Impacts of precipitation variability on water quality in the Nakdong River Estuary: multi-year (2016–2021) data analysis

Physicochemical and biological parameters (temperature, salinity, pH, dissolved inorganic nutrients, and chlorophyll-a) were analyzed to evaluate the effects of precipitation variability associated with climate change on the water quality in the Nakdong River Estuary, South Korea. Multi-year monitoring data (2016–2021) were collected seasonally (February, May, August, and November) throughout the study period. Extreme rainfall events caused pronounced estuarine freshening (salinity < 1) and sharply enhanced riverine nutrient fluxes, with wet-to-dry season increases of 4–70 times for dissolved inorganic nitrogen, 4–36 times for phosphorus, and 9–740 times for silicate, showing strong positive correlations with precipitation (r² = 0.76–0.82, p < 0.001). Time-series and self-organizing map classifications revealed estuarine that the water quality was strongly controlled by seasonal precipitation and river discharge, whereas offshore waters exhibited weaker but detectable responses. Notably, extreme rainfall events altered the chlorophyll-a distribution, suppressing phytoplankton accumulation in the estuary because of dilution and flushing, while enhancing chlorophyll-a concentrations in offshore waters through nutrient-enriched river plume dispersion. These results demonstrated that extreme rainfall driven by climate change can enhance the terrestrial nutrient input into coastal waters, thereby increasing the potential for eutrophication and harmful algal blooms.

1 Introduction

Coastal environments are highly sensitive to climate change driven by global warming, particularly through alterations in precipitation, temperature, sea-level rise, and the frequency of extreme weather events. Changes in precipitation and temperature changes regulate freshwater inflow, stratification, and biogeochemical cycling (Paerl, 2006; Trenberth et al., 2007), whereas rising sea-level enhances coastal flooding, saltwater intrusion, and shoreline erosion (Nicholls and Cazenave, 2010; Talukder et al., 2021). Moreover, the intensification of extreme events, such as storms, typhoons, and heatwaves exacerbates physical disturbances, alters sediment transport, and disrupts the ecological stability of estuarine and coastal systems (Scavia et al., 2002; Leal Filho et al., 2022). According to the U.S. Environmental Protection Agency (USEPA), the global precipitation over land has increased at a rate of approximately 0.76 mm per decade from 1901 to 2023 (USEPA, 2024). In addition, the frequency and intensity of heavy precipitation events are likely increased since the 1950s, and once-in-10-year extreme precipitation events are likely approximately 1.3 times more frequent and 6.7% more intense in 2021 than 1850–1900 (IPCC, 2021). Collectively, these trends highlight that the precipitation variability associated with climate change is a key factor controlling ecological dynamics and water quality in estuarine and coastal systems.

Increased precipitation has been shown to alter estuarine environments by enhancing freshwater fluxes, which in turn increases nutrient loading and decreases dissolved oxygen concentrations (Breitburg et al., 2018; Kennish et al., 2023). Such climate-driven processes, particularly elevated inputs of dissolved inorganic nitrogen (DIN) and dissolved inorganic phosphorus (DIP) from precipitation-driven runoff into estuarine and coastal environments, accelerate eutrophication and stimulate harmful algal blooms (HABs) (Seitzinger et al., 2010; Paerl et al., 2018; Kennish, 2025). These findings highlight the vulnerability of estuaries to climate change, with precipitation-driven nutrient enrichment being recognized as a critical factor influencing estuarine biogeochemistry (Warwick et al., 2018; Montefiore et al., 2023).

