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Article

Characteristics of Stream Water Quality on Draining of Planted Coniferous and Natural Deciduous Forest Catchments in South Korea

by
Sooyoun Nam
,
Qiwen Li
,
Byoungki Choi
,
Hyung Tae Choi
and
Honggeun Lim
*
Forest Disaster and Environmental Research Department, National Institute of Forest Science, Seoul 02455, Republic of Korea
*
Author to whom correspondence should be addressed.
Water 2025, 17(10), 1535; https://doi.org/10.3390/w17101535
Submission received: 25 April 2025 / Revised: 16 May 2025 / Accepted: 18 May 2025 / Published: 20 May 2025
(This article belongs to the Special Issue Soil Erosion and Sedimentation by Water)
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Abstract

:
The quality characteristics of runoff water during selected precipitation events in planted coniferous (CP) and natural deciduous (DN) forest stands in Pocheon-si, 27.0 km north of Seoul, were assessed via the mean event concentrations and discharge loads. The relationship between stream water quality and the runoff time differential (dQ/dt) indicated that the characteristics of the latter differed during the rising and falling stages of the two catchments. Pearson’s product moment correlation analysis revealed that chemical oxygen demand was significantly correlated with total organic carbon in the rising and falling limbs of the two catchments. When discharge loads were transported with actual precipitation events, the event load at the two sites increased with increasing discharge load. In particular, the total organic carbon and total nitrogen were higher in the CP catchment than in the DN catchment, whereas biological oxygen demand, total suspended solids, total nitrogen, and total phosphorus were higher in the DN catchment than in the CP catchment. Sequences of high and intense precipitation elevated discharge loads, with differences in loads related to the vegetation conditions in headwater areas (≤100 ha) with steep slopes (>20°) and narrow valleys.

1. Introduction

Stream water quality differs both spatially and temporally [1,2,3,4]. Arocena [5] explained that forest canopies and floors play important roles in nutrient cycling, stream water chemistry, and stream water quality in forest ecosystems. Several researchers [6,7,8,9] have also indicated that forest tree species differ in their litter quality and quantity, which influence the decomposition rate, nutrient release, and cycling in forest ecosystems. Haygarth and Jarvis [10] explained that hydrological events from storms and long-term precipitation are responsible for transporting high percentages of catchment substance loads, such as sediment, phosphorus, and nitrogen. Constituent concentrations can vary on different timescales, including event [11], daily [12], seasonal [13], and inter-annual scales [14]. Therefore, a range of influencing factors of natural or anthropogenic origins must be identified in catchments, and their spatiotemporal variabilities must be determined to improve and manage stream water quality and develop effective treatment strategies.
On the Korean Peninsula, 63.2% of the land is covered by forests, which includes 38.8% coniferous, 33.4% deciduous, and 27.8% mixed forests [15]. Headwater streams (i.e., third-order streams) account for 88.9% of the nationwide cumulative stream length [16]. In particular, headwater streams represent a highly variable source of contaminants because the amount and quality of each contaminant can be modified by in-channel storage, dilution, biological uptake, diminution, and chemical transformation [17]. For instance, Meyer et al. [18] reported that the upstream region is diverse as well as species-rich and helps to maintain healthy mountain stream ecosystems throughout the water system. Park et al. [19] explained that water from headwater streams, which reflect first-class water quality standards, flows into downstream rivers and lakes and affects drinking water. Therefore, forested headwater streams are an important source of potable water for downstream communities worldwide, and, in several cases, they are managed specifically for this purpose [20].
Under these conditions, the sustainable supply of clean water of a water system is determined by water purification, which occurs as water drains through planted coniferous and natural deciduous forests [21,22]. To understand the distribution and properties of stream water quality, forest conditions (e.g., forest floor, canopy cover, and tree species) must be determined to develop essential water resource strategies because of the longer water routing processes and greater storage capacity of these ecosystems [23,24]. The introduction of chemicals from different tree species to soil and stream water has not been well documented because tree species with different chemical and physical forest floor characteristics can influence the amounts of surface and subsurface flow and release nutrients that affect stream water quality [25]. Similarly, Eisalou et al. [23] reported that forest stands are important components of forest ecosystems that affect the water quality in watershed streams. However, domestic studies have only been conducted in a few geographical areas [26,27,28,29]. Hence, insufficient information is available on how forest conditions affect the responses of stream water chemical quality to precipitation events.
Additionally, the successful management of stream water quality depends on adequate and appropriate monitoring [30]. Although single events can cause significant changes in stream nutrients and biogeochemical dynamics [31], such changes may not be noticed using traditional grab sampling techniques. Dramatic increases in sampling frequency, made possible by recent developments in continuous water quality monitoring [32,33,34,35], permit an increased quantitative understanding of the biogeochemical responses to these events. To optimize the mitigation and management action plans in catchments with varying substance sources, the processes and mechanisms that affect the mobilization of the total suspended solids, particulate phosphorus, and other particle bound polluting loads must be better understood [36,37]. Thus, the impacts of polluting loads on stream quality need to be investigated in headwater areas. Moreover, adaptive water management strategies must be applied to identify important indicators of aquatic ecosystem health [38,39]. Therefore, this study aimed to (1) investigate stream water quality to precipitation and runoff responses during an actual precipitation event, (2) identify the potential factors that affect stream water quality, and (3) quantify the loading substances in stream water during precipitation events in two different forested catchments.

