Water Air Soil Pollut (2014) 225:2122 DOI 10.1007/s11270-014-2122-7
Comparing the Export Coefficient Approach with the Soil and Water Assessment Tool to Predict Phosphorous Pollution: The Kan Watershed Case Study Madjid Delkash & Furat A. M. Al-Faraj & Miklas Scholz
Received: 22 April 2014 / Accepted: 8 August 2014 / Published online: 28 September 2014 # Springer International Publishing Switzerland 2014
Abstract Water quality protection has become a key concern in water resources development and management. Uncontrolled nutrient input may challenge the quality of some water bodies. This study uses the relatively steep Kan watershed located in the north-west of Tehran (Iran) as an example case study, where an artificial lake is currently under construction for recreational purposes. Two approaches to predict the total annual phosphorous load were assessed: the soil and water assessment tool (SWAT) and the export coefficient approach. River discharge and sediment transport were simulated prior to modeling of the total phosphorous (TP) load in SWAT to make the model more accurate. In addition, an upstream to downstream calibration method was utilized. Findings reveal that the SWAT-simulated phosphorous load had sound Nash–Sutcliffe efficiency (ENS) values (ENS of 75 % for calibration and ENS of 52 % for validation). The relative error in estimating annual TP load was 7 %. The export coefficient approach assigning coefficients of export for each land use is known as an alternative method that can be used for estimating the TP load. Four sets of export coefficients were selected from the literature to examine their M. Delkash Department of Civil and Environmental Engineering, University of Delaware, Newark, DE 19716, USA F. A. M. Al-Faraj : M. Scholz (*) Civil Engineering Research Group, School of Computing, Science and Engineering, The University of Salford, Newton Building, Salford M5 4WT, UK e-mail:
[email protected]
suitability in TP load prediction. The results showed significant errors in TP load prediction, which indicates that export coefficients are likely to be watershed-specific. Likewise, the export coefficients were found to vary through four wet months with errors ranging from 9 % to 33 %. This paper demonstrates that the export coefficient method may estimate the pollution load in the Kan watershed with less data than the advance SWAT model. However, it is associated with a higher level of error. Keywords Error minimization . Nutrient control . River discharge . Sediment transport . Watershed management . Water quality modeling
1 Introduction 1.1 Background The discharge of nutrients such as phosphorous and nitrogen into water bodies is a major concern in water quality management (Shrestha et al. 2008). High loads of nutrients not only diminish the quality of water bodies, but it also increases the burden of treatment expenses. Generally, pollution sources are categorized in two groups: point sources, which can be located relatively easily (Carpenter et al. 1998), and non-point sources, which are associating with land use changes (Novotny 1999) and diffuse pollution (Scholz 2010). As humans disturb nature, which leads to changes in nutrient cycles, the importance of non-point source pollution
2122, Page 2 of 17
control becomes more important (Scholz 2010; Saunders and Kalff 2001). Hydrologic models have been utilized to study the impacts of various management strategies, and climate and land use changes. These models are usually divided into physical and empirical ones. Physical models often lead to acceptable results, but they need numerous data and require a high number of calculations for larger catchments (Singh et al. 2005). SWAT, Areal Non point Source Watershed Environment Response Simulation, Simulator for Water Resources in Rural Basins, and Basins are some examples for this modeling approach. The SWAT model can be considered as a semicontinuous model in space; some parameters are fed into the model as distributed parameters, while others have one value for the entire watershed. Furthermore, SWAT is continuous in time, simulating the hydrology of watersheds based on physical, chemical, and biological processes. It was developed by Arnold et al. (1998) to simulate sediment, nutrient, pesticide, pathogen, and hydrological responses to land use and climate changes (Gou et al. 2008; Lou and Zhang 2009; Laurent and Ruelland 2011; Dixon and Earls 2012; Munkundan et al. 2013). Shang et al. (2012) calculated pollution proportions to a watercourse upstream of Lake Erhai (China) using SWAT. They identified that agricultural activities and raw domestic wastewater discharges are the main contamination sources. Moreover, they indicated that there is a lack of knowledge regarding the fate of pollutants between their sources and the watershed outflow point. A further study using SWAT was conducted by Bosch et al. (2011) on discharges, and sediment and nutrient loads in the Lake Erie (USA) watershed. They concluded that SWAT is reliable in modeling discharges and sediments. In contrast, nutrient simulations were less accurate. Empirical models need less input data than physical models. The export coefficient approach is considered as a reliable approach by Worrall and Burt (1999). This method considers effluent masses in specific climatic and land use scenarios. Export coefficients are derived from discharge and pollutant measurements. Land use, climate, soil type, and topography often affect the results. Major assumptions of this approach include the steady and uniform distribution of parameters. The export coefficient method applied to land use classification led to significant progress in the 1970s. For example, it was utilized by Omernik (1976) to
Water Air Soil Pollut (2014) 225:2122
