
Smart Water Systems: Advances in Monitoring, Modeling, and Management of Water Resources in China
DOI:
https://doi.org/10.30564/jees.v8i8.13406Abstract
Smart water systems are a revolutionary way of utilizing water resources, based on the use of modern technologies (sensors, data analysis, machine learning, and real-time monitoring). Such systems are becoming part and parcel of water management practices in China in order to deal with the fact that the country is facing water scarcity, problems of pollution, and even an unpredictable climate. This article examines the design and deployment of smart waters in China and how monitoring, modeling, and decision-making technologies have been integrated into water management systems in different sectors of water management, such as river basins, urban utilities, agriculture, and groundwater management. We consider how decision support systems (DSS) can be used to improve water allocation, infrastructure management processes, and resilience to floods and droughts. The article, however, also notes that there are various challenges to scaling these systems, such as the lack of technological interoperability, data management concerns, cybersecurity risks, and financial limitations. Moreover, institutional fragmentation and social refusal to accept also complicate the popularisation of smart water technologies. The article wraps up by underscoring the fact that more investment needs to be made in data standards, cross-agency coordination, and capacity building to eliminate these obstacles and to make smart water systems a success in the long term. The activities of China in this respect are a worthy example to other countries that are trying to deal with the challenge of water management, like China.
Keywords:
Smart Water Systems; Water Resource Management; Data Integration; Decision Support Systems; ChinaReferences
[1] Wang, J., Li, Y., Huang, J., et al., 2017. Growing water scarcity, food security and government responses in China. Global Food Security. 14, 9–17.
[2] Ma, T., Sun, S., Fu, G., et al., 2020. Pollution exacerbates China’s water scarcity and its regional inequality. Nature Communications. 11(1), 650.
[3] Zahoor, I., Mushtaq, A., 2023. Water pollution from agricultural activities: A critical global review. International Journal of Chemical and Biochemical Sciences. 23(1), 164–176.
[4] Madhav, S., Ahamad, A., Singh, A.K., et al., 2019. Water pollutants: Sources and impact on the environment and human health. In: Pooja, D., Kumar, P., Singh, P., et al. (Eds.). Sensors in Water Pollutants Monitoring: Role of Material. Springer: Singapore. pp. 43–62.
[5] Ndubuisi, O.G., Obiorah, C.A., Ugah, T.A., et al., 2025. Integrated water resource management in a changing climate: Assessing adaptive strategies, technologies, and policies for resilient water system. International Journal of Innovative Science, Engineering and Technology Research. 13(2), 264–277.
[6] Dai, Y., Huang, Z., Khan, N., et al., 2025. Smart water management: Governance innovation, technological integration, and policy pathways toward economic and ecological sustainability. Water. 17(13), 1932.
[7] Li, J., Yang, X., Sitzenfrei, R., 2020. Rethinking the framework of smart water system: A review. Water. 12(2), 412.
[8] Gupta, A.D., Pandey, P., Feijóo, A., et al., 2020. Smart water technology for efficient water resource management: A review. Energies. 13(23), 6268.
[9] Liu, Q., Yang, L., Yang, M., 2021. Digitalisation for water sustainability: Barriers to implementing circular economy in smart water management. Sustainability. 13(21), 11868.
[10] Sun, A.Y., Scanlon, B.R., 2019. How can Big Data and machine learning benefit environment and water management: A survey of methods, applications, and future directions. Environmental Research Letters. 14(7), 073001.
[11] Zhang, K., Zargar, A., Achari, G., et al., 2014. Application of decision support systems in water management. Environmental Reviews. 22(3), 189–205.
[12] Dlamini, V., 2007. Local government implementation of policies for integrated water services provision: The practice in Bushbuckridge Local Municipality. CGIAR Challenge Program on WATER&FOOD: Montpellier, France.
[13] Kamyab, H., Khademi, T., Chelliapan, S., et al., 2023. The latest innovative avenues for the utilization of artificial Intelligence and big data analytics in water resource management. Results in Engineering. 20, 101566.
[14] Zhu, Z., Zhang, H., Zhang, S., et al., 2025. Intelligent Monitoring and Control Systems for Smart Water Management. In: Garg, M.C., Rajput, V.D., Minkina, T., et al. (Eds.). Nano-Solutions for Sustainable Water and Wastewater Management: From Monitoring to Treatment. Springer: Cham, Switzerland. pp. 369–390.