Estuaries, as transitional zones connecting terrestrial and marine environments, are dynamic systems influenced by freshwater and seawater inputs. These regions play crucial roles in global biogeochemical cycles as pathways for the transport of terrestrial materials (nutrients, trace metals, and carbon) to the oceans (Meybeck, 1982; Brunskill et al., 2003; Smith et al., 2003; Paerl, 2006; Mallick et al., 2022; Wang et al., 2023). Several studies have reported that variations in nutrient fluxes in estuarine environments are controlled by natural processes (variability in precipitation, river discharge, and tidal mixing) and human activities (wastewater discharge and agricultural runoff) (Statham, 2012; Moore et al., 2013). Natural climatic events, including monsoonal rainfall and tropical cyclones can drastically enhance freshwater inflow and nutrient delivery to estuaries, thereby modifying salinity structures and stimulating primary productivity in estuarine zones (Justić et al., 1995; Paerl, 2006). Furthermore, long-term observations in the Changjiang River (1960–2020) have revealed that variations in the fluxes of DIN, DIP, and dissolved inorganic silicate (DSi) are largely driven by human activities (Wu et al., 2023). These changes in natural and anthropogenic nutrient fluxes have caused serious environmental problems, such as HABs in estuarine zones (Moncheva et al., 2001; Davidson et al., 2014).

Large rainfall-induced increases in nutrient fluxes during the wet season have been widely reported in monsoon estuarine systems. For example, studies conducted in monsoonal estuaries along the west coast of India have demonstrated that intense wet-season rainfall enhanced river discharge and the associated nutrient loads, leading to substantial increases in estuarine nutrient fluxes (Fernandes et al., 2025). Similarly, in the tropical coastal systems on Hainan Island, China, monsoon-related extreme rainfall events significantly intensified runoff, resulting in elevated inputs of dissolved inorganic nitrogen and silicate to adjacent coastal waters (Li et al., 2014). In particular, annual precipitation in South Korea has increased by approximately 16.3 mm per decade over the past 106 years, a rate considerably higher than the global average (0.76 mm per decade) (Kim et al., 2018). Additionally, the frequency of heavy precipitation days almost doubled during 1991–2020 compared to 1961–1990 (Do et al., 2023). This trend is closely related to the geographic setting of the Korean Peninsula, which lies within the East Asian monsoon region, and is strongly influenced by seasonal monsoon rainfall and typhoons.

These increases have in precipitation caused significant changes to the estuarine and coastal environments of the Korean Peninsula. For example, in the Nakdong River Estuary, enhanced freshwater discharge associated with increased precipitation alters salinity structures and stratification, leading to seasonal hypoxia and eutrophication (Lee et al., 2024). In addition, a previous study conducted in the Geum River Estuary showed that variations in river flow, strongly linked to precipitation and barrage operations, significantly affect nutrient fluxes and phytoplankton dynamics (Lee et al., 2025a). These studies highlight the sensitivity of Korean estuarine and coastal systems to precipitation variability under climate change. However, studies that simultaneously investigate the long-term and seasonal variations in the biochemistry of estuarine and offshore regions remain limited. Previous studies on precipitation-driven changes in coastal water quality have primarily focused on estuarine environments, emphasizing short-term or event-based responses of salinity, nutrients, and phytoplankton dynamics. However, the influence of precipitation variability on adjacent offshore waters remains poorly understood, despite their direct hydrodynamic connectivity with estuaries through river plume dispersion and coastal circulation.

Therefore, in this study, we investigated the trends in water quality parameters (temperature, salinity, pH, dissolved inorganic nutrients, and chlorophyll-a (Chl-a)) and precipitation over a six-year period (2016–2021). Using seasonal and spatial datasets, we evaluated the effects of precipitation variability on coastal water quality in the Nakdong River Estuary, including both estuarine water (EW) and offshore water (OW). In this study, the offshore region refers to coastal waters located seaward of the Nakdong River Estuary mouth, which are periodically influenced by freshwater and nutrient inputs during high river discharge and extreme rainfall events. Offshore waters respond differently to estuaries, exhibiting delayed or contrasting biogeochemical and biological responses. Additionally, statistical analyses combined with a self-organizing map (SOM) were applied to characterize the spatiotemporal variability of coastal waters through clustering based on water quality.

