Japan Geoscience Union Meeting 2023

Presentation information

[E] Online Poster

A (Atmospheric and Hydrospheric Sciences ) » A-HW Hydrology & Water Environment

[A-HW18] Material transportation and cycling at the land-sea interface: from headwaters to the ocean

Thu. May 25, 2023 10:45 AM - 12:15 PM Online Poster Zoom Room (5) (Online Poster)

convener:Takahiro Hosono(Faculty of Advanced Science and Technology, Kumamoto University), Syuhei Ban(The University of Shiga Prefecture), Mitsuyo Saito(Graduate School of Advanced Science and Engineering, Hiroshima University), Adina Paytan(University of California Santa Cruz)


On-site poster schedule(2023/5/26 17:15-18:45)

10:45 AM - 12:15 PM

[AHW18-P06] Evaluation of the performance of SWAT model in a karstic watershed by different calibration time steps

*Nang Yu War1, Shin-ichi Onodera1, Kunyang Wang1, Yuta Shimizu2, Mitsuyo Saito1 (1.Graduate School of Advanced Science and Engineering, Hiroshima University, 2.Western Region Agricultural Research Center, National Agriculture and Food Research Organization)

Keywords:SWAT, streamflow, karst, modelling, calibration

The Soil and Water Assessment Tool (SWAT) is a widely used hydrological model to simulate the streamflow and water quality, especially for large agricultural catchment. However, SWAT has not been applied widely for the karstic catchment since the model simulate the catchment as a geomorphological heterogeneity. Karstic catchment has complex surface and groundwater flow with subterranean hydrological processes. Takahashi River watershed in Okayama Prefecture has limestone in some parts of the river upstream areas, forming a partially karstic watershed. This study is to compare the statistical performance of SWAT model in a karstic watershed by different calibration time step (daily and monthly). The streamflow was simulated daily and monthly by SWAT. The calibration years are from 2002 – 2004 and validation from 2005 – 2007. The daily model simulation results are satisfactory with NSE, R² and PBIAS 0.74, 0.75, and -7.9 for calibration, and 0.66, 0.67, and -14.3 respectively for validation.

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