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Article

Responses of Stream Water Temperature to Water Levels in Forested Catchments of South Korea

by
Sooyoun Nam
,
Honggeun Lim
,
Byoungki Choi
,
Qiwen Li
,
Haewon Moon
and
Hyung Tae Choi
*
Forest Environment and Conservation Department, National Institute of Forest Science, Seoul 02455, Republic of Korea
*
Author to whom correspondence should be addressed.
Forests 2023, 14(10), 2085; https://doi.org/10.3390/f14102085
Submission received: 20 September 2023 / Revised: 13 October 2023 / Accepted: 16 October 2023 / Published: 18 October 2023
(This article belongs to the Special Issue Hydrology and Ecosystem Services in Forests)
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Abstract

:
Event flow characteristics were evaluated based on temperature and level of stream water in 22 forested catchments (area: 13.2–281.4 ha) to investigate sustainable flood management measures. Temperature and stream water levels were during 346 rainfall events in the summer season (July–September) from 2020 to 2022. Rising stream water levels responded to falling stream water temperature between ≤100 and >100 ha forested catchments in two types of time of concentration. Stream water temperature decreased by 3.0 °C when the stream water level increased by up to 0.9 m during rainfall events. Falling stream water temperature at two types of time of concentration was negatively correlated with total precipitation and rising stream water level. Based on the relatively high value of regression and cumulative frequency distribution, the estimated rising stream water level was appropriate in small catchments (≤100 ha) when the stream water temperature decreased, and the stream water level increased during rainfall events. Rising stream water levels and falling stream water temperatures are responses to catchment-scale effects, which are influenced by the nature and rapidity of the hydrological responses. Therefore, the results of the present study indicate that spatial and temporal differences in thermal responses of stream water temperature to water levels were controlled by catchment-scale effects under rapidly changing rainfall.

1. Introduction

The average global temperature is currently rising owing to climate change events [1,2]. IPCC [3] reported that the global average surface air temperature increased by 0.85 °C between 1880 and 2012, whereas the rate of increase has been much higher since 1971 (0.2 °C/decade). Rising temperatures have increased the frequency and intensity of extreme weather events [4,5]. Wu et al. [6] explained that extreme weather events, which usually have a frequency of less than 5%, will become more recurrent due to climate change. Consequently, such events are likely to affect water demand planning and lead to changes in precipitation and runoff patterns because of imbalances in the supply and demand of water resources and their management [7,8,9].
In the Republic of Korea, 63% of the land is covered by forests, and streams (i.e., tertiary streams) account for 88.9% of the total length of streams nationally [10,11]. Therefore, the streams play a significant role in water supply and management [12]. However, the streams located in the upper parts of catchments have relatively low flow rates, with ephemeral streams, when compared with rivers and lakes located in the lower part of the catchment [13,14]. Therefore, the streams can occur quickly in response to the start of rainfall events [15]. Moreover, variation in discharge in drainages smaller than 100 ha is greater than that in drainages larger than 100 ha, based on the representative elementary area concept [16,17]. For instance, previous studies [16,18] have noted that hydrological processes within 100 ha areas are governed by hillslope processes related to soil depth, topography, rainfall intensity, and vegetation. Such site factors establish greater variation in the unit area discharge. In contrast, hydrological responses in catchments larger than 100 ha are affected more by routing processes and the structure and extent of floodplains [17]. Finally, disturbances (e.g., landslides, debris flows, and floods) may control the patch distribution of organisms in and around stream systems [19,20]. Montgomery [21] also demonstrated that geomorphic processes set the templates for biological processes of disturbance, river continuum, and patch dynamics using the process–domain concept.
In general, stream water temperature (WT), as a supplementary tracer, is used to identify and evaluate the water sources contributing to runoff processes at forested catchments [22,23]. WT fluctuates over time within a given catchment and is also a key driver of drinking water quality and aquatic ecosystem health [24,25,26,27]. The natural and anthropogenic mechanisms driving several facets of WT regimes are also well understood and explain how thermal regimes may vary over time and space [28,29]. Although WT is generally applied to water quality and aquatic ecosystems [29,30], approaches using both WT and stream water level (WL) responses are also effective for identifying event-driven indicators for unexpected rainfall events and flash floods. For instance, Subehi et al. [31] indicated that WL, which depends on the season and slope gradient, jointly affects the relative proportions of flow paths during rainfall events, thereby influencing changes in specific discharge and water temperature. This is because WT and WL are sensitive to climatological and hydrological variables that induce changes in climate or water flow, which may have important implications for WT and WL thermal regimes [32,33]. Moreover, we need to consider spatial and temporal variations in hydrologic and geomorphic processes in forested catchments because WL and WT are sensitive to vertical distributions between upstream and downstream storm directions [34,35,36].
Although WT and WL can respond to rainfall events and flash floods, the direct application of WT with WL as the event-driven indicator has not been fully examined in the forested catchment. Effective application using WT with WL can possibly applied for sustainable flood management, which focuses on the role played by ecohydrology in flood risk management. From the ecohydrological perspective, a stream floodplain is an extremely important and capacious ecosystem that, being periodically flooded peaks, may minimize the danger of flooding (e.g., [37,38,39]). Moreover, when the application is investigated, the hydrological, ecological, and ecotechnological factors can be used to develop sustainable approaches to minimizing flood risk in a given catchment and managing floodplain systems (e.g., [40,41]). According to Rivaes et al. [42], interannual variability is represented by changes in the frequency of floods and variations in annual rainfall, whereas intraannual variability (seasonality) is represented by periods of water surplus interspersed with hydric scarcity. In particular, the frequency and severity of heavy rainfall events in forested catchments have escalated owing to increasing land use and rapid global climate change, with such events frequently resulting in flash flood disasters [43]. Heavy rainfall events often alter environmental conditions, which can influence the composition and structure of biotic communities in terrestrial and aquatic ecosystems [44,45]. In addition, flash floods cause considerable direct and indirect economic losses by damaging socioeconomic systems and infrastructure [46]. In particular, the summer season (mid-June to mid-September), which accounts for 90% of the total rainfall, is concentrated during the East Asian monsoon (late June to mid-September) and typhoons (late July to mid-September) in the Republic of Korea [47,48,49]. Therefore, assessment of how extreme weather events impact environmental conditions and the consequences for biotic interactions and ecosystem functions and services is critical (e.g., [50]). Such assessed trends have been observed globally, and the damage to the environment is increasing as a result [51,52].
Therefore, there is a significant demand for researchers and governments to construct reliable and accurate flood prediction models and plan and implement sustainable flood risk management measures with an emphasis on prevention and preparedness (e.g., [53]). The objectives of the present were to: (1) examine water level and temperature responses in the time of concentration between rising water level and falling water temperature; (2) identify rising water level and falling water temperature characteristics driven by rainfall events; and (3) determine event-driven indicators using a combination of temperature and water level of stream water in forested catchments.