estimate the eutrophication status of lakes. Export coefficients were also utilized to assess nutrient and other organic pollutants of the Fuji River (Japan). Shrestha et al. (2008) reported that forest (62 %) and agriculture (20 %) had the most significant contributions in terms of phosphorus pollution. In another investigation, the phosphorous pollution of an English plain was studied by Bowes et al. (2005) using export coefficients. It was noted that domestic wastewater was associated with the greatest pollution proportion. Approximately 80 % of wastewater treatment led to a 52 % reduction in TP load. The export of pollution is a function of various variables such as rainfall and runoff volume, and season and land use (Cooper and Thomson 1988; Dils and Heathwaite 1996). Agricultural land runoff has often high nutrient loads during the start of major storms (first flush effect), while domestic land use has frequently a constant load throughout the year (Poor and McDonnell 2007). Seasonal export coefficients can often predict nutrient export better compared with annual export coefficients. It has been reported that 42 % of the annual phosphorous load was exported from a catchment during only three storm days (Hoare 1982; Poor and McDonnell 2007), which infers that phosphorus comes from non-point sources (diffuse pollution), such as soil erosion, and depends strongly on rainfall volume (Scholz 2010). It follows that runoff analysis plays an important role in identifying contributions associated with land use. The volume of rainfall impacts on runoff and sediment transport (Ding et al. 2010). The identification of export coefficients for typical rainfall events associated with characteristic sub-basins in terms of slope, area, and land use can be a promising approach.
1.2 Challenge, Rationale, and Aim Modeled responses of a watershed to pollution are considered to be case-study specific (Buck et al. 2004; Lee et al. 2009), because of different soil characteristics, landscape features, hydrological cycles, and farming activities (e.g., fertilizer application, crop rotation, and number of cattle). In contrast, export coefficients derived from agricultural land use are applicable to other agricultural lands according to Johnes (1996) and Ding et al. (2010)). Considering that it is difficult to judge by a practitioner, which position to adopt for a watershed, it
Water Air Soil Pollut (2014) 225:2122
is important to assess the advantages and disadvantages of applying different watershed response methods. This paper used the Kan watershed as a representative case study to assess the reliability of different watershed response methods, particularly to phosphorus discharges. The selected case study experiences rapid land use changes and is important, because it meets a large proportion of Tehran’s water demand. The watershed is relatively small, steep, and the soil is rich in phosphorous. An urban lake is fed by the Kan River and requires protection in terms of its water quality. The river is polluted by phosphorous. The accurate estimation of the total daily maximum load (TMDL) of phosphorus to protect the river and lake is required for successful water quality control. However, no estimation of the TMDL was carried out for the examined watershed in this paper, because of limited resources and the high complexity of the watershed. Using a physical model may lead to good results but requires considerable data input. Alternatively, an empirical approach may help, but a lower accuracy is likely. Only a few literature sources quantify performances of physical and empirical models on relevant watersheds. This article aims to compare the performance of the SWAT model and the export coefficient approach specifically regarding phosphorous flow simulations using the Kan watershed as an example to evaluate the accuracy of both methods. Hydrological simulation in advance of water quality modeling, sediment simulation, and model calibration was undertaken before phosphorous simulation. Furthermore, a step-by-step simulation of both upstream and downstream water quality stations using SWAT was also undertaken to improve the simulation results. The transferability of export coefficients from one watershed to another one was also tested. The export coefficients were also derived for four rather stormy months to obtain more accurate results. The findings of both improved standard approaches are compared with each other in this paper, and shortcomings of the methods tested are highlighted where appropriate.
2 Methodology 2.1 Study Area and Available Data The Kan watershed is located between 51.167° east longitude and 35.750° north latitude, and 51.383° east longitude and 35.967° north latitude, and has an average
Page 3 of 17, 2122
elevation of 2,377 meters above sea level (masl.). The highest and the lowest elevations are 3,822 and 1.327 masl, respectively. This watershed has a drainage area of 215 km2 and a perimeter of 74 km. The annual precipitation is about 550 mm. Approximately 70 % of the total rainfall occurs in winter and spring. Most of the precipitation is snow during fall (early December) and winter, because of the high elevation of the watershed and cold temperature (annual average of 11.5 °C). The mean temperature is about 0 °C during colder months. Snow melting occurs in spring. Discharge observations are consistent with this description. The maximum discharge can be observed during the first few days of spring as the temperature increases. The land use map of the Kan watershed is shown in Fig. 1. Poor vegetation is a salient feature of this watershed. The upper part of basin is usually covered by rock and brush ranch. Agricultural activities are rare in this region while agricultural activities are high in the south of the watershed. Urban land use is prevalent in the east of the watershed. The watershed attracts visitors from its populated neighborhood, predominantly Tehran. The reaction time of the Kan basin is about 3 h, if the runoff speed is 2.5 m/s. Water is deviated just downstream of the Old Bridge. Discharge and phosphorous were measured from the Old Bridge (Fig. 1) to simulate the fate of phosphorous in this watershed. Field observations confirmed that grazing by cattle is not wide-spread. The major pollution sources are raw wastewater discharge and soil erosion throughout the watershed. The locations of tributaries joining the Kan River are shown in Fig. 2. The important tributaries of the Kan River are Kiga and Rendan in the East, and Keshar located to the west of the river (Fig. 3). The measurement stations are represented by points in Fig. 2. The eutrophication of the urban lake (under constructed) was studied. Therefore, monthly measurements of discharge, TP, and total nitrogen (TN) were undertaken for each tributary just before they were joining the main stream in 2011. The ratio of TN to TP measured in the Kan River is tabulated in Table 1, indicating that phosphorous is the limiting nutrient for this water body. This ratio increased during the warm seasons at Rendan, Keshar, and Soloqan, and decreased in Kiga. Keshar had the highest ratio indicating that phosphorous is scarce in comparison to nitrogen.