2 Materials and methods

2.1 Study area

The Nakdong River is the longest river (525 km) in South Korea (Figure 1) and is adjacent to five major metropolitans (Daegu, Ulsan, Gyeongbuk, Gyeongnam, and Busan) (An, 2014). The estuary is located on the southeastern coast near Busan, where it is discharged into the Korea Strait. The Nakdong River plays a critical role in securing freshwater resources for agricultural and industrial uses, serving approximately ten million people (Hong et al., 2016). The seasonal variability in the Nakdong River Estuary is strongly influenced by the East Asian monsoon system. During the summer monsoon period (typically July to September), a large proportion of annual precipitation occurs over a short time span, resulting in substantially increased river discharge. This enhanced discharge delivers large volumes of freshwater and land-derived nutrients to the estuary, leading to pronounced seasonal changes in salinity, nutrient concentrations, and biological productivity. The Nakdong River Barrage was constructed in 1987 to regulate freshwater outflow and manage water resources in this region. The Nakdong Estuary Barrage is normally operated in a stable regime, with the left-bank sluice opened regularly at low tide for water-level control and the right-bank sluice opened only intermittently during flood events (Kim et al., 2017).

Figure 1

Figure 1. A map showing estuarine (station 1–8; red circles) and offshore (station 11–14; black circles) water sampling stations in Nakdong River Estuary, South Korea.

2.2 Data collection

Water quality data (temperature, salinity, pH, dissolved inorganic nutrients, and Chl-a) from 2016 to 2021 were obtained from the Marine Environmental Monitoring Program operated by the Ministry of Oceans and Fisheries (www.meis.go.kr). Precipitation and river discharge data were provided by the Korea Water Resources Corporation (www.water.or.kr).

2.3 Sampling and measurements

Coastal water samples were collected at a depth of 1 m below the surface using a Niskin sampler (General Oceanics Inc., Miami, FL, USA). Seasonal sampling campaigns were conducted in February (winter), May (spring), August (summer), and November (autumn) at 12 stations (stations 1–8 in EW and stations 11–14 in OW; Figure 1) from 2016 to 2021. Water temperature and salinity were measured at each station using a CTD profiler (Seabird 19plus, Sea-Bird electronics Inc., USA). The pH was measured onboard using a calibrated pH meter (Orion Star A329, Thermo Scientific, USA).

The analytical methods and measurements accuracy for the dissolved inorganic nutrients and Chl-a have been described in detail elsewhere (Lee et al., 2009, 2025). Briefly, seawater samples for dissolved inorganic nutrients (DIN, DIP, and DSi) were filtered using a GF/F filter (Whatman, pore size: 0.7 μm) and stored frozen (-20°C) until analysis. The concentrations of dissolved inorganic nutrients (DIN, DIP, and DSi) were measured using an automated nutrient analyzer (Seal Analytical, Germany) following standard colorimetric methods described in previous studies (Kim et al., 2022). DIN is defined as the sum of NH₄⁺, NO₃^-, and NO₂^-, DIP as PO₄^-3, and DSi as Si(OH)₄. For Chl-a analysis, 500 mL of seawater was filtered through a membrane filter (pore size: 0.45 μm), which was subsequently rinsed with deionized water and stored at -20°C until analysis. Chl-a concentrations were determined fluorometrically after extraction with 90% acetone, using a calibrated fluorometer (Turner Designs 10-AU, USA), following established protocols (Welschmeyer, 1994; Barnett et al., 2019). The fluorometer was calibrated using a Chl-a standard solution provided by Turner Designs. Before conducting correlation analysis between the precipitation and dissolved inorganic nutrient (DIN, DIP, and DSi) fluxes, outliers were identified and removed using Cook’s distance (>1) (Cook, 1977).

