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Supporting Information: Practical data-driven flood forecasting based on dynamical systems theory: Case studies from Japan


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Created: Feb 12, 2020 at 9:36 a.m.
Last updated: Feb 12, 2020 at 10:24 a.m.
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Abstract

This document provides an overview of the accompanying data files used in the manuscript entitled "Practical data-driven flood forecasting based on dynamical systems theory: Case studies from Japan."

db_kagetsu.csv:
Past hourly data on Kagetsu gauging station and 4 precipitation stations (Tsurukochi, Kagetsu, Yokohata, and Mikuma) downloaded from the website of the Water Information System (http://www1.river.go.jp/).

db_hiwatashi.csv
Past hourly data on 5 gauging stations (Takeshita, Otobou, Hirose, Ohjibashi, and Hiwatashi) and 14 precipitation stations (Sunoura, Nojiri, Kensetsutakaharu, Shika, Sano, Kirishima, Miike, Hiwatashi, Aoidake, Mimata, Kabayama, Takeshita, Hisokino, and Sueyoshi) downloaded from the website of the Water Information System (http://www1.river.go.jp/).

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References

Sources

Derived From: Water Information System: http://www1.river.go.jp/

How to Cite

Okuno, S., K. Ikeuchi, K. Aihara (2020). Supporting Information: Practical data-driven flood forecasting based on dynamical systems theory: Case studies from Japan, HydroShare, http://www.hydroshare.org/resource/dfcea9afcba94976a2df14f42a5d1a97

This resource is shared under the Creative Commons Attribution CC BY.

 http://creativecommons.org/licenses/by/4.0/
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