應用 HSPF 模式評估鳶山堰集水區污染潛勢之空間變異性

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1 臺灣水利第 64 卷第 2 期民國 105 年 6 月出版 Taiwan Water Conservancy Vol. 64, No. 2, June 2016 Applying HSPF to Assess Spatial Variation of Pollution Potential in the Yuanshanyan Watershed 應用 HSPF 模式評估鳶山堰集水區污染潛勢之空間變異性 Chia-Ling Chang 1*, Jen-Yang Lin 2, Chi-Feng Chen 3 and Shyh-Fang Kang 4 1 Department of Water Resources Engineering and Conservation, Feng Chia University. 2 Water Environment Research Center, National Taipei University of Technology. 3 Department of Natural Resources, Chinese Culture University. 4 Department of Water Resources and Environmental Engineering, Tamkang University. ABSTRACT This study applies the Hydrological Simulation Program FORTRAN (HSPF) model to predict non-point source pollution exports from 25 subbasins of the Yuanshanyan watershed. The spatial variability of the pollution potential in each space is a useful reference for relevant watershed environmental protection agencies as it can be used to improve landuse management strategies. The results show that the prediction effectiveness of non-point source pollution is satisfied by the HSPF model. The pollution potential in downstream subbasins are higher than in upstream subbasins. The pollution potential in Subbasin 1 is the highest among all the subbasins; thus, land-use restrictions should be stricter in Subbasin 1 than in the other 24 subbasins considered. Besides land-use management strategies, structural best management practices can also be implemented in Subbasin 1 to reduce its impact on watershed environment. Keywords: HSPF, non-point source pollution, pollution potential, watershed modeling. 摘 要 集水區污染潛勢之空間變異性是土地管理策略研擬相當重要的資訊, 對集水區相關管理單位具參考價值, 本研究應用 HSPF 模式預測鳶山堰集水區 25 個子集水區之非點源污染產出量, 並評估其污染潛勢之空間變異性 本研究結果顯示 HSPF 模式在非點源污染及水質模擬的預測效果佳, 作為集水區模擬是相當適合的工具 由非點源污染潛勢空間變異分析結果可知, 下游子集水區之污染潛勢高於上游子集水區, 在接近集水區集流點的子集水區 1 之非點源污染潛勢高於其他 24 個子集水區 ; 因此, 子集水區 1 應施以較嚴格的土地管理策略, 除了土地管理策略外, 本研究建議可在子集水區 1 內設置結構性最佳管理措施, 以減少本子集水區非點源污染對於集水區環境之衝擊 關鍵詞 : 集水區模式, 非點源污染, 污染潛勢, 集水區模擬 * Corresponding author. clchang@fcu.edu.tw ( 29 )

2 1. Introduction Although the level of precipitation in Taiwan is higher than in many other countries around the world, water resources are still limited. This is because Taiwan s natural environmental properties, such as uneven distribution of rainfall and rugged terrain, increase the difficulty to store water. As a result of global climate change, water resource management has become a significant challenge. In order to counter the major environmental challenges facing modern society, substantial changes in environmental policy and individual attitudes toward water usage should be considered. Watershed conservation is critical to maintaining water quality, protecting the environment and the species which inhabit our waterways and, as such, is an important issue in water resource management (Lin et al., 2000; Lu et al., 2001). A proper watershed management strategy should carefully consider how to control pollution and how to protect water quality. Point source pollution mainly includes industrial wastewater and municipal sewage discharge. Non-point source pollution is caused by rainfall and comes from many diffuse sources (Wu and Chen, 2013). Compared to point source pollution, non-point source pollution is more diffuse and difficult to control and manage (Pegram and Bath, 1995; Maciej, 2000; Ouyang et al., 2009). Non-point source pollution, such as suspended solid, nutrient, fertilizer, and pesticide, contribute significant pollutant loading to water bodies and worsens water quality (Lai et al., 2011; Lai et al., 2013). Therefore, non-point source pollution control has attracted growing attention (Nasr et al., 2007; Ongley et al., 2010; Darradi et al., 2012). The amount of non-point source pollution is related to rainfall characteristics and land-use conditions. The pollution potential represents the possible environmental impact resulting from diffuse pollution. Spatial variability of rainfall and land use results in spatial variations of the pollution potential (Faures et. al., 1995; Sekhar and Raj, 1995; Chaubey et al., 1999; Dijk et al., 2002; Chang et al., 2006; Chang et al., 2008). Those tasked with creating suitable land-use policies should consider the spatial variability of the pollution potential in a watershed environment (Wang, 2001; Chang and Hsu, 2009; Chang and Hsu, 2011). For these reasons, this study assesses the spatial variability of the pollution potential in a watershed. A watershed model is applied to predict non-point source pollution exports from the subbasins. The discussion in this study can be a helpful reference for watershed management agencies. 2. Methods 2.1 Site description The Yuanshanyan watershed provides the setting for this case study. The watershed area of the site is about km 2, as shown in Figure 1. The watershed is divided into 25 subbasins. The Yuanshan Weir is located in the midstream of the Dahan River. The main function of this weir is to block or direct the flow of water from the Shimen Reservoir in order to provide the Banxin Water plant with water for treatment and utilization. The Banxin Water plant provides domestic water for about 1.87 downstream area Fig. 1. Case area: the Yuanshanyan watershed. ( 30 )

