Articles | Volume 26, issue 18
https://doi.org/10.5194/hess-26-4707-2022
© 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-26-4707-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Global assessment of subnational drought impact based on the Geocoded Disasters dataset and land reanalysis
Institute of Engineering Innovation, The University of Tokyo, Tokyo 113-8656, Japan
Yohei Sawada
Institute of Engineering Innovation, The University of Tokyo, Tokyo 113-8656, Japan
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Sneha Kulkarni, Yohei Sawada, Yared Bayissa, and Brian Wardlow
Hydrol. Earth Syst. Sci., 29, 4341–4370, https://doi.org/10.5194/hess-29-4341-2025, https://doi.org/10.5194/hess-29-4341-2025, 2025
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How drought impacts communities is complex and not yet fully understood. We examined a disaster dataset and compared various drought measures to pinpoint affected regions. Our new combined drought indicator (CDI) was found to be the most effective in identifying drought events compared to other traditional drought indices. This underscores the CDI's importance in evaluating drought risks and directing attention to the most impacted areas.
Yohei Sawada
EGUsphere, https://doi.org/10.48550/arXiv.2403.06371, https://doi.org/10.48550/arXiv.2403.06371, 2024
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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
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
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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.
Yohei Sawada, Rin Kanai, and Hitomu Kotani
Hydrol. Earth Syst. Sci., 26, 4265–4278, https://doi.org/10.5194/hess-26-4265-2022, https://doi.org/10.5194/hess-26-4265-2022, 2022
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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.
Futo Tomizawa and Yohei Sawada
Geosci. Model Dev., 14, 5623–5635, https://doi.org/10.5194/gmd-14-5623-2021, https://doi.org/10.5194/gmd-14-5623-2021, 2021
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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.
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Short summary
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.
This study explores the link between hydrometeorological droughts and their socioeconomic impact...