日本地球惑星科学連合2024年大会

講演情報

[E] 口頭発表

セッション記号 A (大気水圏科学) » A-HW 水文・陸水・地下水学・水環境

[A-HW22] 流域圏生態系における物質輸送と循環:源流から沿岸海域まで

2024年5月30日(木) 10:45 〜 11:30 201A (幕張メッセ国際会議場)

コンビーナ:前田 守弘(岡山大学)、入野 智久(北海道大学 大学院地球環境科学研究院)、宗村 広昭(岡山大学)、Paytan Adina(University of California Santa Cruz)、座長:宗村 広昭(岡山大学)

11:00 〜 11:15

[AHW22-08] High temporal resolution SWAT modeling for assessing extreme hydrological events and sediment transport in steep catchments

*Nang Yu War1Shin-ichi Onodera1Kunyang Wang1Yuta Shimizu2Mitsuyo Saito1 (1.Hiroshima University、2.Western Region Agricultural Research Center, National Agriculture and Food Research Organization)

キーワード:sub-daily, SWAT, flood events, extreme climate, steep catchment

The steep slopes of Japanese river catchments are vulnerable to floods and sediment disasters. This study presents an advanced application of the Soil and Water Assessment Tool (SWAT) to model high-flow events in steep catchments on an hourly scale. SWAT is widely used for simulating both long-term continuous and short-term event-based processes. Focusing on the Takahashi River in Japan, known for its steep topography and heavy rainfall, the research evaluates the SWAT model's capability to simulate extreme flood events in hourly time steps, simulate flood events and sediment transport with a lack of observed data, and analyze the model's performance with the antecedent conditions of the pre-events. Among the flood events from 2002 to 2007, events with a daily average streamflow greater than 500 m^3/s are selected for calibration and validation. We found that the SWAT model can very well simulate the hourly extreme flood events. It is observed that the model's performance on both streamflow and sediment is more satisfactory if the events have moist antecedent conditions with low total precipitation during the event. This work contributes to the understanding of the complex hydrological processes in steep catchments and offers insights for effective flood and sediment management in similar environments, highlighting the importance of high-resolution modeling in predicting and mitigating disaster risks.

This study is supported by the APN Project (CRRP2019-09MY-Onodera).