衛星観測を活用したデータ駆動型の水文季節予報手法の開発
【研究キーワード】
lake surface area / remote sensing / water big data / データ駆動型モデリング / 水文季節予報 / 衛星観測 / テレコネクション / 人工知能 / 衛星高度計 / 河川の水位 / 海水面温度 / 陸域水貯留 / ニューラルネットワーク / 長期リードタイム予測 / data-driven modeling / seasonal prediction / satellite remote sensing
【研究成果の概要】
Pandemic situation has not been alleviated enough, and, therefore, we could not take any international travel to exchange. Instead, we heavily utilized remote meeting solutions and could make several significant achievements. Referring to Pekel’s global surface water data, in total, 1.4 million global lakes listed in the HydroLAKES database have been investigated for their long-term monthly variability during recent 34 years. Originally, it was planned to utilize Google Earth Engine, but we successfully implemented a set of native analysis codes and achieved a significant performance gain. This is the first dataset available in the world. Two papers are under preparation, and these dataset will be available to the public onward. It has been found that the global lake surface area is slowly decreasing during recent two decades, and the seasonal variations is gradually increasing.
【研究代表者】
【研究分担者】 |
| 渡部 哲史 | 京都大学 | 防災研究所 | 特定准教授 | (Kakenデータベース) |
| 内海 信幸 | 京都先端科学大学 | ナガモリアクチュエータ研究所 | 助教 | (Kakenデータベース) |
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【研究種目】国際共同研究加速基金(国際共同研究強化(B))
【研究期間】2018-10-09 - 2023-03-31
【配分額】17,810千円 (直接経費: 13,700千円、間接経費: 4,110千円)