| 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.
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.