2. Materials and Methods

2.1. Study Sites

This study was conducted in two catchment categories, including catchment areas less than and or equal to 100 ha (FC≤100) and catchment areas over 100 ha (FC>100), in South Korea (Figure 1). The study area was managed by the National Institute of Forest Science. The distinguishing of two catchment categories was based on structural differences and the continuous versus discontinuous nature of processes [17]. This was because process characteristics differ in the two catchment categories, which needs to be considered when establishing management guidelines for hydrologic, geomorphic, and biological processes in forested catchments [17,54,55].
The FC≤100 areas (C1–C10) were from 13.2 to 59.0 ha, with the altitude ranging from 260 to 1368 m. According to the weather stations of the Korea Meteorological Administration (KMA), the annual precipitation in the regions for 20 years (2003–2022) was 1301.2 ± 370.1 mm (mean ± standard deviation (SD), range: 596.5–2235.2 mm), of which 60%–67% occurred from July to September. The annual temperature was 12.2 ± 1.2 °C (mean ± SD, range: 9.2–14.4 °C). The mean of dominant slope gradients on the FC≤100 ranged from 20.4 to 34.9°. The underlying geology consists of metamorphic (C1, C4, C6, C8, and C9), sedimentary (C2, C5, and C7), and igneous (C3 and C10) rocks. Most catchments were dominated by deciduous broad-leaved forests, except for C2 and C7, which were covered by mixed forests, and C8, which was covered by coniferous plantations (Table S1).
The FC>100 areas (C11–C22) ranged from 101.8 to 281.4 ha, with altitudes ranging from 210 m to 1340 m. According to the weather stations of the KMA, the mean annual precipitation ± SD in the regions for 20 years was 1320.5 ± 345.4 mm (589.2–3000.5 mm), of which 59%–66% occurred from July to September. The mean annual temperature ± SD was 11.2 ± 1.2 °C (9.1–14.4 °C). The mean of dominant slope gradients on the FC≤100 ranged from 23.4 to 32.8°. The underlying geology consisted of metamorphic (C11, C12, C14–C16, and C18), igneous (C13, C17, C19, C20, and C22), and sedimentary (C21) rocks. Most catchments were dominated by mixed forests, except for C11, C14, and C16, which were covered by broadleaved forests (Table S1).

