Articles | Volume 29, issue 12
https://doi.org/10.5194/hess-29-2655-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-29-2655-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A multiagent socio-hydrologic framework for integrated green infrastructures and water resource management at various spatial scales
Mengxiang Zhang
Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China
Ting Fong May Chui
CORRESPONDING AUTHOR
Department of Civil Engineering, The University of Hong Kong, Hong Kong SAR, China
Related authors
No articles found.
Yang Yang and Ting Fong May Chui
Hydrol. Earth Syst. Sci., 25, 5839–5858, https://doi.org/10.5194/hess-25-5839-2021, https://doi.org/10.5194/hess-25-5839-2021, 2021
Short summary
Short summary
This study uses explainable machine learning methods to model and interpret the statistical correlations between rainfall and the discharge of urban catchments with sustainable urban drainage systems. The resulting models have good prediction accuracies. However, the right predictions may be made for the wrong reasons as the model cannot provide physically plausible explanations as to why a prediction is made.
Cited articles
Akhbari, M. and Grigg, N. S.: A framework for an agent-based model to manage water resources conflicts, Water Resour. Manag., 27, 4039–4052, https://doi.org/10.1007/s11269-013-0394-0, 2013. a
Archfield, S. A. and Vogel, R. M.: Map correlation method: Selection of a reference streamgage to estimate daily streamflow at ungaged catchments, Water Resour. Res., 46, W10513, https://doi.org/10.1029/2009WR008481, 2010. a
Askarizadeh, A., Rippy, M. A., Fletcher, T. D., Feldman, D. L., Peng, J., Bowler, P., Mehring, A. S., Winfrey, B. K., Vrugt, J. A., AghaKouchak, A., Jiang, S. C., Sanders, B. F., Levin, L. A., Taylor, S., and Grant, S. B.: From rain tanks to catchments: use of low-impact development to address hydrologic symptoms of the urban stream syndrome, Environ. Sci. Technol., 49, 11264–11280, https://doi.org/10.1021/acs.est.5b01635, 2015. a
Askew-Merwin, C.: Natural Infrastructure's Role in Mitigating Flooding Along the Mississippi River, Northeast-Midwest Institute Report, 16 pp., https://www.nemw.org/wp-content/uploads/2020/03/Natural-Infrastructures-Role-Mitigating-Flooding.pdf (last access: 5 August 2021), 2020. a
Bach, P. M., Mccarthy, D. T., and Deletic, A.: Can we model the implementation of water sensitive urban design in evolving cities?, Water Sci. Technol., 71, 149–156, https://doi.org/10.2166/wst.2014.464, 2015. a
Baldwin, C. K. and Lall, U.: Seasonality of streamflow: the upper Mississippi River, Water Resour. Res., 35, 1143–1154, https://doi.org/10.1029/1998WR900070, 1999. a
Bankes, S. C.: Agent-based modeling: A revolution?, P. Natl. Acad. Sci. USA, 99, 7199–7200, https://doi.org/10.1073/pnas.072081299, 2002. a
Barreteau, O. and Abrami, G.: Variable time scales, agent-based models, and role-playing games: The PIEPLUE river basin management game, Simulat. Gaming, 38, 364–381, https://doi.org/10.1177/1046878107300668, 2007. a, b
Bellman, R.: Dynamic programming, Science, 153, 34–37, https://doi.org/10.1126/science.153.3731.34, 1966. a, b, c
Berger, T. and Ringler, C.: Tradeoffs, efficiency gains and technical change-Modeling water management and land use within a multiple-agent framework, Quarterly Journal of International Agriculture, 41, 119–144, 2002. a