[15] Behmel, S., Damour, M., Ludwig, R., et al., 2021. Intelligent decision-support system to plan, manage and optimize water quality monitoring programs: Design of a conceptual framework. Journal of Environmental Planning and Management. 64(4), 703–733.
[16] Araral, E., Wu, X., 2016. Comparing water resources management in China and India: Policy design, institutional structure and governance. Water Policy. 18(S1), 1–13.
[17] Xia, G., Bao, C., 2025. Evolution and Mechanism of Intergovernmental Cooperation in Transboundary Water Governance: The Taihu Basin, China. Water. 17(11), 1582.
[18] Silveira, A., Junier, S., Hüesker, F., et al., 2016. Organizing cross-sectoral collaboration in river basin management: case studies from the Rhine and the Zhujiang (Pearl River) basins. International Journal of River Basin Management. 14(3), 299–315.
[19] World Bank, 2019. Watershed: A New Era of Water Governance in China-Thematic Report. World Bank: Washington, DC, USA.
[20] Blanke, A., Rozelle, S., Lohmar, B., et al., 2007. Water saving technology and saving water in China. Agricultural Water Management. 87(2), 139–150.
[21] Zevenbergen, C., Fu, D., Pathirana, A., 2018. Transitioning to sponge cities: Challenges and opportunities to address urban water problems in China. Water. 10(9), 1230.
[22] Qi, Y., Chan, F.K.S., Thorne, C., et al., 2020. Addressing challenges of urban water management in Chinese sponge cities via nature-based solutions. Water. 12(10), 2788.
[23] Chen, J., Chen, S., Fu, R., et al., 2022. Remote sensing big data for water environment monitoring: Current status, challenges, and future prospects. Earth's Future. 10(2), e2021EF002289.
[24] Bandara, R.M.P.N.S., Jayasignhe, A.B., Retscher, G., 2025. The integration of IoT (Internet of Things) sensors and location-based services for water quality monitoring: A systematic literature review. Sensors. 25(6), 1918.
[25] Lee, S., 2021. Water Plan and Governance System. In China's Water Resources Management: A Long March to Sustainability. Springer: Cham, Switzerland. pp. 105–152.
[26] Gallaher, S., Heikkila, T., 2014. Challenges and opportunities for collecting and sharing data on water governance institutions. Journal of Contemporary Water Research & Education. 153(1), 66–78.
[27] Lin, J., Bryan, B.A., Zhou, X., et al., 2023. Making China’s water data accessible, usable and shareable. Nature Water. 1(4), 328–335.
[28] Liu, T., Zhang, W., Wang, R.Y., 2022. How does the Chinese government improve connectivity in water governance? A qualitative systematic review. International Journal of Water Resources Development. 38(4), 717–735.
[29] Koech, R., Cardell-Oliver, R., Syme, G., 2021. Smart water metering: Adoption, regulatory and social considerations. Australasian Journal of Water Resources. 25(2), 173–182.
[30] Zhong, L., Mol, A.P., Fu, T., 2008. Public-private partnerships in China’s urban water sector. Environmental Management. 41(6), 863–877.
[31] Chan, A.P.C., Lam, P.T.I., Wen, Y., et al., 2015. Cross-sectional analysis of critical risk factors for PPP water projects in China. Journal of Infrastructure Systems. 21(1), 04014031.
[32] Lima, S., Brochado, A., Marques, R.C., 2021. Public-private partnerships in the water sector: A review. Utilities Policy. 69, 101182.
[33] Owen, D.L., 2023. Smart water management. River. 2(1), 21–29.
[34] Deng, Y., Brombal, D., Farah, P.D., et al., 2016. China's water environmental management towards institutional integration. A review of current progress and constraints vis-a-vis the European experience. Journal of Cleaner Production. 113, 285–298.
[35] Wang, Y., Chen, X., 2020. River chief system as a collaborative water governance approach in China. International Journal of Water Resources Development. 36(4), 610–630.
[36] Adjovu, G.E., Stephen, H., James, D., et al., 2023. Overview of the application of remote sensing in effective monitoring of water quality parameters. Remote Sensing. 15(7), 1938.
[37] Cloete, N.A., Malekian, R., Nair, L., 2016. Design of smart sensors for real-time water quality monitoring. IEEE Access. 4, 3975–3990.
[38] Samboko, H., Abas, I., Luxemburg, W.M.J., et al., 2020. Evaluation and improvement of remote sensing-based methods for river flow management. Physics and Chemistry of the Earth, Parts A/B/C. 117, 102839.