2.2. Field Measurement and Data Analysis

To investigate stream water temperature and level, a water level gauge was installed at each site in a 90° or 120° V-shaped or square notch. The data were measured using capacitance water stage data loggers (OTT-Orpheus Mini Water Level Logger, OTT Messtechnik, Kempten, Germany) at 10-min intervals. Precipitation was monitored at 10-min intervals using a HOBO tipping-bucket rain gauge (RG3, Onset Computer Corporation, Bourne, MA, USA) in an open area in the study catchment.
Data for the rising stream water level (LR) in response to falling stream water temperature (TF) during the observed rainfall events in the summer season (July–September) from 2020 to 2022 were used to indicate event-driven water levels. Here, the LR was calculated from the minimum to the maximum water level (e.g., [31,56]). The TF was calculated from the maximum to minimum water temperature. To examine the effects of the time of concentration between TF and LR, two types were tested: TF = LR and TF > LR. TF = LR indicates the same time of concentration between decreases in stream water temperature (WT) and increases in stream water level (WL), and TF > LR indicates the fast time of concentration from the decreases in WT before increasing WL. We used 25 and 50 observations for FC≤100 and FC>100 for TF = LR. We used 113 and 158 observations for FC≤100 and FC>100 for TF > LR. We then estimated the LR by TF using regression equations in two catchment categories according to the two types for time of concentration.
The root mean square error (RMSE) and Nash-Sutcliffe Efficiency (NSE) were selected to evaluate the performances of the estimated LR in the present study. This was because the RMSE and NSE alone are not adequate indicators [57]. They are some of the indicators recommended for estimation of efficiency in hydrology (e.g., [58,59,60]).
The RMSE is a commonly used metric for comparing values estimated using the values actually observed [61,62] and was calculated with the following equation:
R M S E = 1 N ∑ i = 1 N L R i − L R ^ i 2
where L R i is the observed LR at time i (m), L R ^ i is the estimated LR at time i (m), and N is the total number of rainfall events observed. The RMSE can show the estimated errors, with a relatively small value denoting superior estimation [61].
NSE is a trusted tool for evaluating the reliability of developed models [63]. In addition, NSE is a normalized statistic that determines the relative magnitude of the residual variance compared to the measured data variance [64] and is computed using the following equation:
N S E = 1 − ∑ i = 1 N L R i − L R ^ i 2 ∑ i = 1 N L R i − L R ¯ i 2
L R ¯ i is the mean of observed LR (m). NSE ranges from −∞ to 1. As usual, investigators seek an NSE value close to 1. A negative NSE indicates unacceptable estimated performance [65].
The RMSE and NSE values were considered accurate when using the estimated LR as a flood estimation model. Subsequently, the residual was calculated by subtracting the observed LR from the estimated LR during a given event in FC≤100 and FC>100 according to the two types of time of concentration. All statistical analyses were performed using R version 4.1.2 (R Foundation for Statistical Computing, Vienna, Austria) and IBM SPSS Statistics 19 (IBM Corp., Armonk, NY, USA).

