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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.

The severity and incidence of flash floods are increasing in forested regions, causing significant harm to residents and the environment. Consequently, accurate estimation of flood peaks is crucial. As conventional physically based prediction models reflect the traits of only a small number of areas, applying them in ungauged catchments is challenging. The interrelationship between catchment characteristics and flood features to estimate flood peaks in ungauged areas remains underexplored, and evaluation standards for the appropriate number of flood events to include during data collection to ensure effective flood peak prediction have not been established. Therefore, we developed a machine-learning predictive model for flood peaks in ungauged areas and determined the minimum number of flood events required for effective prediction. We employed rainfall-runoff data and catchment characteristics for estimating flood peaks. The applicability of the machine learning model for ungauged areas was confirmed by the high predictive performance. Even with the addition of rainfall-runoff data from ungauged areas, the predictive performance did not significantly improve when sufficient flood data were used as input data. This criterion could facilitate the determination of the minimum number of flood events for developing adequate flood peak predictive models.

Continuously increasing demand for freshwater makes many scientists study water yield prediction. In South Korea, of which two-third is covered by forests, understanding the water cycle especially in forests is even more important. Throughfall is penetrated rainfall through the tree canopy and it is the basic source for groundwater recharge which is directly related to the water yield on the catchment scale. Therefore, understanding the throughfall characteristics is essential for sustainable water management. This study is conducted to develop simple throughfall simulation models and estimate prediction uncertainty from developed models by different forest stand characteristics. National Institute of Forest Service (NIFoS) has collected throughfall data for 2 years from 7 different forest stand sites. Rutter model was used for the structure of the simple throughfall simulation model and it had several parameters for simulating. And over a million Monte Carlo experiments and generalized likelihood uncertainty estimation (GLUE) methodology were used for selecting parameters sets of behavioural models from comparing simulated throughfall and observed throughfall. From the range of behaviours in a period, we successfully estimated the prediction uncertainty. We also compared the features of behavioural parameter sets by different forest stand characteristics. We expect developed models can be applied for several forest stands in South Korea with various physical-based hydrological models.

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&rsquo, 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&rsquo, s forest policy for water resource management is presented for the future.

This study was carried out to investigate coefficient and exponent of runoff energy in MUSLE for small forest watershed, Hwachoen watershed in Gangwon-do. For 15 rainfall events, runoff volume, peak discharge and sediment yield were measured and these data were used to calculate coefficient and exponent of runoff energy. The results of this study showed that factors of MUSLE were affected by slope steepness. The coefficient and exponent of runoff energy were validated with coefficient of efficiency of 0.92 and these values were suggested to 0.002 and 0.81 respectively. The comparison of the coefficients and exponents between Hwacheon and other forest watersheds showed that these values would reflect the effect of forest management within watershed.

This study was carried to figure out appropriate method for time of concentration used in the Rational method at design flood estimation. For 6 small forest watershed, time of concentration was obtained from time of concentration formulas including Kirpich Rziha, Kraven (I), Kraven (II) and SCS lag method and the results from these were compared to the results from cross correlation function based on real rainfall runoff events. The results of cross correlation function ranged from 12.9 to 28.8 minutes and the average difference between SCS lag method and cross correlation function was 2.5 minutes. SCS lag method would be feasible to estimate time of concentration in small forest watershed.

This study was carried out to investigate the characteristics of suspended solid concentration in small forest watershed, Hwacheon, Gangwondo. For five rainfall events from July 2013 to August 2013, rainfall, discharge, and suspended solid load has been measured. The results showed that the fist flush effect was observed for suspended solid in each rainfall event, sediment rating curve was obtained with at rising limb and at falling limb, and EMC (event mean concentration) of suspended solid was calculated to 9.4 mg/L. EMC was compared to the values from the watershed that has various land use types and EMC from forest watershed was much lower that from the crop, paddy or low covered forest watershed.

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.