the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 11 Nov 2024
Reconciling the strategic goals of irrigated food production, energy production with environmental flows under water transfer project in the Yellow River Basin
Abstract. Reconciling the nexus relationship between water, energy and land (WEL) is critical for achieving sustainable development. Pathways to reconcile the WEL nexus in river basins remain unclear due to the lack of comprehensive assessments. In this paper, we provide a quantitative investigation using an engineering-economic optimization method to explore how and to what extent the water transfer project reconciles irrigated food production, energy production with environmental flows in the Yellow River Basin. The results show that, maintaining environmental flows at 30 % of the river runoff will cause water for irrigation to be drained by energy production. Water transfer (~2.8 km3/year) mitigates such trade-offs, decreasing water for the energy sector by 1.8 % (0.14 km3/year), and replenishing water for agriculture by 0.5 % (0.09 km3/year). Groundwater use decreases by 0.8 % (0.13 km3/year). Water transfer also builds synergies between water consumption and the economic costs of energy production, with these co-benefits in the lower reaches spilling over to the upper and middle reaches. Compared to irrigated food production, the operational costs of energy production are sensitive to water policies, implying that energy sector transformation is critical to sustainable pathways for reconciling the WEL nexus in the Yellow River Basin. Our study underscores the role of water transfer in alleviating water conflicts within the WEL nexus. Moreover, it provides valuable insights into transformative technological pathways toward a sustainable future in the Yellow River Basin and beyond.
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Status: open (until 23 Dec 2024)
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RC1: 'Comment on egusphere-2024-3393', Anonymous Referee #1, 27 Nov 2024
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Huang et al. used a Nexus Solution Tool linking a distributed hydrologic model with an engineering-economic resource supply planning model to model water, energy, and land-use systems. I am sorry that I am not that positive to this work. The NEST used involves a large amount of input parameters and the interconnections between sectors. The validation is totally missing in this study and the sensitivity analysis is not convincing. At least, the validation of the CWatM with observed streamflow and water table depth for some baselines is necessary. Regarding the structure of NEST, it is hard to say if the interconnections between the sectors match the real situation in the study area without any investigations and adaptions, i.e., if the irrigation approaches, the energy generation, the land use etc. in the study area are consistent with the model. In addition, the quality of the input data is also a concern. I acknowledge that this paper connects many new important concepts in the changing world together, but I don’t think the model generate any convincing results as I pointed above. I suggest rejecting this paper. But editors may consider other reviewer’s comments for decision.
Citation: https://doi.org/10.5194/egusphere-2024-3393-RC1 -
AC1: 'Reply on RC1', Yichu Huang, 28 Nov 2024
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Thank you for your valuable suggestions. Below, I address your major concerns:
- Validation of Model Results:
We compared model results and data from the Water Resource Bulletin for groundwater and surface water consumption in 2020 under the scenario of constraining environmental flow (see Supplement Fig.1). The validation shows that the model generally aligns with historical situation in the Yellow River Basin. As noted in Section 4.3, it is often challenging to validate model results due to the lack of survey data. Therefore, we chose the Water Resource Bulletin of the Yellow River as our reference, because it is an authoritative source and corresponds well to our study area. The primary limitation is the spatial resolution, which is at the provincial level. Additionally, the sensitivity analysis focuses on the robustness of our findings considering the uncertainty.
- Quality of Input Data:
In addition to the data sources listed in Section 2.4, we also performed calibrations to improve the quality of input data (see Supplement Table 1). Specifically, for surface runoff, we chose to use ISIMIP data instead of the CWatM module after discussions with the author of the NEST. Considering surface runoff is the most critical input for this model, we also used the China Natural Runoff Dataset (CNRD) for calibration to further improve accuracy.
- Applicability of the NEST Framework:
The interconnections among the three sectors in NEST are represented through technologies. The NEST framework is adaptable to other river basins since it includes detailed technologies for water, energy and land sectors. For instance, electricity generation technologies include thermal power (coal, oil, natural gas, etc.), renewable energy (hydro, solar, wind, etc.), and others. What differs in big river basins is the parameters and structure of these technologies. In this research, we did investigations and adaptations to adjust the technology parameters according to local studies (see Tables S1-4). Moreover, by modifying the input data for historical technology capacities, the structure was also tailored to represent the Yellow River Basin.
Given the points outlined above, we believe our results are reasonable, and other research can provide evidence for our conclusions (Sections 4.1 and 4.2). If you have further questions, we would be pleased to engage in a more detailed discussion. Additionally, we acknowledge that certain details were omitted from the manuscript for brevity. Thank you again for your insightful suggestions.