3. Results and Discussion

3.1. Distribution of Precipitation, Temperature, and Level Responses in Stream Water

The 346 rainfall events were observed over the entire monitoring period, with 75 occurring in the same time of concentration between decreases in stream water temperature (WT) and increases in stream water level (WL) (TF = LR) (Figure 2a) and 271 occurring in the fast time of concentration from the decreases in WT before increasing WL (TF > LR) (Figure 2b). Here, the occurrence of the TF > LR was similar in both catchment categories (Figure 2b), whereas the TF = LR occurred more in the ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments (Figure 2a). The falling stream water temperature (TF) ranged from −3.1 to −0.1 °C, and the rising stream water level (LR) ranged from 0.001 to 0.9 m. This showed that the WT decreased by 3.0 °C when WL increased by up to 0.9 m during rainfall events. Similarly, Irons et al. [66] showed WT decreases of −1 °C at a 0.10 m WL for an Alaskan river following precipitation, which was explained by enhanced groundwater upwelling. Hannah et al. [67] indicated a depressed WT to 0.40 m WL associated with winter storm flow for a mountain stream. According to Moore [68], WL is affected by the initial stream source (e.g., groundwater seeps or glaciers), as well as the subsequent effects of energy and water exchanges across the stream water surface.
In the two types of time of concentration, the total precipitation (PT), WL, and WT were compared and analyzed based on the two catchment categories (Table 1 and Figure 3). Figure 3a shows that the TF of TF = LR was −0.4 ± 0.5 °C (mean ± SD) in the FC≤100 and −0.6 ± 0.6 °C in the FC>100. The LR was 0.1 ± 0.2 m in the FC≤100 and 0.2 ± 0.2 m in the FC>100 with spatial differences between the two catchment categories (Mann–Whitney U test, p < 0.05). The mean time of concentration (TA) was 2.4 and 5.8 h in FC≤100 and FC>100, respectively, with PT of 14.0 and 33.7 mm. Figure 3b shows that the TF of the TF > LR was −0.3 ± 0.5 °C in the FC≤100 and −0.4 ± 0.5 °C in the FC>100. The LR was 0.1 ± 0.1 m in the two catchment categories. The time of concentration (TA) in the TF was 3.5 and 5.2 h in both FC≤100 and FC>100, respectively, with a PT of 19.1 and 19.2 mm, respectively. In addition, the mean TA in the LR of the TF > LR was 5.0 and 6.5 h in the FC≤100 and FC>100, respectively, with a PT of 25.1 and 23.2 mm, respectively. The spatial differences between the two catchment categories in TA were significant (Mann–Whitney U test, p < 0.05). This could be associated with the steeper slope during repetitive heavy rain and drought processes, which originated from potential direct and/or indirect runoff [68,69]. Because of a small catchment (≤100 ha), which contains ephemeral or temporal channels emerging from zero-order basins [17,70,71], the ephemeral streams flow only as a result of surface runoff generated using precipitation of high intensity and short duration [72]. According to Camarasa-Belmonte and Segura-Beltra [73], the ephemeral runoff, which depends almost exclusively on rainfall, is clearly related to the drainage basin characteristics.
In the two types of time of concentration, the changes in TF in the FC>100 tended to be greater than that in the FC≤100. Here, the LR of the TF = LR was more altered in the FC>100 than in the FC≤100 (Table 1). It is difficult to perform the first high-temporal-resolution hydrometeorological assessment of the water column and stream thermal variability associated with storm events of different magnitudes, durations, and intensities, not just within a forested basin but also for any catchment-scale effects [56]. Moreover, higher PT and longer TA occurred in the FC>100 than in the FC≤100 in the TF = LR (Figure 3a). This could be associated with the functional relationships between geomorphic processes in space and time, which are recognized as controls for the continuity of material transport in stream ecosystems [17]. Such dynamics can have potential impacts on the overall aquatic environment as ecological consequences [74,75]. Researchers [76,77] have demonstrated temporal and spatial linkages between hydrologic and geomorphic processes with respect to rainfall–landslide thresholds and channel network development.

3.2. Factor Affecting Falling Temperature and Rising Levels in Stream Water

Principal component analysis (PCA) was used to analyze the influence parameters considering TF and LR responses monitored in the two catchment categories (i.e., ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments) during the two types of time of concentration (i.e., same time of concentration between decreases in stream water temperature (WT) and increases in stream water level (WL) (TF = LR) and fast time of concentration from the decreases in WT before increasing WL (TF > LR)) using the entire dataset (Table 2 and Figure 4). For the two concentrations, the variance rate was over 90% for Factors 1–3. Table 2 shows the values and proportions of the explained variance and cumulative variance explained by PCA. The PCA identified key parameters of TF and LR responses and revealed that major latent factors are influenced by catchment-scale effects. During the two types of time of concentration. In addition, the relative temporal fluctuation of peak flows in small catchments (≤100 ha) was greater than in large catchments (FC>100) because storm flow responds rapidly to intense rainfall in small catchments because of their relatively small storage capacity and shorter flow paths [13,17,78].
In TF = LR, the total precipitation (PT), including time of concentration (TA) and rising stream water level (LR), showed high factor loadings for Factor 1, both in FC≤100 and FC>100. This is because flow responses to storms appear to be driven by rapidly routed precipitation (i.e., direct precipitation/channel interception, rapid surface flow over impermeable bedrock/thin alpine soils, and subsurface flow through highly weathered screens) [56]. However, Factor 2 showed that falling stream water temperature (TF) and LR had high negative factor loadings in both catchment categories (Figure 4a). It is most likely that the stream temperature response to PT results from advected energy inputs, primarily from the surface and near-subsurface hillslope pathways [56]. In addition, climatic drivers, stream morphology, groundwater influence, and riparian canopy conditions reportedly affect stream thermal regimes [28,29,79]. In TF > LR, high factor loadings for Factors 1–3 differed according to the catchment category; however, TF was Factor 3 in both catchment categories (Figure 4b). It seemed that factors affecting the TF and LR responses were difficult to identify exactly in the TF > LR.

