Socio-hydrological modeling and its issues in Japan: a case study in Naganuma District, Nagano City

Shibata, Naoya; Nakai, Fuko; Otsuyama, Kensuke; Nakamura, Shinichirô

doi:10.3178/hrl.16.32

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…The first category are river flood factors selected to determine the magnitude of flood hazards along river networks. The condition of flood hazard is one of key parameters in socio‐hydrological modeling relating levee heightening (Shibata et al., 2022). The catchment area (CA) reflects the size of upstream catchments and was derived from the flow accumulation map in the MERIT Hydro dataset (Yamazaki et al., 2019).…”

Section: Study Area and Data Preparationmentioning

confidence: 99%

Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling

Zhao

Bates

Neal

et al. 2023

Water Resources Research

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Floods are the most frequent type of natural hazards worldwide, and have caused significant loss of life and severe economic impacts on populations and property during the past two decades (CRED and UNDRR, 2020). To reduce the negative impact of floods, numerous types of flood defenses, such as levee systems, have been built to protect cities, towns, and farms in almost every country (Ubilla et al., 2008;Z. Wang & Liu, 2019). According to a report by the United States (U.S.) Army Corps of Engineers (USACE), approximately 11 million people and $1.3 trillion of property value existed in flood-defended areas in the U.S. as of 2018 (USACE, 2018). The number and standard of flood defenses continuously improve over time to meet societal needs and keep pace with rapid urbanization in floodplains (Leonard, 2008;T. Zhu et al., 2007). Flood defenses have significantly changed the regional flooding distribution and also residents' exposure (Di Baldassarre et al., 2009;Ludy & Kondolf, 2012), and this needs to be considered in flood hazard assessment.With the increase of computing power and advances in remote sensing techniques, it is now possible to map flood hazards on a large scale at high resolution (<100 m) (Ward et al., 2015). High resolution global hydrography datasets, such as MERIT Hydro (Yamazaki et al., 2019) and HydroBASINS (Lehner & Grill, 2013) have been released; however, information on detailed flood defenses for most rivers in the world is severely limited (Aerts et al., 2020;Sampson et al., 2015). Existing state-of-the-art global flood hazard models either assume a simplified high flood defense standard (FDS) or assume no protection when applied (Aerts et al., 2020;Scussolini et al., 2016;Ward et al., 2015). This assumption causes the misestimations of flood hazards when the flooded areas are actually protected by existing flood defenses, and therefore induces a distorted flood hazard and risk assessment. The first global flood defense database (called FLOPROS) was built by collecting FDS data worldwide at the sub-country scale (Scussolini et al., 2016). FLOPROS assumes that the FDSs in a vast area are the same (i.e., most states in the US or all of Australia have the same FDS) and ignores the heterogeneity of FDSs between rivers. The coarse resolution of the FDS data in FLOPROS cannot meet the requirement of large-scale flood hazard modeling at high resolution.

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Section: Study Area and Data Preparationmentioning

confidence: 99%

Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling

Zhao

Bates

Neal

et al. 2023

Water Resources Research

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“…In this model, the changes in floodplain dynamics, such as memory, population density, and levee height were explained based on the intensity of fluvial flood conditions. Although this model has been applied to various case studies worldwide (Ciullo et al, 2017;Di Baldassarre et al, 2017Pande and Sivapalan, 2017;Albertini et al, 2020;Ridolfi et al, 2021;Perera and Nakamura, 2022;Shibata et al, 2022), no method to measure the exact memory decay process has been proposed.…”

mentioning

confidence: 99%

Conceptualizing the effectiveness of flood risk information with a socio-hydrological model: A case study in Lower Kelani River Basin, Sri Lanka

Perera

Nakamura

2023

Front. Water

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In rapidly changing human-flood systems, dynamic models can assist with system thinking, policy-making, and response. Previous research has shown that flood memory is important for better responses during disasters. To date, socio-hydrological research has primarily focused on memory accumulation after flood events and the related dynamics and feedback mechanisms. Notably, the impact of risk information has not yet been considered in any modeling exercise. Accordingly, this study improved upon the socio-hydrological model (SHM) by incorporating the impact of risk information on collective memory and associated dynamics. Probable flood maps were used to assess the response of a floodplain community from the Lower Kelani River Basin, Sri Lanka, via two interview surveys conducted at a 6-month interval. The surveys were conducted under two categories: A—after distributing maps and conducting awareness sessions, and B—after showing and distributing flood maps. The results showed that the flood maps helped to improve the risk perception of floodplain communities. Of the two categories, the memory decay process was slower for Category A. Furthermore, the SHM application showed that flood damage in the study area could be reduced by 10–30% when flood maps were distributed, and awareness sessions were conducted at 1–5-yr intervals.

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Improvement of socio-hydrological model to capture the dynamics of combined river and urban floods: a case study in Lower Kelani River Basin, Sri Lanka

Perera

Nakamura

2022

Hydrological Research Letters

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Socio-hydrological modeling and its issues in Japan: a case study in Naganuma District, Nagano City

Cited by 3 publications

References 31 publications

Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling

Flood Defense Standard Estimation Using Machine Learning and Its Representation in Large‐Scale Flood Hazard Modeling

Conceptualizing the effectiveness of flood risk information with a socio-hydrological model: A case study in Lower Kelani River Basin, Sri Lanka

Improvement of socio-hydrological model to capture the dynamics of combined river and urban floods: a case study in Lower Kelani River Basin, Sri Lanka

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