EGU General Assembly 2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.

Development of Throughfall Simulation Models and Prediction Uncertainty Estimation by Different Forest Stand Characteristics

Hyunje Yang, Honggeun Lim, Hyung Tae Choi, and Jiyoung Lee
Hyunje Yang et al.
  • National Institute of Forest Science, Forest Environment and Conservation Department, Seoul, Republic of Korea (

Continuously increasing demand for freshwater makes many scientists study water yield prediction. In South Korea, of which two-third is covered by forests, understanding the water cycle especially in forests is even more important. Throughfall is penetrated rainfall through the tree canopy and it is the basic source for groundwater recharge which is directly related to the water yield on the catchment scale. Therefore, understanding the throughfall characteristics is essential for sustainable water management. This study is conducted to develop simple throughfall simulation models and estimate prediction uncertainty from developed models by different forest stand characteristics. National Institute of Forest Service (NIFoS) has collected throughfall data for 2 years from 7 different forest stand sites. Rutter model was used for the structure of the simple throughfall simulation model and it had several parameters for simulating. And over a million Monte Carlo experiments and generalized likelihood uncertainty estimation (GLUE) methodology were used for selecting parameters sets of behavioural models from comparing simulated throughfall and observed throughfall. From the range of behaviours in a period, we successfully estimated the prediction uncertainty. We also compared the features of behavioural parameter sets by different forest stand characteristics. We expect developed models can be applied for several forest stands in South Korea with various physical-based hydrological models.

How to cite: Yang, H., Lim, H., Choi, H. T., and Lee, J.: Development of Throughfall Simulation Models and Prediction Uncertainty Estimation by Different Forest Stand Characteristics, EGU General Assembly 2022, Vienna, Austria, 23–27 May 2022, EGU22-6657,, 2022.


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