2.4 Self-organizing map

The self-organizing map (SOM), also known as a Kohonen map (Kohonen, 1982), was used to visualize the high-dimensional data. The SOM is recognized as a powerful learning method for analyzing non-linear relationships among water quality parameters, providing an ordered two-dimensional representation of complex multivariate data with minimal information loss, thereby facilitating the grouping and interpretation of correlations between variables (Li et al., 2018; Park et al., 2020). In this study, SOM computation and mapping were performed using the SOM Toolbox 2.0 (http://www.cis.hut.fi/projects/somtoolbox) for MATLAB (Vesanto et al., 2000), including the processes of initialization, training, and visualization. The map size of the SOM was initially estimated using a heuristic formula (Vesanto and Alhoniemi, 2000) and subsequently optimized by evaluating quantization and topographic errors (Céréghino and Park, 2009). To examine spatiotemporal variability, SOM clustering was conducted separately for estuarine and offshore parameters based on monthly averaged data for water temperature, salinity, pH, DIN, DIP, DSi, and Chl-a.

3 Results

3.1 Precipitation and river discharge

The monthly precipitation and river discharge from the Nakdong River during 2016–2021 are shown in Figure 2a. Overall, monthly precipitation and river discharge exhibited similar seasonal patterns, with extreme rainfall events leading to increased discharge. As expected, both variables were higher during the summer monsoon season (July–September) than during the winter season (December–February). Monthly precipitation ranged from 1.5 to 408.1 mm month^-1 with an average of 91.6 mm month^-1, while river discharge ranged from 0.2×10¹⁰ to 39.5×10¹⁰ m³ month^-1 with an average of 4.4×10¹⁰ m³ month^-1.

Figure 2

Figure 2. (a) Monthly precipitation and river discharge variation from 2016 to 2021. (b) Yearly precipitation (bar) and precipitation in each summer season (from July to September) (pink circle) between 2016 and 2021. The number represents the proportion of summer precipitation to the annual total. (c) Daily precipitation from July to August 2020 (blue bar) and the average daily precipitation for the same period in 2020 (black line) and 2016, 2017, 2018, 2019, and 2021 (red line).

The annual and summer season (July to September) precipitation during the study period is shown in Figure 2b. The annual precipitation (average: 1,099.3 mm) showed large variation, with a relatively dry year in 2017 (741.8 mm) and exceptionally wet conditions in 2020 (1,361.9 mm). The high precipitation in 2020 resulted in increased river discharge from the Nakdong Barrage. The proportion of summer precipitation to the annual total ranged from 45.1% (551.8 mm) to 66.2% (901 mm), with a maximum in 2020 because of consecutive and extreme rainfall events (Figure 2b), highlighting extreme precipitation events in summer 2020. During the summer of 2020, South Korea experienced record-breaking rainfall due to 15 consecutive heavy rain fall events from mid-June to early September (Park et al., 2021), and a total of 48 rainfall days were recorded during July and August in 2020 (Figure 2c). Furthermore, the daily average precipitation (11.5 mm day^-1) in July–August 2020 was approximately two times greater than that (6.2 mm day^-1) in the same months of the years between 2016 and 2021, except for 2020 (Figure 2c).

