An Integrated Framework for Urban Flood Governance: A Participatory Spatial Decision-Making Approach

Document Type : Applied Article

Author

Ahmad Radmehr

Faculty of Governance, College of Management, University of Tehran, Tehran, Iran.

10.22067/jwsd.v13i1.2511-1469

Abstract

Urban water governance is increasingly relying on adaptive and integrated systems alongside conventional structural measures. This paper proposes an innovative and integrated spatial decision-making approach to enhance urban water governance. The study emphasizes collaborative stakeholder engagement within a GIS-based multi-criteria decision analysis framework. A decision-support toolbox developed in a Geographic Information System environment employs the Analytic Hierarchy Process. The proposed model integrates spatial analysis and stakeholder participation into a unified spatial planning support system. The results of a case study conducted in Tehran demonstrate the practical applicability of the proposed method in the field of urban water governance. The final flood hazard map classified the study area into six distinct risk levels. Approximately 6% of the area was categorized as very high vulnerability, primarily located in areas characterized by low slopes, high density, and insufficient drainage infrastructure. Classes two and three, representing moderate to high vulnerability, accounted for about 43% of the area, while 42% fell within the low-vulnerability category. Least-cost path analysis, considering land use, slope, and geological criteria, was applied to identify suitable routes for urban runoff collection. The findings indicate that the immediate upgrading of the drainage system in the southeastern areas is essential. The proposed routes, by leveraging existing infrastructure and favorable topography, can reduce construction costs while helping to minimize potential negative economic and social impacts, such as resident displacement. Overall, the proposed model provides a replicable framework for integrating technical, social, and institutional considerations in urban flood governance.

The results of a case study conducted in Tehran demonstrate the practical applicability of the proposed method in the field of urban water governance. The final flood hazard map classified the study area into six distinct risk levels. Approximately 6% of the area was categorized as very high vulnerability, primarily located in areas characterized by low slopes, high density, and insufficient drainage infrastructure. Classes two and three, representing moderate to high vulnerability, accounted for about 43% of the area, while 42% fell within the low-vulnerability category.

Least-cost path analysis, considering land use, slope, and geological criteria, was applied to identify suitable routes for urban runoff collection. The findings indicate that the immediate upgrading of the drainage system in the southeastern areas is essential. The proposed routes, by leveraging existing infrastructure and favorable topography, can reduce construction costs while helping to minimize potential negative economic and social impacts, such as resident displacement. Overall, the proposed model provides a replicable framework for integrating technical, social, and institutional considerations in urban flood governance.

Keywords

Water Governance

Collaborative Planning

Geographic Information System

Multi-Criteria Decision Analysis

Subjects

• Water Management and Economy

---

References

Arik, A. D., Chionne, D., Renou, Y., Brochet, A., Blanchet, J., Kharbouche, M., Ruin, I., & Creutin, J. D. (2023). The limits of scalability: Uncovering friction between levels of flood risk governance in the French Alps. Disaster Risk Reduction, 97, 104044. https://doi.org/10.1016/j.ijdrr.2023.104044

Challies, E., Newig, J., Thaler, T., Kochskämper, E., & Levin-Keitel, M. (2016). Participatory and collaborative governance for sustainable flood risk management: An emerging research agenda. Environmental Science and Policy, 55 (2016), 55, 275-280. https://doi:10.1016/j.envsci.2015.09.012

Fadmastuti, M., Nowak, D., & Crompvoets, J. (2024). Flood data platform governance: Identifying the technological and socio-technical approach (es) differences. Environmental Science & Policy, 162, 103938. https://doi.org/10.1016/j.envsci.2024.103938

Fernández, D. S., & Lutz, M. A. (2010). Urban flood hazard zoning in Tucumán Province, Argentina, using GIS and multicriteria decision analysis. Engineering Geology, 111(1-4), 90-98. https://doi.org/10.1016/j.enggeo.2009.12.006

Francesch-Huidobro, M. (2015). Collaborative governance and environmental authority for adaptive flood risk: Recreating sustainable coastal cities: Theme 3: Pathways towards urban modes that support regenerative sustainability. Cleaner Production, 107, 568-580. https://doi.org/10.1016/j.jclepro.2015.05.045

Guo, J., Bian, Y., Li, M., & Du, J. (2024). Assessing resilience through social networks: A case study of flood disaster management in China. Disaster Risk Reduction, 108, 104583. https://doi.org/10.1016/j.ijdrr.2024.104583

Haapasaari, P., Marttunen, M., Salokannel, V., & Similä, J. (2024). Navigating the pathway from collaborative governance to impacts under uncertainty: A theory of change for watershed visions. Environmental Science & Policy, 162, 103937. https://doi.org/10.1016/j.envsci.2024.103937

Hampton, S., & Curtis, J. (2022). A bridge over troubled water? Flood insurance and the governance of climate change adaptation. Geoforum, 136, 80-91.https://doi.org/10.1016/j.geoforum.2022.08.008

Kaiser, Z. R. M., Abdullah, A., & Akter, F. (2025). From risk to resilience and sustainability: Addressing urban flash floods and waterlogging. Risk Sciences, 100011. https://doi.org/10.1016/j.risk.2025.100011

Kavzoĝlu, T. (2001). An investigation of the design and use of feed-forward artificial neural networks in the classification of remotely sensed images. Doctoral dissertation, University of Nottingham, Nottingham, United Kingdom (UK).

