Articles | Volume 26, issue 16
https://doi.org/10.5194/hess-26-4265-2022
© Author(s) 2022. 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-26-4265-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Impact of cry wolf effects on social preparedness and the efficiency of flood early warning systems
Yohei Sawada
CORRESPONDING AUTHOR
Institute of Engineering Innovation, University of Tokyo, Tokyo, Japan
Rin Kanai
Department of Civil Engineering, University of Tokyo, Tokyo,
Japan
Hitomu Kotani
Institute of Engineering Innovation, University of Tokyo, Tokyo, Japan
Department of Urban Management, Kyoto University, Kyoto, Japan
Department of Natural Resources, Graduate School of Global
Environmental Studies, Kyoto University, Kyoto, Japan
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It is generally difficult to control large-scale and complex systems, such as Earth systems, using small forces. In this paper, a new method to control such systems is proposed. The new method is inspired by the similarity between simulation-observation integration methods in geoscience and model predictive control theory in control engineering. The proposed method is particularly suitable to find the efficient strategies of weather modification.
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The Nash–Sutcliffe efficiency (NSE) is a widely used score in hydrology, but it is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. This approach is shown to yield profound explanations to many open problems related to NSE. A generalized NSE that can be used in general cases is proposed.
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A new method to predict chaotic systems from observation and process-based models is proposed by combining machine learning with data assimilation. Our method is robust to the sparsity of observation networks and can predict more accurately than a process-based model when it is biased. Our method effectively works when both observations and models are imperfect, which is often the case in geoscience. Therefore, our method is useful to solve a wide variety of prediction problems in this field.
Yohei Sawada and Risa Hanazaki
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In socio-hydrology, human–water interactions are investigated. Researchers have two major methodologies in socio-hydrology, namely mathematical modeling and empirical data analysis. Here we propose a new method for bringing the synergic effect of models and data to socio-hydrology. We apply sequential data assimilation, which has been widely used in geoscience, to a flood risk model to analyze the human–flood interactions by model–data integration.
Yohei Sawada
Hydrol. Earth Syst. Sci., 24, 3881–3898, https://doi.org/10.5194/hess-24-3881-2020, https://doi.org/10.5194/hess-24-3881-2020, 2020
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Hydrologic data assimmilation is the area in which methods to integrate hydrological models and observations are investigated. Recently, hydrological or land models have been increasing their complexity, with very high spatial resolution. However, it is unclear that the current data assimilation method can directly be applied to those hyperresolution models, so that I investigated the applicability and limitation of the existing method by minimalistic numerical experiments.
Yohei Sawada
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Manuscript not accepted for further review
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Hydrologic data assimmilation is the area in which methods to integrate hydrological models and observations are investigated. Recently, hydrological or land models are increasing their complexity with very high spatial resolution. However, it is unclear that the current data assimilation method can directly be applied to those hyperresolution models so that I investigated the applicability and limitation of the existing method by minimalistic numerical experiments.
Yohei Sawada
EGUsphere, https://doi.org/https://doi.org/10.48550/arXiv.2403.06371, https://doi.org/https://doi.org/10.48550/arXiv.2403.06371, 2024
Preprint archived
Short summary
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It is generally difficult to control large-scale and complex systems, such as Earth systems, using small forces. In this paper, a new method to control such systems is proposed. The new method is inspired by the similarity between simulation-observation integration methods in geoscience and model predictive control theory in control engineering. The proposed method is particularly suitable to find the efficient strategies of weather modification.
Le Duc and Yohei Sawada
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The Nash–Sutcliffe efficiency (NSE) is a widely used score in hydrology, but it is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. This approach is shown to yield profound explanations to many open problems related to NSE. A generalized NSE that can be used in general cases is proposed.
Yuya Kageyama and Yohei Sawada
Hydrol. Earth Syst. Sci., 26, 4707–4720, https://doi.org/10.5194/hess-26-4707-2022, https://doi.org/10.5194/hess-26-4707-2022, 2022
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This study explores the link between hydrometeorological droughts and their socioeconomic impact at a subnational scale based on the newly developed disaster dataset with subnational location information. Hydrometeorological drought-prone areas were generally consistent with socioeconomic drought-prone areas in the disaster dataset. Our analysis clarifies the importance of the use of subnational disaster information.