2122, Page 4 of 17
Water Air Soil Pollut (2014) 225:2122
Fig. 1 Map of land use for the Kan watershed
2.2 SWAT Model Description The SWAT model is known as a process-based hydrological model simulating the ecohydrological features of a watershed. The model processes are based on the water balance Eq. 1 (Arnold et al. 1998). SW t ¼ SW 0 þ
t X i¼1
Ri −Qi− ET i −wseep −QRi
ð1Þ
where SWt is the soil moisture after t days, SW0 is the initial soil moisture on day i, R is the rainfall, Q is the runoff, ET is the evapotranspiration, wseep is the water seepage, and QR is the base flow. The SWAT model divides each watershed into hydrologic response units (HRU), which respond uniformly to hydrological events and are defined in terms of soil, slope, and land use. The aggregation of HRU creates the watershed. The SWAT approach
Water Air Soil Pollut (2014) 225:2122
Page 5 of 17, 2122
Fig. 2 The geographical position of the Kan River watershed
utilizes modified soil conservation society runoff curves to route surface water, and the revised rational method for peak of hydrograph and groundwater features such as percolation, base flow, and transmission loss. Meteorological data such as rainfall, minimum and maximum temperatures, the digital elevation model (DEM), and soil and land use maps are needed as input data.
The SWAT model was run initially. Then the outcome of the model was calibrated with observations obtained by sequential uncertainty domain parameter fitting (SUFI2), which is a support program to calibrate SWAT models. The calibration procedure is general, feed-forwarding, sequential, and iterative. The program initiates changing input parameters according to the uncertainty domain until it finds the optimum values
2122, Page 6 of 17
Water Air Soil Pollut (2014) 225:2122
equal to 1. The latter indicates a perfect simulation. n X i¼1
2 2
ðOi −S i Þ
E NS ¼ 1− X n
Oi −O¯
ð2Þ
i¼l
where Oi is the observation of the ith sample, Si is the ith simulation, and O and S are the observation and simulation averages, respectively. Hydrological parameters associated with the river were initially calibrated to find the contribution of each pollution source for this watershed. Considering that nutrients have strong correlations with total suspended solids (TSS) according to Goonetilleke et al. (2005), sediment was calibrated to increase the level of accuracy in TP simulation. Hydrological and TSS calibrations of the upper stations were undertaken using a data set ranging between 2000 and 2010. An increase in the number of calibration data would have led to a more accurate and reliable model. The calibration of the upstream stations Kiga, Rendan, and Keshar in advance of the target calibration at Old Bridge reduced the accumulative error. Fig. 3 The position of sub-basins upstream of the Old Bridge
of input parameters and their levels of sensitivity for the watershed. The initial values of sensitive parameters were replaced by revised values calculated from calibration runs. The accuracy of the simulations was evaluated by using the Nash–Sutcliffe efficiency (Eq. 2; (Nash and Sutcliff (1970)), which is usually used to assess hydrological models. This efficiency is always less than or Table 1 Ratio of total nitrogen over total phosphorus measured in tributaries of the Kan River in 2011 (Eshtooki 2012) Month
Kiga
Rendan
Keshar
Soloqan
Jan
18
11
17
10
Feb
17
15
7
10
March
22
17
28
22
April
16
13
30
21
May
20
16
11
15
June
5
23
40
27
July
6
40
21
19
Mean
13
23
24
19
2.3 Export Coefficients This approach is based on the export of pollutants subject to land use. Generally, this method tries to find the load of pollutants based on Eq. 3. Z T L¼ QC dt ð3Þ 0
where Q is the river discharge, C is the concentration of a pollutant, and T is the time of interval. In general, discharge and pollutant concentrations are not a continuous function in time. Therefore, the integral in Eq. 1 should be converted to summation. Equation 4 represents the mathematic model suggested by Hodge and Armstrong (1993) and used to determine the role of each land use area in polluting the watercourse. L ¼ a þ
n X m X
Ai Ek
ð4Þ
i¼1 k¼1
where L is the expected output load, a the point source contribution, m the total number of area, n is the number of land use classes, Ai is the area of each land use, and Ek is the export coefficient.
