In South Korea, since small forest catchments are located upstream of most river basins, the baseflow from these catchments is important for a clean water supply to downstream areas. Baseflow recession analysis is widely recognized as a valuable tool for estimating the baseflow component of the stream hydrograph. However, few studies have applied this tool to small forest catchments. So, this study was conducted to assess the applicability of the recession analysis methods proposed in previous studies. The data used were long-term rainfall-runoff data from 1982 to 2011 in the Gwangneung coniferous (GC) and deciduous (GD) forest catchment in Gyeonggi-do, South Korea. For the applicability assessment, six recession constant estimation methods, which were used by previous studies, were selected. The recession constants of the GC and GD catchments were calculated, and applicability assessments were conducted by comparing the recession predictions and baseflow separations. As a result, the recession constants for GC and GD were 0.8480 and 0.9235, respectively. This clear difference may be due to the different forest cover in each area. The correlation regression line, AR(1) model, and the Vogel and Kroll method showed lower error rates and appropriate baseflow indexes compared with other methods.

The saturated hydraulic conductivity ($K_s$) is one of the most important soil properties for many hydrological simulation models. Especially in South Korea, analyzing the $K_s$ of the forest soil is essential for understanding the water cycle throughout the country, because forests cover almost two-thirds of the whole country. However, few studies have focused on the forest soil in the temperate climate zone on a nationwide scale. In this study, 1456 forest soil samples were collected throughout South Korea and pedo-transfer functions employed to predict the $K_s$ were developed. The non-linearities of the soil and topographic features were considered with the pretreatment of variables, and the variance inflation factor was used for treating the multicollinearity problem. The forest stand and site characteristics were also categorized by an ANOVA and post hoc test due to their diversity. As a result, the $K_s$ values were different for various forest stands and site characteristics, which was statistically significant. Additionally, the model performance was higher when both soil properties and topographic features were considered. The sensitivity analysis showed that the $K_s$ was highly affected by the bulk density, sand fraction, slope, and upper catchment area. Therefore, the topographic features were as important in predicting the $K_s$ as the soil properties of the forest soil.

Soil water holding capacities (SWHCs) are among the most important factors for understanding the water cycle in forested catchments because they control available plant water that supports evapotranspiration. The direct determination of SWHCs, however, is time consuming and expensive, so many pedotransfer functions (PTFs) and digital soil mapping (DSM) models have been developed for predicting SWHCs. Thus, it is important to select the correct soil properties, topographies, and environmental features when developing a prediction model, as well as to understand the interrelationships among variables. In this study, we collected soil samples at 971 forest sites and developed PTF and DSM models for predicting three kinds of SWHCs: saturated water content (${\theta }_S$) and water content at pF1.8 and pF2.7 (${\theta }_{1.8}$ and ${\theta }_{2.7}$). Important explanatory variables for SWHC prediction were selected from two variable importance analyses. Correlation matrix and sensitivity analysis based on the developed models showed that, as the matric suction changed, the soil physical and chemical properties that influence the SWHCs changed, i.e., soil structure rather than soil particle distribution at ${\theta }_S$, coarse soil particles at ${\theta }_{1.8}$, and finer soil particle at ${\theta }_{2.7}$. In addition, organic matter had a considerable influence on all SWHCs. Among the topographic features, elevation was the most influential, and it was closely related to the geological variability of bedrock and soil properties. Aspect was highly related to vegetation, confirming that it was an important variable for DSM modeling. Information about important variables and their interrelationship can be used to strengthen PTFs and DSM models for future research.

Forests and water are closely related to each other. Thus, forest management is crucial for the sustainable clean water supply. Forest thinning is one of the fundamental forest management practices, as it can change runoff by controlling the density of trees. In this study, the effect of forest thinning on long-term runoff changes was evaluated, based on the long-term rainfall-runoff data of a coniferous plantation forest catchment in Korea. From the double mass curve and Pettitt’s test, a statistically significant increase in runoff rates was identified. A simple linear regression model of the double mass curve can successfully quantify the net effect of forest thinning on the runoff increase. Furthermore, it was also confirmed that forest thinning does not significantly increase the risk of flooding. About ten years after forest thinning, crown closure rates of the coniferous plantation forest reached a level similar to the pre-thinning period, and runoff rates returned to the pre-thinning level, due to forest growth. As a result of this study, a proposed direction for Korea’s forest policy for water resource management is presented for the future.

Machine learning models are now capable of delivering coveted digital soil mapping (DSM) benefits (e.g., field capacity (FC) prediction); therefore, determining the optimal sample sites and sample size is essential to maximize the training efficacy. We solve this with a novel optimal sampling algorithm that allows the authentic augmentation of insufficient soil features using machine learning predictive uncertainty. Nine hundred and fifty-three forest soil samples and geographically referenced forest information were used to develop predictive models, and FCs in South Korea were estimated with six predictor set hierarchies. Random forest and gradient boosting models were used for estimation since tree-based models had better predictive performance than other machine learning algorithms. There was a significant relationship between model predictive uncertainties and training data distribution, where higher uncertainties were distributed in the data scarcity area. Further, we confirmed that the predictive uncertainties decreased when additional sample sites were added to the training data. Environmental covariate information of each grid cell in South Korea was then used to select the sampling sites. Optimal sites were coordinated at the cell having the highest predictive uncertainty, and the sample size was determined using the predictable rate. This intuitive method can be generalized to improve global DSM.

In ground-based harvesting, machine traffic can cause soil physical disturbances including excessive compaction, the displacement of the soil surface and topsoil, and rutting. These impacts can indirectly hamper seedling survival and tree growth because of reduced soil permeability and root growth. However, the extent of soil disturbance in mechanized row thinning by harvester and forwarder systems in South Korea is unclear. Therefore, our objectives were to determine the impacts of two types of harvesters, soil textures, and extraction methods on soil compaction and rutting in a Korean Pine stand. The results showed that the machine passes affected soil disturbances. The soil bulk density inside the tracks (at 0–10 and 10–20 cm soil depths) after harvester passes (wheeled vs. tracked) increased by 17 and 27% and 30 and 20%, respectively. The largest impact was recorded for the forwarding track and significant differences were observed between the track and reference locations. Furthermore, the rutting depth in the forwarding trails was significantly higher than in the harvester trails. Thus, the application of a brush mat on harvester and forwarder trails may reduce machine-induced soil compaction. These results provide useful information to help forest engineers and field managers design environmentally sound ground-based harvesting operations.