2. Materials and Methods

2.1. Study Site

This study was conducted in the Gwangneung Forest catchment in Pocheon-si, which is 27.0 km north of Seoul (Figure 1). Geologically, the area comprises metamorphic rocks covered with brown forest soil. The mean annual precipitation for 20 years (2002–2021) at the Gwangneung Automatic Weather Station (AWS) was 1349 mm (standard deviation (SD) of 365 mm), of which 68% occurred from July to September. The mean annual temperature is 10 °C (SD 1 °C). In this study, Normalized Difference Vegetation Index (NDVI) data were obtained from the Sentinel-2 dataset. For the two catchments, the NDVI values were 0.27–0.89 and 0.19–0.87 based on available data series from 2019 to 2021.
Figure 1 presents the topography of the catchments. Two paired adjacent headwater catchments (planted coniferous (CP) and natural deciduous (DN)) were established in the Gwangneung Forest catchment. The two catchments are 1.8 km apart and managed by the National Institute of Forest Science (NiFoS). The CP and DN catchments have areas of 0.7 and 22.0 ha (Figure 1). The elevations of the CP and DN catchments range from 165 m to 306 m and 241 m to 460 m above sea level, respectively. Hillslope gradients ranged from 0.5° to 33.0° (mean of 22.6°) in the CP catchment and 0.0 to 54.2° (mean of 20.0°) in the DN catchment. The forest-type map (1:5000) revealed that the CP catchment was dominated by Korean pine (Pinus koraiensis), and the DN catchment was dominated by Quercus, Fraxinus, and Zelkova species.
The CP catchment had relatively low flow rates and exhibited ephemeral stream characteristics, whereas the DN catchment had relatively high flow rates and exhibited perennial stream characteristics during normal periods. The CP catchment was originally managed as hillslope agricultural land via slash-and-burn farming [40] but was reforested with coniferous trees in 1976. A total of 45% of stems were uniformly thinned within the two catchments in 1996.

2.2. Sampling and Observation

We observed thirteen selected precipitation events concentrated during the summer season (July–September) in 2021. Five precipitation events occurred in the CP catchment and eight occurred in the DN catchment. Gauging stations managed by NiFoS were used to monitor the streams (Figure 1).
Precipitation was monitored during actual rain events in the open area of each catchment outlet using a tipping-bucket rain gauge with a data logger (HOBO, Onset Computer Corporation, Bourne, MA, USA). Subsequently, we calculated the precipitation in 2 h intervals based on the collected water samples. The event precipitation, maximum precipitation intensity, and antecedent precipitation index (API) were calculated by monitoring the occurrence of the precipitation events in the forested mountainous catchments. API refers to the amount of precipitation that occurs in the period before a precipitation event and affects the current precipitation and runoff responses [41,42]. In this study, the accumulated precipitation was calculated 2, 5, 7, and 30 days before the start of the precipitation event [43,44,45]. Here, API2, API5, and API7 reflect the soil moisture conditions in the surface soil, whereas API30 represents the moisture in the deep soil matrix [46,47,48,49].
Water levels were monitored in 10 min intervals in sharp-crested weirs (i.e., 90° and 120° V-notch weirs) using a capacitance water level recorder (OTT-Orpheus Mini Water Level Logger, OTT Messtechnik, Kempten, Germany) for each catchment outlet. Runoff (mm) was divided by the area of each catchment (ha).
An ISCO automatic water sampler (Teledyne ISCO Inc., Lincoln, NE, USA) was installed to collect stream water samples in 2 h intervals during precipitation events. A water sampler using a tipping-bucket rain gauge was installed to capture precipitation. Water samples were immediately transported to the laboratory. For some events, the peak runoff and recession limb of the hydrograph were included as storm-flow samples.