Water Air Soil Pollut (2014) 225:2122
Page 7 of 17, 2122
Each sub-basin located upstream of the Old Bridge was defined in ArcGIS 10 based on outlets to utilize the export coefficient. The area of each land use in any subbasin was subsequently calculated by raster computation. Examination of the generalization of export coefficients was considered at first. The multiplication of suggested export coefficients by area of each land use obtained the expected output load. Summation of each expected load from each land use with point sources resulted in the TP load. The same export coefficients are often used for both agriculture and cattle land uses, because both categories have similar responses to pollution loads (Johnes 1996; Ding et al. 2010). Equation 4 was applied to estimate the phosphorous load associated with domestic wastewater. Four studies were selected to examine the accuracy of this approach. Table 2 shows the export coefficients used in this study. The phosphorous loads associated with the outlets of the four major sub-basins (Kiga, Rendan, Sangan, and Keshar) were used to derive monthly export coefficients for the Kan watershed during wet months (December 2010 to March 2011). Multiple regression analysis was used to find the export coefficients for each land use. The dominant land uses in these four sub-basins were range (brush) and rock (sometimes used as farmland). There was no significant urban land use upstream of the Old Bridge. No rainfall was recorded after May, resulting in the absence of any significant diffuse pollution. Therefore, the export coefficient method did not apply for the non-rainy season, because point source discharges were the only cause of pollution. The geographical characteristics of the four studied sub-basins are tabulated in Table 3. The areas vary between 830 ha for Kiga to 4,751 ha for Sangan. The elevation variation is about 500 m. Rendan is the steepest region and almost covered entirely by rock. Table 2 Four selected studies for the evaluation of the export coefficient method (kilograms per hectare per year)
Model number
2.4 Input Data The essential input data quantity and quality is a function of the model complexity. The SWAT model is known as an advanced model requiring more input data than the export coefficient approach. For example, SWAT requires a DEM in addition to meteorological, soil, land use, discharge, and water and soil quality data. The DEM at a resolution of 1:24,000 describes the topography, stream routing, and slope (ASTER GDEM 2014). Various soil and land use information was obtained from the Forest, Range, and Watershed Organization located in Tehran, Iran. The essential meteorological data (minimum and maximum daily temperature and precipitation) were obtained from the hydrometric stations located in the watersheds Kiga, Keshar, and Rendan. Discharge, phosphorous, and TSS were measured weekly by the Institute of Water and Energy of Sharif University of Technology. The water quantity and quality were measured at the Old Bridge location during two separate periods (2011, and 2012 to 2013). Measurements were undertaken during spring, summer, and fall. A few measurements were also taken during winter. The phosphorus content of soil was measured in addition to TSS to assess the role of soil erosion. The range and number of samples during the study period are tabulated in Table 4. The peak discharge was 13.5 m3/ s in the mid of April, when the snow was melting. The soil phosphorous content was high, enhancing the negative effect of soil erosion by generating non-point source pollution. Generally, the ratio of TN/TP was high, indicating that phosphorous is the limiting nutrient in this watershed. The peak in sediment load coincides with the peak of discharge, which is a further indication of considerable soil erosion in this region. In order to improve the ENS, the monthly discharge measurements and meteorological data were obtained
Authors
Land usages Urban
Farms using irrigation
Rock
Range
1
Shrestha et al. (2008)
1.73
0.42
0.00
0.59
2
Ierodiaconou et al. (2005)
1.400
0.500
0.085
0.015
3
May et al. (2001)
0.83
0.65
0.00
0.30
4
River Avon catchment (Bowes et al. 2005)
0.83
0.66
0.02
0.20
2122, Page 8 of 17 Table 3 Geographical characteristics of the studied sub-basins
Water Air Soil Pollut (2014) 225:2122
Subbasins
Kiga
Area (ha)
830
Average slope (%) 40–60
> 60
32
26
Mean elevation (masl)
Dominant land use
2,577
Rock (51 %) and range (49 %)
Rendan
3,338
34
46
2,722
Rock (95 %)
Keshar
3,495
34
23
2,274
Range (73 %) and agriculture (15 %)
Sangan
4,751
34
31
2,439
Range (51 %), rock (27 %), and agriculture (22 %)