Berger, T., Birner, R., Mccarthy, N., DíAz, J., and Wittmer, H.: Capturing the complexity of water uses and water users within a multi-agent framework, Water Resour. Manag., 21, 129–148, https://doi.org/10.1007/s11269-006-9045-z, 2007. a
Berglund, E. Z.: Using agent-based modeling for water resources planning and management, J. Water Res. Pl., 141, 04015025, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000544, 2015. a
Brannen, R., Spence, C., and Ireson, A.: Influence of shallow groundwater–surface water interactions on the hydrological connectivity and water budget of a wetland complex, Hydrol. Process., 29, 3862–3877, https://doi.org/10.1002/hyp.10563, 2015. a
Chang, J.-x., Bai, T., Huang, Q., and Yang, D.-w.: Optimization of water resources utilization by PSO-GA, Water Resour. Manag., 27, 3525–3540, https://doi.org/10.1007/s11269-013-0362-8, 2013. a
Chen, J., Liu, Y., Gitau, M. W., Engel, B. A., Flanagan, D. C., and Harbor, J. M.: Evaluation of the effectiveness of green infrastructure on hydrology and water quality in a combined sewer overflow community, Sci. Total Environ., 665, 69–79, https://doi.org/10.1016/j.scitotenv.2019.01.416, 2019. a
Chiew, F. and McMahon, T.: Modelling runoff and diffuse pollution loads in urban areas, Water Sci. Technol., 39, 241–248, https://doi.org/10.1016/S0273-1223(99)00340-6, 1999. a
Cooley, H., Phurisamban, R., and Gleick, P.: The cost of alternative urban water supply and efficiency options in California, Environ. Res. Commun., 1, 042001, https://doi.org/10.1088/2515-7620/ab22ca, 2019. a
Coutts, A. M., Tapper, N. J., Beringer, J., Loughnan, M., and Demuzere, M.: Watering our cities: The capacity for Water Sensitive Urban Design to support urban cooling and improve human thermal comfort in the Australian context, Prog. Phys. Geogr., 37, 2–28, https://doi.org/10.1177/0309133312461032, 2013. a
Daigger, G. T.: Evolving urban water and residuals management paradigms: Water reclamation and reuse, decentralization, and resource recovery, Water Environ. Res., 81, 809–823, https://doi.org/10.2175/106143009X425898, 2009. a
Daigger, G. T. and Crawford, G. V.: Enhancing water system security and sustainability by incorporating centralized and decentralized water reclamation and reuse into urban water management systems, Journal of Environmental Engineering and Management, 17, 1–10, 2007. a
Dallman, S., Chaudhry, A. M., Muleta, M. K., and Lee, J.: The value of rain: benefit-cost analysis of rainwater harvesting systems, Water Resour. Manag., 30, 4415–4428, https://doi.org/10.1007/s11269-016-1429-0, 2016. a
Dandy, G. C., Marchi, A., Maier, H. R., Kandulu, J., MacDonald, D. H., and Ganji, A.: An integrated framework for selecting and evaluating the performance of stormwater harvesting options to supplement existing water supply systems, Environ. Modell. Softw., 122, 104554, https://doi.org/10.1016/j.envsoft.2019.104554, 2019. a
Darbandsari, P., Kerachian, R., Malakpour-Estalaki, S., and Khorasani, H.: An agent-based conflict resolution model for urban water resources management, Sustain. Cities Soc., 57, 102112, https://doi.org/10.1016/j.scs.2020.102112, 2020. a
Dierauer, J. R. and Zhu, C.: Drought in the twenty-first century in a water-rich region: modeling study of the Wabash River Watershed, USA, Water, 12, 181, https://doi.org/10.3390/w12010181, 2020. a
Dietz, M. E.: Low impact development practices: A review of current research and recommendations for future directions, Water Air Soil Pollut., 186, 351–363, https://doi.org/10.1007/s11270-007-9484-z, 2007. a