3.3. Relationship between Falling Temperature and Rising Level in Stream Water

To evaluate the correlations among the monitoring data between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments in the same time of concentration between decreases in stream water temperature (WT) and increases in stream water level (WL) (TF = LR) and the fast time of concentration from the decreases in WT before increasing WL (TF > LR), correlation analyses were performed for a time of concentration (TA), total precipitation (PT), falling stream water temperature (TF), and rising stream water level (LR) characteristics (Table 3).
To assess the correlation between the characteristics in the two catchment categories, the significance level was set at 0.01 or less, which implied a high correlation. The results are summarized in Table 3. Here, the LR of TF = LR was positively correlated with TA and PT in both catchments (correlation coefficient: 0.59–0.74, p < 0.01). The LR of the TF > LR also showed a significant correlation with TA and PT parameters (correlation coefficient: 0.46–0.91, p < 0.01). However, the TF of the two types for the time of concentration was negatively correlated with TA, PT, and LR, with correlation coefficients ranging from −0.78 to −0.42 in both catchment categories (p < 0.01) (Table 3). Previous studies have indicated that PT has the greatest influence on stream water with seasonal changes [68,80,81]. The researchers [23,56] also discussed that PT may cause changes in WL owing to direct inputs (i.e., channel interception) and by inducing WL from various hydrological stores and pathways. Lee et al. [82] showed that annual variations in streamflow timing and volume depend on the seasonal cycles of PT in the upper part of the basin. Thus, PT can affect changes in the temperature and level of stream water as a function of climate variability within a catchment (e.g., [83,84]), even if TF and LR have different correlation patterns.
In the two types of time of concentration, TF decreased with increasing LR in both catchment categories. The LR and TF patterns were similar in both catchment categories in the TF = LR (Figure 5a). In the TF > LR, the LR with TF patterns of the FC>100 was larger than that of the FC≤100 (Figure 5b). This was due to spatial and temporal differences in thermal response to storm events, which were controlled by PT and WL [23,31,56]. Brown and Hannah [56] discussed that the catchment characteristics influencing event-driven thermal variability can be speculated upon at present because temperature data are not routinely collected in studies of runoff generation processes. In addition, according to Oware and Peterson [85], storm events strongly influence WT in the direction of the pre-storm thermal gradient between the stream and substrate temperatures. Furthermore, storm flow responds rapidly to intense rainfall in the FC≤100 because of their relatively low storage capacity and shorter flow paths [17]. Storm flow generation in the FC≤100 is also affected by the responses of hillslopes and zero-order basins to changing antecedent moisture conditions [86,87]. In other words, the relationship between TF and LR can be combined with the response to the stream surface energy balance and catchment-scale effects. The TF is, therefore, less sensitive to uncertainties in precipitation, whereas these uncertainties potentially have a large impact on the simulated LR (e.g., [88,89,90]). Our methodological approach using relationships between temperature and water level could facilitate the formulation of sustainable flood management strategies with variations in commonly used spatial and temporal scales (e.g., [56,91]).

3.4. Approaches to Estimating Rising Levels Using Falling Temperature in Stream Water

Table 4 shows the regression analysis results for the two catchment categories at the same time of concentration between decreases in stream water temperature (WT) and increases in stream water level (WL) (TF = LR). Significant results were obtained, with coefficients of determination (R2) ranging from 0.52 to 0.69 at a 99% significance level. On the other hand, the two equations in the fast time of concentration from the decreases in WT before increasing WL (TF > LR) did not have a relatively fit to estimate rising stream water level (LR) using falling stream water temperature (TF).
The estimated LR (mean ± SD) in the TF = LR was 0.1 ± 0.1 m (range: 0.01–0.5 m) and 0.2 ± 0.1 m (0.05–0.6 m) in the FC≤100 and FC>100, respectively (Figure 6a). In the TF > LR, the estimated LR (mean ± SD) was similar to 0.1 ± 0.1 m (0.1–0.4 m and 0.1–0.7 m in the FC≤100 and FC>100) in both catchment categories (Figure 6b). As illustrated in Figure 6, the relationship between the observed and estimated LR was suitable for determining the event-driven indicator using a combination of temperature and level of stream water, particularly when the LR was estimated using the TF in the two catchment categories in TF = LR (Figure 6a). The differences may have been caused by the time of concentration between the TF and LR with catchment scale effects. Stream and event flow generation processes modify the biological community structure and life cycle of aquatic fauna from upstream to downstream systems [92,93,94,95]. Therefore, the estimated LR of the two catchment categories was greater when TF = LR than when TF > LR. In particular, during the TF = LR, the estimated LR in the FC≤100 was greater than that in the FC>100 because water inputs strongly affect hillslope and channel conditions because of the close coupling of hydrologic and geomorphic processes within confined and steep valleys of FC≤100 [87]. Water temperature in stream channels is closely related to the soil pore structure and bedrock fractures in hillslopes and zero-order basins [17]. In the FC≤100, subsurface discharge from hillslopes contributes base flow and storm flow to stream channels, initiates certain erosion processes, and is important for the development of catchment topography [77].
The residual between the observed and estimated LR during the TF = LR ranged from −0.2 to 0.3 m and −0.3 to 0.5 m in the FC≤100 and FC>100, respectively (Figure 7a). In the TF > LR, the residual is within a similar range, at −0.3–0.6 m and −0.2–0.5 m in the FC≤100 and FC>100, respectively (Figure 7b). When the TF = LR, the NSE values were 0.69 and 0.52 with 0.09 and 0.14 RMSE values in the two catchment categories (Figure 6a), the estimated accuracy on the FC≤100 was higher than that of the FC>100 (Figure 7a). In contrast, the NSE values with RMSE in the TF > LR were 0.26 with 0.12 and 0.39 with 0.11 in the FC≤100 and FC>100 (Figure 6b), respectively, i.e., the estimated accuracy was low (Figure 7b).
Therefore, the above results indicate that the estimated LR was appropriate in small catchments (FC≤100) during the TF = LR. Subehi et al. [31] indicated that change in WT is influenced more by changes in specific WL. Our estimated rising stream water level was appropriate in small catchments (≤100 ha) and could be included in the expansion of hydrologically active areas (e.g., riparian zones, zero-order basins, and bogs) during periods of increasing wetness, which increases the probability of mass movements and alters flow paths between terrestrial and aquatic environments [17,31,96,97]. Therefore, hydrologists studying rainfall-runoff processes in catchment scale effects, particularly small catchments (≤100 ha) (e.g., [17,87,98]) could greatly contribute to the understanding of the processes incorporating WT measurements alongside WL and confirm the application of WT with WL as event-driven indicators in the forested catchments.