[39] Boyle, T., Giurco, D., Mukheibir, P., et al., 2013. Intelligent metering for urban water: A review. Water. 5(3), 1052–1081.
[40] Lambrou, T.P., Anastasiou, C.C., Panayiotou, C.G., et al., 2014. A low-cost sensor network for real-time monitoring and contamination detection in drinking water distribution systems. IEEE Sensors Journal. 14(8), 2765–2772.
[41] Li, H., Wan, W., Ji, R., et al., 2023. Inspects and prospects of satellite remote sensing monitoring ability for land surface water in China. National Remote Sensing Bulletin. 27(7), 1554–1573.
[42] Sun, J., Ding, L., Li, J., et al., 2018. Monitoring temporal change of river islands in the Yangtze River by remotely sensed data. Water. 10(10), 1484.
[43] Rhee, J., Im, J., Carbone, G.J., 2010. Monitoring agricultural drought for arid and humid regions using multi-sensor remote sensing data. Remote Sensing of Environment. 114(12), 2875–2887.
[44] Acharya, B.S., Bhandari, M., Bandini, F., et al., 2021. Unmanned aerial vehicles in hydrology and water management: Applications, challenges, and perspectives. Water Resources Research. 57(11), e2021WR029925.
[45] Wang, R., Sun, Y., Zong, J., et al., 2024. Remote sensing application in ecological restoration monitoring: A systematic review. Remote Sensing. 16(12), 2204.
[46] Wu, Y., Washbourne, C., Haklay, M., 2022. Citizen science in China’s water resources monitoring: current status and future prospects. International Journal of Sustainable Development & World Ecology. 29(3), 277–290.
[47] Pattinson, N.B., Taylor, J., Dickens, C.W.S., et al., 2023. IWMI Working Paper 210: Digital Innovation in Citizen Science to Enhance Water Quality Monitoring in Developing Countries. International Water Management Institute (IWMI): Colombo, Sri Lanka.
[48] Restuccia, F., Ghosh, N., Bhattacharjee, S., et al., 2017. Quality of information in mobile crowdsensing: Survey and research challenges. ACM Transactions on Sensor Networks. 13(4), 1–43.
[49] Dong, J., Wang, G., Yan, H., et al., 2015. A survey of smart water quality monitoring system. Environmental Science and Pollution Research. 22(7), 4893–4906.
[50] Luo, L., Lan, J., Wang, Y., et al., 2022. A novel early warning system (EWS) for water quality, integrating a high-frequency monitoring database with efficient data quality control technology at a large and deep lake (Lake Qiandao), China. Water. 14(4), 602.
[51] Liu, Q., 2021. Intelligent water quality monitoring system based on multi-sensor data fusion technology. International Journal of Ambient Computing and Intelligence. 12(4), 43–63.
[52] Blasch, E., Pham, T., Chong, C.-Y., et al., 2021. Machine learning/artificial intelligence for sensor data fusion–opportunities and challenges. IEEE Aerospace and Electronic Systems Magazine. 36(7), 80–93.
[53] Glasgow, H.B., Burkholder, J.M., Reed, R.E., et al., 2004. Real-time remote monitoring of water quality: A review of current applications, and advancements in sensor, telemetry, and computing technologies. Journal of Experimental Marine Biology and Ecology. 300(1–2), 409–448.
[54] Kunungo, S., Ramabhotla, S., Bhoyar, M., 2018. The integration of data engineering and cloud computing in the age of machine learning and artificial intelligence. Iconic Research and Engineering Journals. 1(12), 79–84.
[55] Andronie, M., Lăzăroiu, G., Iatagan, M., et al., 2023. Big data management algorithms, deep learning-based object detection technologies, and geospatial simulation and sensor fusion tools in the internet of robotic things. ISPRS International Journal of Geo-Information. 12(2), 35.
[56] Butler, D., Ward, S., Sweetapple, C., et al., 2017. Reliable, resilient and sustainable water management: The Safe & SuRe approach. Global Challenges. 1(1), 63–77.
[57] Liu, Y., Gupta, H., Springer, E., et al., 2008. Linking science with environmental decision making: Experiences from an integrated modeling approach to supporting sustainable water resources management. Environmental Modelling & Software. 23(7), 846–858.
[58] Cong, Z., Zhao, J., Yang, D., et al., 2010. Understanding the hydrological trends of river basins in China. Journal of Hydrology. 388(3–4), 350–356.
