Articles | Volume 27, issue 7
https://doi.org/10.5194/hess-27-1607-2023
© Author(s) 2023. 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-27-1607-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
17 Apr 2023
Research article |
| 17 Apr 2023
| 17 Apr 2023
Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes
Yangtze Institute for Conservation and Development, Hohai University, Nanjing, China
Feng Wu
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing, China
Hao Jiang
State Environmental Protection Key Laboratory of Integrated Surface
Water–Groundwater Pollution Control, School of Environmental Science and
Engineering, Southern University of Science and Technology, Shenzhen, China
Naliang Guo
Key Laboratory of Land Surface Pattern and Simulation, Institute of
Geographic Sciences and Natural Resources Research, Chinese Academy of
Sciences, Beijing, China
Yong Tian
State Environmental Protection Key Laboratory of Integrated Surface
Water–Groundwater Pollution Control, School of Environmental Science and
Engineering, Southern University of Science and Technology, Shenzhen, China
Chunmiao Zheng
CORRESPONDING AUTHOR
EIT Institute for Advanced Study, Ningbo, Zhejiang, China
State Environmental Protection Key Laboratory of Integrated Surface
Water–Groundwater Pollution Control, School of Environmental Science and
Engineering, Southern University of Science and Technology, Shenzhen, China
Related authors
No articles found.
Zhenghang Chen, Meili Feng, Matthew F. Johnson, Nigel Wright, Ying Weng, Faith Ka Shun Chan, and Feng Wu
EGUsphere, https://doi.org/10.5194/egusphere-2026-937, https://doi.org/10.5194/egusphere-2026-937, 2026
This preprint is open for discussion and under review for Natural Hazards and Earth System Sciences (NHESS).
Short summary
Short summary
Mountainous flash floods are becoming more frequent due to climate change. This review synthesizes 25 years of global research to understand their mechanisms and control measures. We identify significant research gaps in Africa and South America and find that simple rainfall thresholds are often ineffective due to complex local conditions. We propose a new adaptive framework combining engineering, nature-based solutions, and spatial planning to better manage these risks in vulnerable regions.
Cited articles
Abebe, Y. A., Ghorbani, A., Nikolic, I., Vojinovic, Z., and Sanchez, A.: A
coupled flood-agent-institution modelling (CLAIM) framework for urban flood
risk management, Environ. Modell. Softw., 111, 483–492,
https://doi.org/10.1016/j.envsoft.2018.10.015, 2019.
Aerts, J. C. J. H., Botzen, W. J., Clarke, K. C., Cutter, S. L., Hall, J.
W., Merz, B., Michel-Kerjan, E., Mysiak, J., Surminski, S., and Kunreuther,
H.: Integrating human behaviour dynamics into flood disaster risk
assessment, Nat. Clim. Change, 8, 193–199,
https://doi.org/10.1038/s41558-018-0085-1, 2018.
Alam, M. J., Habib, M. A., and Pothier, E.: Shelter locations in evacuation:
A Multiple Criteria Evaluation combined with flood risk and traffic
microsimulation modeling, Int. J. Disaster Risk R., 53, 102016,
https://doi.org/10.1016/j.ijdrr.2020.102016, 2021.
Alçada-Almeida, L., Tralhão, L., Santos, L., and Coutinho-Rodrigues,
J.: A multiobjective approach to locate emergency shelters and identify
evacuation routes in urban areas, Geogr. Anal., 41, 9–29,
https://doi.org/10.1111/j.1538-4632.2009.00745.x, 2009.
Alonso Vicario, S., Mazzoleni, M., Bhamidipati, S., Gharesifard, M.,
Ridolfi, E., Pandolfo, C., and Alfonso, L.: Unravelling the influence of
human behaviour on reducing casualties during flood evacuation, Hydrolog. Sci.
J., 65, 2359–2375, https://doi.org/10.1080/02626667.2020.1810254, 2020.
Barendrecht, M. H., Viglione, A., Kreibich, H., Merz, B., Vorogushyn, S.,
and Blöschl, G.: The Value of Empirical Data for Estimating the
Parameters of a Sociohydrological Flood Risk Model, Water Resour. Res., 55,
1312–1336, https://doi.org/10.1029/2018WR024128, 2019.