4. Summary and Conclusions

We investigated event flow characteristics based on the level and temperature of stream water during 346 rainfall events across the summer season (July–September) from 2020 to 2022 in 22 forested catchments (area: 13.2–281.4 ha). To indicate event-driven water levels, we used event data for rising stream water levels (LR) that responded to falling stream water temperature (TF) between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments in the two types of time of concentration (i.e., TF = LR and TF > LR). Our main findings are as follows: (1) stream water temperature decreased by 3.0 °C, when stream water level increased by up to 0.9 m; (2) the falling stream water temperature in the two types of time of concentration was negatively correlated with total precipitation and rising stream water level (correlation coefficient: −0.78–0.42, p < 0.01) due to water column and stream thermal variability associated with storm events; (3) the TF decreased with increasing LR in both catchment categories at both types of time of concentration; (4) in addition, the rising stream water level pattern of the FC>100 was greater, due to changes in falling stream water temperature, than that of the FC≤100 in the TF > LR, due to combined effects of stream surface energy balance and catchment scale effects in response to the start of rainfall; and (5) based on relative high regression and cumulative frequency distribution, the estimated rising stream water level was appropriate for a small catchment (≤100 ha) during the same time of concentration between decreases in stream water temperature and increase in stream water level during rainfall events. This could be associated with the expansion of hydrologically active areas (e.g., riparian zones, zero-order basins, and bogs) during concentrated rainfall periods, which alter the flow paths between terrestrial and aquatic environments in forested catchments. Our results indicate that the unique aspects of our study design allowed us to draw inferences about event flow characteristics based on the contribution of temperature and stream water level in small catchments (≤100 ha) during the time of concentration sequences. Furthermore, our results could facilitate the integration of the falling curve of stream water temperature in response to rising stream water levels, which need to consider the catchment-scale effects, particularly in the small catchments (≤100 ha) for aquatic ecosystem and event-driven indicators of the potential environmental and ecological consequences.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/f14102085/s1, Table S1: Summary of observed sites in forested catchments.

Author Contributions

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

Funding

This study was carried out with the support of the “R&D Program for Forest Science Technology (Project No. 2021343B10-2323-CD01)” provided by the Korea Forest Service (Korea Forestry Promotion Institute).

Data Availability Statement

Not applicable.

Conflicts of Interest

The authors declare they have no conflict of interest.

