다양한 지표모형을 활용한 토양수분 예측 성능 평가 연구
A Study on Soil Moisture Estimates Performance Using Various Land Surface Models
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초록
Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, variousprocess-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending onthe 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 moistureprediction) 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 soilmoisture prediction.
Abstract
Soil moisture is significantly related to crop growth and plays an important role in irrigation management. To predict soil moisture, variousprocess-based model has been developed and used in the world. Various models (Land surface model) may have different performance depending onthe 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 moistureprediction) 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 soilmoisture prediction.
참고문헌 (19)
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