between 2000 and 2011 to calibrate hydrology parameters of the upstream stations Sangan, Kiga, and Rendan (Table 5). Precipitation is non-uniformly distributed in this steep watershed. Precipitation and elevation correlate well between 1962 and 2006; precipitation (millimeters)=0.33×elevation (masl)−135 with R2 =0.85 (Tehran Municipilary’s Consultant 2010). This makes runoff prediction harder compared with a plain watershed with fairly evenly distributed precipitation. It is therefore likely that discharge simulations are associated with considerable errors. The flow hydrograph of the Kan River during 2012 is shown in Fig. 4. There is a peak in April, which relates to snow melting due to an increase in temperature. Rare and insignificant precipitation events were noted during summer and most of fall. The major contribution to river discharge is associated with domestic wastewater discharge to the river during dry months. The TP load fluctuation is displayed in Fig. 5. Comparing Figs. 4 and 5 with each other reveals that an increase in river discharge is also associated with an augmentation in phosphorous load. Non-point source pollutants play the central role in phosphorous pollution for this region. The peak of phosphorous load coincides with a peak of discharge during the snow melt. The
corresponding load during summer is related to domestic wastewater discharges. Cattle grazing were sporadically observed in this region. Fall precipitation washes the manure that was left in summer into the watercourses resulting in first foul flush events (Scholz 2010). The seasonal variation of discharge versus soil phosphorous content is shown in Fig. 6. It can be seen that, as the discharge increases, the soil is enriched with phosphorous in spring and fall. Generally, the soil phosphorus content is very high, and soil erosion plays the pivotal role in phosphorous pollution. The seasonal variation of TSS versus discharge is presented in Fig. 7. It can be seen that there is a positive correlation between TSS and discharge in spring. It follows that an increase in discharge leads to a higher TSS load. Figure 6 reveals that TSS varies without noticeable discharge variation in summer. This can be related to domestic wastewater discharge in summer, particularly at times when visitor numbers to the area are high. The population distribution in the Kan River watershed is shown in Table 6. These data were obtained from Eshtooki (2012). It can be seen that the population density is low, i.e., less than 2,000 inhabitants. Visitors produced approximately 8 kg/day of phosphorous during weekends in summer and spring. This equates to about 192 kg/year.
Table 4 Configurations of measured parameters Parameter
Range
3
Number of measurements
Discharge (m /s)
0.005–13.500 30
Total phosphorus (mg/L)
0.02–0.20
Table 5 Geographic position of meteorological stations Station
Latitude
Longitude
Annual precipitation (mm)
31
Total phosphorus in soil (mg/kg) 810–2,250
25
Sangan
36.00
51.23
660
Total nitrogen (mg/L)
1.1–3.7
32
Kiga
36.04
51.19
717
Total suspended solids (mg/L)
3–1,100
23
Rendan
35.89
51.28
750
Water Air Soil Pollut (2014) 225:2122
Page 9 of 17, 2122
Fig. 4 Annual discharge variation at Old Bridge in 2012
3 Results and Discussion 3.1 Quantity and Quality Validation in SWAT 3.1.1 Discharge Discharge calibration was undertaken before modeling of phosphorus in SWAT. This approach reduced the error associated with the final phosphorous simulation. Another measure taken to improve the results was the step-by-step calibration of upstream stations. Generally, the discharge was sensitive to snow melt, routing, and soil water capacity parameters. The groundwater parameters impacted insignificantly on discharge calibration, because of the steep topography. The key parameters and their corresponding calibration values for each station are summarized in Table 7. Kiga and Rendan, which are located at higher elevation, are more sensitive to snow melt parameters. Calibration results for Fig. 5 Phosphorous load variation at Old Bridge in 2012
discharge varied between 80 % ENS for Kiga and 93 % ENS for Old Bridge. In comparison, validation values were between 71 % ENS for Old Bridge and 94 % ENS for Kiga. The discharge calibration results at Old Bridge are illustrated in Fig. 8. The peak of discharge is around the middle of April, which can be explained by the snow melt. The model underestimates the peak of discharge. The discharge is at its low in summer and fall. The model predicts the base flow well, but it overestimates the rainfall during fall. The simulated discharge is less than the observed one during the warmer seasons. The discharge measurements are less than the simulated ones in fall. This could be explained by model weaknesses associated with unsaturated flow simulations. These shortcomings have also been reported by Arnold et al. (2000). Considering that the watershed is very steep and mountainous, groundwater parameters are less effective to simulate. At high elevations and low temperature,
2122, Page 10 of 17
Water Air Soil Pollut (2014) 225:2122 Table 6 Population distribution and wastewater production Village
Fig. 6 Seasonal total phosphorous (TP) content variation in the soil versus discharge