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Figure 1. Location of observation sites in ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments. FC≤100 and FC>100 are included from C1 to C10 and C11 to C22, respectively.
Figure 1. Location of observation sites in ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments. FC≤100 and FC>100 are included from C1 to C10 and C11 to C22, respectively.
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Figure 2. Percentage of different size catchment distributions (i.e., ≤100 ha (FC≤100) and >100 ha (FC>100)) forested catchments in the two types of time of concentration, (a) TF = LR and (b) TF > LR. FC≤100 and FC>100 are included from C1 to C10 and C11 to C22, respectively.
Figure 2. Percentage of different size catchment distributions (i.e., ≤100 ha (FC≤100) and >100 ha (FC>100)) forested catchments in the two types of time of concentration, (a) TF = LR and (b) TF > LR. FC≤100 and FC>100 are included from C1 to C10 and C11 to C22, respectively.
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Figure 3. Distribution of time of concentration (TA) (time of concentration of falling water temperature (TF), and rising water level (LR)), total precipitation (PT) (PT of TF and LR) between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments in the two types of time of concentration, (a) TF = LR and (b) TF > LR. Mann–Whitney U test results are indicated in separate bold letters (a,b) above the bar graph (p < 0.05).
Figure 3. Distribution of time of concentration (TA) (time of concentration of falling water temperature (TF), and rising water level (LR)), total precipitation (PT) (PT of TF and LR) between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments in the two types of time of concentration, (a) TF = LR and (b) TF > LR. Mann–Whitney U test results are indicated in separate bold letters (a,b) above the bar graph (p < 0.05).
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Figure 4. Principal component analysis for six parameters associated with falling stream water temperature (TF) and rising stream water level (LR) between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments during the two types for the time of concentration, (a) TF = LR and (b) TF > LR. TA: time of concentration. TLR: time of concentration of LR, TTF: time of concentration of TF, PT: total precipitation; PLR: total precipitation of LR, PTF: total precipitation of TF.
Figure 4. Principal component analysis for six parameters associated with falling stream water temperature (TF) and rising stream water level (LR) between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments during the two types for the time of concentration, (a) TF = LR and (b) TF > LR. TA: time of concentration. TLR: time of concentration of LR, TTF: time of concentration of TF, PT: total precipitation; PLR: total precipitation of LR, PTF: total precipitation of TF.
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Figure 5. Relationship between falling stream water temperature (TF) and rising stream water level (LR) between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments during the two types for the time of concentration, (a) TF = LR and (b) TF > LR. Thick and broken lines were estimated using regression analysis (see Table 4) for two catchment categories.
Figure 5. Relationship between falling stream water temperature (TF) and rising stream water level (LR) between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments during the two types for the time of concentration, (a) TF = LR and (b) TF > LR. Thick and broken lines were estimated using regression analysis (see Table 4) for two catchment categories.
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Figure 6. Relationship between the observed and estimated rising stream water level between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments during the two types of time of concentration, (a) TF = LR and (b) TF > LR.
Figure 6. Relationship between the observed and estimated rising stream water level between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments during the two types of time of concentration, (a) TF = LR and (b) TF > LR.
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Figure 7. Cumulative frequency distributions for differences between observed and estimated rising water levels between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments in the two types of time of concentration, (a) TF = LR and (b) TF > LR.
Figure 7. Cumulative frequency distributions for differences between observed and estimated rising water levels between ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments in the two types of time of concentration, (a) TF = LR and (b) TF > LR.
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Table 1. Summary of precipitation, temperature, and level in stream water.
Table 1. Summary of precipitation, temperature, and level in stream water.
TF = LRTF > LR
FC≤100FC>100FC≤100FC>100
TA (h)TF2.4 ± 2.8
(0.2–12.7)		5.8 ± 4.8
(0.2–20.2)		3.5 ± 3.8
(0.2–16.2)		5.2 ± 5.3
(0.2–23.3)
LR5.0 ± 4.5
(0.3–19.7)		6.5 ± 5.6
(0.3–24.3)
PT (mm)TF14.0 ± 19.4
(0.5–83.5)		33.7 ± 29.6