3.2 Water quality

The water quality parameters from eight estuarine (stations 1–8) and four offshore (stations 11–14) sampling sites collected between 2016 and 2021 are shown in Figures 3 and 4. Water temperature and salinity ranged from 5.9 °C to 29.3 °C (average: 17.7 °C) and from 0.1 to 34.5 (average: 25.5), respectively. The pH ranged from 7.1 to 8.7 (average: 8.2). Both EW and OW showed higher temperatures during summer than during winter seasons. The salinity of EW was relatively lower than that of OW, primarily because of fresh river water discharge from the upstream Nakdong River, especially during the summer monsoon. The pH in both regions showed high or low values with each other, with the EW pH exhibiting greater fluctuations than the OW pH. The concentrations of DIN, DIP, and DSi were in the range of 0.3–102.7 μM (average: 39.0 μM), 0.0–2.0 μM (average: 0.4 μM), and 4.3–169.5 μM (average: 23.5 μM), respectively (Figures 4a–c). Chl-a concentrations ranged from 0.1 to 17.2 μg L^-1 (average: 4.0 μg L^-1) (Figure 4d). Water quality parameters showed clear spatial and seasonal differences between estuarine and offshore waters (Figures 3, 4). Estuarine waters exhibited lower salinity and higher concentrations of dissolved inorganic nutrients and Chl-a than offshore waters, with nutrient and Chl-a concentrations peaking in summer in response to enhanced monsoon-driven river discharge. Offshore waters showed lower nutrient concentrations and weaker seasonal variability, although episodic increases occurred during high-discharge events, highlighting the contrasting estuarine–offshore responses to freshwater inputs.

Figure 3

Figure 3. Box plots and scattering plots showing statistical analysis results for the average (a) temperature, (b) salinity, and (c) pH of the estuarine (station 1–8; box plots) and offshore (station 11–14; blue circles) waters in February, May, August, and November between 2016 and 2021. Median and average values are represented by a solid line and a red line, respectively. The edges of the box represent the 25%and 75% quartiles, and the bar indicates the 10% and 90% data spread.

Figure 4

Figure 4. Box and scattering plots showing statistical analysis results for the concentrations of (a) DIN (μM) (b) DIP (μM) (c) DSi (μM), and (d) Chl-a (μg L^-1) in estuarine (station 1–8; box plots) and offshore (station 11–14; blue circles) waters in February, May, August, and November between 2016 and 2021. Median and average values are represented by a solid line and a red line, respectively. The edges of the box represent the 25% and 75% quartiles, and the bar indicates the 10% and 90% data spread.

3.3 SOM-based classification

The SOM analysis classified both the estuarine (C_e1–C_e5) and offshore (C_o1–C_o5) environments into five groups (Table 1). The SOM-based classification integrated multiple water quality parameters, with the identified clusters, reflecting the combined patterns of water temperature, salinity, pH, DIN, DIP, DSi, and Chl-a. In the estuarine region, the five identified groups exhibited distinct differences in nutrient and Chl-a concentrations. C_e1 and C_e2 exhibited moderate or low nutrient and Chl-a concentrations. C_e3 showed high nutrient, but low-Chl-a concentrations, whereas C_e4 (August 2020) exhibited the highest nutrient concentrations and the lowest Chl-a concentrations. C_e5 (August 2016, 2018, 2019, and 2021) was characterized by moderate nutrient concentrations and elevated Chl-a concentrations. The five offshore groups were categorized according to water temperature, nutrients, and Chl-a concentrations. C_o1 and C_o2 showed the lowest temperature and Chl-a concentrations, and moderate temperature with high Chl-a concentrations, respectively. C_o3 was associated with moderate temperature and low nutrient concentrations, whereas C_o4 (August 2017 and 2020) exhibited the highest nutrient and the highest Chl-a concentrations. C_o5 was characterized by the highest temperature and low nutrient and Chl-a concentrations.

Table 1

Table 1. Cluster classification results of estuarine (C_e) and offshore (C_o) water quality data derived from self-organizing map analysis.

SOM-based classification provides an integrated representation of water quality. The identified clusters reflected the combined patterns of water temperature, salinity, pH, DIN, DIP, DSi, and Chl-a. In particular, clusters associated with summer conditions corresponded to periods of enhanced river discharge and elevated nutrient concentrations, whereas clusters representing winter and spring conditions captured relatively stable, low-discharge environments. Thus, the SOM results synthesized the precipitation-driven hydrological forcing and resulting biogeochemical responses observed in the preceding sections.