3 million people in New Taipei City. The pollution in the Yuanshanyan watershed influences the water quality of the Banxin Water plant. Therefore, it is important to understand the spatial variability of the pollution potential when creating suitable watershed management strategies in the Yuanshanyan watershed. R 2 = n MAPE = n i = 1 Pi P Oi P P i i = 1 i = 1 1 n n i = 1 2 n Pi Oi O i O Oi 2 O (1) (2) 2.2 HSPF model The HSPF is one module in the Better Assessment Science Integration Point and Nonpoint Sources (BASINS) model and is a geographicallybased watershed assessment tool. It can be integrated with a Geographic Information System (GIS). Most of the data can be directly extracted from GIS data. It can even act as an integrated decision-making system. Thus, this model has been commonly applied in environmental science and management (Laroche et al., 1996; Whittemore and Beebe, 2000; Luzio et al., 2002; Al-Abed and Whiteley, 2002; Albek et al., 2004). This study applies the HSPF model to predict hydrologic responses and non-point source pollution. Meteorological and geographical data from 2008 and 2009 are utilized for model calibration and validation. Daily flow data and monthly water quality data are compared with the predicted values by the HSPF model. In order to decrease interference and additional model uncertainty arising from weir operation, only one sub-watershed of the Yuanshanyan watershed is selected for model calibration and validation. The effectiveness of the model calibration and validation can be evaluated by a number of criteria. For instance, the R-squared (R 2 ) between the predicted and observed value is usually applied for evaluating the reliability of runoff predictions. Whereas the mean absolute percentage error (MAPE) is usually utilized for evaluating the reliability of pollution and water quality predictions. The R 2 and MAPE are defined as eqn. (1) and eqn. (2). where P i and O i are the i th predicted and observed values; P and O are the average predicted and observed values; n is the number of simulation records. After model calibration and validation, the parameters of the HSPF model can be fixed for further prediction and analysis. This study evaluates the spatial variation of the pollution potential by using the HSPF model. 2.3 Classification method Classification methods use various sets of features or parameters to characterize objects and put the objects into different categories (Erbek et al., 2004). This study utilizes the natural breaks method and the equal interval method to classify the subbasins into five grades; the subbasins with the highest pollution exports are classified into the 1 st grade. The cutoff points for each classification method are various. The equal interval method divides a set of attribute values into groups that contain an equal range of values. The natural breaks method seeks to partition data into classes based on natural groups in the data distribution and minimizes the variation within a class. For pollution potential, one must consider SS, BOD, TP, and TN exports. This study normalizes the values of pollution exports. The normalized values are fixed between 0-1. The sum of the four normalized values represents the degree of the pollution potential of each subbasin. 3. Results and Discussion 3.1 Model calibration and validation ( 31 )