2.3. Water Sample Analysis

Stream water quality was analyzed to determine the following polluting loads: pH, electrical conductivity (EC), biological oxygen demand (BOD), chemical oxygen demand (COD), total organic carbon (TOC), BOD/COD (B/C) ratio, total suspended solids (TSSs), total nitrogen (TN), and total phosphorus (TP).
pH and EC were measured using a pH meter (HM-30R, DKK-TOA Corp., Tokyo, Japan) and an EC meter (CM-30R, DKK-TOA Corp., Tokyo, Japan), respectively. BOD was determined using the BOD5 method, which requires five days of incubation at 20 °C using a specially designed incubator [50]. CODMn was determined using the acid titration method. TOC was determined by high-temperature combustion using a TOC analyzer (TOC-L, Shimadzu Corp., Kyoto, Japan). The organic composition was described based on the biodegradable BOD/COD (B/C) ratio, as organic-matter-containing wastewater is readily broken down in the environment [51,52]. TSS was measured using filtration methods employing membrane filters (Whatman GF/F 47 mm glass fiber filter) and a vacuum pump (gravimetric method). TN was determined using the continuous-flow method with an autoanalyzer (SYNCA, BLTEC Corp., Tokyo, Japan). TP was analyzed using a UV–Vis spectrometer (Cary 60, Varian Corp., Palo Alto, CA, USA).

2.4. Data Analysis

To determine the concentration and discharge of stream water, we applied the time differential of water discharge (dQ/dt) according to the hydrograph regime, which included rising (dQ/dt > 0) and falling (dQ/dt < 0) limbs [53]. This approach was required because stream water concentrations tend to have different values at identical stream discharges because of hysteresis effects, which represent the primary drawback to applying the transport material curve for precipitation events [54]. After that, we used Pearson’s product moment correlation analysis, linear regression analysis, and principal component analysis (PCA) to identify the polluting loads contributed by variables affecting stream water quality in the CP and DN catchments during the rising and falling limbs.
The event mean concentration (EMC) for the receiving water was determined by dividing the event load by the precipitation event volume according to Göbel et al. [55]. These average EMCs can normalize the fluxes in the water discharge amount and can be used to compare different events at different times or sites [56,57]. The EMC was calculated based on the relationship between the mass of the substance contained in the water discharge event and the total volume of flow during the event [58].
The different discharge loads for single precipitation events were calculated by multiplying the load concentrations with the corresponding discharges [57,59]. The load of a precipitation event is the sum of these products and equals the area of the time series plotted against the product of the discharge and load concentration [60]. Thus, the load was calculated based on the relationship between different loads and stream water discharges for single precipitation events [57,61].
The two-sample Student’s t-test and non-parametric Mann–Whitney statistical test were used based on assumptions of normality and variance equality, e.g., [62], to compare the mean differences in the CP and DN catchments. All statistical analyses were performed in SPSS (Statistical Package for Social Sciences, version 29) and R (version 4.1.2).

