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다양한 지표모형을 활용한 토양수분 예측 성능 평가 연구
장예근 ( Jang Ye-geun ),신승훈 ( Sin Seoung-hun ),이태화 ( Lee Tae-hwa ),장원석 ( Jang Won-seok ),신용철 ( Shin Yong-chul ),장근창 ( Jang Keun-chang ),천정화 ( Chun Jung-hwa ),김종건 ( Kim Jong-gun ) 한국농공학회 2022 한국농공학회논문집 Vol.64 No.1
Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, various process-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending on the model parameters and structures that causes the different model output for the same modeling condition. In this study, the three land surface models (Noah Land Surface Model, Soil Water Atmosphere Plant, Community Land Model) were used to compare the model performance (soil moisture prediction) and develop the multi-model simulation. At first, the genetic algorithm was used to estimate the optimal soil parameters for each model, and the parameters were used to predict soil moisture in the study area. Then, we used the multi-model approach based on Bayesian model averaging (BMA). The results derived from this approach showed a better match to the measurements than the results from the original single land surface model. In addition, identifying the strengths and weaknesses of the single model and utilizing multi-model methods can help to increase the accuracy of soil moisture prediction.