snow is the dominant form of precipitation. Thus, the snow melt is the most important event in the hydrological cycle of the case study watershed. Figure 6 indicates that the soil phosphorous content is relatively high. Table 8 shows the TSS calibration results for stations located upstream of the Old Bridge (Fig. 2). The findings vary between 57 % ENS for Rendan and 83 % ENS for Keshar. The validation values are between 61 % ENS for Kiga and 88 % ENS for Keshar. The observed and simulated TSS values at the Old Bridge are displayed in Fig. 9. An increase in discharge led to a higher TSS load, because the discharge originated from precipitation, which was responsible for the sediment-rich runoff. The peaks of both TSS and discharge coincided. 3.1.2 Phosphorous The phosphorous contamination of the Kan River is predominantly associated with soil erosion and Fig. 7 Seasonal total suspended solids (TSS) variation versus discharge
Population
Producedwastewater range (kg/day)
Phosphorous (kg/day)
Rendan
187
165–127
0.37
Kiga
333
240–227
0.67
Keshar
724
523–494
1.45
Sangan
749
511–541
1.50
wastewater discharge. TSS in the river water were measured to study sediment movement. Furthermore, the soil phosphorous content was analyzed to interpret the phosphorous sources within the Kan watershed. TSS were simulated in the river after discharge simulation and before phosphorous simulation to obtain more accurate phosphorous modeling results. Figures 6, 7, and 9, and Table 8 indicate TSS simulation findings applying SWAT. After discharge and sediment calibrations at the upper stations over a measurement period of 10 years, phosphorous was calibrated and validated for two separate measurement regimes: The first took place monthly in December 2010, and the second was initiated in 2012 and was undertaken weekly. Three parameters related to the soil phosphorous content were known as sensitive parameters. The calibrated values of these parameters are summarized in Table 9. The results of the model simulation are shown in Fig. 10. The SWAT model consistently predicted that the peak of phosphorous load was less than the observed one. This phenomenon has been referred to some unknown processes happening in watersheds by Abbaspour (2012). Moreover, the watershed is rather
Water Air Soil Pollut (2014) 225:2122
Page 11 of 17, 2122
Table 7 Discharge calibration results Parameter
Description
Minimum Maximum Calibrated value Rendan Kiga
Keshar Old Bridge
ALPHA_BNK.rte
Baseflow alpha factor
0.00
1.00
0.37
0.34
0.35
0.34
CH_K2.rte
Effective hydraulic conductivity in the main channel (mm/h) Manning’s n value for the main channel
0.0
150.0
56.5
53.1
49
44.8
0.000
0.300
0.001
0.0009 0.0007 0.0005
90
81
85
79
77
CH_N2.rte CN2.mgt ESCO.hru
Initial Soil Conservation Society runoff curve number 20 for moisture condition II Soil evaporation compensation factor 0.01
1.00
0.68
0.64
0.74
0.77
SFTMP.bsn
Snowfall temperature
−5.00
5.00
0.01
0.01
0.01
0.01
SMFMN.bsn
Minimum melt rate during the year (mm H2O/C/day)
0.0
10.0
2.1
2.1
2.3
2.4
SMFMX.bsn
Maximum melt rate during the year (mm H2O/C/day) 0.00
10.00
3.92
3.80
3.57
3.50
SMTMP.bsn
Snow melt base temperature
−5.00
5.00
0.81
0.78
0.73
0.70
0
500
257
260
247
264
SNOCOVMX.bsn Minimum snow water content that corresponds to 100 % snow cover(mm H2O) SOL_AWC(1).sol Available water capacity of first soil layer (mm/mm)
0.00
1.00
0.01
0.01
0.01
0.01
SOL_BD(1).sol
Moist bulk density of first soil layer (mg/m3)
1.1
2.5
1.1
1.1
1.1
1.1
SOL_K(1).sol
Saturated hydraulic conductivity of the first soil layer (mm/h) Snow pack temperature lag factor
0.00
0.65
0.51
0.48
0.46
0.43
0.01
1.00
0.96
0.93
0.91
0.88
TIMP.bsn
steep, so accumulative errors stem from uncertainties in discharge, sediment, and phosphorous calibration. The HRU method disintegrates watersheds into subcatchments according to soil type, land use, and slope. This separation can strengthen the correlation between land use and water quality parameters (Lee et al. 2009; Kibena and Gumindoga 2013). Slope can affect the water quality significantly (Sliva and Williams 2001). Considering that the land use category does not capture
Fig. 8 The result of discharge simulation in Old Bridge
a lot of detail, correlations between land use and water quality may give spurious results. For example, the land use category agriculture comprises both wheat and rice cultivation, which are different in terms of fertilizer composition requirements. It is likely that the introduction of sub-categories for land use would result in more accurate results. For example, Hartcher and Post (2008)) split-up the forest land use category into evergreen, deciduous, and pine
2122, Page 12 of 17
Water Air Soil Pollut (2014) 225:2122
Table 8 Total suspended solids calibration results Parameter
Description
Minimum Maximum Calibration Rendan
Kiga
Keshar
Old Bridge
0.60
0.58
0.58
CH_EROD.rte
Channel erodibility factor
0.00
0.60
0.60
PRF.bsn
Peak rate adjustment factor for sediment routing in the channel Phosphorous availability index