3. Results and Discussion

3.1. Stream Water Quality and Event Precipitation–Runoff Responses

Event precipitation was 6.0–76.2 mm with 5.0–32.0 mm for a max. of 2 h of precipitation during the observed precipitation events in two catchments. Additionally, API2, API5, and API7 ranged from 0.0 to 58.5 mm (Table 1). API30 was 41.6–143.1 mm for the two catchments (Table 1). The NDVIs from June to September were 0.78–0.89 and 0.36–0.84 for the CP and DN catchments, respectively.
Under these conditions, the total runoff was 0.23–3.57 and 0.65–6.19 mm/event in the CP and DN catchments, respectively. The mean pH value ranged from 6.2 to 7.0 and 7.0 to 7.4, and the EC ranged from 79.7 to 122.8 and 59.1 to 82.0 μS/cm. The differences in the EC and pH could have been associated with potential soil buffering capacity and/or ion leaching between the two catchments. Here, the coniferous trees could have promoted forest soil acidification (resulting in lower soil pH and lower base saturation) compared to deciduous trees. This might have been due to coniferous litter being more resistant to biological decomposition and leaching more organic acids [63,64,65]. Similarly, Dijkstra et al. [66] showed that ion exchange could influence the soil neutralization in oak and beech forests.
The mean values of the organic matter (i.e., BOD, COD, and TOC) were 1.2–12.6 and 1.6–10.0 for the CP and DN catchments, respectively. The B/C ratio ranged from 0.10 to 0.18 in the CP catchment and 0.08 to 0.73 in the DN catchment. The mean TSS concentrations ranged from 1.8 to 3.6 and 0.8 to 16.4 mg/L, and the nutrient concentrations (i.e., TN and TP) ranged from 0.008 to 12.7 and 0.005 to 1.7 mg/L (Table 1). The precipitation characteristics (i.e., event precipitation, max. 2 h precipitation, and API) can considerably affect the pollutant emissions of forest stands, e.g., [67,68,69]. For these reasons, topographic characteristics (i.e., elevation, slope, and aspect) affect rainfall and surface runoff by influencing the hydrothermal distribution and vegetation growth in the catchment, which can affect the generation and transport of non-point source pollution [70]. Several researchers [71,72,73,74,75,76] have revealed that several factors (e.g., stand characteristics, rainfall characteristics, and environmental variables) affect precipitation event partitioning in forest stands. Moreover, the NDVI of the DN catchment was relatively higher than that of the CP catchment, while the spatial variability in the NDVI was lowest in the CP catchment and highest in the DN catchment. Our study was performed during the flood season (June–September), which generally corresponds to the growing season of the deciduous forests in the DN catchment. Therefore, the available potential water in the growing season may have been limited by the differences in the covered forest stands [70].
The time differential of runoff (dQ/dt) and both precipitation and runoff as well as stream water qualities showed rising and falling stages in the two catchments. The dQ/dt was based on interactions among precipitation, runoff (Figure 2a), and stream water quality variables (Figure 2b), even when the catchment and channel contributions to the stream flow were not precisely separated. The variables were scattered in relation to dQ/dt during the rising stage, particularly in the CP catchment.
Much of the chemical variation in stream water occurs during periods of increased water discharge [77]. Sampling was performed over one day; therefore, stormflow sampling sometimes occurred on the rising limb of the hydrograph and sometimes occurred on the falling limb of the hydrograph [54,78]. The stream water quality variables were scattered in relation to dQ/dt during the rising stage, particularly in the CP catchment. During the falling stage, the stream water quality variables were almost constant, and their values decreased with decreasing dQ/dt in both catchments (Figure 2b). This relationship between stream water quality variables and dQ/dt values observed in the CP and DN catchments indicated that the characteristics of the stream water quality variables differed during the rising and falling stages. Discharge and concentration hysteresis also occur whenever differences occur in the relative timing or form of solute and discharge responses [79].

3.2. Characteristics of Stream Water Quality Depended on Stream Water Flow

The distribution of the water quality in the two catchments was separated into rising and falling limbs (Figure 3). The TSS values in the two catchments were similar for the two limbs. In addition, the pH, BOD, and B/C ratio tended to be higher in the DN catchment than in the CP catchment, whereas the EC, COD, TOC, TN, and TP tended to be higher in the CP catchment than in the DN catchment. In total, eight water quality variables (i.e., pH, EC, B/C ratio, COD, TOC, TN, and TP) presented significant differences between the two catchments according to the two-sample Student’s t-test and non-parametric Mann–Whitney statistical test (p < 0.05) (Figure 3). In particular, summer storms (June–September), which are affected by the East Asian monsoon [80,81], caused sudden and ephemeral runoff formation, characterized by sharp, narrow hydrographs and short time lags after event precipitation [82]. This is because stream water concentration tends to have different values at identical stream discharges, as previously discussed [54].
To assess the correlation between the characteristics of the two catchments, the significance level (p) was set at 0.05 or less, which implied a high correlation depending on the rising and falling limbs (Table 2). The COD and TOC were significantly correlated in the rising and falling limbs for the two catchments (correlation coefficient: 0.71–0.99, p < 0.01) (Table 2). For a given water quality variable, the appropriate graph was selected based on the regression statistics (R2, p-value) and the significance of the linear regression coefficients, provided that the residuals of the linear regression were normally distributed [83]. Finally, the goodness-of-fit of the statistically significant results, e.g., [84], was evaluated using a scatter plot and simple linear regression of the COD versus TOC in the CP and DN catchments within the rising and falling limbs (R2 = 0.51–0.97, p-value ≤ 0.01). The COD increased linearly with increasing TOC in both limbs (Figure 4a,b). Moreover, the COD and TOC presented stronger correlations in the CP catchment than in the DN catchment.
In coniferous forests, organic matter is separated into positively and negatively charged ions with water flow in the hydrograph regimes. This is evident because a larger amount of organic matter, such as litter, is supplied to soil surfaces in a dissolved form, which results in higher COD and TOC concentrations during high event flows in coniferous forests, e.g., [85,86]. Coniferous litter is usually richer in organic matter and humic acids than deciduous litter [87]. Moreover, coniferous litter generally includes COD and TOC with more refractory (e.g., hydrophobic acid and lignin) and aromatic compounds and a relatively larger proportion of high-molecular-weight compounds than deciduous litter [88].