4 Model calibration and validation is an important step before simulation and analysis. Figures 2 and 3 show the results of the calibration and validation of the model for the runoff and pollution predictions. Table 1 summarizes the values of R 2 and MAPE in the model s calibration and validation. The results indicate that the reliability of the runoff prediction is satisfactory. The values of R 2 between the predicted and observed runoff are 0.65 and 0.8 in the model calibration and validation, respectively. Moreover, the reliability of the water quality predictions are acceptable. Most values of MAPE in the water quality simulation are less than 50%. These results demonstrate that the HSPF model is a useful tool for predicting watershed responses. Although many studies have addressed recommended values of parameters, regional parameters are significant for watershed simulation. The major parameters in the HSPF model are summarized in Table 2. Model users need to adjust the parameters in the process of model calibration and validation. Because many parameters are required for the HSPF model, it increases the difficulty when applying this model. Sensitivity analysis results can help model users save a lot of time when preparing the parameters. In the PWATER program, the parameters IFES, CEPS, UZDN and INFILT have the greatest influence on hydrologic simulations. The results show that the soil type and infiltration process are highly related to hydrologic responses and pollution transportation. (a) Model calibration for runoff predictions. (b) Model validation for runoff predictions. Fig. 2. Model calibration and validation for runoff predictions. Table 1. Effectiveness of model calibration and validation model calibration model validation simulation criteria value criteria value runoff simulation R R pollution simulation BOD MAPE 21% MAPE 40% SS MAPE 44% MAPE 149% TP MAPE 39% MAPE 46% TN MAPE 41% MAPE 49% ( 32 )

5 (a) Model calibration for SS predictions. (b) Model validation for SS predictions. (c) Model calibration for BOD predictions. (d) Model validation for BOD predictions. (e) Model calibration for TP predictions. (f) Model validation for TP predictions. (g) Model calibration for TN predictions. (h) Model validation for TN predictions. Fig. 3. Model calibration and validation for pollution predictions. ( 33 )

6 Table 2. Main parameters in the HSPF model PWATER program Range (unit)* Calibrated value Infiltration capacity of the soil (INFILT) ~100 (in/hr) 50 Length of the assumed overland flow plan (LSUR) 1~none (ft) 150 Slope of the overland flow plane (SLSUR) ~ Behavior of groundwater recession flow (KVARY) 0 (1/in) 4 Basic groundwater recession rate (AGWRC) 0.001~0.999 (1/day) 0.98 Lower zone E-T parameter (LZETP) 0~ Interception storage (CEPS) 0~100 (in) 0.1 Initial upper zone storage (UZS) 0.001~100 (in) 0.1 Interflow storage (IFWS) 0~100 (in) 1 Interception storage capacity (CEPSC) 0~10 (in) 0.1 Upper zone nominal storage (UZSN) 0.01~10 (in) 0.01 Manning s n for the overland flow plane (NSUR) 0.001~1 0.4 Interflow inflow parameter (INTFW) 0~none 4 Interflow recession parameter (IRC) 1.0E-30~0.999 (1/day) 0.9 Lower zone storage (LZS) 0.001~100 (in) 1 Active groundwater storage (AGWS) 0~100 (in) 0.01 Index to groundwater slope (GWVS) 0~100 (in) 0.02 Lower zone nominal storage (LZSN) 0.01~100 (in) 0.02 SEDMNT program Range (unit)* Calibrated value Management Practice (P) factor from USLE (SMPF) 0.001~1 0.5 Coefficient in the soil detachment equation (KRER) 0~none 0.3 Exponent in the soil detachment equation (JRER) none 2 Daily reduction in detachment sediment (AFFIX) 0~1 (1/day) 0.03 Fraction land surface protected from rainfall (COVER) 0~1 0.9 Atmospheric additions to sediment storage (NVSI) none (lb/ac-day) 0 Coefficient in the sediment washoff equation (KSER) 0~none 0.1 Exponent in the sediment washoff equation (JSER) none 8 Coefficient in soil matrix scour equation (KGER) 0~none 0.01 Exponent in soil matrix scour equation (JGER) none 8 *(Bicknell et al., 2001). 3.2 Non-point source pollution prediction The composition of land-use types in the 25 subbasins are shown in Table 3. The agricultural and industrial areas are about km 2 and km 2 in the Yuanshanyan watershed. Compared to water protection areas, there is a higher degree of urbanization in this area. Pollution exports will differ among the 25 subbasins as a result of various properties, such as the composition of land-use types, soil, slope, and meteorological conditions. This study applies HSPF to predict the SS, BOD, TP, and TN exports from the 25 subbasins in the Yuanshanyan watershed. The predicted results are shown in Table 4. As the total amount of pollution exports is related to the total area of each subbasin, this study evaluates pollution exports in on a per unit area basis. The results show that the five subbasins with the highest pollution exports in each unit area are Subbasins 1, 13, 14, 22, and 15. Many factors, such as rainfall characteristics, landuse types, and location, can influence the pollution ( 34 )

