Surface water quality worldwide is under increasing pressure from climate change, urbanization, and intensified human activity, threatening the United Nations Sustainable Development Goals for clean water and healthy aquatic ecosystems. Despite decades of progress, water quality models often remain in local contexts, with limited cross-border learning and inconsistent standards. In a new review article published in Water & Ecology , an international team of researchers from 17 institutions across China, Australia, and New Zealand calls for a fundamental shift toward coordinated, transnational collaboration in water quality modeling.
“China excels in real-time monitoring and urban system modeling, while Australia and New Zealand lead in stakeholder engagement and ecological benchmarking,” says corresponding author Yong Liu. “The three nations have nonetheless developed complementary approaches.”
China has achieved 90.4% surface water meeting national standards and is shifting toward basin-scale low-carbon management. Australia's Paddock to Reef program integrates monitoring, modeling, and reporting to protect the Great Barrier Reef. New Zealand pioneers the integration of ecological and Māori cultural values through its National Policy Statement for Freshwater Management 2020.
The researchers identified four collaboration themes: emerging technologies (AI, high-performance computing, remote sensing); uncertainty quantification; tackling climate change and urbanization; and shared modeling guidelines. China's UV–VIS spectroscopy and machine learning for real-time river management could transform Australian and New Zealand stormwater monitoring. Conversely, Australian and New Zealand experience in stakeholder engagement and Good Modeling Practice offers lessons for China's governance.
“These are urgently needed practical directions,” says Liu. “Hybrid models, high-performance computing frameworks, and AI are already improving predictive capacity for extreme weather and data-scarce regions. Also, digital twins and cloud platforms are making models more accessible to decision-makers.”
However, the researchers noted the limitations, including insufficient decision-maker training, short project lifecycles, and cross-border data integration complexity. “Phased implementation starting with shared benchmark datasets and an international repository of modeling cases—successes and failures—offers the most realistic pathway,” adds Liu.
The researchers proposed establishing Good Modeling Practice standards, promoting technology transfer and policy sharing. and jointly addressing emerging contaminants and climate-resilient infrastructure. They emphasized that sharing failed cases is as valuable as successes for collective learning.
“Water quality degradation knows no borders,” says Liu. “By combining China's technological scale, Australia's catchment experience, and New Zealand's cultural-ecological integration, we can turn scientific innovation into policy action.”
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Contact author name, affiliation, email address:
Jiping Jiang
-State Key Laboratory of Urban-Rural Water Resource and Environment, School of Eco-Environment, Harbin Institute of Technology, Shenzhen 518055, China
-School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen 518055, China
jiangjp@hit.edu.cn
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Water & Ecology
Systematic review
Not applicable
Priorities and Future Outlook for Cross-country Collaboration in Surface Water Quality Modeling: Collective Insights from China, Australia, and New Zealand
The authors declare the following financial interests which may be considered as potential competing interests: Qian Liang is currently employed by PowerChina Water Environmental Technology Co., Ltd. Weijun Zhang is currently employed by Ewaters New Zealand Co., Ltd.