3.3. Stream Water Quality Affected Forest Stand Separated by Rising and Falling Limbs

The Kaiser–Meyer–Olkin (KMO) statistics and Bartlett’s test were calculated to examine the dataset’s suitability for determining how the selective stream water quality affected the two different forest stands in the rising and falling limbs [89]. The KMO’s sample adequacy value of 0.54 to 0.71 and the significance levels for Bartlett’s test being under 0.001 indicated that PCA could be implemented (Table 3). Using PCA, the three principal components in the two limbs accounted for 84.5 and 94.1% of the total variance, respectively. The values and proportions of variance and cumulative variance were explained based on the PCA. The high component loadings of components 1–3 varied more in the rising limb than in the falling limb (Table 3).
In the rising limb, the COD, TOC, and TP exhibited high loadings in component 1 in both catchments, whereas components 2 and 3 showed different loadings between the CP and DN catchments (Figure 5a). In the falling limb, the COD and TOC also had high loadings for component 1 in both catchments, the TP had high loadings in component 2, and different loadings in component 3 were observed between the CP and DN catchments (Figure 5b). The polluting load components in the two limbs were likely affected by the high COD and TOC, which were caused by the differences in the activity of the ecosystems in each catchment [90]. Although the response of concentration to discharge may be clear, it rarely takes a simple linear or curvilinear form [79]. When stream flows were increased by a precipitation event, higher concentrations of these parameters also occurred differently. This was likely associated with discharge and flow velocities under increasing precipitation and intensity as well as faster saturation of the soil in steep mountainous catchments [82,91]. For instance, Hendrickson and Kreiger [92] and Toler [93] observed cyclical relationships between discharge and the concentration of dissolved solids, whereby the concentration at a given discharge in the rising limb of the hydrograph differed from that at the same discharge in the falling limb. Similarly, Toler [93] reported that solute concentrations vary systematically with respect to rising and falling limb discharge on the storm hydrograph. Such variations often result in non-unique solute concentrations for a given value of stream discharge or hysteresis [94].

3.4. Discharge Load with Event Precipitation in Different Forest Stands

The EMCs of the polluting loads (i.e., BOD, COD, TOC, TSS, TN, and TP) in the streams were compared between the CP and DN catchments (Table 4). Here, the EMC of organic matter was 1.3–12.6 and 1.6–9.0 mg/L in the CP and DN catchments, respectively; the EMC of the TSSs was 2.2–4.3 and 0.9–18.7 mg/L in the CP and DN catchments, respectively; while the EMC of nutrients was 0.01–12.4 and 0.005–1.7 mg/L in the CP and DN catchments, respectively The EMCs of the organic matter and nutrients in the CP catchment tended to be greater than those in the DN catchment. The EMC of the BOD was only higher in the DN catchment than in the CP catchment.
The observed EMCs were within the ranges reported in some previous studies, although the results have been scarce under similar study catchment topographical conditions on the Korean Peninsula. For instance, Kang et al. [27] determined that the ranges of the EMCs (mg/L) were 0.8–13.4 for organic matter, 3.1–291.8 for TSSs, and 0.01–3.9 for nutrients in a deciduous forest catchment (21.7 ha). Yur and Kim [26] indicated that the ranges in the EMCs (mg/L) were 0.3–16.5 for organic matter, 0.1–37.0 for TSSs, and 0.1–2.2 for nutrients in a forested watershed (338 ha). In addition, Maniquiz et al. [95] identified that the EMC of the suspended solids in a natural deciduous forest (mean ± standard deviation, 14.8 ± 12.2 mg/L) was greater than in a planted coniferous forest (1.5 ± 1.8 mg/L).
The organic matter event loads ranged from 0.005 to 0.41 kg/ha/event in the CP catchment and from 0.01 to 0.29 kg/ha in the DN catchment; the TSS event load ranged from 0.01 to 0.15 kg/ha in the CP catchment and from 0.02 to 0.44 kg/ha in the DN catchment. The nutrient event loads ranged from 0.0001 to 0.42 kg/ha in the CP catchment and from 0.00003 to 0.08 kg/ha in the DN catchment. The EMCs and event loads of different loads can lead to the direct elution of soil surface material and forest litter into streams (Table 4). The primary factors influencing the loading of the loads in the stream water, associated with soil surface disturbances induced by the loss of surface cover and raindrop splash erosion, may have differed between the two forest stands [96,97].
To determine the discharge load in the stream water from the catchments, we changed the event load response to event precipitation. The event load at the two sites increased with increasing discharge load. Here, this was primarily represented by general polluting loads during the washing process with increased water flow (Figure 6). In addition, this was related to the increasing precipitation and intensity as well as higher discharge and flow velocities associated with the faster saturation of the soil in steep mountainous catchments [98]. The TOC and TN event loads of the CP catchment were greater than those of the DN catchment (Figure 6c,e); however, the BOD, TSS, and TP event loads of the CP catchment were lower than those of the DN catchment (Figure 6a,d), whereas the COD event load pattern was similar between the catchments (Figure 6c). This was largely attributable to differences in the stand characteristics in their response to the rainfall partitioning between the CP and DN catchments. Similarly, Hirobe et al. [99] and Klimaszyk and Rzymski [85] noted that different types of forest litter, including coniferous, mixed, and deciduous litter, may have different impacts on runoff chemistry due to varying quantities of chemical compounds in the litter produced by different tree species. Here, leftovers can accumulate differently as lignin, lipids, and waxes, depending on the forest stands, e.g., [100,101]. In addition, some fractions of dissolved organic matter can be sorbed more strongly by components of the solid soil (minerals, organic matter), systematically changing the stream water quality, e.g., [102,103].
This is consistent with our finding that the BOD levels (i.e., EMC and event load) were higher in the DN catchment (Table 4), which may be counterintuitive given the typically slower decomposition of coniferous litter. Similarly, Mo et al. [104] showed that the litter decomposition of mature, evergreen, broad-leaf forest stands was faster than that of pine forest stands. Several researchers [101,105] have reported that coniferous forests decompose more slowly than deciduous broad-leaf forests. The leaves slowly decompose, which may lead to oligotrophic conditions [106]. Thus, N retention mechanisms may have developed a relationship with the dominant vegetation and the climatic conditions.
For precipitation events that occur at the same time, the actual approaching rain time differs in mountainous terrain [107], which is characterized by steep hills and narrow valleys [108]. In particular, our small, forested headwater (≤100 ha) is located on steep mountainous terrain with steep slopes (>20°); thus, water flow could propagate much faster as channel flow [109] with low infiltration capacity [110]. In addition, steep slopes may promote water erosion [111]. Passalacqua et al. [112] also suggested that topography contributes to rapid floodwater flow and high peak flow as well as the movement of discharge loads. Under our topographical conditions, sequences of high and intense precipitation elevated discharge loads, with differences in loads related to the vegetation conditions. Consequently, the characteristics of the stream water quality could be largely attributed to the differences in the discharge loads in the forest stand characteristics between the CP and DN catchments.