Du, E., Tian, Y., Cai, X., Zheng, Y., Li, X., and Zheng, C.: Exploring spatial heterogeneity and temporal dynamics of human-hydrological interactions in large river basins with intensive agriculture: A tightly coupled, fully integrated modeling approach, J. Hydrol., 591, 125313, https://doi.org/10.1016/j.jhydrol.2020.125313, 2020. a
Ebrahimian, A., Wadzuk, B., and Traver, R.: Evapotranspiration in green stormwater infrastructure systems, Sci. Total Environ., 688, 797–810, https://doi.org/10.1016/j.scitotenv.2019.06.256, 2019. a
Ellis, J. B.: Sewer infiltration/exfiltration and interactions with sewer flows and groundwater quality, in: 2nd International Conference Interactions between sewers, treatment plants and receiving waters in urban areas–Interurba II, Citeseer, 19–22, https://api.semanticscholar.org/CorpusID:11794888 (last access: 18 September 2022), 2001. a
Ellis, J. B.: Sustainable surface water management and green infrastructure in UK urban catchment planning, J. Environ. Plann. Man., 56, 24–41, https://doi.org/10.1080/09640568.2011.648752, 2013. a
Endreny, T. and Collins, V.: Implications of bioretention basin spatial arrangements on stormwater recharge and groundwater mounding, Ecol. Eng., 35, 670–677, https://doi.org/10.1016/j.ecoleng.2008.10.017, 2009. a
Engelbrecht, A. P.: Fundamentals of computational swarm intelligence, John Wiley & Sons, Inc., ISBN 0470091916, 2006. a
Ennenbach, M. W., Concha Larrauri, P., and Lall, U.: County-scale rainwater harvesting feasibility in the United States: Climate, collection area, density, and reuse considerations, JAWRA J. Am. Water Resour. As., 54, 255–274, https://doi.org/10.1111/1752-1688.12607, 2018. a
Esri: World Topographic Map, http://www.arcgis.com/home/item.html?id=30e5fe3149c34df1ba922e6f5bbf808f (last access: 19 February 2012), 2012. a
Fenicia, F., Savenije, H. H. G., Matgen, P., and Pfister, L.: Is the groundwater reservoir linear? Learning from data in hydrological modelling, Hydrol. Earth Syst. Sci., 10, 139–150, https://doi.org/10.5194/hess-10-139-2006, 2006. a
Fielding, K. S., Gardner, J., Leviston, Z., and Price, J.: Comparing public perceptions of alternative water sources for potable use: The case of rainwater, stormwater, desalinated water, and recycled water, Water Resour. Manag., 29, 4501–4518, https://doi.org/10.1007/s11269-015-1072-1, 2015. a
Fletcher, T. D., Mitchell, V., Deletic, A., Ladson, T. R., and Seven, A.: Is stormwater harvesting beneficial to urban waterway environmental flows?, Water Sci. Technol., 55, 265–272, https://doi.org/10.2166/wst.2007.117, 2007. a
Fletcher, T. D., Andrieu, H., and Hamel, P.: Understanding, management and modelling of urban hydrology and its consequences for receiving waters: A state of the art, Adv. Water Resour., 51, 261–279, https://doi.org/10.1016/j.advwatres.2012.09.001, 2013. a
Garbrecht, J. and Brunner, G.: Hydrologic channel-flow routing for compound sections, J. Hydraul. Eng., 117, 629–642, https://doi.org/10.1061/(ASCE)0733-9429(1991)117:5(629), 1991. a, b, c
Gini, C.: Measurement of inequality of incomes, Econ. J., 31, 124–126, https://doi.org/10.2307/2223319, 1921. a
Giuliani, M. and Castelletti, A.: Assessing the value of cooperation and information exchange in large water resources systems by agent-based optimization, Water Resour. Res., 49, 3912–3926, https://doi.org/10.1002/wrcr.20287, 2013. a, b, c
Glendenning, C., Van Ogtrop, F., Mishra, A., and Vervoort, R.: Balancing watershed and local scale impacts of rain water harvesting in India – A review, Agr. Water Manage., 107, 1–13, https://doi.org/10.1016/j.agwat.2012.01.011, 2012. a, b, c, d
