Rethinking urban water resilience: Scientists blueprint three integrated paths beyond concrete
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Graphic Abstract
view moreCredit: Ziyan Lin, Zhibang Xu, Hua Zheng
Urban water systems worldwide are buckling under the combined pressures of climate change, rapid urbanization, and ecological degradation. Intensifying rainfall, droughts, and pollution are pushing cities toward a crisis point, yet conventional management remains anchored in gray infrastructure—concrete channels, dams, and storm drains. While these systems remain essential, they may be insufficient on their own, and inflexible or single-purpose designs can create environmental trade-offs or shift risks elsewhere.
In a new review published in Water & Ecology, a research team led by Hua Zheng at the Chinese Academy of Sciences proposes a fundamental shift from static engineering to dynamic resilience thinking.
“Resilience is not simply about building bigger walls or wider pipes,” explains Zheng. “It is about the capacity of urban aquatic ecosystems to resist shocks, recover essential functions, and adapt—or even transform—when persistent change makes old configurations unsustainable.”
The review grounds urban water ecological resilience (UWER) in ecological resilience theory, defining it as the capacity of urban aquatic ecosystems, shaped by supporting infrastructure and governance arrangements, to absorb disturbances and reorganize or transform under change. “To diagnose how this capacity emerges or erodes, we integrate two conceptual lenses: the Social–Ecological–Technological Systems (SETS) framework, which maps interactions among ecological processes, engineered infrastructure, and institutions; and the Three Horizons (3H) framework, which sequences short-term maintenance, transitional innovations, and long-term transformation,” shares Zheng.
Four interacting drivers recurrently shape UWER: hydroclimatic forcing, land-use intensity, ecological integrity, and institutional capacity. These drivers can interact across scales, generating nonlinear feedback and threshold effects. “Expanding impervious surfaces, for example, can produce flashier flows, greater pollutant delivery, habitat fragmentation, and simplified aquatic communities—a pattern known as the “urban stream syndrome”,” says Zheng. “When chronic pressures combine with extreme events, aquatic systems may be pushed across ecological thresholds into less desirable regimes.”
Against this backdrop, the reseaerchers identified three complementary enhancement pathways:
- Technological pathways: Nature-based solutions and hybrid measures,, including green roofs, urban wetlands, sponge-city approaches, and blue–green–gray coastal protection, together with decentralized infrastructure and digital tools such as real-time sensor networks and digital twins.
- Ecological pathways: Restoring biodiversity and functional redundancy, rebuilding habitat connectivity through blue–green corridors, and preserving natural buffers like riparian zones and wetlands.
- Governance and institutional pathways: Integrated planning across water, land-use, and climate sectors; stable financing and performance-based incentives; participatory co-creation with local communities; and adaptive policies that reward multifunctionality.
The researchers called for measurable resilience targets explicitly linked to resistance, recovery, and adaptation capacities; before–after evaluation frameworks; and equity-aware governance that tracks who benefits from resilience investments and who bears residual risks.
“Urban water resilience is not a fixed destination,” says Zheng. “It is a continuous capacity to learn, adjust, and sustain both ecological integrity and human well-being amid uncertainty. Closing the gap between concept and action is the urgent next step.”
Contact the Author:
Hua Zheng
-State Key Laboratory of Regional and Urban Ecology, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
-State Key Laboratory of Regional and Urban Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
-University of Chinese Academy of Sciences, Beijing 100049, China
hzheng@iue.ac.cn
The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 300 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).
Journal
Water & Ecology
Method of Research
Literature review
Beyond chemical snapshots: AI and biological “living sensors” transform urban drainage risk management
Urban drainage systems contribute to over half of water-related ecological risks in China, yet conventional management still relies on static chemical snapshots that fail to capture how living ecosystems respond to complex contaminant mixtures and dynamic environmental conditions.
In a new perspective published in Water & Ecology, a research team led by Aijie Wang at Harbin Institute of Technology details how artificial intelligence (AI) and biological big data can together revolutionize urban water safety.
“The future of water management is not just measuring chemicals—it is about the understanding of how living systems respond to those chemicals in real time,” says Wang. “We need to treat urban waterways as dynamic ecosystems, not passive pipelines.”
The perspective introduces three complementary layers of biological information for dynamic diagnosis. First, high-throughput imaging and computer vision track behavioral and morphological changes in indicator organisms such as zooplankton and protozoa in near real time. Systems like ROMIDAS™ have already generated more than 15 terabytes of continuous microscopic image data, enabling AI-driven feature extraction and risk assessment.
Second, metabolic biomarkers—including chlorophyll and algal toxins—captured through remote sensing, in situ spectroscopy, and autonomous underwater robots can reveal physiological stress and subsurface bloom precursors before surface expansion.
Third, functional gene biomarkers provide mechanistic signals linking contaminant transformation to ecological health effects, offering early warnings before visible damage occurs.
Building on these biological data layers, the authors propose the Integrated Chemical–Biological Environmental Risk Assessment framework, designed for the small-sample, high-dimensional datasets common in real-world environmental monitoring. “By combining interpretable machine learning, stability testing, and cross-model agreement, the framework prioritizes potential risk factors that remain consistent across analyses, helping turn complex environmental data into actionable priorities for management,” says Wang.
The authors also outlined a five-module intelligent management platform: monitoring-network optimization, ecological risk diagnosis, pollution source tracing and early warning, plant–network–river coordinated operation, and sponge city stormwater source control. “Together, these modules form a closed-loop workflow from real-time sensing to adaptive management,” explains Wang. “Real-world examples show what this could look like in practice: anomaly warnings for the Maozhou River could be issued within approximately 30 minutes, while a separate multi-agent AI control study reported 25.4% lower overflow volume and 18.0% lower river pollutant concentrations.”
“This is about shifting from reactive cleanup to predictive resilience,” adds senior and co-corresponding author Bin Liang. “By integrating biological intelligence with AI, we can create urban water systems that diagnose their own health and suggest interventions before crises emerge.”
The researchers acknowledged that deployment still faces hurdles, including sensor reliability, data security, and infrastructure costs. “We recommend a phased implementation strategy starting with existing monitoring stations and scaling progressively toward autonomous intelligent systems,” says Liang.
Contact Authors:
Aijie Wang
-State Key Laboratory of Urban-rural Water Resources and Environment, School of Environment, Harbin Institute of Technology, Harbin 150090, China
-State Key Laboratory of Urban-rural Water Resources and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
waj0578@hit.edu.cn
Bin Liang
-State Key Laboratory of Urban-rural Water Resources and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
liangbin1214@hit.edu.cn
The publisher KeAi was established by Elsevier and China Science Publishing & Media Ltd to unfold quality research globally. In 2013, our focus shifted to open access publishing. We now proudly publish more than 300 world-class, open access, English language journals, spanning all scientific disciplines. Many of these are titles we publish in partnership with prestigious societies and academic institutions, such as the National Natural Science Foundation of China (NSFC).
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