4. Summary and Conclusions

We investigated the changes in stream water quality caused by different pollutants during precipitation events in two forest stands consisting of planted coniferous (CP) and natural deciduous (DN) forests during the flood season (June–September). The discharge loads in the runoff under different forest conditions, including forest stands, understory vegetation, overstory species, and management history, correlated between the two stands based on actual precipitation events. During the rising stage, the stream water quality variables were scattered in relation to the runoff time differential (dQ/dt), particularly in the CP catchment. During the falling stage, stream water quality variables were almost constant, and values decreased with decreasing dQ/dt in the two catchments. A significant relationship was observed between the COD and TOC for the two catchments in the rising and falling limbs. When increased discharge loads were reflected in event precipitation, both the TOC and TN were higher in the CP catchment than the DN catchment, while the BOD, TSS, TN, and TP were higher in the DN catchment than in the CP catchment. The high and intense precipitation that occurred during the flood season increased the discharge loads, with differences in loads related to the vegetation conditions in the catchments.
The representative forest stands (i.e., planted coniferous and natural deciduous forest) located in forested headwater areas (≤100 ha) with steep slopes (>20°) and narrow valleys are specific catchment topographical conditions on the Korean Peninsula. Our findings suggest that stream water can experience localized pollution during precipitation events in these forests. The effect of rapid water flow pathways on discharge loads in runoff has not yet been established during actual precipitation events in forested areas. Therefore, water quality evaluation strategies for forest stands that present different physical partitionings of rainfall and different chemical characteristics may provide insights into different ecosystem services and functions; thus, further research is required for adaptive different ecosystem services attributed to the characteristics of various forests.

Author Contributions

Conceptualization, S.N. and H.L.; methodology, S.N. and H.L.; software, S.N.; validation, S.N.; formal analysis, S.N. and Q.L.; investigation, Q.L. and H.L.; resources, H.L.; data curation, S.N.; writing—original draft preparation, S.N. and H.L.; writing—review and editing, S.N., B.C., and Q.L.; visualization, S.N.; supervision, H.T.C. and H.L.; project administration, B.C. and H.T.C.; funding acquisition, B.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not available publicly as they are from a project for obtaining specific research results and because of the intellectual property rights at the National Institute of Forest Science.

Conflicts of Interest

The authors declare no conflicts of interest.