Bayram, V., Tansel, B. T., and Yaman, H.: Compromising system and user
interests in shelter location and evacuation planning, Transp. Res. B,
72, 146–163, https://doi.org/10.1016/j.trb.2014.11.010, 2015.
Bernardini, G., Santarelli, S., Quagliarini, E., and Orazio, M. D.: Dynamic
guidance tool for a safer earthquake pedestrian evacuation in urban systems,
Comput. Environ. Urban Syst., 65, 150–161,
https://doi.org/10.1016/j.compenvurbsys.2017.07.001, 2017.
Bhatt, G., Kumar, M., and Duffy, C. J.: A tightly coupled GIS and
distributed hydrologic modeling framework, Environ. Modell. Softw., 62,
70–84, https://doi.org/10.1016/j.envsoft.2014.08.003, 2014.
Braess, D., Nagurney, A., and Wakolbinger, T.: On a Paradox of Traffic
Planning, Transp. Sci., 39, 446–450,
https://doi.org/10.1287/trsc.1050.0127, 2005.
Brunner, M. I., Papalexiou, S., Clark, M. P., and Gilleland, E.: How
Probable Is Widespread Flooding in the United States?, Water Resour. Res.,
56, 1–16, https://doi.org/10.1029/2020WR028096, 2020.
Chen, C., Lindell, M. K., and Wang, H.: Tsunami preparedness and resilience
in the Cascadia Subduction Zone: A multistage model of expected evacuation
decisions and mode choice, Int. J. Disaster Risk Re., 59, 102244,
https://doi.org/10.1016/j.ijdrr.2021.102244, 2021.
Chen, C., Mostafizi, A., Wang, H., Cox, D., and Chand, C.: An integrative
agent-based vertical evacuation risk assessment model for near-field tsunami
hazards, Risk Anal., 1, 1–15, https://doi.org/10.1111/risa.13881, 2022.
Chen, X. and Zhan, F. B.: Agent-based modelling and simulation of urban
evacuation: Relative effectiveness of simultaneous and staged evacuation
strategies, J. Oper. Res. Soc., 59, 25–33,
https://doi.org/10.1057/palgrave.jors.2602321, 2008.
Chen, X., Wang, D., Tian, F., and Sivapalan, M.: From channelization to
restoration: Sociohydrologic modeling with changing community preferences in
the Kissimmee River Basin, Florida, Water Resour. Res., 52, 1227–1244,
https://doi.org/10.1002/2015WR018194, 2016.
Dias, C., Rahman, N. A., and Zaiter, A.: Evacuation under flooded
conditions: Experimental investigation of the influence of water depth on
walking behaviors, Int. J. Disaster Risk Re., 58, 102192,
https://doi.org/10.1016/j.ijdrr.2021.102192, 2021.
Di Baldassarre, G., Viglione, A., Carr, G., Kuil, L., Salinas, J. L., and Blöschl, G.: Socio-hydrology: conceptualising human-flood interactions, Hydrol. Earth Syst. Sci., 17, 3295–3303, https://doi.org/10.5194/hess-17-3295-2013, 2013.
Du, E., Rivera, S., Cai, X., Myers, L., Ernest, A., and Minsker, B.: Impacts
of human behavioral heterogeneity on the benefits of probabilistic flood
warnings: An agent-based modeling framework, J. Am. Water Resour. As.,
53, 316–332, https://doi.org/10.1111/1752-1688.12475, 2016.
Du, E., Cai, X., Sun, Z., and Minsker, B.: Exploring the Role of Social
Media and Individual Behaviors in Flood Evacuation Processes: An Agent-Based
Modeling Approach, Water Resour. Res., 53, 9164–9180,
https://doi.org/10.1002/2017WR021192, 2017.
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.
Du, E., Wu, F., Jiang, H., Guo, N., Tian, Y., and Zheng, C.: Dataset for the article “Development of an integrated socio-hydrological modeling framework for assessing the impacts of shelter location arrangement and human behaviors on flood evacuation processes” (Version 1.0), Zenodo [data set], https://doi.org/10.5281/zenodo.7797361, 2023.