0.00
2.00
Not sensitive Not sensitive Not sensitive 1.32
0.01
0.70
Not sensitive Not sensitive Not sensitive 0.52
SLSUBBSN.hru Average slope length (m)
10.00
150.00
36.05
SPCON.bsn
0.0001
0.010
Not sensitive Not sensitive Not sensitive 0.0001
1.00
2.00
Not sensitive Not sensitive Not sensitive 1.42
0.00
0.65
0.46
0.48
0.46
0.46
1.0
0.1
0.1
0.1
0.1
PSP.bsn
USLE_K.sol
Linear parameters for max. reentrained sediment Linear parameters for reentrained sediment USLE equation soil erodibility
USLE_P.mgt
USLE equation support practice factor 0.1
SPEXP.bsn
plantations. They compared data obtained for a single forest land use category based on images taken in 1995 with the corresponding three sub-land use covers determined in 2003 for the Mae Chaem watershed (3900 km2) located in Thailand. In terms of TSS, Hartcher and Post (2008)) found that a separation of the forest land use category into sub-categories leads to improved results for some HRU. The ENS indicator was used to assess the accuracy of simulations. An efficiency value higher than 65 % was considered as a very good simulation, values between 54 % and 65 % were regarded as good and values between 50 % to below 54 % were seen as satisfactory (after Moriasi et al. (2007)). Phosphorus calibration and verification findings are shown in Table 10. The calibration results were better than the verification ones, because the number of calibration data was greater than Fig. 9 Results of total suspended solids (TSS) simulation at the Old Bridge
39.28
33.72
31.05
the one for verification. Nevertheless, both the calibration and verification efficiencies were in acceptable ranges, and the model simulated the watershed appropriately. The proportion of each season in phosphorous pollution load is displayed in Fig. 11. The major sources of pollution were soil erosion, and domestic wastewater and cattle grazing. Figure 11 indicates that the precipitation plays a central role in phosphorous contamination. So, soil erosion was the major source of contamination. Considering that there is no significant precipitation in summer, the phosphorous load should be directly related to the domestic wastewater discharge. The proportions of domestic wastewater in summer and spring are greater than in fall, because of relatively high numbers of visitors coming to this recreational area in summer and spring. Soil
Water Air Soil Pollut (2014) 225:2122
Page 13 of 17, 2122
Table 9 Phosphorous calibration results Parameter
Description
Minimum
Maximum
Calibrated
ERORGP.hru
Organic phosphorus enrichment ratio
0
5
1
PPERCO.bsn
Phosphorous percolation coefficient
10.00
17.50
14.71
PHOSKD.bsn
Phosphorous soil partitioning coefficient
100.00
200.00
183.03
erosion was the main source of pollution (about 60 %) in the Kan River watershed. This was followed by domestic wastewater pollution (36 %) due to tourist activities during warm weather. Cattle grazing took sparsely place during the warmer seasons. Moreover, there was little precipitation to wash off cattle manure containing phosphorous into watercourses. It follows that the first major precipitation event during fall usually leads to a foul flush runoff full of nutrients. Nevertheless, cattle grazing were only responsible for a minor annual contribution in phosphorous pollution (3 %). This figure was obtained by dividing the cattle grazing annual contribution by the total load (Table 11). The pollution contributions of each sub-basin are summarized in Table 11. Soil erosion plays the central role in phosphorous pollution. The topography is steep in the upper part of the watershed, and the soil is barely covered by vegetation. Domestic wastewater originated from villages and recreational centers. Cattle grazing were simulated according to field observations.
Fig. 10 Results of the Soil and Water Assessment Tool phosphorous calibration
3.2 Export Coefficient The export coefficient method considers both point sources and non-point sources regardless of their nature. For instance, this method combines all non-point sources such as soil erosion, and fertilizer and pesticide applications on farm lands together, and the user cannot separate them. However, in this study, the analysis (confirmed by the authors’ knowledge of the Kan watershed) revealed that non-point source pollution is predominantly associated with soil erosion. Wastewater contamination was minimal. The land use map shown in Fig. 1 demonstrates that agricultural and human activities are not dominant in the area upstream of the Old Bridge location. Therefore, fertilizer and pesticide applications are not responsible for phosphorous pollution at the Old Bridge location. Studies concerning similar land uses (as observed for this case study) were reviewed before the application of the export coefficient method. However, other watersheds were subject to not the same combination of land uses making a direct comparison difficult.