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Water 17 01535 g001
Figure 1. Location of the study site and topographical map of the Gwangneung forest catchment. The photos provide an overview of the monitored catchments.
Figure 1. Location of the study site and topographical map of the Gwangneung forest catchment. The photos provide an overview of the monitored catchments.
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Figure 2. Relationships between the differential of water runoff (dQ/dt) and both (a) precipitation and runoff and (b) stream water quality (pH, EC, B/C ratio, BOD, COD, TOC, TSS, TN, and TP) in the CP (open circles) and DN (crosses) catchments.
Figure 2. Relationships between the differential of water runoff (dQ/dt) and both (a) precipitation and runoff and (b) stream water quality (pH, EC, B/C ratio, BOD, COD, TOC, TSS, TN, and TP) in the CP (open circles) and DN (crosses) catchments.
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Figure 3. Stream water qualities between the CP and DN catchments in the rising (left) and falling (right) limbs. Two-sample Student’s t-test (i.e., pH, EC, B/C ratio, TN, and TP in the rising limb) and non-parametric Mann–Whitney statistical test (i.e., BOD, COD, TOC, and TSS in the rising limb and nine stream water variables in the falling limb) results are indicated in separate bold letters (a,b) above and below the box graph (p < 0.05). Lowercase letters (a,b) indicate that pairwise tests were performed to compare the means of two groups.
Figure 3. Stream water qualities between the CP and DN catchments in the rising (left) and falling (right) limbs. Two-sample Student’s t-test (i.e., pH, EC, B/C ratio, TN, and TP in the rising limb) and non-parametric Mann–Whitney statistical test (i.e., BOD, COD, TOC, and TSS in the rising limb and nine stream water variables in the falling limb) results are indicated in separate bold letters (a,b) above and below the box graph (p < 0.05). Lowercase letters (a,b) indicate that pairwise tests were performed to compare the means of two groups.
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Figure 4. Correlations between COD and TOC in the CP and DN catchments in the (a) rising and (b) falling limbs. Thick and broken lines indicate estimates based on the regression analysis for the CP and DN catchments, respectively; thick and broken lines were estimated using regression analysis for the two catchments, respectively.
Figure 4. Correlations between COD and TOC in the CP and DN catchments in the (a) rising and (b) falling limbs. Thick and broken lines indicate estimates based on the regression analysis for the CP and DN catchments, respectively; thick and broken lines were estimated using regression analysis for the two catchments, respectively.
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Figure 5. Principal component analysis of stream water quality variables in the CP and DN catchments in the (a) rising and (b) falling limbs.
Figure 5. Principal component analysis of stream water quality variables in the CP and DN catchments in the (a) rising and (b) falling limbs.
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Figure 6. Relationship between event precipitation and event load for (a) BOD, (b) COD, (c) TOC, (d) TSS, (e) TN, and (f) TP in the CP and DN catchments. Thick and broken lines represent estimates based on a regression analysis for the CP and DN catchments, respectively.
Figure 6. Relationship between event precipitation and event load for (a) BOD, (b) COD, (c) TOC, (d) TSS, (e) TN, and (f) TP in the CP and DN catchments. Thick and broken lines represent estimates based on a regression analysis for the CP and DN catchments, respectively.
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Table 1. Summary for precipitation events during which samples were collected.
Table 1. Summary for precipitation events during which samples were collected.
CP CatchmentDN Catchment
Event precipitation (mm)11.0–76.26.0–39.5
Max. 2 h precipitation (mm)11.0–26.35.0–32.0
Duration of precipitation (h)2.0–24.04.0–20.0
API2 (mm)0.0–35.00.0–43.4
API5 (mm)3.0–35.00.0–43.4
API7 (mm)3.0–35.00.0–58.5
API30 (mm)62.0–124.541.6–143.1
