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\begin{document}

\title{Spatial Prediction of the Forest Soil Water Holding Capacities in
Temperate Region on a National Scale With Random Forest Models}



\author[1]{Hyunje Yang}%
\author[1]{Honggeun Lim}%
\author[1]{Hyung Tae Choi}%
\affil[1]{National Institute of Forest Science}%


\vspace{-1em}



  
  \date{May 08, 2024}


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\begin{abstract}
Soil water holding capacities (SWHCs) is important input factor in
hydrological simulation models for sustainable water management. Forests
that covered 63\% of South Korea are the main source of clean water, and
it is essential to estimate SWHCs on a nationwide scale for effective
forest water resources management. However, there are a few studies
estimating SWHCs on a nationwide scale in the temperate regions
especially in South Korea. Fortunately, forest spatial big data have
been collected on a national scale, and the nationwide prediction of the
SWHC can be possible with this dataset. In this study, spatial
prediction of forest SWHCs (saturated water content, water content at
pF1.8 and 2.7) was conducted with 953 forest soil samples and forest
spatial big dataset. 4 soil properties and 14 environmental covariates
were used for predicting SWHCs. Simple linear regression and random
forest model were compared for selecting the optimal predictive model.
From the variable importance analysis, environmental covariates had as
big importance as soil properties had. And prediction performance of the
model with environmental covariates as the input data was higher than
that of the model with soil properties. Comparing two models, the random
forest model could accurately and stably predict SWHCs than the simple
linear model. As a result of spatial prediction of SWHCs at the national
scale through the random forest model and the forest spatial big
dataset, it was confirmed that higher SWHCs were distributed along with
the Baekdudaegan, the watershed-crest-line in South Korea.%
\end{abstract}%



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\textbf{Hosted file}

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