Golden, H. E. and Hoghooghi, N.: Green infrastructure and its catchment-scale effects: an emerging science, Wiley Interdisciplinary Reviews: Water, 5, e1254, https://doi.org/10.1002/wat2.1254, 2018. a
Goonrey, C. M., Perera, B., Lechte, P., Maheepala, S., and Mitchell, V. G.: A technical decision-making framework: stormwater as an alternative supply source, Urban Water J., 6, 417–429, https://doi.org/10.1080/15730620903089787, 2009. a
Guo, Q.: Strategies for a resilient, sustainable, and equitable Mississippi River basin, River, 2, 336–349, https://doi.org/10.1002/rvr2.60, 2023. a
Guo, T., Englehardt, J., and Wu, T.: Review of cost versus scale: water and wastewater treatment and reuse processes, Water Sci. Technol., 69, 223–234, https://doi.org/10.2166/wst.2013.734, 2014. a
Gupta, H. V., Sorooshian, S., and Yapo, P. O.: Status of automatic calibration for hydrologic models: Comparison with multilevel expert calibration, J. Hydrol. Eng., 4, 135–143, https://doi.org/10.1061/(ASCE)1084-0699(1999)4:2(135), 1999. a
Hardin, G.: The tragedy of the commons, in: Classic Papers in Natural Resource Economics Revisited, Routledge, 145–156, ISBN 9781315695730, 2018. a
Hardy, M., Kuczera, G., and Coombes, P.: Integrated urban water cycle management: the UrbanCycle model, Water Sci. Technol., 52, 1–9, https://doi.org/10.2166/wst.2005.0276, 2005. a
He, X.: Droughts and Floods in a Changing Environment Natural Influences, Human Interventions, and Policy Implications, PhD thesis, Princeton University, http://arks.princeton.edu/ark:/88435/dsp019593tx925 (last access: 4 August 2020), 2019. a
Houle, J. J., Roseen, R. M., Ballestero, T. P., Puls, T. A., and Sherrard Jr., J.: Comparison of maintenance cost, labor demands, and system performance for LID and conventional stormwater management, J. Environ. Eng., 139, 932–938, https://doi.org/10.1061/(ASCE)EE.1943-7870.0000698, 2013. a
Hu, Z., Wei, C., Yao, L., Li, L., and Li, C.: A multi-objective optimization model with conditional value-at-risk constraints for water allocation equality, J. Hydrol., 542, 330–342, https://doi.org/10.1016/j.jhydrol.2016.09.012, 2016. a, b
Hung, F. and Yang, Y. E.: Assessing adaptive irrigation impacts on water scarcity in nonstationary environments – a multi-agent reinforcement learning approach, Water Resour. Res., 57, e2020WR029262, https://doi.org/10.1029/2020WR029262, 2021. a
Jang, J.-S. R., Sun, C.-T., and Mizutani, E.: Neuro-fuzzy and soft computing-a computational approach to learning and machine intelligence [Book Review], IEEE T. Automat. Contr., 42, 1482–1484, https://doi.org/10.1109/TAC.1997.633847, 1997. a
Jayasooriya, V. and Ng, A.: Tools for modeling of stormwater management and economics of green infrastructure practices: a review, Water Air Soil Pollut., 225, 1–20, https://doi.org/10.1007/s11270-014-2055-1, 2014. a
Kallis, G.: Coevolution in water resource development: The vicious cycle of water supply and demand in Athens, Greece, Ecol. Econ., 69, 796–809, https://doi.org/10.1016/j.jenvman.2022.116049, 2010. a
Kanta, L. and Zechman, E.: Complex adaptive systems framework to assess supply-side and demand-side management for urban water resources, J. Water Res. Pl., 140, 75–85, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000301, 2014. a
