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Title

Applying Data Assimilation with HYDRUS for Digital-twin Modeling of Soil-water and Heat Transport of Rain-fed Soybean Field throughout Japan

Details
Date Fri, Oct14, 2022
Time 4:20 pm - 4:40 pm (5 pm - 5:20 pm Discussion, 5:20 pm - Free discussion (optional))
City Online
Place

Online seminar on Zoom

  • If you are not affiliated with R-CCS and would like to attend R-CCS Cafe, please email us at r-ccs-cafe[at]ml.riken.jp.
Language Presentation Language: English
Presentation Material: English
Speakers

Sakiur RAHMAN

Data Assimilation Research Team
Postdoctoral Researcher

Abstract

Monitoring field water balance conditions continuously is a critical issue for crops cultivated under rain-fed conditions such as soybean fields in Japan. Numerical hydrological models simulate soil water and heat fluxes which can complement the expensive and time-consuming direct field measurements. This study aims to characterize the soil-water and heat transport dynamics explicitly by combining a state-of-the-art hydrological model known as HYDRUS-1D and field data during the soybean crop growing period under different hydroclimatic conditions in Japan. HYDRUS-1D can simulate the transport process of water, heat, and solutes in the vadose zone simultaneously. Since HYDRUS-1D can simulate crops' water uptake from different soil layers, it can provide a better understanding of the soil-water-atmospheric interactions during crop growing seasons. To understand the actual field conditions during the soybean crop growing period, soil water content and temperature were measured in a number of soybean fields throughout Japan during the last crop growing period. In addition, meteorological stations were installed to measure meteorological variables near the soybean fields, and soil samples were collected and analyzed. The meteorological and soil data gathered were fed into the HYDRUS-1D. The simulated temporal changes in soil water contents and temperature during the soybean growing period were compared to the same variables observed in the field to ensure model performance. In addition, ensemble-based data assimilation was applied with HYDRUS-1D to enhance the accuracy and quantify the uncertainties. The outcomes of this study will be fed into the soybean crop models later to develop a coupled crop-hydrological model that will provide the mechanistic details of the soybean crops cultivated in Japan.

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(Oct 5, 2022)