Author links open overlay panelYing Yang a1
, Jing Wen a1
, Qionghong Chen cShow moreAdd to MendeleyShareCite
https://doi.org/10.1016/j.watres.2025.123899Get rights and content
Highlights
- •The MRIO table was compiled for a multi-core multi-level urban agglomeration.
- •A diagnostic framework was established by coupling ENA and MRIO approaches.
- •Water quantity-water quality linkage was considered in the diagnostic framework.
- •The IWMN was less vigorous and less organized than the QWMN.
- •The IWMN tended slightly towards mutualism but had more negative collaborations.
Abstract
Urban agglomerations (UAs) are compelled to scrutinize the health of their water systems as the frequency of water crises increases. An urban water system’s health is closely related to metabolism processes. To date, water systems in multi-core multi-level UAs have not been analyzed using water quantity and water quality because of methodological constraints. To address this research gap, we developed an integrated water quality–water quantity model for diagnosing water metabolism systems that could process nested multi-region input-output (MRIO) tables. We coupled the MRIO tables and established two networks, an integrated water quantity–quality metabolism network (IWMN) and a water quantity metabolism network (QWMN). We tested the two networks with data from the Guangdong-Hong Kong-Macao UA and assessed four aspects of the networks’ health, namely vigor, organization, resilience, and collaboration, using ecological network analysis. We discovered that IWMN exhibited lower vigor (internal circulation 10.4 %) and organization dominated by dependency (total contribution intensity σ = -23) compared to the QWMN. Polity-driven disparities shaped the robustness distribution, while a mutualism tendency coexisted with a complex exploitation relationship (52.4 %), particularly in the core large-sized city of Hong Kong, where 58 new competitive pairs emerged. Thus, we recommend prioritizing Guangdong-Hong Kong-Macao trade optimization for high-water-content products to enhance system health.
Graphical abstract
Introduction
The surface water deficit experienced in 482 of the world’s largest cities is projected to reach 6.75 million tons by 2050 because of an imbalance between the water supply and the demand (Flörke et al., 2018). This trend has prompted growing interest in resource allocation and environmental protection within urban agglomerations (UAs). UAs are composed of multiple geographically adjacent cities with diverse sizes and characteristics (Fang et al., 2015). Diverse UAs with multi-core structures (classified by comprehensive urban engine functions) and multi-level systems (quantified by social indicators) face challenges due to high heterogeneity in population size and spatial resource allocation (Han et al., 2019; Chirigati, 2022; Zhao et al., 2021). Water quantity and water quality are important attributes of water resources. Changes in the water quantity caused by a lack of rainfall or heavy rainfall events affect the water quality by concentrating pollutants or diluting. Conversely, degraded water quality diminishes the availability of water resources (Li et al., 2023) and has direct effects on urban aquatic ecosystems (Liu and Yang, 2012). Therefore, to optimize water management in multi-core multi-level UAs, we need to know more about the combined effects of water quality and water quantity on the water resources.
When optimizing water management in urban areas, the water metabolism mechanism of the system should be analyzed, and key issues should be identified (Cao et al., 2021; He et al., 2020b; Liu et al., 2022). The concept of water metabolism originates from urban metabolism (Wolman, 1965), which describes water cycle processes (e.g., water input, output, and storage) driven by social activities in different cities (Wang and Chen, 2010). This concept can effectively identify hidden risks resulting from the allocation of social resources—such as population, industry and environment within UAs, thus challenging the traditional multilevel paradigm of urban water management. In assessing the health of water systems based on water metabolism mechanisms, processes analogous to those in natural ecosystems, such as vigor and collaboration (Y.J. Yang et al., 2020; Zhu et al., 2020), sustained and stable organization, and adaptability to external pressures (Yan et al., 2014), are employed. However, to date, most research has primarily focused on the efficiency of consumptive activities (Nishimura et al., 2021; Qi et al., 2021; Xu et al., 2020), while ignoring the underlying water metabolism processes.
Network methods are effective for characterizing critical resource metabolism processes (Liang et al., 2020). Ecological Network Analysis (ENA) (Hannon B, 1973) quantifies metabolic features via resource fluxes (Fath, 2004; Ulanowicz et al., 2009), offering insights into system health. For example, resource footprint circulation rates reflect node vigor; balanced control-dependency relationships enhance organizational capacity; maintaining metabolic orderliness optimizes resilience thresholds; and niche complementarity indices help analyze co-evolutionary collaboration. There is concern about the approaches used to quantitatively assess the resource flows within a network. A bottom-up approach uses industrial processes to track water flows (Vanham and Bidoglio, 2013), but a top-down approach quantitatively assesses the resource flows within a network (Feng et al., 2011). For example, input-output analysis (IOA), an accepted method for quantifying water flows in a water metabolism system, is preferred over bottom-up approaches because it can link industrial economic data to water consumption using input-output tables and produce a high-resolution view of the networked water flow transactions, helping us to address issues caused within UAs by economic trade, such as water-related resource flows, ecosystem services, and health status (Hubacek and Feng, 2016). However, our ability to carry out a comprehensive and accurate assessment of water system health within UAs is hampered by a lack of high-resolution MRIO data for multi-core multi-level UAs, which has resulted from the poor alignment of statistical standards used for trade data across cities of different levels.
To date, there is little clarity about how the combination of water quantity and water quality influences the health of water metabolism systems in UAs. Cao et al. (2021) were the first to evaluate the health of water networks using an assessment model that focused on water quantity, but excluded water quality. Adequate water quantity and sufficient water quality are essential for the sustainable use of urban water resources (Cai et al., 2023). A water footprint, which incorporates both water quantity and water quality, can be used to assess water flows (Hoekstra and Mekonnen, 2012). Various water footprints have been defined, and the blue water footprint (BWF) and grey water footprint (GWF) have been used to quantify both water quantity and water quality (Chapagain and Hoekstra, 2011; Yu et al., 2022). In previous studies, researchers have focused on either water quantity or water quality when assessing the intensity of resource transfers (Cai et al., 2023; Zhao et al., 2016) and the factors that influenced them (Cai and Guo, 2023; Guan et al., 2014). Some researchers have also simulated and evaluated the performance of metabolism systems using either water quantity or water quality as the independent metabolism medium (He et al., 2020b, 2020a; Liu et al., 2022). The conventional separation of water quantity and quality in current research paradigms makes it difficult to reveal the cascading effects of their synergistic interactions on multiscale metabolism systems, which may lead to ecological cognitive bias in system health assessments. As synergistic variables within regional metabolism system, the mechanisms underlying the interactions between water quantity and water quality remain underexplored. It is imperative to conceptualize water quantity and quality as an integrated metabolism medium and develop a corresponding theoretical framework to elucidate how their synergistic metabolic processes influence system health.
The diagnoses of water metabolism system health at the UA scale are constrained by a) a lack of MRIO tables, which hinders the accurate assessment of water flow within UAs with multi-core and multi-level cities, and b) a limited understanding of how the health of metabolism systems is influenced when water quantity and water quality are combined into a single metabolism medium. To address these issues, we proposed a method for compiling MRIO tables for multi-core multi-level UAs that resolved the methodological limitations associated with assessments of water flow. We created two networks based on MRIO and ENA, one that integrated water quantity and water quality and another for water quantity only, and assessed four attributes of the health of the two networks, namely vigor, organization, resilience, and collaboration. We then tested the method with data from the Guangdong-Hong Kong-Macao Greater Bay Area UA (GBA).
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https://www.sciencedirect.com/science/article/abs/pii/S0043135425008073?via%3Dihub
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