4 Discussion

4.1 Variations in water quality and nutrients

The results revealed clear differences in water quality variability between EW and OW (Figure 3). Water temperature exhibited pronounced seasonal fluctuations in both regions, whereas EW showed slightly greater variability, reflecting the influence of river water input. Salinity showed strong contrasts between the two regions: OW salinity remained relatively stable, whereas EW salinity exhibited wide variability and frequently decreased to low values during periods of extreme precipitation and enhanced river discharge (August 2019, 2020, and 2021). During these periods, OW salinity also decreased, indicating the influence of freshwater plumes extending offshore. The pH of OW remained relatively constant (Feely et al., 2009), whereas EW exhibited lower pH values (~7.7) during summers with extreme rainfall, likely due to freshwater inflow, organic matter degradation, and tidal mixing. These results demonstrated that extreme precipitation exerted a stronger influence on water quality in EW than in OW, leading to episodic alterations across the estuarine–offshore system. Although extreme precipitation and river discharge were the primary focus of this study, other physical factors, such as water depth and coastal circulation may also have influenced the estuarine–offshore contrasts. However, a detailed assessment of these processes was beyond the scope of this study because of the absence of hydrodynamic observations.

During the study period, the average concentrations of DIN, DIP, and DSi were consistently higher and more variable in EW than in OW (Figures 4a–c), with the highest values observed in August 2020. These elevated nutrient concentrations were closely associated with extreme precipitation events that substantially increased river discharge into the Nakdong River Estuary. Intense summer rainfall delivered large volumes of nutrient-enriched river water derived from terrestrial runoff, fertilizer use, and wastewater effluents (Seitzinger et al., 2010; Howarth et al., 2011), whereas enhanced DSi inputs reflected the increased transport of silicate from weathered soils and geological substrates (Ronchi et al., 2013). These results highlight extreme rainfall events as critical drivers of high nutrient loading and episodic biogeochemical perturbations in estuarine systems.

4.2 Precipitation impacts on nutrient fluxes and Chl-a concentrations

The dissolved inorganic nutrient fluxes delivered to the Nakdong River Estuary were estimated using river discharge and average nutrient concentrations to evaluate the impacts of precipitation on the estuarine and coastal environments of the estuary. The calculated nutrient fluxes ranged from 0.4×10¹⁴ to 4.2×10¹⁶ for DIN, 0.1×10¹⁰ to 2.1×10¹² for DIP, and 0.8×10¹⁸ to 7.1×10²¹ μmol month^-1 for DSi (Figures 5b–d). Nutrient fluxes were highest during summer because of increased river discharge and nutrient concentrations, with exceptionally elevated values in the summer of 2020 driven by consecutive extreme rainfall events. These events produced approximately 50% higher precipitation than the climatological average, representing the highest summer rainfall since 1971 and resulting in substantial impacts on marine biogeochemical processes.

Figure 5

Figure 5. Temporal variation of (a) monthly precipitation and river water–derived fluxes of (b) DIN, (c) DIP, (d) DSi in February, May, August, November from 2016 to 2021. (e) average concentrations of Chl-a in estuarine (station 1–8; box plots) and offshore (station 11–14; blue circles) waters.

