A Multiagent Socio-hydrologic Framework for Integrated Green Infrastructures and Water Resource Management at Various Spatial Scales

Zhang, Mengxiang; Chui, Ting Fong May

doi:https://doi.org/10.5194/hess-2024-232

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https://doi.org/10.5194/hess-2024-232

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03 Sep 2024

| 03 Sep 2024

Status: this preprint is currently under review for the journal HESS.

A Multiagent Socio-hydrologic Framework for Integrated Green Infrastructures and Water Resource Management at Various Spatial Scales

Mengxiang Zhang and Ting Fong May Chui

Abstract. Green infrastructures have been widely used to manage urban stormwater, especially in water-stressed regions. They also pose new challenges to urban and watershed water resources management. This paper focuses on the green infrastructure-induced dynamics of water sharing in a watershed from three spatial scales. A multiagent socio-hydrologic model framework is developed to provide an optimization-simulation method for city-, inter-city-and watershed-scale integrated green infrastructures and water resource management (IGWM) that comprehensively considers the watershed circumstances-, the urban water managers-and the watershed manager-urban water managers interactions. We apply the framework to conduct three simulating experiments in the Upper Mississippi River Basin, the US. Four patterns in city-scale IGWM are classified and two dynamics of cost and equity in inter-city- and watershed-scale IGWM are characterized through various sensitivity, scenario, and comparative analyses. The modeling results could advance our understanding of the role of green infrastructures in urban and watershed water resources management and assist water managers in making associated decisions.

Received: 24 Jul 2024 – Discussion started: 03 Sep 2024

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Mengxiang Zhang and Ting Fong May Chui

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RC1:
'Comment on hess-2024-232', Cyndi Vail Castro, 23 Sep 2024 reply
The overall paper is an important contribution to the science of decision-making with GIs across spatial scales. There are a few conceptual questions I had that I was not able to follow in the paper, but it appears the work is there. A bit further clarification would be helpful, and in that case, I would accept the manuscript for publication.
Please see below a few comments:
The grammar and writing style is excellent. I do not have any major comments on the technical writing. However, while I understand the rationale for doing so, the many use of acronyms reads to me as confusing. Several of these acronyms mean essentially the same thing at the decision-making scale. Perhaps consider condensing the number of acronyms if feasible? Not necessary, it just was hard for me to follow. I see the graphic in Figure 1, which does help explain this concept a bit across the 3 spatial scales, but it is still hard to follow when reading the introduction. Perhaps re-state the acronyms in Fig. 1 caption and use all in the graphic? (e.g., UWB, SM are missing).

Very minor corrections noted here:

Line 116: "a agent-based framework" should be "an agent-based framework"

Line 159: "dynamic(s) of (the) watershed"?

Figure 1: Should the text near WM Agent state "Bi-level multiagent system"?

Line 578: Period instead of ;?

The socio-hydrological application at various spatial scales applies to GIs in any urban community, not just alongside rivers. Perhaps consider re-phrasing the references to how GIs interact with hydrology near river networks.

Most of the mentionings of the "socio" part of GIs being part of a socio-hydrological system in the introduction refer to housing types or anthropogenic activities in upstream portions of the drainage basin. While this is true, the socio component of GI systems extends far beyond these considerations and might be worth a mention. For example, how GIs become community recreational meeting spots, have been shown to reduce human health issues, improve mental well-being, provide urban sources of food, improve heat island effects, reduce noise, etc. Or, are you mostly referring to the "socio" component being the complex decision-making required? This was a bit confusing to me.

Conceptually, while I agree that the planning paradigm described at the city-level in Figure 1 is ideally how city-scale GIs should be constructed, in practice, I do not think this is happening. Instead, due to the long timeframe associated with stormwater funding and construction, GIs tend to be developed sporatically on a project-by-project basis, not in real-time, tightly coupled with the water use and demand, as depicted here. Although I understand that in order to model this as an ABM, you had to make such a coupling decision, perhaps consider a time delay in the model, or mention this limitation in reflecting real-world decision-making patterns. This also applies to the hydrologic connections in Fig 1B, between each urban area linked by real-time riverine flows between them. The city-scale decision-making of GI does not align temporally with the hydrologic flows of connecting river systems, even though the time steps appear to be monthly.

