The water retention capacity of forest leaf litter was estimated through lysimeter measurements under field conditions. Six lysimeters were placed in Pinus koraiensis and Quercus acutissima forests and filled with the surrounding leaf litter to represent the effects of litter type on the water retention capacity. Two years of measurements for rainfall and litter weight have been conducted in all lysimeters at 30 min intervals. Field measurements showed that P. koraiensis litter stored more water during rainfall periods than did Q. acutissima litter. As a result, immediately after the cessation of rainfall, 1.82 mm and 3.00 mm of water were retained per unit mass of Q. acutissima and P. koraiensis litter, respectively. Following rainfall, after the gravitational flow had entirely drained, the remaining water adhered to the litter was estimated to be 1.66 ± 1.72 mm and 2.72 ± 2.82 mm per unit mass per rainfall event for Q. acutissima and P. koraiensis litter, respectively. During the study period, approximately 83.7% of incident rainfall drained into the uppermost soil layer below the Q. acutissima litter, whereas 84.5% of rainfall percolated through the P. koraiensis litter. The moisture depletion curves indicated that 50% of the water retained in the Q. acutissima and P. koraiensis litter was lost via evaporation within 27 h and 90 h after the cessation of rainfall, respectively. This study demonstrated the water retention storage of leaf litter and its contribution to the water balance over floor litter according to litter and rainfall characteristics. The results also proved that lysimetry is a reliable method to quantify the variation of litter moisture under natural conditions.

Fires can alter the hydraulic properties of burned soils through the consumption of organic matter on the ground surface. This study examined the effects of rainfall on the presence of soil pore clogging with varying ash layer thickness using laboratory rainfall simulator experiments. The image analysis with resin impregnation showed that rainfall impact caused plugging of soil pores at 22.2% with soil particles and 14.3% with ash particles on near surface soils (0–5 mm below). High rainfall intensities enhanced soil pore clogging by ash particles, particularly at shallow soil depths (0–10 mm). Ash deposits on the soil surface increased the water-absorbing capacity of ash-covered soils compared with that of bare soils. The rainfall simulation experiments also showed that ash cover led to a reduction in soil hydraulic conductivity, owing to the combined effects of surface crust formation and soil pore clogging. The complementary effects of soil pore clogging and water absorption by ash cover could hamper the accurate understanding of the soil hydrologic processes in burned soils.

Forest fire occurrence prediction remains challenging despite advances in operational fire danger rating systems. In South Korea, the Korea Forest Fire Danger Rating Index (KFDRI) incorporates meteorological conditions, terrain (elevation, aspect), and forest type to assess regional fire danger. While KFDRI successfully assesses environmental fire danger at the pixel level, it does not explicitly account for human activity patterns that create substantial occurrence variability among locations with similar environmental conditions. This limitation is critical in human-dominated landscapes where where the main source of fire occurrence is anthropogenic. This study developed a Random Forest (RF) model to predict forest fire occurrence probability and propose management priorities during the forest fire prevention season (November–May) along the eastern coast of Korea, explicitly integrating human proximity variables (distance to agricultural areas and roads) with topographical (elevation, slope, aspect), surface fuel load, and meteorological variables (SMAP soil moisture, cumulative precipitation). Using forest fire occurrence records (1112 fire occurrence records) and background samples from 2015 to 2024, the model was trained with monthly stratified sampling and 10-fold cross-validation. The model achieved stable classification performance, with an overall F1-score of 0.515 and accuracy of 0.733. According to the SHAP (SHapley Additive exPlanations) analysis, distance to agricultural areas, elevation, slope, aspect, 5-day cumulative precipitation, and forest type were the most influential predictors. In particular, occurrence probability tended to increase in areas close to agricultural land (<180 m), at low elevations (≤200 m), on moderately steep slopes (≥8°), on south- and west-facing aspects, and under dried conditions. These results emphasize that fire occurrence risk is primarily structured by human proximity within areas of similar environmental danger. We propose an operational integration in which the RF model provides a 30 m “where-to-focus” occurrence layer that is used alongside KFDRI’s daily danger rating to prioritize prevention and patrol efforts.

Fires can burn canopy fuel and replace leafy crowns with charred branches and trunks, thereby affecting hydrological flow and water chemistry. However, little is known about the effects of fire on throughfall volumes and chemical fluxes in temperate forests. Therefore, we aimed to monitor the volume and chemistry of throughfall in pine trees (Pinus densiflora) damaged by the 2022 Gangneung-Donghae Forest fire in the Republic of Korea. Immediately after the forest fire, funnel-type measurements were performed to collect the throughfall beneath five trees at foliage necrosis and crown consumption sites. The amount of water that penetrated in a specified period was continually measured and analyzed in terms of the water quality components. Crown consumption resulted in the passage of more water due to the removal of the tree canopy; however, the ratio of throughfall to total rainfall remained constant as the rainfall amount increased. The throughfall volume was not significantly different owing to the fire damage. The solute concentrations of Ca and TOC at the crown consumption site were higher than those at the foliage necrosis site after the fire; however, no significant difference was observed three months after the fire. In this study, the changes in the amount and water quality of throughfall due to fire were examined over a relatively short period, providing fundamental data for nutrient cycling management of wildfire-damaged soil.

The presence of macroroughness elements directly affects the flow velocity in mountain headwater streams. Hydraulic roughness is the dominant resistance to flow caused by objects protruding into the water, but it is not measurable in the field. This study quantified the reach-average hydraulic roughness based on the channel morphology in two mountain streams. The average flow velocities of the reaches were measured using the dye-tracing method. The magnitude of the hydraulic roughness was derived from the grain size of the streambed materials (D₅₀ and D₈₄) and the cross-sectional/longitudinal bed roughness. The observations for low flows (0.04–0.43 m²/s discharge per unit width) indicated that the longitudinal 90% inter-percentile range ($IP{R}_{90}^L$) seemed to have considerable merit in examining the influences of large roughness elements on flow conveyance. A dimensionless hydraulic geometry relation that can reflect the field measurements over a limited range of hydraulic characteristics was also developed for estimating the reach-average flow velocity in steep and rough streams. Thus, the research framework used appears to provide a reliable method of quantifying reach-average hydraulic roughness from local data in mountain headwater streams.

Litter fuel moisture governs the ignition and early spread of forest fires, yet its meteorological controls in Korean conifer and broadleaf stands, and the correspondence of existing fire-weather index classes to locally observed moisture, remain poorly characterised. We directly measured litter fuel moisture in paired conifer and broadleaf stands at four regions in Korea during one spring fire season and classified moisture class (Dry/Intermediate/Moist) from 26 meteorological and soil predictors under time-blocked, nested cross-validation. Five classifiers performed moderately and indistinguishably (macro-F1 0.39–0.51; quadratic weighted kappa 0.20–0.47), all above naive baselines, and performance declined under leave-one-region-out transfer. A multiple-testing-corrected interaction test detected a forest-type-dependent response only for soil water content, and neither a forest-type indicator nor separate per-type models improved prediction over a single pooled model. The Fine Fuel Moisture Code (FFMC) was significantly correlated with measured litter moisture (r = −0.57 conifer, −0.48 broadleaf) and was more sensitive to Dry conditions than any classifier, but its ignition-probability-derived class boundary corresponded weakly to the observed classes, and a moisture-equivalent boundary produced a complementary rather than superior error pattern. Meteorological data provide site-calibrated discrimination of litter-moisture class; forest-type differences are present but narrow, and FFMC class boundaries require local calibration before operational use.