Etkin, D., Medalye, J., and Higuchi, K.: Climate warming and natural
disaster management: An exploration of the issues, Clim. Change, 112,
585–599, https://doi.org/10.1007/s10584-011-0259-6, 2012.
Farkas, K., Nagy, A., Tomas, T., and Szabo, R.: Participatory sensing based
real-time public transport information service, in: IEEE International
Conference on Pervasive Computing and Communication Workshops, Budapest, Hungary, 24–28 March 2014,
141–144,
https://doi.org/10.1109/PerComW.2014.6815181, 2014.
Frank, M.: The Braess paradox, Math. Program., 20, 283–302,
https://doi.org/10.1007/BF01589354, 1981.
Fu, L., Sun, D., and Rilett, L. R.: Heuristic shortest path algorithms for
transportation applications: State of the art, Comput. Oper. Res., 33,
3324–3343, https://doi.org/10.1016/j.cor.2005.03.027, 2006.
Fuchs, S., Karagiorgos, K., Kitikidou, K., Maris, F., Paparrizos, S., and Thaler, T.: Flood risk perception and adaptation capacity: a contribution to the socio-hydrology debate, Hydrol. Earth Syst. Sci., 21, 3183–3198, https://doi.org/10.5194/hess-21-3183-2017, 2017.
Gallo, G. and Pallottino, S.: Shortest path algorithms, Ann. Oper. Res., 13,
1–79, 1988.
Girons Lopez, M., Di Baldassarre, G., and Seibert, J.: Impact of social
preparedness on flood early warning systems, Water Resour. Res., 53,
522–534, https://doi.org/10.1002/2016WR019387, 2017.
Goodarzi, L., Banihabib, M. E., and Roozbahani, A.: A decision-making model
for flood warning system based on ensemble forecasts, J. Hydrol., 573,
207–219, https://doi.org/10.1016/j.jhydrol.2019.03.040, 2019.
Guo, K., Guan, M., and Yu, D.: Urban surface water flood modelling – a comprehensive review of current models and future challenges, Hydrol. Earth Syst. Sci., 25, 2843–2860, https://doi.org/10.5194/hess-25-2843-2021, 2021.
Harvey, E. P., Cardwell, R. C., McDonald, G. W., van Delden, H., Vanhout,
R., Smith, N. J., Kim, J. hwan, Forgie, V. E., and van den Belt, M.:
Developing integrated models by coupling together existing models; land use,
economics, demographics and transport in Wellington, New Zealand, Comput.
Environ. Urban, 74, 100–113,
https://doi.org/10.1016/j.compenvurbsys.2018.07.004, 2019.
Hasan, S., Ukkusuri, S., Gladwin, H., and Murray-Tuite, P.: Behavioral model
to understand household-level hurricane evacuation decision making, J.
Transp. Eng., 137, 341–348,
https://doi.org/10.1061/(ASCE)TE.1943-5436.0000223, 2011.
He, M., Chen, C., Zheng, F., Chen, Q., Zhang, J., Yan, H., and Lin, Y.: An
efficient dynamic route optimization for urban flooding evacuation based on
Cellular Automata, Comput. Environ. Urban, 87, 101622,
https://doi.org/10.1016/j.compenvurbsys.2021.101622, 2021.
Hino, M. and Nance, E.: Five ways to ensure flood-risk research helps the
most vulnerable, Nature, 595, 27–29,
https://doi.org/10.1038/d41586-021-01750-0, 2021.
Hofflinger, A., Somos-Valenzuela, M. A., and Vallejos-Romero, A.: Response time to flood events using a social vulnerability index (ReTSVI), Nat. Hazards Earth Syst. Sci., 19, 251–267, https://doi.org/10.5194/nhess-19-251-2019, 2019.
Horni, A.: Multi-agent Transport Simulation Matsim, Ubiquity Press, London,
https://doi.org/10.5334/baw, 2016.
Huang, S., Lindell, M. K., Prater, C. S., Wu, H., and Siebeneck, L. K.:
Household evacuation decision making in response to Hurricane Ike, Nat.