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A new method to predict chaotic systems from observation and process-based models is proposed by combining machine learning with data assimilation. Our method is robust to the sparsity of observation networks and can predict more accurately than a process-based model when it is biased. Our method effectively works when both observations and models are imperfect, which is often the case in geoscience. Therefore, our method is useful to solve a wide variety of prediction problems in this field.
Yohei Sawada and Risa Hanazaki
Hydrol. Earth Syst. Sci., 24, 4777–4791, https://doi.org/10.5194/hess-24-4777-2020, https://doi.org/10.5194/hess-24-4777-2020, 2020
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In socio-hydrology, human–water interactions are investigated. Researchers have two major methodologies in socio-hydrology, namely mathematical modeling and empirical data analysis. Here we propose a new method for bringing the synergic effect of models and data to socio-hydrology. We apply sequential data assimilation, which has been widely used in geoscience, to a flood risk model to analyze the human–flood interactions by model–data integration.
Yohei Sawada
Hydrol. Earth Syst. Sci., 24, 3881–3898, https://doi.org/10.5194/hess-24-3881-2020, https://doi.org/10.5194/hess-24-3881-2020, 2020
Short summary
Short summary
Hydrologic data assimmilation is the area in which methods to integrate hydrological models and observations are investigated. Recently, hydrological or land models have been increasing their complexity, with very high spatial resolution. However, it is unclear that the current data assimilation method can directly be applied to those hyperresolution models, so that I investigated the applicability and limitation of the existing method by minimalistic numerical experiments.
Yohei Sawada
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2019-324, https://doi.org/10.5194/hess-2019-324, 2019
Manuscript not accepted for further review
Short summary
Short summary
Hydrologic data assimmilation is the area in which methods to integrate hydrological models and observations are investigated. Recently, hydrological or land models are increasing their complexity with very high spatial resolution. However, it is unclear that the current data assimilation method can directly be applied to those hyperresolution models so that I investigated the applicability and limitation of the existing method by minimalistic numerical experiments.
Related subject area
Subject: Engineering Hydrology | Techniques and Approaches: Theory development
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A signal-processing-based interpretation of the Nash–Sutcliffe efficiency
Impact of detention dams on the probability distribution of floods
Hess Opinions: An interdisciplinary research agenda to explore the unintended consequences of structural flood protection
Managing uncertainty in flood protection planning with climate projections
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Ideal point error for model assessment in data-driven river flow forecasting
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Tao Zhang, Qinhong Hu, Behzad Ghanbarian, Derek Elsworth, and Zhiming Lu
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Tight rock is essential to various emerging fields of energy geosciences such as EGS and CCUS, but its ultra-low permeability is not easily measurable as a rigorous and rapid theory-based measurement technique for sub-nanodarcy levels is lacking. For the first time, we resolve this by providing an integrated technique (termed gas permeability technique) with coupled theoretical development, experimental procedures, and a data interpretation workflow.
Le Duc and Yohei Sawada
Hydrol. Earth Syst. Sci., 27, 1827–1839, https://doi.org/10.5194/hess-27-1827-2023, https://doi.org/10.5194/hess-27-1827-2023, 2023
Short summary
Short summary
The Nash–Sutcliffe efficiency (NSE) is a widely used score in hydrology, but it is not common in the other environmental sciences. One of the reasons for its unpopularity is that its scientific meaning is somehow unclear in the literature. This study attempts to establish a solid foundation for NSE from the viewpoint of signal progressing. This approach is shown to yield profound explanations to many open problems related to NSE. A generalized NSE that can be used in general cases is proposed.
Salvatore Manfreda, Domenico Miglino, and Cinzia Albertini
Hydrol. Earth Syst. Sci., 25, 4231–4242, https://doi.org/10.5194/hess-25-4231-2021, https://doi.org/10.5194/hess-25-4231-2021, 2021
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In this work, we introduce a new theoretically derived probability distribution of the outflows of in-line detention dams. The method may be used to evaluate the impact of detention dams on flood occurrences and attenuation of floods. This may help and support risk management planning and design.