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AC1: 'Reply on RC1', Yichu Huang, 28 Nov 2024
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RC2: 'Comment on egusphere-2024-3393', Anonymous Referee #2, 01 Dec 2024
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I like the broader aspect of analyzing the water transfer project in terms of WEL nexus. The following aspects need to be addressed before it is suitable for publication
- As the focus is on interbasin water transfers, large interbasin transfers and their studies across the globe may be included in the introduction
- Few of the Lines 56-63 may be moved to study area section
- My concern about the NEST tool is the validation of different parameters in the model specific to the study area.
- I understand the objective function is minimizing the cost of supply systems. For the different parameters in equation 1, what is the methodology to calculate different costs for each region? I see the cost tables in supplementary; the question is how are they defined across different regions and the interactions between them
- Authors may highlight the novelty of methods as this reads as an implementation of NEST for a study area
- Is there any cost defined for not satisfying the minimum environmental flows?
- The results presented do not imply the interactions in the nexus as mentioned in line 340
- I like section 4.3 on looking into the sensitivity of parameters. This may be improved based on how the analysis was performed; details of methodology and why only specific parameters are selected
- The paper focuses on the performance of the recipient basin and is silent on the donor basin impacts. Was this modeled?
Citation: https://doi.org/10.5194/egusphere-2024-3393-RC2 -
AC2: 'Reply on RC2', Yichu Huang, 02 Dec 2024
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Thank you for your insightful and constructive comments on our manuscript. Your advice is very helpful in identifying areas for improvement and ensuring the clarity and rigor of our work. Here I provide detailed responses to your specific concerns:
- The introduction only focused on the South-to-North Water Diversion (SNWD) project (Lines 68-74). We agree that incorporating large inter-basin transfers across the globe will broaden the context for our research. We will revise the introduction to include relevant global case studies.
- In Lines 56-63, we aimed to introduce water management measures implemented in the Yellow River Basin, the “87” Water Division Scheme and the SNWD project. However, the introduction on the former seems too lengthy. Your suggestion to move them to the study area section helps for a better organization. We will revise the part accordingly to ensure the logical flow of the manuscript.
- The localization of the NEST tool is achieved through adjustments of input data and technology parameters. The input data sources can be found in Section 2.4. Sources of technology parameters are listed in Table S1-4. All parameters were referenced as much as possible from studies conducted within China. Other parameters were referenced from international organizations or global studies, which is also a standard practice in model parameterization.
- The model assumes that technology parameters are the same for different regions. For each region, the differences lie in the supply and demand of water, energy and land (WEL). For example, region SDO_1, located in the lower reaches, is densely populated and has high demand for WEL. For the energy sector, new power plants are needed to meet the growing energy demand, then the investment cost for new plants and operational cost for existing plants arise. The model also allows for the establishment of electricity transmission lines, enabling electricity generated in other regions to help alleviate energy demand in SDO_1. For the water sector, the NEST model utilizes a simplified river network to connect each region. If SDO_1 requires excessive water resources, regions in the upper and middle reaches will make adjustments to spare water resources for the lower reaches. For the land sector, costs are mainly driven by land-use changes. Considering the demand for food is predicted to decline in the future, corresponding changes in land use are expected as well.
- While our research focuses on implementing the NEST for the Yellow River Basin, the novelty also lies in setting new scenarios, particularly the SNWD scenario. This scenario represents an important policy implemented to alleviate water stress in the Yellow River Basin. We will revise the manuscript to emphasize these contributions more clearly in the methods and discussion sections.
- In the EF scenario, we defined the minimum environmental flows according to the “87” Water Division Scheme. The scheme was devised specifically for the Yellow River Basin and was based on reality rather than theory. Thus, the model ensures that the minimum environmental flows are always met in this study. If the minimum environmental flows were set too high to break the water balance, the model would fail to produce an optimization result.
- We acknowledge that more details should be replenished to illustrate the interactions in our model. We will revise Section 4.1 to explicitly highlight the interdependencies and trade-offs between WEL.
- Thank you for your positive feedback on Section 4.3. We will expand this section to provide more details on the methodology, including the selection criteria for specific parameters and the rationale for focusing on these parameters. This will enhance the robustness and transparency of the sensitivity analysis.
- The current study primarily focuses on the recipient basin, and the impacts on the donor basin were not modeled, since the water-supplying basin is located in the Yangtze River Basin. However, we recognize the importance of addressing this aspect and will include a discussion on potential donor basin impacts based on existing studies and model limitations.
Citation: https://doi.org/10.5194/egusphere-2024-3393-AC2
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