Hazards Rev., 13, 283–296,
https://doi.org/10.1061/(ASCE)NH.1527-6996.0000074, 2012.
Huang, S.-K., Lindell, M. K., and Prater, C. S.: Multistage Model of
Hurricane Evacuation Decision: Empirical Study of Hurricanes Katrina and
Rita, Nat. Hazards Rev., 18, 05016008,
https://doi.org/10.1061/(asce)nh.1527-6996.0000237, 2017.
Islam, K. A., Marathe, M., Mortveit, H., Swarup, S., and Vullikanti, A.: A
Simulation-based Approach for Large-scale Evacuation Planning, in: IEEE
International Conference on Big Data, 1338–1345,
https://doi.org/10.1109/BigData50022.2020.9377794, 2020.
Jiang, R., Yu, X., Xie, J., Zhao, Y., Li, F., and Yang, M.: Recent changes
in daily climate extremes in a serious water shortage metropolitan region, a
case study in Jing-Jin-Ji of China, Theor. Appl. Climatol., 134, 565–584,
https://doi.org/10.1007/s00704-017-2293-4, 2018.
Jongman, B., Ward, P. J., and Aerts, J. C. J. H.: Global exposure to river
and coastal flooding: Long term trends and changes, Glob. Environ. Chang.,
22, 823–835, https://doi.org/10.1016/j.gloenvcha.2012.07.004, 2012.
Khalilpourazari, S. and Pasandideh, S. H. R.: Designing emergency flood
evacuation plans using robust optimization and artificial intelligence, J.
Comb. Optim., 41, 640–677, https://doi.org/10.1007/s10878-021-00699-0,
2021.
Koch, Z., Yuan, M., and Bristow, E.: Emergency Response after Disaster
Strikes: Agent-Based Simulation of Ambulances in New Windsor, NY, J.
Infrastruct. Syst., 26, 06020001,
https://doi.org/10.1061/(asce)is.1943-555x.0000565, 2020.
Kreibich, H., van den Bergh, J. C. J. M., Bouwer, L. M., Bubeck, P.,
Ciavola, P., Green, C., Hallegatte, S., Logar, I., Meyer, V., Schwarze, R.,
and Thieken, A. H.: Costing natural hazards, Nat. Clim. Chang., 4, 303–306,
https://doi.org/10.1038/nclimate2182, 2014.
Kreibich, H., Bubeck, P., Van Vliet, M., and De Moel, H.: A review of
damage-reducing measures to manage fluvial flood risks in a changing
climate, Mitig. Adapt. Strat. Gl., 20, 967–989,
https://doi.org/10.1007/s11027-014-9629-5, 2015.
Lämmel, G., Klüpfel, H., and Nagel, K.: The MATSim Network Flow
Model for Traffic Simulation Adapted to Large-Scale Emergency Egress and an
Application to the Evacuation of the Indonesian City of Padang in Case of a
Tsunami Warning, in: Pedestrian Behavior, edited by: Timmermans, H., Emerald
Group Publishing Limited, 245–265,
https://doi.org/10.1108/9781848557512-011, 2009.
Lämmel, G., Grether, D., and Nagel, K.: The representation and
implementation of time-dependent inundation in large-scale microscopic
evacuation simulations, Transp. Res. Part C, 18, 84–98,
https://doi.org/10.1016/j.trc.2009.04.020, 2010.
Lee, K. S., Eom, J. K., and Moon, D.: Applications of TRANSIMS in
transportation: A literature review, Procedia Comput. Sci., 32, 769–773,
https://doi.org/10.1016/j.procs.2014.05.489, 2014.
Li, A. C. Y., Nozick, L., Xu, N., and Davidson, R.: Shelter location and
transportation planning under hurricane conditions, Transp. Res. Part E, 48,
715–729, https://doi.org/10.1016/j.tre.2011.12.004, 2012.
Li, B., Hou, J., Ma, Y., Bai, G., Wang, T., Xu, G., Wu, B., and Jiao, Y.: A
coupled high-resolution hydrodynamic and cellular automata-based evacuation
route planning model for pedestrians in flooding scenarios, Nat. Hazards,
110, 607–628, https://doi.org/10.1007/s11069-021-04960-x, 2022.
