Articles | Volume 17, issue 12
https://doi.org/10.5194/essd-17-6669-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/essd-17-6669-2025
© Author(s) 2025. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Data description article
| 
01 Dec 2025
Data description article |
| 01 Dec 2025
| 01 Dec 2025
Global spatially-distributed sectoral GDP map for disaster risk analysis
Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Kiyoharu Kajiyama
Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Dai Yamazaki
Graduate School of Engineering, The University of Tokyo, Tokyo, 113-8656, Japan
Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Yuki Kita
Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
Gaia Vision Inc., Tokyo, Japan
Megumi Watanabe
Institute of Industrial Science, The University of Tokyo, Tokyo, 153-8505, Japan
LIRA, Observatoire de Paris, Paris, 75014, France
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Muhammad Hasnain Aslam, Yukiko Hirabayashi, Dai Yamazaki, Gang Zhao, Yuki Kita, and Do Ngoc Khanh
Geosci. Model Dev., 19, 3709–3724, https://doi.org/10.5194/gmd-19-3709-2026, https://doi.org/10.5194/gmd-19-3709-2026, 2026
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Menaka Revel, Xudong Zhou, Prakat Modi, Jean-François Cretaux, Stephane Calmant, and Dai Yamazaki
Earth Syst. Sci. Data, 16, 75–88, https://doi.org/10.5194/essd-16-75-2024, https://doi.org/10.5194/essd-16-75-2024, 2024
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As satellite technology advances, there is an incredible amount of remotely sensed data for observing terrestrial water. Satellite altimetry observations of water heights can be utilized to calibrate and validate large-scale hydrodynamic models. However, because large-scale models are discontinuous, comparing satellite altimetry to predicted water surface elevation is difficult. We developed a satellite altimetry mapping procedure for high-resolution river network data.
Md Safat Sikder, Jida Wang, George H. Allen, Yongwei Sheng, Dai Yamazaki, Chunqiao Song, Meng Ding, Jean-François Crétaux, and Tamlin M. Pavelsky
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Digital elevation models and derived flow directions are crucial to distributed hydrological modeling. As the spatial resolution of models is typically coarser than these data, we need methods to upscale flow direction data while preserving the river structure. We propose the Iterative Hydrography Upscaling (IHU) method and show it outperforms other often-applied methods. We publish the multi-resolution MERIT Hydro IHU hydrography dataset and the algorithm as part of the pyflwdir Python package.
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We developed TCHOIR, a hydrologic simulation framework, to solve fluvial- and thermodynamics of the river–lake continuum. This provides an algorithm for upscaling high-resolution topography as well, which enables the representation of those interactions at the global scale. Validation against in situ and satellite observations shows that the coupled mode outperforms river- or lake-only modes. TCHOIR will contribute to elucidating the role of surface hydrology in Earth’s energy and water cycle.
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Short summary
This study addressed the essential need for detailed spatial economic data to accurately assess global economic losses from natural disasters by developing high-resolution global maps of sector-specific (Service, Industry, and Agriculture) Gross Domestic Product (GDP) for 2010, 2015, and 2020. The new maps showed improved accuracy in reflecting local economies, revealing that sectoral GDP distribution differs by country and affects disaster impact estimates.
This study addressed the essential need for detailed spatial economic data to accurately assess...
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