Investigation of correlation between surface runoff rate and stream water quality

The purpose of this study was to investigate the relationship between stream water quality and the surface runoff rate defined as the ratio of annual surface runoff to annual average precipitation. The surface runoff rate was first estimated in the Han River basin located in South Korea using the calibrated and validated HSPF model. Then a linear regression analysis was performed to investigate the correlation between the computed surface runoff rate and the observed water quality. It was found that there were statistically significant relationships between the surface runoff rate and concentrations of BOD, COD, and T-P and higher surface runoff rate led to the deterioration of water quality in streams. Finally, the applicability of the surface runoff rate as an indicator to measure the impact of land development on stream water quality was evaluated using a receiver operating characteristic (ROC) curve analysis. The ROC curve analysis indicated that the surface runoff rate could be utilized as a useful indicator to illustrate the degradation of stream water quality at the watershed scale. The results from this study also suggest that the surface runoff rate needs to be managed and controlled within about 15% to prevent the degradation of stream water quality.


INTRODUCTION
Non-point source (NPS) pollution, which is the polluted runoff from the land, is an important factor affecting the water quality of rivers (Li et al. ). NPS pollution occurs as water moves across the land or underground. Thus, the factors that affect accumulation of pollutants on the lands surface or the mechanisms that transport pollutants from the land surface have a direct impact on NPS pollution (Donigian & Crawford ). In particular, the quantity of surface runoff, which occurs when the rainfall amount exceeds the infiltration capacity of the soil, substantially influences NPS pollution because a significant quantity of non-point pollutants such as sediment, nutrients and pesticides are transported to nearby streams via surface runoff (Novotny & Olem ; Ritter & Shirmohammadi ). The expansion of urban areas has also created more impervious surfaces that prevent rainfall from infiltrating the soil, resulting in an increase in surface runoff (Shuster et al. ). These impervious surfaces and surface runoff directly affect the transport of NPS pollutants (Chithra et al. ). During the storm events, the surface runoff from urban areas is drained into urban drainage systems such as separate or combined sewer systems. In separate sewer systems, the increase in surface runoff may degrade the stream water quality because pollutants that accumulate on the surface from traffic, litter, street dust and other sources are washed off by the surface runoff into and run into the sewer system (Novotny & Olem ). In combined sewer systems, during rainfall events when the transport capacity of the sewer system is insufficient, the combined sewer overflows (CSOs) occur, resulting in the discharge with a mixture of untreated wastewater and urban runoff into the nearest watercourse (Even et  However, researchers frequently debate whether this approach is rather inefficient and whether the percentage of total impervious areas or effective impervious areas can be applied to all watershed environments as local factors such as terrain, soils, geology, and rainfall patterns (Pettigrove ). Pettigrove () suggested that a more effective approach is to develop an understanding of the impact of urban runoff on all receiving waters because the impact of flows is likely to be strongest at the sub-watershed level. However, previous research has not adequately investigated the relationship between hydrological components such as total runoff and surface runoff and water quality in different geographical areas under different scales.
Understanding this correlation is necessary to identify the primary threats to water quality and to provide theoretical support for improving water quality. Thus, the purposes of this study are (1) to estimate the surface runoff rate, which is defined as a proportion of annual surface runoff to annual average precipitation using the HSPF model, (2) to investigate the correlation between the computed surface runoff rate and observed water quality, and (3) to evaluate the applicability of the surface runoff rate as an indicator to measure the impacts of land development on stream water quality.

Study area
This study was conducted in the Han River Basin (HRB), which is located in the central part of South Korea (Figure 1).
The HRB is the largest river basin in South Korea, covering approximately 34,428 km 2 . The HRB has a typical semihumid continental monsoon climate with an average annual precipitation of 1,300 mm. About 70% of the annual precipitation occurs during the summer season (June-September) (Kim et al. ). As a result, the maximum discharge occurs during the rainy summer season. The North and South Han Rivers in the HRB are the major sources of drinking water for more than 24 million people including the residents of the densely populated Seoul metropolitan area. As land development pressure continues in the suburban areas in Seoul, the urban area is expected to gradually expand. The HRB includes the 14 multipurpose dams and three multifunction weirs to ensure a stable water supply and mitigate floods.

Model calibration and validation
The calibration of the model was performed manually by adjusting the major parameters related to hydrology. Thir-

Selection of sub-watershed
The evaluation of the correlation between surface runoff rate and water quality was not conducted for all sub-watersheds in HRB (Figure 1). The sub-watersheds where there were extensive impoundments or dams within the stream network were excluded. In addition, the sub-watersheds including the water quality monitoring stations within 1 km from major point sources of pollutant discharge such as a wastewater treatment plant were excluded. This approach has often been used to minimize the effect of major point sources or dam discharge on water quality observed from monitoring stations (Kim et al. ). In this study, 42 sub-watersheds among the sub-watersheds were finally selected for analysis ( Figure 1).

Water quality data
Three water quality parameters including biochemical oxygen demand (BOD), chemical oxygen demand (COD), and total phosphorus (T-P) were used in the analysis because these parameters have been regulated by water quality standards for managing stream quality in Korea.
The water quality parameters have been monitored and ana-

Linear regression analysis
To evaluate the relationship between the surface runoff rate and stream water quality in the study area, the five-year was considered statistically significant.

Receiver operating characteristic curve analysis
In this study, the threshold of the surface runoff rate was used to represent the point at which the water quality abruptly degrades with respect to the surface runoff rate. Receiver  Class II is considered to be not polluted: the regulation levels of BOD, COD, and T-P are 3.0, 5.0, and 0.10 mg/L, respectively. Therefore, the water quality levels for Class II were used to determine the threshold value of the surface runoff rate for stream water quality management. This threshold value indicates that the water quality drops below Class II, caused by a change in the surface runoff rate of the sub-watershed.

Model calibration and validation
During the calibration period (2010-2012), R 2 ranges from 0.60 to 0.98 and NSE from 0.47 to 0.97 (Table 2) Correlation between the surface runoff rate and water quality Figure 3 shows the scatter plots of the surface runoff rate and water quality. The results of the linear regression 'bad status (worse than Class II of the water quality standard)' because all the AUC values were more than 0.94. The cutoff (or threshold) value of the surface runoff rate at which the degradation of stream water quality occurs ranged from 11.1% to 15.1% for BOD, COD, and T-P ( Figure 5). This result indicates that managing the surface runoff rate to be approximately within 15% can strategically be required to prevent the degradation of stream water quality caused by a higher surface runoff rate in the watershed and to satisfy the stream water quality standards for Class II which is the target of stream water quality management in Korea.

CONCLUSIONS
In this study, the HSPF model and statistical analysis including the linear regression analysis and ROC curve analysis were applied to investigate the relationship between the surface runoff rate and stream water quality. The results derived from this study reveal that the surface runoff rate significantly affects the stream water quality parameters including BOD, COD, and T-P. In addition, it was found that since higher proportions of surface runoff could lead to higher concentrations of BOD, COD, and T-P in streams, managing and controlling the surface runoff rate within approximately 15% could be a fundamental strategy to prevent the degradation of stream water quality.
The results from this study could be slightly different from those derived from other hydrologic models because the others models could employ different methods for simulating the hydrologic components including surface runoff.
Thus, more research may be needed to compare the results simulated by various hydrologic models. In addition, various other ranges of possible surface rate in the watersheds were not sampled as this study was performed using observed water quality data in order to analyze the impact of the surface runoff rate on stream water quality. Therefore, further research based on simulated water quality data using the calibrated and validated model for water quality is recommended to analyze the relationship between the surface runoff rate and stream water quality.