Li, X., Zhang, L., Zheng, Y., Yang, D., Wu, F., Tian, Y., Han, F., Gao, B.,
Li, H., Zhang, Y., Ge, Y., Cheng, G., Fu, B., Xia, J., Song, C., and Zheng,
C.: Novel hybrid coupling of ecohydrology and socioeconomy at river basin
scale: A watershed system model for the Heihe River basin, Environ. Modell.
Softw., 141, 105058, https://doi.org/10.1016/j.envsoft.2021.105058, 2021.
Lindell, M., Sorensen, J., Baker, E., and Lehman, W.: Community response to
hurricane threat: Estimates of household evacuation preparation time
distributions, Transp. Res. Part D, 85, 102457,
https://doi.org/10.1016/j.trd.2020.102457, 2020.
Lindell, M. K., Lu, J.-C., and Prater, C. S.: Household decision making and
evacuation in response to hurricane Lili, Nat. Hazards Rev., 6, 171–179,
https://doi.org/10.1061/(ASCE)1527-6988(2005)6:4(171), 2005.
Liu, X. and Lim, S.: Integration of spatial analysis and an agent-based
model into evacuation management for shelter assignment and routing, J.
Spat. Sci., 61, 283–298, https://doi.org/10.1080/14498596.2016.1147393,
2016.
Mahmud, K. and Town, G. E.: A review of computer tools for modeling electric
vehicle energy requirements and their impact on power distribution networks,
Appl. Energ., 172, 337–359,
https://doi.org/10.1016/j.apenergy.2016.03.100, 2016.
McClymont, K., Morrison, D., Beevers, L., and Carmen, E.: Flood resilience:
a systematic review, J. Environ. Plann. Man., 63, 1151–1176,
https://doi.org/10.1080/09640568.2019.1641474, 2020.
Melnikov, V. R., Krzhizhanovskaya, V. V, Lees, M. H., and Boukhanovsky, A.
V: Data-driven Travel Demand Modelling and Agent-based Traffic Simulation in
Amsterdam Urban Area, Procedia Comput. Sci., 80, 2030–2041,
https://doi.org/10.1016/j.procs.2016.05.523, 2016.
Milevich, D., Melnikov, V., Karbovskii, V., and Krzhizhanovskaya, V.:
Simulating an impact of road network improvements on the performance of
transportation systems under critical load: Agent-based Approach, Procedia
Comput. Sci., 101, 253–261, https://doi.org/10.1016/j.procs.2016.11.030,
2016.
Mostafizi, A., Wang, H., Cox, D., Cramer, L., and Dong, S.: Agent-based
tsunami evacuation modeling of unplanned network disruptions for
evidence-driven resource allocation and retrofitting strategies, Nat.
Hazards, 88, 1347–1372, https://doi.org/10.1007/s11069-017-2927-y, 2017.
Mostafizi, A., Wang, H., Cox, D., and Dong, S.: An agent-based vertical
evacuation model for a near-field tsunami: Choice behavior, logical shelter
locations, and life safety, Int. J. Disaster Risk Re., 34, 467–479,
https://doi.org/10.1016/j.ijdrr.2018.12.018, 2019.
Moulds, S., Buytaert, W., Templeton, M. R., and Kanu, I.: Modeling the
Impacts of Urban Flood Risk Management on Social Inequality, Water Resour.
Res., 57, e2020WR029024, https://doi.org/10.1029/2020WR029024, 2021.
Muhammad, A., De Risi, R., De Luca, F., Mori, N., Yasuda, T., and Goda, K.:
Are current tsunami evacuation approaches safe enough?, Stoch. Environ. Res.
Risk Assess., 35, 759–779, https://doi.org/10.1007/s00477-021-02000-5,
2021.
Murchland, J. D.: Braess's paradox of traffic flow, Transp. Res., 4,
391–394, https://doi.org/10.1016/0041-1647(70)90196-6, 1970.
Murray-Tuite, P. and Wolshon, B.: Evacuation transportation modeling: An
overview of research, development, and practice, Transp. Res. Part C, 27,
25–45, https://doi.org/10.1016/j.trc.2012.11.005, 2013.