Kennedy, J. and Eberhart, R.: Particle swarm optimization, in: Proceedings of ICNN'95-international conference on neural networks, vol. 4, Perth, WA, Australia, 27 November–1 December 1995, 1942–1948, IEEE, https://doi.org/10.1109/ICNN.1995.488968, 1995. a
Kenway, S., Gregory, A., and McMahon, J.: Urban water mass balance analysis, J. Ind. Ecol., 15, 693–706, https://doi.org/10.1111/j.1530-9290.2011.00357.x, 2011. a
Khorshidi, M. S., Izady, A., Nikoo, M. R., Al-Maktoumi, A., Chen, M., and Gandomi, A. H.: An Agent-based Framework for Transition from Traditional to Advanced Water Supply Systems in Arid Regions, Water Resour. Manag., 38, 2565–2579, https://doi.org/10.1007/s11269-024-03787-y, 2024. a
Kim, J. E., Humphrey, D., and Hofman, J.: Evaluation of harvesting urban water resources for sustainable water management: Case study in Filton Airfield, UK, J. Environ. Manage., 322, 116049, https://doi.org/10.1016/j.jenvman.2022.116049, 2022. a, b
Kirshen, P. H., Larsen, A. L., Vogel, R. M., and Moomaw, W.: Lack of influence of climate on present cost of water supply in the USA, Water Policy, 6, 269–279, https://doi.org/10.2166/wp.2004.0018, 2004. a
Kling, H., Fuchs, M., and Paulin, M.: Runoff conditions in the upper Danube basin under an ensemble of climate change scenarios, J. Hydrol., 424, 264–277, https://doi.org/10.1016/j.jhydrol.2012.01.011, 2012. a, b
Kock, B. E.: Agent-based models of socio-hydrological systems for exploring the institutional dynamics of water resources conflict, PhD thesis, Massachusetts Institute of Technology, http://hdl.handle.net/1721.1/44199 (last access: 5 May 2021), 2008. a
Langevin, C. D., Hughes, J. D., Banta, E. R., Niswonger, R. G., Panday, S., and Provost, A. M.: Documentation for the MODFLOW 6 groundwater flow model, Tech. rep., US Geological Survey, https://doi.org/10.3133/tm6A55, 2017. a
Li, H., Ding, L., Ren, M., Li, C., and Wang, H.: Sponge city construction in China: A survey of the challenges and opportunities, Water, 9, 594, https://doi.org/10.3390/w9090594, 2017. a
Liang, J. J., Qin, A. K., Suganthan, P. N., and Baskar, S.: Comprehensive learning particle swarm optimizer for global optimization of multimodal functions, IEEE T. Evolut. Comput., 10, 281–295, https://doi.org/10.1109/TEVC.2005.857610, 2006. a
Likas, A., Vlassis, N., and Verbeek, J. J.: The global k-means clustering algorithm, Pattern Recogn., 36, 451–461, https://doi.org/10.1016/S0031-3203(02)00060-2, 2003. a
Lin, Z., Lim, S. H., Lin, T., and Borders, M.: Using agent-based modeling for water resources management in the Bakken Region, J. Water Res. Pl., 146, 05019020, https://doi.org/10.1061/(ASCE)WR.1943-5452.0001147, 2020. a
Loucks, D. P. and Van Beek, E.: Water resource systems planning and management: An introduction to methods, models, and applications, Springer, https://doi.org/10.1007/978-3-319-44234-1, 2017. a
Lu, E., Takle, E. S., and Manoj, J.: The relationships between climatic and hydrological changes in the upper Mississippi River basin: A SWAT and multi-GCM study, J. Hydrometeorol., 11, 437–451, https://doi.org/10.1175/2009JHM1150.1, 2010. a
Marquardt, D. W.: An algorithm for least-squares estimation of nonlinear parameters, J. Ind. Appl. Math., 11, 431–441, https://doi.org/10.1137/0111030, 1963. a
McArdle, P., Gleeson, J., Hammond, T., Heslop, E., Holden, R., and Kuczera, G.: Centralised urban stormwater harvesting for potable reuse, Water Sci. Technol., 63, 16–24, https://doi.org/10.2166/wst.2011.003, 2011. a