Khodaei, H., Nasiri Saleh, F., Nobakht Dalir, A., & Zarei, E. (2025). Future flood susceptibility mapping under climate and land use change. Scientific Reports, 15(1), 12394. https://doi.org/10.1038/s41598-025-97008-0

Latham, Z., Barrett-Lennard, G., & Opdyke, A. (2024). Archetypes of local governance for flood risk reduction decision-making under uncertain climate change futures. Sustainable Cities and Society, 112, 105632. https://doi.org/10.1016/j.scs.2024.105632

Liu, Y., Zhang, X., Liu, J., Wang, Y., Jia, H., & Tao, S. (2025). A flood resilience assessment method of green-grey-blue coupled urban drainage system considering backwater effects. Ecological Indicators, 170, 113032. https://doi.org/10.1016/j.ecolind.2024.113032

Lu, Y., Zhai, G., & Zhou, S. (2024). An integrated Bayesian networks and GIS approach for flood disaster risk assessment: Yinchuan case study. Ecological Indicators, 166, 112322. https://doi.org/10.1016/j.ecolind.2024.112322

Luan, G., Hou, J., Wang, T., Zhou, Q., Xu, L., Sun, J., & Wang, C. (2024). Method for analyzing urban waterlogging mechanisms based on a 1D-2D water environment dynamic bidirectional coupling model. Environmental Management, 360, 121024. https://doi.org/10.1016/j.jenvman.2024.121024

Lukman, T., Yahaya, A. K., & Gordon, N. (2023). External stakeholders in the collaborative governance of natural resources in Ghana: Experiences from the Wa West District. Environmental challenges, 13, 100769. https://doi.org/10.1016/j.envc.2023.100769

Malczewski, J. (1999). GIS and multicriteria decision analysis. John wiley & sons. NewYork, USA.

McNaught, R., Nalau, J., Hales, R., Pittaway, E., Handmer, J., & Renouf, J. (2024). Innovation and deadlock in governing disasters and climate change Collaboratively-Lessons from the Northern Rivers region of New South Wales, Australia. Disaster Risk Reduction, 105, 104366. https://doi.org/10.1016/j.ijdrr.2024.104366

Minano, A., Thistlethwaite, J., & Henstra, D. (2024). Conceptualizing and evaluating the role of a data platform as an entry-point for strengthening flood risk governance in Canada. Disaster Risk Reduction, 103, 104297. https://doi.org/10.1016/j.ijdrr.2024.104297 

Minucci, G., Molinari, D., Gemini, G., & Pezzoli, S. (2020). Enhancing flood risk maps by a participatory and collaborative design process. Disaster Risk Reduction, 50, 101747. https://doi.org/10.1016/j.ijdrr.2020.101747

Nguyen, H., Pham, B. T., Prakash, I., Dou, J., Trinh, P. T., & Le, H. V. (2022).A hybrid flood susceptibility modeling framework using GIS‑based multi‑criteria analysis and machine‑learning approaches (ANN, SVM, RF). Hydrology,  605, 127304. https://doi.org/10.1016/j.jhydrol.2021.127304

Nick, F. C., Sänger, N., van der Heijden, S., & Sandholz, S. (2023). Collaboration is key: exploring the 2021 flood response for critical infrastructures in Germany. Disaster Risk Reduction, 91, 103710. https://doi.org/10.1016/j.ijdrr.2023.103710

O’Shea, T. E., Grobusch, L. C., Zhang, M., Neal, J., Daron, J., Jones, R. G., Jack, C., McClure, A., Siame, G., Ndhlovu, D., & Bharwani, S. (2025). Integrating social narratives of flood events into a text network analysis-based decision support framework to reduce vulnerability to climate change in Africa. Climate Services, 37, 100538. https://doi.org/10.1016/j.cliser.2024.100538

Phua, M. H., & Minowa, M. (2005). A GIS-based multi-criteria decision making approach to forest conservation planning at a landscape scale: a case study in the Kinabalu Area, Sabah, Malaysia. Landscape and urban planning, 71(2-4), 207-222. https://doi.org/10.1016/j.landurbplan.2004.03.004

Pourzangbar, A., Oberle, P., Kron, A., & Franca, M. J. (2025). Analysis of the utilization of machine learning to map flood susceptibility. Flood Risk Management, 18(2), e70042. https://doi.org/10.1111/jfr3.70042

Qi, W., Ma, C., Xu, H., Lian, J., Xu, K. & Yao, Y. (2024). An exploratory framework to urban flood collaborative mitigation strategy considering synergistic effect of inundation volume. Hydrology, 628, 130555. https://doi.org/10.1016/j.jhydrol.2023.130555  

Saaty, T. L. (1996). The analytic hierarchy process: planning, priority setting, resource allocation, RWS Publication. Pittsburgh, USA.

Shirzadi, A., Salvati, A., Tahan, M. H., Shahabi, H., Nodoushan, E. J., Ramezani, M., Hashim, M., & Clague, J. J. (2025). Urban flood susceptibility mapping using deep and machine learning algorithms as a management tool: A case study of Sanandaj City, Iran. Ecological Indicators, 178, 113886. https://doi.org/10.1016/j.ecolind.2025.113886

Sun, Q., Kushner, H., & Yang, Y. E. (2024). Identifying barriers to decentralized stormwater infrastructure implementation at different levels of urban flood governance–A case study in Eastern Pennsylvania, US. Environmental Science & Policy, 154, 103686. https://doi.org/10.1016/j.envsci.2024.103686

Winter, A. K., & Karvonen, A. (2022). Climate governance at the fringes: Peri-urban flooding drivers and responses. Land use policy, 117, 106124. https://doi.org/10.1016/j.landusepol.2022.106124

Yang, J., Liu, K., Wang, M., & Yue, Q. (2025). A new algorithm for generation of urban underground stormwater networks and its application for enhanced urban flood simulation. Hydrology, 661, 133571. https://doi.org/10.1016/j.scs.2024.105632