stress is one of the key outcome indicators, but the ecological-flow requirement needs clearer explanation. The manuscript defines the ecological stress index as the ratio of ecological flow deficit to ecological requirement, using simulated streamflow.
8. Make the equity index interpretation more precise. The Blue-water Availability Equity Index is based on the complement of an area-weighted Gini coefficient. This is a useful index, especially for comparing spatial differences in blue-water availability among sub-basins. However, the term “equity” can have multiple meanings in water governance. The index used here captures distributional equity, not procedural equity or perceived fairness.
9. The three management scenarios are helpful for comparing supply augmentation, efficiency improvement, and water pricing. However, the chosen magnitudes, such as +20% supply augmentation, conveyance efficiency improvement, and water price increase, should be described as either realistic policy options or stylized exploratory experiments.
10. Refine the transferability discussion. The manuscript draws broader conclusions for irrigation districts. This is appropriate, but the boundary conditions should be clearer.
11.The font and citation format in the manuscript should be revised in accordance with the journal's requirements; please consult the journal's homepage.
12. Reduce overlap between Figure 1 and Figure 2. Figure 1 and Figure 2 both describe the coupled framework. Figure 1 is more conceptual, while Figure 2 is more operational. This distinction should be made clearer.