McDonald, R. I., Weber, K., Padowski, J., Flörke, M., Schneider, C., Green, P. A., Gleeson, T., Eckman, S., Lehner, B., Balk, D., Boucher, T., Grill, G., and Montgomery, M.: Water on an urban planet: Urbanization and the reach of urban water infrastructure, Global Environ. Chang., 27, 96–105, https://doi.org/10.1016/j.gloenvcha.2014.04.022, 2014. a
Meng, X.: Understanding the effects of site-scale water-sensitive urban design (WSUD) in the urban water cycle: a review, Blue-Green Systems, 4, 45–57, https://doi.org/10.2166/bgs.2022.026, 2022. a
Montalto, F. A., Bartrand, T. A., Waldman, A. M., Travaline, K. A., Loomis, C. H., McAfee, C., Geldi, J. M., Riggall, G. J., and Boles, L. M.: Decentralised green infrastructure: the importance of stakeholder behaviour in determining spatial and temporal outcomes, Struct. Infrastr. E., 9, 1187–1205, https://doi.org/10.1080/15732479.2012.671834, 2013. a
Moravej, M., Renouf, M. A., Lam, K. L., Kenway, S. J., and Urich, C.: Site-scale Urban Water Mass Balance Assessment (SUWMBA) to quantify water performance of urban design-technology-environment configurations, Water Res., 188, 116477, https://doi.org/10.1016/j.watres.2020.116477, 2021. a, b
Motlaghzadeh, K., Eyni, A., Behboudian, M., Pourmoghim, P., Ashrafi, S., Kerachian, R., and Hipel, K. W.: A multi-agent decision-making framework for evaluating water and environmental resources management scenarios under climate change, Sci. Total Environ., 864, 161060, https://doi.org/10.1016/j.scitotenv.2022.161060, 2023. a
Müller, M. F., Müller-Itten, M. C., and Gorelick, S. M.: How Jordan and Saudi Arabia are avoiding a tragedy of the commons over shared groundwater, Water Resour. Res., 53, 5451–5468, https://doi.org/10.1002/2016WR020261, 2017. a
Nash, J. E. and Sutcliffe, J. V.: River flow forecasting through conceptual models part I – A discussion of principles, J. Hydrol., 10, 282–290, https://doi.org/10.1016/0022-1694(70)90255-6, 1970. a
Nicklow, J., Reed, P., Savic, D., Dessalegne, T., Harrell, L., Chan-Hilton, A., Karamouz, M., Minsker, B., Ostfeld, A., Singh, A., and Zechman, E.: State of the art for genetic algorithms and beyond in water resources planning and management, J. Water Res. Pl., 136, 412–432, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000053, 2010. a
Nishi, A., Shirado, H., Rand, D. G., and Christakis, N. A.: Inequality and visibility of wealth in experimental social networks, Nature, 526, 426–429, https://doi.org/10.1038/nature15392, 2015. a
National Oceanic and Atmospheric Administration (NOAA): National Centers for Environmental Information, https://www.ncei.noaa.gov/ (last access: 5 May 2024), 2015. a
Palla, A. and Gnecco, I.: Hydrologic modeling of Low Impact Development systems at the urban catchment scale, J. Hydrol., 528, 361–368, https://doi.org/10.1016/j.jhydrol.2015.06.050, 2015. a
Parsapour-Moghaddam, P., Abed-Elmdoust, A., and Kerachian, R.: A heuristic evolutionary game theoretic methodology for conjunctive use of surface and groundwater resources, Water Resour. Manag., 29, 3905–3918, https://doi.org/10.1007/s11269-015-1035-6, 2015. a
Poustie, M. S., Deletic, A., Brown, R. R., Wong, T., de Haan, F. J., and Skinner, R.: Sustainable urban water futures in developing countries: the centralised, decentralised or hybrid dilemma, Urban Water J., 12, 543–558, https://doi.org/10.1080/1573062X.2014.916725, 2015. a
Prior, C. H., Schneider, R., and Durum, W. H.: Water Resources of the Minneapolis-St. Paul Area, Minnesota, vol. 274, US Geological Survey, https://doi.org/10.3133/cir274, 1953. a
