The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model

Barendrecht, Marlies; Viglione, Alberto; Kreibich, Heidi; Merz, Bruno; Vorogushyn, Sergiy; Blöschl, Günter

doi:10.1029/2018wr024128

Cited by 49 publications

(72 citation statements)

References 61 publications

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“…This model conceptualizes human-flood interactions by a set of simple equations which describe the states of flood, economy, technology, politics, and society. Based on this original model of Di Baldassarre et al (2013), many similar flood risk models have been proposed, validated, and applied (e.g., Viglione et al, 2014;Ciullo et al, 2017;Barendrecht et al, 2019). Here we briefly describe this model.…”

Section: Modelmentioning

confidence: 99%

“…Then, we evaluated the sensitivity of the observation network (i.e., the observable variables and the observation intervals) to the SIRPF algorithm's performance. Although it is not straightforward to observe the social memory M, several previous studies obtained the proxy of the social memory from interview data (Barendrecht et al, 2019) and a number of Google searches (Gonzales and Ajami, 2017).…”

Section: Observation System Simulation Experimentsmentioning

confidence: 99%

“…The other limitation of the data-driven approach is that the quantification of the causal relationship cannot be easily done by empirical data only (e.g., Dang and Konar, 2018). Considering the advantages and disadvantages of model and data, previous studies used social statistics to calibrate and validate their socio-hydrological models (e.g., Barendrecht et al, 2019;Roobavannan et al, 2017;Ciullo et al, 2017;van Emmerik et al, 2014;Gonzales and Ajami, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…Data assimilation sequentially adjusts the predicted state variables and parameters of dynamic models by integrating observation data into models based on Bayes' theorem. Data assimilation has been widely applied to numerical weather prediction (e.g., Miyoshi and Yamane, 2007;Bauer et al, 2015;Poterjoy et al, 2019;Sawada et al, 2019), atmospheric reanalysis (e.g., Kobayashi et al, 2015;Hersbach et al, 2019), and hydrology and land surface modeling (e.g., Moradkhani et al, 2005;Sawada et al, 2015;Rasmussen et al, 2015;Lievens et al, 2017). The applicability of the data assimi-lation approach to socio-hydrological models has yet to be investigated.…”

Section: Introductionmentioning

confidence: 99%

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Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration

Sawada

Hanazaki

2020

Hydrol. Earth Syst. Sci.

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Abstract. In socio-hydrology, human–water interactions are simulated by mathematical models. Although the integration of these socio-hydrological models and observation data is necessary for improving the understanding of human–water interactions, the methodological development of the model–data integration in socio-hydrology is in its infancy. Here we propose applying sequential data assimilation, which has been widely used in geoscience, to a socio-hydrological model. We developed particle filtering for a widely adopted flood risk model and performed an idealized observation system simulation experiment and a real data experiment to demonstrate the potential of the sequential data assimilation in socio-hydrology. In these experiments, the flood risk model's parameters, the input forcing data, and empirical social data were assumed to be somewhat imperfect. We tested if data assimilation can contribute to accurately reconstructing the historical human–flood interactions by integrating these imperfect models and imperfect and sparsely distributed data. Our results highlight that it is important to sequentially constrain both state variables and parameters when the input forcing is uncertain. Our proposed method can accurately estimate the model's unknown parameters – even if the true model parameter temporally varies. The small amount of empirical data can significantly improve the simulation skill of the flood risk model. Therefore, sequential data assimilation is useful for reconstructing historical socio-hydrological processes by the synergistic effect of models and data.

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Section: Modelmentioning

confidence: 99%

Section: Observation System Simulation Experimentsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration

Sawada

Hanazaki

2020

Hydrol. Earth Syst. Sci.

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“…Additionally, the above-mentioned long-term datasets might be used to improve socio-hydrological models (e.g. Barendrecht et al 2019), or other models that could be used to project the dynamics of drought and flood risk. According to Barendrecht et al (2017) and Aerts et al (2018) other models that are able to describe the interaction of hydrological and anthropogenic processes are system-of-systems models (e.g.…”

mentioning

confidence: 99%

Approaches to analyse and model changes in impacts: reply to discussions of “How to improve attribution of changes in drought and flood impacts”

Kreibich

Blauhut

Aerts

et al. 2019

Hydrological Sciences Journal

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We thank the authors, Brunella Bonaccorso and Karsten Arnbjerg-Nielsen for their constructive contributions to the discussion about the attribution of changes in drought and flood impacts. We appreciate that they support our opinion, but in particular their additional new ideas on how to better understand changes in impacts. It is great that they challenge us to think a step further on how to foster the collection of long time series of data and how to use these to model and project changes. Here, we elaborate on the possibility to collect time series of data on hazard, exposure, vulnerability and impacts and how these could be used to improve e.g. socio-hydrological models for the development of future risk scenarios.

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Exploring drought‐to‐flood interactions and dynamics: A global case review

Barendrecht,

Matanó,

Mendoza

et al. 2024

WIREs Water

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This study synthesizes the current understanding of the hydrological, impact, and adaptation processes underlying drought‐to‐flood events (i.e., consecutive drought and flood events), and how they interact. Based on an analysis of literature and a global assessment of historic cases, we show how drought can affect flood risk and assess under which circumstances drought‐to‐flood interactions can lead to increased or decreased risk. We make a distinction between hydrological, socio‐economic and adaptation processes. Hydrological processes include storage and runoff processes, which both seem to mostly play a role when the drought is a multiyear event and when the flood occurs during the drought. However, which process is dominant when and where, and how this is influenced by human intervention needs further research. Processes related to socio‐economic impacts have been studied less than hydrological processes, but in general, changes in vulnerability seem to play an important role in increasing or decreasing drought‐to‐flood impacts. Additionally, there is evidence of increased water quality problems due to drought‐to‐flood events, when compared to drought or flood events by themselves. Adaptation affects both hydrological (e.g., through groundwater extraction) or socio‐economic (e.g., influencing vulnerability) processes. There are many examples of adaptation, but there is limited evidence of when and where certain processes occur and why. Overall, research on drought‐to‐flood events is scarce. To increase our understanding of drought‐to‐flood events we need more comprehensive studies on the underlying hydrological, socio‐economic, and adaptation processes and their interactions, as well as the circumstances that lead to the dominance of certain processes.This article is categorized under: Science of Water > Hydrological Processes Science of Water > Water Extremes

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The Value of Empirical Data for Estimating the Parameters of a Sociohydrological Flood Risk Model

Cited by 49 publications

References 61 publications

Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration

Socio-hydrological data assimilation: analyzing human–flood interactions by model–data integration

Approaches to analyse and model changes in impacts: reply to discussions of “How to improve attribution of changes in drought and flood impacts”

Exploring drought‐to‐flood interactions and dynamics: A global case review

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