the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
| 19 Dec 2024
Development of the global hydro-economic model (ECHO-Global version 1.0) for assessing the performance of water management options
Abstract. Water scarcity is one of the most critical global environmental challenges. Addressing this challenge requires implementing economically-profitable and environmentally-sustainable water management interventions across scales globally. This study presents the development of the global version of the ECHO hydro-economic model (ECHO-Global version 1.0), for assessing the economic and environmental performance of water management options. This global version covers 282 subbasins worldwide, includes a detailed representation of irrigated agriculture and its management, and incorporates economic benefit functions of water use in the agricultural, domestic and industrial sectors calibrated using the positive mathematical programming procedure alongside with the water supply cost. We used ECHO-Global to simulate the impact of alternative water management scenarios under future climate and socio-economic changes, with the aim of demonstrating its value for informing water management decision making. Results of these simulations are overall consistent with previous studies evaluating the global cost of water supply and adaptation to global changes. Moreover, these results show the changes in water use and water supply and their economic impacts in a spatially-explicit way across the world, and highlight the opportunities for reducing those impacts through improved water management. Overall, this study demonstrates the capacity of ECHO-Global to address emerging research and practical questions related to future economic and environmental impacts of global changes on water resources and to translate global water goals (e.g., SDG6) into national and local policies.
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RC1: 'Comment on gmd-2024-238', Anonymous Referee #1, 07 Mar 2025
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General comments
I apologize for the time it took to review this paper. This paper includes advanced content covering extensive elements of global water resources. It took much more time than I had initially estimated to review it (I read through it three times).
This paper is ambitious in that it attempts to estimate the amount of water withdrawn and consumed for agricultural, industrial, and domestic sectors for 300 geographical units globally. It calculates the costs and benefits in each unit by water source and maximizes the net benefits for the entire world. While the conventional method of calculating the amount of water withdrawn by sector is to use a regression model, this research proposes a new technique that is suitable for advanced scenario analyses.
The manuscript is well prepared, but I felt that there were issues with the following: in the introduction section, there was no reference to earlier research by GCAM, which has long propelled economics-based global water resources studies; in the methods section, there was room for improvement in the expression of mathematical formulas; and in the methods, results, and discussion sections, there was no description of the balance between supply and demand of goods and services. For details, please see below.
Specific comments
Lines 52-68: It is surprising to me that the authors do not refer to any works of GCAM (e.g., Kim et al., 2016; Dolan et al., 2021; Niazi et al., 2024). Niazi et al. (2024) are cited in this manuscript but not in the review part. Their works must be referred to, and this study must be contextualized well in the development history of economics-based global water resources modeling.
Line 151 “each headwater gauge”: What is the headwater gauge? I assume it is the uppermost river discharge gauge, but Equation 1 has the inflow term (I_h,t).
Line 189 “but it is considered a more expensive water supply source compared to surface water and renewable groundwater”: Is this relationship static (irrelevant) to groundwater table depletion?
Line 213 “water application per ha, b_a,j,k”: I expect this is a weather-/climate- dependent term, but the text implies this is a tuning parameter. If this term is not weather-/climate-specific, the projected change in irrigation water requirements due to global warming (e.g., Wada et al. 2013) cannot be implemented in this modeling framework. Elaborate on this issue.
Line 265 “alpha_1,a,g,j,k is…”: Do you mean the alpha_1 is negative? Equations 22 and 23 are expressed in general forms, but I guess the authors assume a declining and a convex upward function, respectively. This should be conveyed more clearly.
Line 290 Equation 25: Maximizing the sum of net benefit is understandable, but I wonder whether the supply and demand of goods and services equilibrate with this approach. Another concern is what the author assumes on international trade. These points are critically important in interpreting the results part.
Line 307-307 “1410 river gauges nodes and 1128 demand nodes”: Clarify why they become 282*5 and 282*4.
Line 376 “For irrigated agriculture, country-specific prices of 13 crops”: Do you mean the market price of agricultural products from irrigated cropland? I wonder whether such a price is distinguishable between rainfed and irrigated agriculture.
Line 413 “for the base year 2010. The calibration process consists in adjusting model parameters such as irrigation efficiency…”: Was calibration conducted only for one year? If this is the case, the validation should ideally be done for a different year other than 2010. If addressing is technically tricky, at least the authors can discuss challenges.
Line 524 “the ENV scenario would reduce cereals (wheat, maize, and other cereals) by 36-45%, cotton by 32%, oil crops by 27%, roots by 26%, fruit by 24%, vegetables by 21%, and rice by 14% in 2050”: First, which do you mean reduction in the total crop production or irrigation water? If the former is the case, how will humanity be fed by smaller calorie production than today (see discussion in Gerten et al. 2020)? If the latter is the case, would it accompany a considerable expansion in rainfed cropland (see discussion in Rosa et al. 2018)?