Ravazzani, G., Corbari, C., Morella, S., Gianoli, P., and Mancini, M.: Modified Hargreaves-Samani equation for the assessment of reference evapotranspiration in Alpine river basins, J. Irrig. Drain. E., 138, 592–599, https://doi.org/10.1061/(ASCE)IR.1943-4774.0000453, 2012. a
Reed, T., Mason, L. R., and Ekenga, C. C.: Adapting to climate change in the upper Mississippi river basin: exploring stakeholder perspectives on river system management and flood risk reduction, Environmental Health Insights, 14, 1178630220984153, https://doi.org/10.1177/1178630220984153, 2020. a
Riget, J. and Vesterstrøm, J. S.: A diversity-guided particle swarm optimizer-the ARPSO, Dept. Comput. Sci., Univ. of Aarhus, Aarhus, Denmark, Tech. Rep, 2, https://api.semanticscholar.org/CorpusID:14505221 (last access: 12 December 2022), 2002. a
Rosegrant, M. W., Ringler, C., McKinney, D. C., Cai, X., Keller, A., and Donoso, G.: Integrated economic-hydrologic water modeling at the basin scale: The Maipo River basin, Agr. Econ., 24, 33–46, https://doi.org/10.1111/j.1574-0862.2000.tb00091.x, 2000. a
Rozos, E., Makropoulos, C., and Butler, D.: Design robustness of local water-recycling schemes, J. Water Res. Pl., 136, 531–538, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000067, 2010. a
Sapkota, M., Arora, M., Malano, H., Moglia, M., Sharma, A., George, B., and Pamminger, F.: An overview of hybrid water supply systems in the context of urban water management: Challenges and opportunities, Water, 7, 153–174, https://doi.org/10.3390/w7010153, 2014. a
Schewe, J., Heinke, J., Gerten, D., Haddeland, I., Arnell, N. W., Clark, D. B., Dankers, R., Eisner, S., Fekete, B. M., Colón-González, F. J., Gosling, S. N., Kim, H., Liu, X., Masaki, Y., Portmann, F. T., Satoh, Y., Stacke, T., Tang, Q., Wada, Y., Wisser, D., Albrecht, T., Frieler, K., Piontek, F., Warszawski, L., and Kabat, P.: Multimodel assessment of water scarcity under climate change, P. Natl. Acad. Sci. USA, 111, 3245–3250, https://doi.org//10.1073/pnas.1222460110, 2014. a
Sharma, S. K., Tignath, S., Gajbhiye, S., and Patil, R.: Use of geographical information system in hypsometric analysis of Kanhiya Nala watershed, International Journal of Remote Sensing & Geoscience, 2, 30–35, 2013. a
Simaan, M. and Cruz, J. B.: On the Stackelberg strategy in nonzero-sum games, J. Optimiz. Theory App., 11, 533–555, https://doi.org/10.1007/BF00935665, 1973. a
Sitzenfrei, R., Möderl, M., and Rauch, W.: Assessing the impact of transitions from centralised to decentralised water solutions on existing infrastructures–Integrated city-scale analysis with VIBe, Water Res., 47, 7251–7263, https://doi.org/10.1016/j.watres.2013.10.038, 2013. a
Souto, S. L., Reis, R. P. A., and Campos, M. A. S.: Impact of Installing Rainwater Harvesting System on Urban Water Management, Water Resour. Manag., 37, 583–600, https://doi.org/10.1007/s11269-022-03374-z, 2022. a
Steffen, J., Jensen, M., Pomeroy, C. A., and Burian, S. J.: Water supply and stormwater management benefits of residential rainwater harvesting in US cities, JAWRA J. Am. Water Resour. As., 49, 810–824, https://doi.org/10.1111/jawr.12038, 2013. a
Tu, Y., Zhou, X., Gang, J., Liechty, M., Xu, J., and Lev, B.: Administrative and market-based allocation mechanism for regional water resources planning, Resources, Conservation and Recycling, 95, 156–173, https://doi.org/10.1016/j.resconrec.2014.12.011, 2015. a