Murray-Rust, D., Robinson, D. T., Guillem, E., Karali, E., and Rounsevell,
M.: An open framework for agent based modelling of agricultural land use
change, Environ. Modell. Softw., 61, 19–38,
https://doi.org/10.1016/j.envsoft.2014.06.027, 2014.
Nakanishi, H., Black, J., and Suenaga, Y.: Investigating the flood
evacuation behaviour of older people: A case study of a rural town in Japan,
Res. Transp. Bus. Manag., 30, 100376,
https://doi.org/10.1016/j.rtbm.2019.100376, 2019.
Nappi, M. M. L. and Souza, J. C.: Disaster management: hierarchical
structuring criteria for selection and location of temporary shelters, Nat.
Hazards, 75, 2421–2436, https://doi.org/10.1007/s11069-014-1437-4, 2015.
Nester, T., Komma, J., Viglione, A., and Blöschl, G.: Flood forecast
errors and ensemble spread-A case study, Water Resour. Res., 48, 1–19,
https://doi.org/10.1029/2011WR011649, 2012.
Nigussie, T. A. and Altunkaynak, A.: Modeling the effect of urbanization on
flood risk in Ayamama Watershed, Istanbul, Turkey, using the MIKE 21 FM
model, Nat. Hazards, 99, 1031–1047,
https://doi.org/10.1007/s11069-019-03794-y, 2019.
Oh, W. S., Yu, D. J., and Muneepeerakul, R.: Efficiency-fairness trade-offs
in evacuation management of urban floods: The effects of the shelter
capacity and zone prioritization, PLoS One, 16, e0253395,
https://doi.org/10.1371/journal.pone.0253395, 2021.
Palen, L., Starbird, K., Vieweg, S., and Hughes, A.: Twitter-based
information distribution during the 2009 Red River Valley flood threat,
Bull. Am. Soc. Inf. Sci. Technol., 36, 13–17,
https://doi.org/10.1002/bult.2010.1720360505, 2010.
Pande, S. and Sivapalan, M.: Progress in socio-hydrology: a meta-analysis of
challenges and opportunities, WIREs Water, 4, e1193,
https://doi.org/10.1002/wat2.1193, 2017.
Papaioannou, G., Loukas, A., Vasiliades, L., and Aronica, G. T.: Flood
inundation mapping sensitivity to riverine spatial resolution and modelling
approach, Nat. Hazards, 83, S117–S132,
https://doi.org/10.1007/s11069-016-2382-1, 2016.
Pas, E. I. and Principio, S. L.: Braess' paradox: Some new insights, Transport
Res. B-Meth., 31, 265–276,
https://doi.org/10.1016/S0191-2615(96)00024-0, 1997.
Paul, B. K.: Factors Affecting Evacuation Behavior: The Case of 2007 Cyclone
Sidr, Bangladesh, Prof. Geogr., 64, 401–414,
https://doi.org/10.1080/00330124.2011.609780, 2012.
Rahman, A., Hokugo, A., Ohtsu, N., and Chakma, S.: Evacuation Preparation
Scenarios of Households during Early and Emergency Evacuation: A Case Study
of Cyclone Bulbul in Southwestern Coastal Bangladesh, J. Integr. Disaster
Risk Manag., 11, 108–137, https://doi.org/10.5595/001c.29128, 2021.
Saadi, I., Mustafa, A., Teller, J., and Cools, M.: Investigating the impact
of river floods on travel demand based on an agent-based modeling approach:
The case of Liège, Belgium, Transp. Policy, 67, 102–110,
https://doi.org/10.1016/j.tranpol.2017.09.009, 2018.
Shahabi, K. and Wilson, J. P.: CASPER: Intelligent capacity-aware evacuation
routing, Comput. Environ. Urban, 46, 12–24,
https://doi.org/10.1016/j.compenvurbsys.2014.03.004, 2014.
Shi, H., Du, E., Liu, S., and Chau, K.: Advances in Flood Early Warning:
Ensemble Forecast, Information Dissemination and Decision-Support Systems,
Hydrology, 7, 56, https://doi.org/10.3390/hydrology7030056, 2020.