The calculated nutrient fluxes showed strong, significant, and positive correlations with precipitation rate (Figures 6a–c); r² = 0.78, p <0.001 for DIN, r² = 0.76, p <0.001 for DIP, and r² = 0.82, p <0.001 for DSi. Data from August 2020 were excluded from the correlation analysis using Cook’s distance, because this period represented an extreme precipitation event with exceptionally high river discharge and nutrient fluxes. The inclusion of this outlier would disproportionately affect the regression results and mask precipitation–nutrient relationships under typical hydrological conditions. These strong correlations indicate that climate-driven changes in precipitation are key regulators of estuarine nutrient budgets and should be considered in long-term water quality management and eutrophication modeling. In comparison with other studies, 56% of the annual DIN flux and 75% of the annual DIP flux were discharged during July–August 2011 in the Yeongsan River (Kim et al., 2022). Similarly, in the Seomjin River during 2005–2008, the wet season (July–August) fluxes were approximately 4–10 times higher for DIN, 8–42 times higher for DIP, and 4–97 times higher for DSi compared to the dry season (March) (Park et al., 2014). In our study, seasonal contrasts of nutrient fluxes in the Nakdong River were assessed using dry season (February) and wet season (August) data from 2016 to 2021. We exhibited wet-season enhancement was observed, with wet-to-dry ratios of 4–70 times for DIN, 4–36 times for DIP, and 9–740 times for DSi from 2016 to 2021. Similarly, the fluxes of DIN and DIP during the wet season (June to November) were approximately 4–5 times higher those during the dry season (December to May) in the Bangpakong River Estuary, Thailand (Boonphakdee and Fujiwara, 2008). Additionally, nutrient fluxes of wet season accounted for more than 50% of the annual fluxes in the Mekong River (51.6–61.7% for DIN; 59.8–65.6% for DIP; 47.4–55.6% for DSi) between 1985 and 2011 (Li and Bush, 2015). Furthermore, the fluxes of DIN, DIP, and DSi were approximately two times higher during the wet season than during the dry season in major global estuaries including the Amazon, Orinoco, São Francisco, Paraíba do Sul, Volta, Niger, and Congo River because of high river discharge induced by large amounts of precipitation (Araujo et al., 2014). These results highlighted the critical role of extreme rainfall events in driving nutrient pulses and reshaping estuarine biogeochemical processes.

Figure 6

Figure 6. Correlations between precipitation and (a) DIN flux, (b) DIP flux, and (c) DSi flux. The solid lines represent the regression lines.

Precipitation-driven nutrient fluxes strongly influenced Chl-a concentration variability in estuarine waters (Figure 5), with higher concentrations generally observed in summer in response to enhanced riverine nutrient inputs (e.g., 2018, 2019, and 2021). Chl-a concentrations were generally lower in offshore waters (average: 3.04 μg L^-1) than in estuarine waters (4.55 μg L^-1), with weak seasonal variability, whereas during the extreme rainfall event in August 2020, the estuarine Chl-a concentration remained low (1.70 μg L^-1) while offshore concentrations reached their maximum (17.17 μg L^-1). This anomalous pattern is likely explained by the massive river water inputs during the extreme rainfall event, which could cause strong dilution (average salinity dropped below 1 in August 2020), increased turbidity, and rapid flushing of phytoplankton biomass, thereby suppressing Chl-a accumulation within the estuary. Once discharged into the offshore, the river water plume stabilized and mixed with seawater, providing favorable conditions for phytoplankton growth offshore and leading to elevated Chl-a concentrations in coastal waters. Indeed, between July and August 2020, three red tide occurrences were reported in the Nakdong River coastal region by the National Institute of Fisheries Science. All events were dominated by the dinoflagellate Ceratium furca, with cell densities ranging from 300 to 2,500 cells mL^-1 (www.nifs.go.kr). These red tides were likely triggered by extreme rainfall–induced increases in nutrient fluxes to OW.

4.3 Characteristics of SOM-derived groups

Based on the SOM analysis (Table 1 and Figure 7), C_e1, C_e2, and C_e3 in EW and C_o1, C_o2, and C_o3 in OW mainly corresponded to observations from February, May, and November, whereas C_e4, C_e5, C_o4, and C_o5 were primarily associated with August. C_e1 corresponds to the dry season (autumn and winter), representing relatively stable, low-productivity conditions under limited river water input. C_e2 predominantly occurred during the late winter to spring transition periods (February–May), indicating nutrient depletion following biological uptake. In particular, C_e4 (August 2020) displayed the highest nutrient concentrations and the lowest Chl-a concentrations, corresponding to an extreme rainfall event that caused massive river discharge, dilution of the phytoplankton biomass, and enhanced turbidity in the estuary, as mentioned above (Murrell et al., 2007). C_e5 (August 2016, 2018, 2019, and 2021) showed the highest Chl-a concentrations, representing productive summer monsoon conditions associated with an increased nutrient supply and active biological growth.