7 (a) SS exports (b) BOD exports (c) TP exports (d) TN exports Fig. 4. Spatial variability of pollution exports by the natural breaks method. exports of subbasins. Due to pollution accumulation and transport, most subbasins with higher non-point source pollution are located in downstream areas. This study applies the natural breaks method and the equal interval method to classify the subbasins into five grades. Figures 4 and 5 display the subbasins with different grades of pollution exports by the natural breaks method and by the equal interval method, respectively. The results illustrate that Subbasin 1, with the highest SS, BOD, TP, and TN exports, is classified into the 1 st grade. The pollution exports from Subbasin 1 are much higher than those from the other 24 subbasins; thus, only Subbasin 1 is classified into the 1st grade of pollution exports. Therefore, stricter land-use management strategies and structural best management practices are critical for water quality in Subbasin 1. The results indicate that the natural breaks and the equal interval classification methods result in drastically different categorizations of water quality. More subbasins are classified into the 5th grade by the equal interval method than by the natural breaks method. Therefore we can say that the evaluation made by the equal interval method of non-point source pollution is more optimistic than the natural breaks method. Many researchers have demonstrated that the natural breaks method, one of the most common methods for classifying data, can minimize variation within classes and maximize variation between classes (Jenks, 1967; Luan et al., 2011). The results of this study substantiate previous ( 35 )

8 (a) SS exports (b) BOD exports (c) TP exports (d) TN exports Fig. 5. Spatial variability of pollution exports by the equal interval method. claims and further demonstrates the appropriateness and objectivity of using the natural breaks method for creating proper land-use management strategies in a watershed. 3.3 Spatial analysis of pollution potential Pollution potential of each subbasin is determined by four pollutants, i.e. SS, BOD, TP, and TN. The sum of normalized values of SS, BOD, TP, and TN exports represents the pollution potential. This study also utilizes the natural breaks method and the equal interval method to classify the pollution potential into five grades. Figures 6 and 7 show the pollution potential in the 25 subbasins by the natural breaks method and by the equal interval method, respectively. The grades of the subbasins are different when only considering SS, BOD, TP, or TN exports and when considering the comprehensive pollution potential. These results indicate that the pollution potential is higher in downstream subbasins than in upstream subbasins. The subbasins with higher pollution potential need to implement stricter land-use management strategies. In addition to non-structural land-use restrictions, structural best management practices, such as grassy swale, buffer strip, or bioretention, can also be implemented in downstream areas for reducing pollutants. By using the equal interval method, most of the subbasins are classified into the 5 th grade. Thus, the classification results from the natural breaks method are stricter than those from the equal interval method. ( 36 )

9 Fig. 6. Spatial variability of the pollution potential by the natural breaks method. Fig. 7. Spatial variability of the pollution potential by the equal interval method. Table 3. Land use type composition in the 25 subbasins Subbasins Area Agricultural area Industrial area Forest Water (ha) Area (ha) % Area (ha) % Area (ha) % Area (ha) % ( 37 )

10 Table 4. Predicted non-point source pollution from the 25 subbasins Subbasins SS BOD TP TN Unit: kg/ha/yr. 4. Conclusions This study applies a popular watershed model, HSPF, to evaluate the spatial variability of the pollution potential in the Yuanshanyan watershed. A summary of the findings are as follows: The effectiveness of runoff predictions is better than pollution predictions using the HSPF model. On the whole, reliability of the prediction of watershed responses is satisfied by the HSPF model. The pollution exports in each unit area are more objective than the total amount of pollution exports for evaluating the pollution potential in the subbasins. Pollution potential considers four pollution exports: SS, BOD, TP, and TN. The examination of pollution potential is too optimistic when classified by the equal interval method to implement suitable land-use restrictions. Thus, this study recommends utilizing the natural breaks method to classify the subbasins based on both pollution exports and their respective pollution potentials. Spatial variability of the pollution potential is an important reference for creating proper landuse management strategies in a watershed. The downstream subbasins have higher pollution potential than the upstream subbasins in the Yuanshanyan watershed. The pollution potential ( 38 )

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