U.S. Environmental Protection Agency (U.S. EPA): Nonpoint Source: Urban Areas, https://www.epa.gov/nps/nonpoint-source-urban-areas (last access: 23 July 2021), 2015. a
U.S. Geological Survey (USGS): Water Data for the Nation, https://waterdata.usgs.gov/ (last access: 1 May 2021), 2020. a
U.S. Geological Survey: Watershed Boundary Dataset (WBD), https://www.usgs.gov/national-hydrography/watershed-boundary-dataset (last access: 20 July 2021), 2021. a
Van Dijk, A. and Bruijnzeel, L.: Modelling rainfall interception by vegetation of variable density using an adapted analytical model. Part 1. Model description, J. Hydrol., 247, 230–238, https://doi.org/10.1016/S0022-1694(01)00392-4, 2001. a
Viney, N., Vaze, J., Crosbie, R., Wang, B., Dawes, W., and Frost, A.: AWRA-L v5. 0: Technical description of model algorithms and inputs, https://awo.bom.gov.au/assets/notes/publications/Viney_et_al_2015_AWRA_L_5.0_model_description.pdf (last access: 14 August 2020), 2015. a
Von Stackelberg, H.: Market structure and equilibrium, Springer Science & Business Media, https://doi.org/10.1007/978-3-642-12586-7, 2010. a, b, c
Weinmann, P. E. and Laurenson, E. M.: Approximate flood routing methods: A review, J. Hydr. Eng. Div.-ASCE, 105, 1521–1536, https://doi.org/10.1061/JYCEAJ.0005329, 1979. a, b, c
Wolf, L.: Assessing the influence of leaky sewer systems on groundwater resources beneath the City of Rastatt, Germany Department of Applied Geology. Karlsruhe, PhD thesis, University of Karlsruhe, https://doi.org/10.5445/IR/1000005430, 2006. a, b
Xu, J., Zhang, M., and Zeng, Z.: Hybrid nested particle swarm optimization for a waste load allocation problem in river system, J. Water Res. Pl., 142, 04016014, https://doi.org/10.1061/(ASCE)WR.1943-5452.0000645, 2016. a
Xu, J., Lv, C., Yao, L., and Hou, S.: Intergenerational equity based optimal water allocation for sustainable development: A case study on the upper reaches of Minjiang River, China, J. Hydrol., 568, 835–848, https://doi.org/10.1016/j.jhydrol.2018.11.010, 2019. a, b
Zhan, W. and Chui, T. F. M.: Evaluating the life cycle net benefit of low impact development in a city, Urban For. Urban Gree., 20, 295–304, https://doi.org/10.1016/j.ufug.2016.09.006, 2016. a
Zhan, Z.-H., Zhang, J., Li, Y., and Chung, H. S.-H.: Adaptive particle swarm optimization, IEEE T. Syst. Man Cy. B, 39, 1362–1381, https://doi.org/10.1109/TSMCB.2009.2015956, 2009. a
Zhang, K. and Chui, T. F. M.: A review on implementing infiltration-based green infrastructure in shallow groundwater environments: Challenges, approaches, and progress, J. Hydrol., 579, 124089, https://doi.org/10.1016/j.jhydrol.2019.124089, 2019. a
Zhang, M.: Multiagent_IGWM: Multiagent_IGWM Public (data), Zenodo [data set], https://doi.org/10.5281/zenodo.15704525, 2025. a
Zhou, Q.: A review of sustainable urban drainage systems considering the climate change and urbanization impacts, Water, 6, 976–992, https://doi.org/10.3390/w6040976, 2014. a
Short summary
This study introduces a multiagent socio-hydrologic framework for city-, inter-city-, and watershed-scale integrated green infrastructures (GIs) and water resource management. Applied to the Upper Mississippi River basin, it explores GI-driven water-sharing dynamics in a watershed. It identifies four city-scale water use patterns and characterizes cost and equity on broader scales, thereby enhancing comprehension of the role of GIs in water resource management and aiding decision-making.
This study introduces a multiagent socio-hydrologic framework for city-, inter-city-, and...