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.

Changes in water quality were examined during selected rainfall events in a headwater stream severely damaged by a forest fire on 21–23 February 2021. Seven water quality parameters were analyzed: pH, electrical conductivity, biochemical oxygen demand, chemical oxygen demand, total suspended solids (TSS), total nitrogen, and total phosphorous. First-flush effect and event mean concentration analyses were conducted in spring (dry season) and summer (wet season) immediately after the forest fire. In spring, the non-biodegradable organic matter concentrations increased along with pollutants related to ash-enhanced soil water repellency in water affected by first-flush effects. In summer, TSS and nutrient concentrations increased along with pollutants related to fire-induced soil surface disturbances after a series of rainfall events. First-flush analyses indicated that cumulative pollutant loads were greater in the spring than in the summer due to a higher concentration of forest fire ash in the early storm runoff immediately after the forest fire. The event mean concentrations revealed that pollutant loads were associated with both forest fire ash and storm events in the immediate post-fire period. Therefore, this study indicated that water quality along a headwater stream is affected by short-term effects of large and intense forest fires and rainfall seasonality.