[59] Yang, L., Zhao, G., Tian, P., et al., 2022. Runoff changes in the major river basins of China and their responses to potential driving forces. Journal of Hydrology. 607, 127536.
[60] Zhang, B., Wu, Y., Zhao, B., et al., 2022. Progress and challenges in intelligent remote sensing satellite systems. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing. 15, 1814–1822.
[61] Liu, Z., Zhou, J., Yang, X., et al., 2024. Research on water resource modeling based on machine learning technologies. Water. 16(3), 472.
[62] Najwa Mohd Rizal, N., Hayder, G., Mnzool, M., et al., 2022. Comparison between regression models, support vector machine (SVM), and artificial neural network (ANN) in river water quality prediction. Processes. 10(8), 1652.
[63] Essam, Y., Huang, Y.F., Ng, J.L., et al., 2022. Predicting streamflow in Peninsular Malaysia using support vector machine and deep learning algorithms. Scientific Reports. 12(1), 3883.
[64] Ho, L., Goethals, P., 2022. Machine learning applications in river research: Trends, opportunities and challenges. Methods in Ecology and Evolution. 13(11), 2603–2621.
[65] Santos, L.B., Escobar-Silva, E.V., Satolo, L.F., et al., 2025. Machine learning-based hydrological models for flash floods: A systematic literature review. Smart Construction and Sustainable Cities. 3(1), 21.
[66] Kan, G., He, X., Li, J., et al., 2017. Computer aided numerical methods for hydrological model calibration: An overview and recent development. Archives of Computational Methods in Engineering. 26(1), 35–59.
[67] Peng, J., 2025. Digital twin technology and its application in water governance: China’s practices and achievements. International Journal of Water Resources Development. 41(5–6), 974–989.
[68] Fan, C., Hou, J., Li, X., et al., 2025. Efficient urban flood control and drainage management framework based on digital twin technology and optimization scheduling algorithm. Water Research. 282, 123711.
[69] Nguyen, T.H., Bhattacharya, S., Wong, J.S., et al., 2026. Towards Digital Twin in Flood Forecasting with Data Assimilation Satellite Earth Observations—A Proof-of-Concept. Remote Sensing. 18(5), 685.
[70] Sardar, M.A., Amir, E., Rehaman, M.A., et al., 2025. Integration of Digital Twin Technology for Water Resource Management of Smart Cities and Communities: A Narrative Review. International Journal of Advanced Natural Sciences and Engineering Research. 9, 75–85.
[71] Guzman, J.A., Shirmohammadi, A., Sadeghi, A.M., et al., 2015. Uncertainty considerations in calibration and validation of hydrologic and water quality models. Transactions of the ASABE. 58(6), 1745–1762.
[72] Renard, B., Kavetski, D., Kuczera, G., et al., 2010. Understanding predictive uncertainty in hydrologic modeling: The challenge of identifying input and structural errors. Water Resources Research. 46(5).
[73] Moriasi, D.N., Wilson, B.N., Douglas-Mankin, K.R., et al., 2012. Hydrologic and water quality models: Use, calibration, and validation. Transactions of the ASABE. 55(4), 1241–1247.
[74] Zhou, Y., Li, B., Han, J., et al., 2023. Enabling efficiency-driven and low-impact water management from robust decision making: A risk-and robustness-based multi-objective decision support model. Journal of Cleaner Production. 394, 136277.
[75] Ponte, B., de la Fuente, D., Parreño, J., et al., 2016. Intelligent decision support system for real-time water demand management. International Journal of Computational Intelligence Systems. 9(1), 168–183.
[76] Veintimilla-Reyes, J., Cattrysse, D., De Meyer, A., et al., 2016. Mixed integer linear programming (MILP) approach to deal with spatio-temporal water allocation. Procedia Engineering. 162, 221–229.
[77] Weng, S., Huang, G.H., Li, Y., 2010. An integrated scenario-based multi-criteria decision support system for water resources management and planning—A case study in the Haihe River Basin. Expert Systems with Applications. 37(12), 8242–8254.
[78] Kara, S., Karadirek, I.E., Muhammetoglu, A., et al., 2016. Real time monitoring and control in water distribution systems for improving operational efficiency. Desalination and Water Treatment. 57(25), 11506–11519.
[79] Creaco, E., Campisano, A., Fontana, N., et al., 2019. Real time control of water distribution networks: A state-of-the-art review. Water Research. 161, 517–530.