Simonovic, S. P. and Ahmad, S.: Computer-based model for flood evacuation
emergency planning, Nat. Hazards, 34, 25–51,
https://doi.org/10.1007/s11069-004-0785-x, 2005.
Sivapalan, M., Savenije, H., and Blöschl, G.: Socio-hydrology: A new
science of people and water, Hydrol. Process., 26, 1270–1276,
https://doi.org/10.1002/hyp.8426, 2012.
Smith, A. B. and Matthews, J. L.: Quantifying uncertainty and variable
sensitivity within the US billion-dollar weather and climate disaster cost
estimates, Nat. Hazards, 77, 1829–1851,
https://doi.org/10.1007/s11069-015-1678-x, 2015.
Su, H., Wang, W., Jia, Y., Han, S. C., Gao, H., Niu, C., and Ni, G.: Impact
of urbanization on precipitation and temperature over a lake-marsh wetland:
A case study in Xiong'an New Area, China, Agr. Water Manage., 243, 106503,
https://doi.org/10.1016/j.agwat.2020.106503, 2021.
Sun, B. and Yang, X.: Simulation of water resources carrying capacity in
Xiong'an New Area based on system dynamics model, Water, 11, 1085,
https://doi.org/10.3390/w11051085, 2019.
Sun, J., Chow, A. C. H., and Madanat, S. M.: Multimodal transportation
system protection against sea level rise, Transp. Res. Part D, 88, 102568,
https://doi.org/10.1016/j.trd.2020.102568, 2020.
Sung, K., Jeong, H., Sangwan, N., and Yu, D. J.: Effects of Flood Control
Strategies on Flood Resilience Under Sociohydrological Disturbances, Water
Resour. Res., 54, 2661–2680, https://doi.org/10.1002/2017WR021440, 2018.
Takabatake, T., Fujisawa, K., Esteban, M., and Shibayama, T.: Simulated
effectiveness of a car evacuation from a tsunami, Int. J. Disaster Risk
Re., 47, 101532, https://doi.org/10.1016/j.ijdrr.2020.101532, 2020.
Tanoue, M., Hirabayashi, Y., and Ikeuchi, H.: Global-scale river flood
vulnerability in the last 50 years, Sci. Rep., 6, 36021,
https://doi.org/10.1038/srep36021, 2016.
Tellman, B., Sullivan, J. A., Kuhn, C., Kettner, A. J., Doyle, C. S.,
Brakenridge, G. R., Erickson, T. A., and Slayback, D. A.: Satellite imaging
reveals increased proportion of population exposed to floods, Nature, 596,
80–86, https://doi.org/10.1038/s41586-021-03695-w, 2021.
Teng, J., Jakeman, A., Vaze, J., Croke, B., Dutta, D., and Kim, S.: Flood
inundation modelling: A review of methods, recent advances and uncertainty
analysis, Environ. Modell. Softw., 90, 201–216,
https://doi.org/10.1016/j.envsoft.2017.01.006, 2017.
Troy, T. J., Konar, M., Srinivasan, V., and Thompson, S.: Moving sociohydrology forward: a synthesis across studies, Hydrol. Earth Syst. Sci., 19, 3667–3679, https://doi.org/10.5194/hess-19-3667-2015, 2015.
Urata, J. and Pel, A. J.: People's Risk Recognition Preceding Evacuation and
Its Role in Demand Modeling and Planning, Risk Anal., 38, 889–905,
https://doi.org/10.1111/risa.12931, 2018.
Verkade, J. S. and Werner, M. G. F.: Estimating the benefits of single value and probability forecasting for flood warning, Hydrol. Earth Syst. Sci., 15, 3751–3765, https://doi.org/10.5194/hess-15-3751-2011, 2011.
Viglione, A., Di Baldassarre, G., Brandimarte, L., Kuil, L., Carr, G.,
Salinas, J. L., Scolobig, A., and Blöschl, G.: Insights from
socio-hydrology modelling on dealing with flood risk – Roles of collective
memory, risk-taking attitude and trust, J. Hydrol., 518, 71–82,
https://doi.org/10.1016/j.jhydrol.2014.01.018, 2014.
