Japan Geoscience Union Meeting 2022

Presentation information

[E] Poster

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

[A-HW24] Material transportation and cycling at the land-sea interface: from headwaters to the ocean

Wed. Jun 1, 2022 11:00 AM - 1:00 PM Online Poster Zoom Room (11) (Ch.11)

convener:Jun Yasumoto(University of the Ryukyus, Faculty of Agriculuture), convener:Masahiro Kobayashi(Forestry and Forest Products Research Institute), Noboru Okuda(Kobe University), convener:Adina Paytan(University of California Santa Cruz), Chairperson:Jun Yasumoto(University of the Ryukyus, Faculty of Agriculuture), Noboru Okuda(Kobe University), Masahiro Kobayashi(Forestry and Forest Products Research Institute)

11:00 AM - 1:00 PM

[AHW24-P06] Does weekly data could fill the bridge between daily and monthly data in modeling work?

*Kunyang Wang1, Shin-ichi Onodera1, Mitsuyo Saito2 (1.Graduate School of Advanced Science and Engineering, Hiroshima University, 2.Graduate School of Environmental and Life Science, Okayama Universit)

Keywords:Modeling, Daily, Monthly, Calibration, Nutrient, Concentration

Over the past few decades, modellers have had a high degree of autonomy in the calibration and validation of flow models. They can autonomously decide to use daily or monthly time steps, depending on the needs of the model and the length of the study period. However, modellers have very few options for calibration and validation of nutrient transport models. In most cases, users are forced to perform model calibration work at monthly time steps due to insufficient data volume of observation data to meet the needs of daily calibration. However, monthly observations could not covered the transport peaks in precipitation event well, which also makes models calibrated at monthly time steps likely to underestimate nutrient transport peaks. The Objective of this study was take weekly sampling on the downstream of Yamato River and rate the model according to the weekly sampling data and demonstrate the significance of the weekly sampling data in the modeling work and its influence on the results