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

Abstract

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.

A Systematic Review of Spatial-Temporal Scale Issues in Sociohydrology

“…Ferreira and Ghimire, 2012; Di Baldassarre et al, 2013Baldassarre et al, , 2014Baldassarre et al, , 2016Wilhelmi and Morss, 2013;Braden et al, 2014;O'Connell and O'Donnell, 2014;Schumann and Nijssen, 2014;Viglione et al, 2014;Chang and Huang, 2015;Chen et al, 2016;Grames et al, 2016;Hazarika et al, 2016;Ward and Winter, 2016;Camargo, 2017;Ciullo et al, 2017;Mostert, 2017;Yu et al, 2017;Horn and Elagib, 2018;Robinne et al, 2018;Shelton et al, 2018;Tellman et al, 2018;Abadie et al, 2019;Abebe et al, 2019aAbebe et al, ,b, 2020Barendrecht et al, 2019;Bojórquez-Tapia et al, 2019;Caprario and Finotti, 2019;Haer et al, 2019;Viero et al, 2019;Devkota, 2020;Dziubanski et al, 2020;Haeffner and Hellman, 2020;Hossain et al, 2020;Huang et al, 2020;Lee and Kang, 2020;Michaelis et al, 2020;Ridolfi et al, 2020a,b;Saia et al, 2020;Sarmento Buarque et al, 2020;Sawada and Hanazaki, 2020;Song et al, 2020;Zhao and Mo, 2020;…”

Socio-meteorology: flood prediction, social preparedness, and cry wolf effects

“…The effect of social collective memory on catastrophic disasters in the actual society is also debatable (e.g., Fanta et al 2019). As Mostert (2018) suggested, it is crucially important to perform case study analyses, obtain empirical data, and integrate those data into the dynamic model to deepen our understanding of the hypothesis of the models (e.g., Roobavannan et al 2017;Ciullo et al 2017;Barendrecht et al 2019;Sawada and Hanazaki 2020).…”

Barriers and Possibilities for Interdisciplinary Disaster Science Research: Critical Appraisal of the Literature

“…These were grouped into a single category because they are frequently referenced together and contribute to difficulties in attaining accurate and actionable items from research. Sawada and Hanazaki (2020) connect these factors in stating, "The major limitation of socio-hydrological models is that they are often inaccurate due to the uncertainty in their input forcing, parameters, and descriptions of the processes." Furthermore, situations are frequently encountered in data integration in which not all values are known for a given object in the system.…”