Wang, H., Mostafizi, A., Cramer, L. A., Cox, D., and Park, H.: An
agent-based model of a multimodal near-field tsunami evacuation:
Decision-making and life safety, Transport Res. C-Emerg., 64,
86–100, https://doi.org/10.1016/j.trc.2015.11.010, 2016.
Wang, W., Yang, S., Stanley, H. E., and Gao, J.: Local floods induce
large-scale abrupt failures of road networks, Nat. Commun., 10, 2114,
https://doi.org/10.1038/s41467-019-10063-w, 2019.
Wang, Y., Song, L., Han, Z., Liao, Y., Xu, H., Zhai, J., and Zhu, R.:
Climate-related risks in the construction of Xiongan New Area, China, Theor.
Appl. Climatol., 141, 1301–1311,
https://doi.org/10.1007/s00704-020-03277-2, 2020.
Wang, Z. and Jia, G.: A novel agent-based model for tsunami evacuation
simulation and risk assessment, Nat. Hazards, 105, 2045–2071,
https://doi.org/10.1007/s11069-020-04389-8, 2021.
Wedawatta, G. and Ingirige, B.: Resilience and adaptation of small and
medium-sized enterprises to flood risk, Disaster Prev. Manag.,
21, 474–488, https://doi.org/10.1108/09653561211256170, 2012.
Witkowski, K.: Man's impact on the transformation of channel patterns (the
Skawa River, southern Poland), River Res. Appl., 37, 150–162,
https://doi.org/10.1002/rra.3702, 2021.
Wood, N., Henry, K., and Peters, J.: Influence of demand and capacity in
transportation simulations of short-notice, distant-tsunami evacuations,
Transp. Res. Interdiscip. Perspect., 7, 100211,
https://doi.org/10.1016/j.trip.2020.100211, 2020.
Wu, F., Guo, N., Kumar, P., and Niu, L.: Scenario-based extreme flood risk
analysis of Xiong'an New Area in northern China, J. Flood Risk Manag., 14,
e12707, https://doi.org/10.1111/jfr3.12707, 2021.
Yu, D. J., Haeffner, M., Jeong, H., Pande, S., Dame, J., Di Baldassarre, G.,
Garcia-Santos, G., Hermans, L., Muneepeerakul, R., Nardi, F., Sanderson, M.
R., Tian, F., Wei, Y., Wessels, J., and Sivapalan, M.: On capturing human
agency and methodological interdisciplinarity in socio-hydrology research,
Hydrolog. Sci. J., 67, 1905–1916,
https://doi.org/10.1080/02626667.2022.2114836, 2022.
Zhu, J., Ma, Z., Yan, Z., Yuan, X., and Fu, C.: Problems Faced by
Construction of Xiongan New Area under Climate Change, Bull. Chinese Acad.
Sci., 32, 1231–1236, https://doi.org/10.16418/j.issn.1000-3045.2017.11.008,
2017.
Zhu, Y., Xie, K., Ozbay, K., and Yang, H.: Hurricane Evacuation Modeling
Using Behavior Models and Scenario-Driven Agent-based Simulations, Procedia
Comput. Sci., 130, 836–843, https://doi.org/10.1016/j.procs.2018.04.074,
2018.
Zhuge, C., Bithell, M., Shao, C., Li, X., and Gao, J.: An improvement in
MATSim computing time for large-scale travel behaviour microsimulation,
Transportation, 48, 193–214,
https://doi.org/10.1007/s11116-019-10048-0, 2021.
Zhuo, L. and Han, D.: Agent-based modelling and flood risk management: A
compendious literature review, J. Hydrol., 591, 125600,
https://doi.org/10.1016/j.jhydrol.2020.125600, 2020.
Short summary
This study develops an integrated socio-hydrological modeling framework that can simulate the entire flood management processes, including flood inundation, flood management policies, public responses, and evacuation activities. The model is able to holistically examine flood evacuation performance under the joint impacts of hydrological conditions, management policies (i.e., shelter location distribution), and human behaviors (i.e., evacuation preparation time and route-searching strategy).
This study develops an integrated socio-hydrological modeling framework that can simulate the...
