Floor litter can reduce the amount of water reaching the soil layer through rainfall interception. The rainfall interception capacity of floor litter varies with the physical features of the litter and rainfall characteristics. This study aimed to define the maximum and minimum interception storages ($C_{mx}, C_{mn}$) of litter layers using rainfall simulation experiments, and examine the effects of litter type and rainfall characteristics on rainfall retention and drainage processes that occur in the litter layer. Different types of needle-leaf and broadleaf litters were used: Abies holophylla, Pinus strobus, Pinus rigida, Quercus acutissima, Quercus variabilis, and Sorbus alnifolia. Our results indicate a wide variation in interception storage values of needle leaf litter, regardless of the rainfall intensity and duration. The A. holophylla needle-leaf litter showed the highest $C_{mx}$ and $C_{mn}$ values owing to its short length and low porosity. Conversely, the lowest interception storage values were determined for the P. strobus needle leaf litter. No significant differences in interception storage were established for the broadleaf litter. Moreover, except for A. holophylla litter, the broadleaf litter retained more water than the needle leaf litter. An increase in the intensity or duration of rainfall events leads to an increase in the water retention storage of litter. However, these factors do not influence the litter’s drainage capacity, which depends primarily on the force of gravity.

Forest fires can have a direct and immediate impact on soil properties, particularly soil water repellency. This study investigated the direct impacts of the Gangneung forest fire of 2019 on soil properties and the spatial variability of soil water repellency with vegetation burn severity in the Korean red pine (Pinus densiflora Siebold and Zucc) forest of South Korea. A total of 36 soil samples were collected at depth intervals of 0–5 cm, 10–15 cm, and 20–25 cm from three burned sites, representing surface-fuel consumption (SC), foliage necrosis (FN), and crown-fuel consumption (CC), respectively. An unburned site was also used as a control. Soil properties such as soil texture, pH, bulk density, electrical conductivity (EC), total organic carbon (TOC), and cation exchange capacity (CEC) were analyzed in the laboratory. The increase in the sand fraction near the soil surface after a fire was associated with changes in silt and clay fractions. Moderate to high vegetation burn severity at the FN and CC sites caused a decrease in soil pH due to the thermal destruction of kaolinite mineral structure, but organic matter combustion on the soil surface increased soil pH at the SC site. Forest fires led to increases in total organic carbon at the FN and SC sites, owing to the external input of heat damaged foliage and burnt materials. Molarity of an ethanol droplet (MED) tests were also conducted to measure the presence and intensity of soil water repellency from different locations and soil depths. MED tests showed that vegetation burn severity was important for determining the strength of water repellency, because severely burned sites tended to have stronger water repellency of soil than slightly burned sites. Unburned soils had very hydrophilic characteristics across soil depths, but a considerably thick hydrophobic layer was found in severely burned sites. The soil water repellency tended to be stronger on steep (>30°) slopes than on gentle (<15°) slopes.

Typical small shovel logging system, using manual felling and extraction by small crawler excavator with grapple and crawler carriers, is the predominant logging method in the Republic of Korea, due to the associated high productivity. The trails with ground pressure and one more passes of the shovel and carriers may lead to soil compaction. However, impacts of these bunching-extraction technologies on physical and hydrological properties of soils are not well known. The main objectives of this study were to: (1) determine the bulk density, porosity, and saturated hydraulic conductivity of soils in logging operation areas with three different disturbance types (i.e., undisturbed areas [UAs], bladed trails [BTs], and skid trails [STs]), and (2) compare soil compaction between these disturbance types. The most intense compaction occurred within BTs and STs, with increased bulk density and reduced porosity and hydraulic conductivity. Soil bulk density increased by 27-53% at all depths in BTs and STs compared to UAs, while porosity decreased by 23-49%. On average, saturated hydraulic conductivity at depths of 0-20 cm decreased from 337.5 mm h-1 to 30.5 mm h-1 in the most compacted sections of BTs and STs. Skid trails, which are characterized by trail construction and frequent vehicle movement, also caused greater impacts on soil compaction than BTs. This study provides useful insights to aid forest consultants and field managers in planning more environmentally sound mechanized logging operations.

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.