Line 571 “while the agricultural sector has a smaller share of 7% (23 billion USD/year)”: By reading this point, I started to wonder about the role of governments (the public sector). Agricultural water is often subsidized, and its infrastructure is usually built and managed by central/local governments. Does the public sector cover the cost included in Equations 21-22, and how?
Line 601-604 “Strong et al. (2000)…”: The discussion sounds blurry. Clarify whether their findings/estimates are similar to (support) the authors’ findings or not.
Line 612-615 “Strong et al. (2000)…”: Ibid.
Line 622 “Demand management options can reduce withdrawals by 27% compared to BAU”: Was goods and service supply maintained? Won't a 27% reduction create other problems?
Line 639 “unregulated groundwater pumping could increase substantially by 160% in BAU by 2050”: The claim contrasts that of Niazi et al. (2024). If I understand correctly, the difference stems from the treatment/assumption in the groundwater table. If it is explicitly considered, groundwater pumping becomes, sooner or later, economically unaffordable.
Editorial comments
Line 90 Figure 1: The acronyms used (PMP and BCU) should be explained in the caption.
Line 114 “across subbasins within river basins at the global scale”: I feel this part is a bit awkward. Subbasins are always within river basins…
Line 117 “from inverse water demand functions estimated using the Point Expansion approach (Griffin, 2016)”: This part is not very informative. I understand that readers should read Griffin (2016), but it would be helpful for us if the authors added more understandable information.
Line 120 “using the positive mathematical programming (PMP) procedure to address regional-scale aggregation and overspecialization problems (Baccour et al., 2022; Dagnino and Ward, 2012)”: ibid.
Line 151 “local runoff r_h,t, and inflow from upstream BCUs I_h,t”: The authors use “X” for flows in general, while here, r and I are also used for “flows (in contrast to stocks).” These expressions are confusing to me, which is one of the reasons why I needed three times of reading.
Line 158 “and -1 for nodes that reduce flow”: How streamflow can be reduced? Do you mean “diverted flow?”
Line 164 Equation 4: This expression is very confusing to me. Here, X is used both to express release and evaporation. It is odd to see that S (Storage) and X(Flow/Flux) have the same dimension (kg).
Line 222 Equation 14: Why does the form of the equation differ from Equation 12? I expected the difference to be only with/without the irrigation efficiency term.
Line 266 ”based on the first-order conditions of the agricultural profit maximization problem following the PMP procedure (Dagnino and Ward, 2012)”: This phrase is too technical and not informative. Provide a bit more understandable information.
Line 268 “M&I”: What’s this for?
Line 277 “Then beta_2,M&I is expected to be large and negative”: What does “large” mean?
Line 397 “a more sustainable scenario (RES)”: Maybe it reads ENV.
References
Kim, S. H., Hejazi, M., Liu, L., Calvin, K., Clarke, L., Edmonds, J., Kyle, P., Patel, P., Wise, M., and Davies, E.: Balancing global water availability and use at basin scale in an integrated assessment model, Climatic Change, 136, 217-231, 10.1007/s10584-016-1604-6, 2016.
Dolan, F., Lamontagne, J., Link, R., Hejazi, M., Reed, P., and Edmonds, J.: Evaluating the economic impact of water scarcity in a changing world, Nature Communications, 12, 1915, 10.1038/s41467-021-22194-0, 2021.
Gerten, D., Heck, V., Jägermeyr, J., Bodirsky, B. L., Fetzer, I., Jalava, M., Kummu, M., Lucht, W., Rockström, J., Schaphoff, S., and Schellnhuber, H. J.: Feeding ten billion people is possible within four terrestrial planetary boundaries, Nature Sustainability, 3, 200-208, 10.1038/s41893-019-0465-1, 2020.
Niazi, H., Wild, T. B., Turner, S. W. D., Graham, N. T., Hejazi, M., Msangi, S., Kim, S., Lamontagne, J. R., and Zhao, M.: Global peak water limit of future groundwater withdrawals, Nature Sustainability, 7, 413-422, 10.1038/s41893-024-01306-w, 2024.
Rosa, L., Rulli, M. C., Davis, K. F., Chiarelli, D. D., Passera, C., and D’Odorico, P.: Closing the yield gap while ensuring water sustainability, Environmental Research Letters, 13, 104002, 10.1088/1748-9326/aadeef, 2018.
Wada, Y., Wisser, D., Eisner, S., Floerke, M., Gerten, D., Haddeland, I., Hanasaki, N., Masaki, Y., Portmann, F. T., Stacke, T., Tessler, Z., and Schewe, J.: Multimodel projections and uncertainties of irrigation water demand under climate change, Geophys. Res. Lett., 40, 4626-4632, 10.1002/grl.50686, 2013.
Citation: https://doi.org/10.5194/gmd-2024-238-RC1
Model code and software
ECHO-Global version 1.0 Taher Kahil https://zenodo.org/doi/10.5281/zenodo.14391182
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