(0.5–110.5)		19.1 ± 27.8
(0.4–213.5)		19.2 ± 23.7
(0.5–146.5)
LR25.1 ± 33.3
(1.0–241.0)		23.2 ± 25.7
(0.5–154.5)
WT (°C)Tmax17.5 ± 1.9
(14.6–21.7)		16.6 ± 2.3
(11.2–21.7)		16.8 ± 2.3
(8.8–22.4)		17.4 ± 2.5
(10.0–22.5)
Tmin17.1 ± 1.7
(14.3–21.5)		16.0 ± 2.2
(10.3–21.0)		16.4 ± 2.2
(8.7–22.3)		17.0 ± 2.5
(9.9–21.9)
TF−0.4 ± 0.5
(−1.9–−0.1)		−0.6 ± 0.6
(−2.4–−0.1)		−0.3 ± 0.5
(−2.3–−0.1)		−0.4 ± 0.5
(−3.1–−0.1)
WL (m)Lmin0.1 ± 0.1
(0.03–0.4)		0.1 ± 0.1
(0.01–0.5)		0.2 ± 0.2
(0.01–1.2)		0.1 ± 0.1
(0.01–0.6)
Lmax0.2 ± 0.2
(0.03–0.9)		0.3 ± 0.2
(0.03–0.8)		0.3 ± 0.3
(0.01–1.3)		0.3 ± 0.2
(0.01–1.1)
LR0.1 ± 0.2
(0.001–0.7)		0.2 ± 0.2
(0.001–0.7)		0.1 ± 0.1
(0.002–0.9)		0.1 ± 0.1
(0.001–0.7)
Note: TF = LR: same time of concentration between decreases in stream water temperature (WT) and increases in stream water level (WL). TF > LR: fast time of concentration from the decrease in WT before increasing WL. ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments, TA: time of concentration, PT: total precipitation, Tmax: maximum WT, Tmin: minimum WT, TF: falling stream water temperature, Lmin: minimum WL, Lmax: maximum WL, LR: risings stream water level, ±: SD, bracket: range from minimum to maximum values.
Table 2. Principal component loadings of six parameters associated with falling temperature and rising level in stream water with total precipitation.
Table 2. Principal component loadings of six parameters associated with falling temperature and rising level in stream water with total precipitation.
TF = LRTF > LR
ParameterFactor 1Factor 2Factor 3ParameterFactor 1Factor 2Factor 3
FC≤100
(C1–C10)		TA0.8020.3120.439TTF0.2640.8700.337
TLR0.2760.9310.091
PT0.9390.2320.173PTF0.8270.3240.380
PLR0.9110.3080.225
TF−0.305−0.876−0.373TF−0.317−0.242−0.910
LR0.3350.4980.792LR0.9300.1950.166
FC>100
(C11–C22)		TA0.3020.3330.890TTF0.9040.2680.298
TLR0.9140.2740.253
PT0.9070.2150.294PTF0.4060.6190.595
PLR0.3560.7320.502
TF−0.249−0.876−0.357TF−0.320−0.327−0.859
LR0.6790.6230.211LR0.2330.9070.226
Note: TF: falling stream water temperature; LR: rising stream water level; TF = LR: same time of concentration between decreases in stream water temperature (WT) and increases in stream water level (WL). TF > LR: fast time of concentration from the decrease in WT before increasing WL. TA: time of concentration. TLR: time of concentration of LR, TTF: time of concentration of TF, PT: total precipitation; PLR: total precipitation of LR, PTF: total precipitation of TF.
Table 3. Correlation matrix for total precipitation, falling temperature, and rising level in stream water.
Table 3. Correlation matrix for total precipitation, falling temperature, and rising level in stream water.
TF = LR
TAPTTF TAPTTF
FC≤100
(n = 25)		PT0.85 FC>100
(n = 50)		PT0.59
TF−0.69−0.55 TF−0.67−0.56
LR0.740.59−0.83LR0.630.74−0.72
TF > LR
TTFTLRPTFPLRTF TTFTLRPTFPLRTF
FC≤100
(n = 113)		TLR0.86 FC>100
(n = 158)		TLR0.95
PTF0.650.54 PTF0.720.66
PLR0.570.560.95 PLR0.650.660.93
TF−0.58−0.42−0.66−0.57 TF−0.63−0.61−0.78−0.74
LR0.470.460.840.91−0.51LR0.530.530.730.79−0.63
Note: TF = LR: same time of concentration between decreases in stream water temperature (WT) and increases in stream water level (WL). TF > LR: fast time of concentration from the decrease in WT before increasing WL. ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments, TA: time of concentration, PT: total precipitation, Tmax: maximum WT, Tmin: minimum WT, TF: falling stream water temperature, Lmin: minimum WL, Lmax: maximum WL, LR: rising stream water level, ±: SD, bracket: range from minimum to maximum values. Significant correlations are shown in bold type.
Table 4. Summary of regression analyses to estimate rising stream water levels.
Table 4. Summary of regression analyses to estimate rising stream water levels.
EquationR2nFp
TF = LRFC≤100LR = −0.278(TF) − 0.0210.692551.60<0.001
FC>100LR = −0.259(TF) + 0.0240.525052.24<0.001
TF > LRFC≤100LR = −0.162(TF) + 0.0510.2611338.98<0.001
FC>100LR = −0.199(TF) + 0.0350.39158100.09<0.001
Note: TF: falling stream water temperature; LR: rising stream water level; TF = LR: same time of concentration between decreases in stream water temperature (WT) and increases in stream water level (WL). TF > LR: fast time of concentration from the decrease in WT before increasing WL. ≤100 ha (FC≤100) and >100 ha (FC>100) forested catchments.
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MDPI and ACS Style

Nam, S.; Lim, H.; Choi, B.; Li, Q.; Moon, H.; Choi, H.T. Responses of Stream Water Temperature to Water Levels in Forested Catchments of South Korea. Forests 2023, 14, 2085. https://doi.org/10.3390/f14102085

AMA Style

Nam S, Lim H, Choi B, Li Q, Moon H, Choi HT. Responses of Stream Water Temperature to Water Levels in Forested Catchments of South Korea. Forests. 2023; 14(10):2085. https://doi.org/10.3390/f14102085

Chicago/Turabian Style

Nam, Sooyoun, Honggeun Lim, Byoungki Choi, Qiwen Li, Haewon Moon, and Hyung Tae Choi. 2023. "Responses of Stream Water Temperature to Water Levels in Forested Catchments of South Korea" Forests 14, no. 10: 2085. https://doi.org/10.3390/f14102085

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