[80] Zhang, W., Tooker, N.B., Mueller, A.V., 2020. Enabling wastewater treatment process automation: Leveraging innovations in real-time sensing, data analysis, and online controls. Environmental Science: Water Research & Technology. 6(11), 2973–2992.
[81] Hou, D., Song, X., Zhang, G., et al., 2013. An early warning and control system for urban, drinking water quality protection: China’s experience. Environmental Science and Pollution Research. 20(7), 4496–4508.
[82] Perera, D., Seidou, O., Agnihotri, J., et al., 2019. UNU-INWEH Report Series 08: Flood Early Warning Systems: A Review of Benefits, Challenges and Prospects. UNU-INWEH: Hamilton, ON, Canada.
[83] Zhang, Q., Yao, Y., Li, Y., et al., 2015. Research progress and prospect on the monitoring and early warning and mitigation technology of meteorological drought disaster in northwest China. Advances in Earth Science. 30(2), 196–211.
[84] Office of Research and Development National Homeland Security Research Center, 2005. Technologies and Techniques for Early Warning Systems to Monitor and Evaluate Drinking Water Quality: A State-of-the-Art Review. EPA: Washington, DC, USA.
[85] Rezvani, S., Falcão, M.J., Komljenovic, D., et al., 2023. A systematic literature review on urban resilience enabled with asset and disaster risk management approaches and GIS-based decision support tools. Applied Sciences. 13(4), 2223.
[86] Levy, J.K., 2005. Multiple criteria decision making and decision support systems for flood risk management. Stochastic Environmental Research and Risk Assessment. 19(6), 438–447.
[87] Obura, D., Nabifo, V., Apiny, I., et al., 2024. Application of a decision support system for monitoring and maintenance management of water valley tank facilities: A technological transformation toward sector sustainability. Groundwater for Sustainable Development. 25, 101109.
[88] Ge, Y., Li, X., Huang, C., et al., 2013. A Decision Support System for irrigation water allocation along the middle reaches of the Heihe River Basin, Northwest China. Environmental Modelling & Software. 47, 182–192.
[89] Hauser, A., Roedler, F., 2015. Interoperability: The key for smart water management. Water Science and Technology: Water Supply. 15(1), 207–214.
[90] Lee, S.W., Sarp, S., Jeon, D.J., et al., 2015. Smart water grid: The future water management platform. Desalination and Water Treatment. 55(2), 339–346.
[91] Abu Bakar, A.A., Abu Bakar, Z., Mohd Yusoff, Z., et al., 2025. IoT-Based Real-Time Water Quality Monitoring and Sensor Calibration for Enhanced Accuracy and Reliability. International Journal of Interactive Mobile Technologies. 19(1).
[92] Joseph, K., Sharma, A.K., Van Staden, R., 2022. Development of an intelligent urban water network system. Water. 14(9), 1320.
[93] Rasekh, A., Hassanzadeh, A., Mulchandani, S., et al., 2016. Smart water networks and cyber security. American Society of Civil Engineers. 142(7), 01816004.
[94] Addeen, H.H., Xiao, Y., Li, J., et al., 2021. A survey of cyber-physical attacks and detection methods in smart water distribution systems. IEEE Access. 9, 99905–99921.
[95] Salomons, E., Sela, L., Housh, M., 2020. Hedging for privacy in smart water meters. Water Resources Research. 56(9), e2020WR027925.
[96] Xie, J., 2009. Addressing China's Water Scarcity: Recommendations for Selected Water Resource Management Issues. World Bank: Washington, DC, USA.
[97] Yu, X., Geng, Y., Heck, P., et al., 2015. A review of China’s rural water management. Sustainability. 7(5), 5773–5792.
[98] Goulas, A., Goodwin, D., Shannon, C., et al., 2022. Public perceptions of household IoT smart water “event” meters in the UK—Implications for urban water governance. Frontiers in Sustainable Cities. 4, 758078.
[99] Jiang, Y., 2023. Financing water investment for global sustainable development: Challenges, innovation, and governance strategies. Sustainable Development. 31(2), 600–611.
[100] Boulos, P.F., 2017. Smart water network modeling for sustainable and resilient infrastructure. Water Resources Management. 31(10), 3177–3188.
Downloads
How to Cite
Issue
Article Type
License
Copyright © 2026 Fei Lin, Qing Gao, Lixin Wang

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.




Fei Lin