Another conceptual question I have - the overall model framework depends on GIs being used at-large for water storage and demand. Perhaps this is common in some parts of the world, but in the US, where the case study is conducted, most GIs are used for runoff abatement, which is then linked back into the greywater infrastructure system and sent offsite to reduce flooding issues. Some rainwater harvesting systems are used for on-site capture and use for irrigation, but these are very small-scale in nature (like someone's personal lawn) and not designed systematically to be a major contributor to widespread irrigation needs. At least I am not aware of this being common practice in the US.
It seems to me that the concept being simulated is actually systematic decision-making for detention-pond and reservoir storage at both the city watershed scales, and how inter-city feedbacks can impact the overall cycle. Which is not necessarily "Green Infrastructure" as I understand it to be used in the literature and community.

Perhaps this is me not understanding the Markov property, but it seems counter-intuitive to apply this property, essentially stating the decision-making process is stochastic and its future evolution is completely independent of its history, as the opposite paradigm is key to a system operating "socio-hydrologically".

Case Study:
This is an extremely large watershed area for designing with GI. Was this selected at random, or is there a decision-making entity that manages this trans-state watershed systematically? I looked up the UMRBA association, and they don't seem to actively manage water supply and use in this basin.

I looked at the 1 citation mentioned for using GI in this geographical region (Askey-Merwin, 2020), and this publication addresses mitigating flooding, not storing rainwater via GIs and re-use in widespread irrigation projects. Moreoever, the Mississippi River is one of the highest-flow rivers in the US and has a complex network of laws and regulations regarding water extraction for municipal or industrial use. I am not aware of water quantity being an issue here for irrigation purposes, so I am confused why there is a study suggesting GI is being actively proposed, managed, and constructed at the watershed scale and the city scales in real-time to ensure water availability here. If it is purely for a conceptual purpose of explaining complexities of GI planning in general, that is fine, but in that instance, I wouldn't necessarily limit the model to connecting urban communities along a main river stem, as this limits the application substantially. However, the model is already built under these assumptions, so I do not recommend re-designing. I am just pointing out it conceptually is difficult for me to see the application outside of this theoretical explorative study.

Where are you getting water demand data for irrigation, and how does this change over time? What factors drive this in the model? I see that you simulated "urban" water demand via population and urban layouts, but as mentioned, this is a tiny percentage of the overall water resources in the basin, and is likely to be impacted significantly by urban-scale GI units. I see some mentioning of irrigation demand in Eq A10, but it is unclear to me what these equations mean or how the underlying data were gathered. Is the irrigation a basis of cropland type?

I am not qualified to review the set-up of the ABM model, particle optimization schemes, or economic theory choices. Please ensure one of the other reviewers has this expertise and can comment on the methodology.

I do not see where the channel geometry is used for the Muskingum-Cunge routing method.

I am not following how USGS stations for the Mississippi river could be used to calibrate urban water use.

The discussion of results is very convoluted. Perhaps consider a concluding paragraph with bullet-points of the main take-aways that can be widely applied. For example, key insights about water costs and conflicts among adjacent communities, the importance of communication of watershed-scale managers and city-scale planners, the impact of assuming agent rationality / Markov property / Stackelberg in understanding the overall socio-hydro dynamics, what this means on overall water policy as GIs become more popular, interaction between urban and irrigation water use and demand, what this study informed us about social equity in water decisions, etc.

The Conclusions section is too convoluted for me. I recommend removing a lot of the technical jargon and focusing on the bigger picture here.

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Citation: https://doi.org/10.5194/hess-2024-232-RC1

Mengxiang Zhang and Ting Fong May Chui

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Short summary

This study introduces a multiagent socio-hydrologic framework for city, inter-city, and watershed scale integrated green infrastructures (GI) and water resource management. Applied to the Upper Mississippi River Basin, it explores GI-driven water-sharing dynamics in a watershed. It identifies four city-scale water use patterns and characterizes cost and equity in broader scales, thereby enhancing comprehension of GI's role in water resource management and aiding decision-making.


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