Total runoff (mm)0.23–3.570.65–6.19
Peak flow (mm/2 h)0.12–1.920.13–0.68
Runoff coefficient (%)2.1–7.84.9–22.4
pH6.2–7.07.0–7.4
EC (μS/cm)79.7–122.859.1–82.0
BOD (mg/L)1.2–2.31.6–5.5
COD (mg/L)9.3–12.63.3–10.0
TOC (mg/L)7.0–10.32.2–6.1
B/C ratio0.10–0.180.08–0.73
TSS (mg/L)1.8–3.60.8–16.4
TN (mg/L)6.3–12.70.9–1.7
TP (mg/L)0.008–0.0230.005–0.011
Note: API2, API5, API7, and API30: 2-, 5-, 7-, and 30-day antecedent precipitation indices; range: minimum–maximum values.
Table 2. Correlation matrix of stream water quality variables in the two catchments in the rising and falling limbs.
Table 2. Correlation matrix of stream water quality variables in the two catchments in the rising and falling limbs.
Rising LimbFalling Limb
pHECBODCODTOCB/CTSSTN pHECBODCODTOCB/CTSSTN
CPpH CP
EC−0.33 −0.18
BOD0.210.38 0.220.30
COD−0.500.510.41 −0.100.720.14
TOC−0.560.590.460.99 −0.120.770.060.96
B/C0.490.170.88−0.050.01 0.24−0.030.89−0.31−0.37
TSS−0.320.130.420.660.640.12 −0.030.300.330.100.090.25
TN−0.810.650.190.770.84−0.190.43 −0.650.59−0.220.380.51−0.370.07
TP0.71−0.060.25−0.58−0.570.62−0.29−0.670.360.610.510.680.610.180.14−0.07
DNpH DN
EC0.45 −0.43
BOD−0.310.22 −0.170.26
COD−0.34−0.100.85 −0.340.060.56
TOC−0.26−0.030.870.95 −0.260.070.690.71
B/C−0.070.620.690.280.40 0.140.210.05−0.68−0.49
TSS−0.34−0.160.480.770.620.00 −0.19−0.140.090.350.33−0.34
TN−0.04−0.41−0.070.230.05−0.490.33 0.25−0.50−0.250.180.00−0.470.44
TP−0.38−0.140.730.770.850.290.460.090.05−0.100.300.570.53−0.570.530.15
Note: B/C indicates the BOD/COD (B/C) ratio. Significant correlations at a 95% confidence level are presented in bold. Number of observations: rising limb, 11 CP and 21 DN; falling limb, 28 CP and 33 DN.
Table 3. Loadings of observed stream water quality variables for the two catchments in the rising and falling limbs.
Table 3. Loadings of observed stream water quality variables for the two catchments in the rising and falling limbs.
Rising LimbFalling Limb
Comp 1Comp 2Comp 3Comp 1Comp 2Comp 3
CPBOD0.1320.9350.2280.2280.7080.464
COD0.8240.2530.4350.966−0.0920.051
TOC0.8620.3080.3750.954−0.2300.051
TSS0.2850.1800.9340.0240.0150.958
TN0.9310.0590.1250.382−0.7990.189
TP−0.7850.500−0.1810.7940.5070.090
DNBOD0.8790.281−0.1520.8740.017−0.249
COD0.8040.5540.1420.8150.3080.231
TOC0.9050.376−0.0040.8610.307−0.006
TSS0.3190.9120.2010.0870.8050.372
TN−0.0010.1590.982−0.0500.1700.963
TP0.9260.0960.1080.3600.843−0.027
Note: Comp: component; bold numbers indicate a strong correlation coefficient.
Table 4. Summary of EMC and event load for polluting discharge in the two catchments.
Table 4. Summary of EMC and event load for polluting discharge in the two catchments.
CP CatchmentDN Catchment
EMC
(mg/L)		Organic matterBOD1.3–2.11.6–3.8
COD10.1–12.63.2–9.0
TOC7.6–10.22.2–5.2
TSS2.2–4.30.9–18.7
NutrientTN6.6–12.40.9–1.7
TP0.01–0.020.005–0.01
Event load
(kg/ha)		Organic matterBOD0.005–0.060.01–0.12
COD0.03–0.410.02–0.29
TOC0.02–0.350.01–0.21
TSS0.01–0.150.02–0.44
NutrientTN0.02–0.420.01–0.08
TP0.0001–0.00040.00003–0.0005
Note: EMC: event mean concentration, range: minimum–maximum values.
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Nam, S.; Li, Q.; Choi, B.; Choi, H.T.; Lim, H. Characteristics of Stream Water Quality on Draining of Planted Coniferous and Natural Deciduous Forest Catchments in South Korea. Water 2025, 17, 1535. https://doi.org/10.3390/w17101535

AMA Style

Nam S, Li Q, Choi B, Choi HT, Lim H. Characteristics of Stream Water Quality on Draining of Planted Coniferous and Natural Deciduous Forest Catchments in South Korea. Water. 2025; 17(10):1535. https://doi.org/10.3390/w17101535

Chicago/Turabian Style

Nam, Sooyoun, Qiwen Li, Byoungki Choi, Hyung Tae Choi, and Honggeun Lim. 2025. "Characteristics of Stream Water Quality on Draining of Planted Coniferous and Natural Deciduous Forest Catchments in South Korea" Water 17, no. 10: 1535. https://doi.org/10.3390/w17101535

APA Style

Nam, S., Li, Q., Choi, B., Choi, H. T., & Lim, H. (2025). Characteristics of Stream Water Quality on Draining of Planted Coniferous and Natural Deciduous Forest Catchments in South Korea. Water, 17(10), 1535. https://doi.org/10.3390/w17101535

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