Figure 7

Figure 7. SOM clustering results for (a) estuarine and (b) offshore water quality. Each hexagon represents a SOM neuron, and the colors indicate the clusters (C_e1–C_e5 and C_o1–C_o5).

In the offshore regions, C_o1 appeared during the dry season (autumn and winter), reflecting stable offshore conditions with limited productivity, whereas C_o2 (May 2018 and May 2019) indicates active phytoplankton growth, possibility due to water mixing and light availability. C_o4 (August 2017 and 2020) was characterized by high temperature and elevated concentrations of both nutrients and Chl-a, representing the offshore dispersion of nutrient-enriched freshwater plumes from the estuary during heavy rainfall events. Although offshore waters generally exhibit stable physical conditions, SOM-based clusters represent relative differences in phytoplankton growth responses driven by episodic nutrient inputs and seasonal variability rather than the large-scale instability of the offshore system. These SOM results indicated that the clustering in both EW and OW reflected spatiotemporal variations in water quality, demonstrating that water quality changes in EW and OW evolved sequentially in response to precipitation through a coupled rainfall–discharge–mixing–biological interaction process.

5 Conclusion

This study examined the influence of precipitation variability on water quality in the Nakdong River Estuary. Based on data collected from 2016 to 2021, physicochemical and biological parameters showed distinct seasonal and spatial variability between estuarine and offshore waters. Estuarine waters exhibited strong seasonal fluctuations driven by river water, whereas offshore waters remained relatively stable. Notably, extreme rainfall events, particularly in the summer of 2020, substantially increased riverine nutrient fluxes, leading to episodic changes in salinity, pH, and nutrient dynamics. Strong positive correlations between precipitation and nutrient fluxes highlight precipitation as a key driver of nutrient loading. SOM clustering further revealed that water quality conditions evolved sequentially through a coupled rainfall–discharge–mixing–biological response. These findings emphasize that the intensified precipitation associated with climate change will enhance nutrient transport and eutrophication risks in Korean coastal systems. Therefore, long-term monitoring and integration of hydrological variables into predictive water quality management are essential for sustainable coastal management.

Data availability statement

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding authors.

Author contributions

HK: Data curation, Formal analysis, Software, Visualization, Writing – original draft, Writing – review & editing. IY: Formal analysis, Writing – review & editing. JP: Formal analysis, Writing – review & editing. Y-WL: Conceptualization, Formal analysis, Investigation, Methodology, Writing – original draft, Writing – review & editing. YO: Conceptualization, Data curation, Formal analysis, Software, Supervision, Visualization, Writing – original draft, Writing – review & editing.

Funding

The author(s) declared that financial support was received for this work and/or its publication. This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIT) (NRF-2022R1C1C1012901) and Korea Institute of Marine Science & Technology Promotion (KIMST) funded by the Ministry of Oceans and Fisheries, Korea (RS-2021-KS211535).

Acknowledgments

We would like to thank marine environment monitoring department members at Korea Marine Environment Management Corporation (KOEM) for helping in undertaking the field sampling. This work was supported by the projects titled ‘Marine environmental measuring network’ funded by the Ministry of Oceans and Fisheries, Republic of Korea.

Conflict of interest

The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

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Abbreviations

USEPA, U.S. Environmental Protection Agency; DIN, dissolved inorganic nitrogen; DIP, dissolved inorganic phosphorus; DSi, dissolved inorganic silicate; HABs, harmful algal blooms; Chl-a, chlorophyll-a; EW, estuarine water; OW, offshore water; SOM, self-organizing map; Ce, classified estuarine; Co, classified offshore.

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