Giuliano Di Baldassarre, Heidi Kreibich, Sergiy Vorogushyn, Jeroen Aerts, Karsten Arnbjerg-Nielsen, Marlies Barendrecht, Paul Bates, Marco Borga, Wouter Botzen, Philip Bubeck, Bruna De Marchi, Carmen Llasat, Maurizio Mazzoleni, Daniela Molinari, Elena Mondino, Johanna Mård, Olga Petrucci, Anna Scolobig, Alberto Viglione, and Philip J. Ward
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One common approach to cope with floods is the implementation of structural flood protection measures, such as levees. Numerous scholars have problematized this approach and shown that increasing levels of flood protection can generate a false sense of security and attract more people to the risky areas. We briefly review the literature on this topic and then propose a research agenda to explore the unintended consequences of structural flood protection.
Beatrice Dittes, Olga Špačková, Lukas Schoppa, and Daniel Straub
Hydrol. Earth Syst. Sci., 22, 2511–2526, https://doi.org/10.5194/hess-22-2511-2018, https://doi.org/10.5194/hess-22-2511-2018, 2018
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There is large uncertainty in the future development of flood patterns, e.g., due to climate change. We quantify relevant uncertainties and show how they can be used for flood protection planning. We find that one ought to include an estimate of uncertainty that cannot be quantified from available data (hidden uncertainty), since projections and data at hand often cover only a limited range of the uncertainty spectrum. Furthermore, dependencies between climate projections must be accounted for.
B. Mazzorana, S. Simoni, C. Scherer, B. Gems, S. Fuchs, and M. Keiler
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J. H. Sung and E.-S. Chung
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J. Hall, B. Arheimer, M. Borga, R. Brázdil, P. Claps, A. Kiss, T. R. Kjeldsen, J. Kriaučiūnienė, Z. W. Kundzewicz, M. Lang, M. C. Llasat, N. Macdonald, N. McIntyre, L. Mediero, B. Merz, R. Merz, P. Molnar, A. Montanari, C. Neuhold, J. Parajka, R. A. P. Perdigão, L. Plavcová, M. Rogger, J. L. Salinas, E. Sauquet, C. Schär, J. Szolgay, A. Viglione, and G. Blöschl
Hydrol. Earth Syst. Sci., 18, 2735–2772, https://doi.org/10.5194/hess-18-2735-2014, https://doi.org/10.5194/hess-18-2735-2014, 2014
V. R. N. Pauwels, G. J. M. De Lannoy, H.-J. Hendricks Franssen, and H. Vereecken
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Hydrol. Earth Syst. Sci., 16, 3677–3688, https://doi.org/10.5194/hess-16-3677-2012, https://doi.org/10.5194/hess-16-3677-2012, 2012
S. Uhlenbrook and E. de Jong
Hydrol. Earth Syst. Sci., 16, 3475–3483, https://doi.org/10.5194/hess-16-3475-2012, https://doi.org/10.5194/hess-16-3475-2012, 2012
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Hydrol. Earth Syst. Sci., 16, 3049–3060, https://doi.org/10.5194/hess-16-3049-2012, https://doi.org/10.5194/hess-16-3049-2012, 2012
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Hydrol. Earth Syst. Sci., 15, 3293–3305, https://doi.org/10.5194/hess-15-3293-2011, https://doi.org/10.5194/hess-15-3293-2011, 2011
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Hydrol. Earth Syst. Sci., 15, 141–150, https://doi.org/10.5194/hess-15-141-2011, https://doi.org/10.5194/hess-15-141-2011, 2011
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Short summary
Although flood early warning systems (FEWS) are promising, they inevitably issue false alarms. Many false alarms undermine the credibility of FEWS, which we call a cry wolf effect. Here, we present a simple model that can simulate the cry wolf effect. Our model implies that the cry wolf effect is important if a community is heavily protected by infrastructure and few floods occur. The cry wolf effects get more important as the natural scientific skill to predict flood events is improved.
Although flood early warning systems (FEWS) are promising, they inevitably issue false alarms....