2122, Page 14 of 17
Water Air Soil Pollut (2014) 225:2122
Table 10 Results of phosphorous calibration and verification at the Old Bridge Calibration (%)
Table 11 Pollution sources for each sub-basin Sub-basin
Soil
Wastewater
Cattle
Verification (%) 1
44.0
R2
R2
–
–
ENS
ENS
2
15.2
7.0
–
91
75
94
52
3
14.6
10.5
0.7
4
13.4
23.8
–
5
29.5
23.8
0.9
7
6.0
–
–
8
9.7
22.9
6.6
9
14.9
–
–
ENS Nash–Sutcliffe efficiency, R2 coefficient of determination
Equation 4 was used for the export coefficient approach calculations. There were two pollution point source types in this watershed. The constant phosphorus load of residents producing wastewater was added to the variable wastewater contribution by visitors. The area of each land use was multiplied by the corresponding export coefficients, and the estimated phosphorus loads were derived for each sub-basin. The results of phosphorus pollution associated with non-point sources are shown in Table 12. Summation of the point source and non-point source contributions resulted in the TP load. A comparison of the total loads associated with the export coefficient model and the observed loads (Table 12) indicates relative errors between 78 % and 194 %. These comparatively high errors can be explained by few input data, high soil phosphorous content, and the use of export coefficients calibrated for other watersheds (e.g., 370 mg/kg for Bad Lauchstädt in Germany (Blake et al. 2000), 996 mg/kg for a watershed in China (Bai et al. 2010), and 480 mg/kg for Rothamsted in the UK (Blake et al. 2000)). Bowes et al. (2005) proposed high farmland coefficients and low coefficients for both range and rock surfaces. Therefore, it is likely that agricultural activities along the Kan River have a profound impact on the
water quality. In comparison, the impact of range and rock should be rather low. Four water sample measurements were undertaken for the four major tributaries (Kiga, Rendan, Keshar, and Sangan) of the Kan River during wet months to obtain a better understanding of the role of each land use. Water was sampled between December 2010 and March 2011. The results are summarized in Tables 13 and 14. Model results were best for December when the precipitation was low. Agricultural farmlands had the lowest export coefficients. Range and rock had reduced impacts on the phosphorus load of the Kan River for December. The model underestimated the phosphorous load for this month. The highest error of 33 % was noted for January (Table 14), which was associated with a huge storm. The river discharge was subsequently very high. There might have been considerable soil erosion, which the model failed to predict. The greatest challenges were noted for Kiga and Rendan, which were associated with the highest elevation and steepest surfaces, making accurate runoff predictions difficult. There was a positive correlation between precipitation (based on 34 years of rainfall data) and elevation: precipitation (millimeters)= 0.33×elevation (meters)+135 with R2 =0.84. It follows that the volume of precipitation was relatively high at
Table 12 Comparison between the actual phosphorous load and the estimated load based on Table 2 Load
Actual Model load #1
Phosphorus load 3,610 (kg/yr) Relative error (%) – Fig. 11 Pollution source contributions for the Kan watershed
Model #2
Model #3
Model #4
9,293.1 8,032.6 6,501.0 5,614.7 194
154
106
78
Water Air Soil Pollut (2014) 225:2122
Page 15 of 17, 2122
Table 13 Derived export coefficients for the wet months of the Kan Watershed (December 2010 to March 2011) Land use
Monthly export coefficient (kg/ha/year) December
January
February
March
Range
0.1
−0.6
1.1
1.1
Rock
0.2
0.4
0.8
0.2
Farm
0.9
7.8
5.5
4.0
Kiga and Rendan, resulting in the real pollutant load being greater than the estimated one. February had similar characteristics to January, and the model was therefore unable to predict accurate phosphorous export coefficients from Kiga and Rendan. Furthermore, the use of long-term rainfall data (34-year period) is limited, because rainfall may also vary with elevation, impacting on the model prediction performance. This is a particular challenge if the user is interested in modeling precipitation in small areas located in the mountains. The model performed better for Keshar and Sangan, which are located in less steep areas than Kiga and Rendan. The model performance was better in December, where the discharge was low and the runoff volume was less than compared with other months associated with the rainy seasons. The agricultural export coefficients were high during two stormy months. Heavy rainfall was responsible for the erosion of soil from agricultural lands. Trends in export coefficient variations show that agricultural land use contributed least to diffuse pollution during December when rainfall was low. Storms occurring in January and February washed out agricultural soil containing phosphorus. Moreover, the snow melt in the mountains started during March.
Range was the dominant land use and was relatively poor in phosphorus. The observed and predicted phosphorous loads are shown in Table 14. Export coefficients are fixed for particular land uses and do not account for biochemical processes. For example, watersheds have the general ability to purify polluted water (Seeboonruang 2012). Clay beds are capable of removing phosphorus from runoff (CalabiFloody et al. 2012; Eveborn et al. 2012). However, processes such as self-purification phenomena have not been considered by the export coefficient approach, which can be considered as a limitation. Advanced models consider nutrient adsorption to soil. Soil phosphorous adsorption is a function of various variables such as the phosphorous concentration in runoff, soil clay percentage, and contact time. These factors may vary during the year, and advanced models consider them, while the export coefficient model is based on regression between area and load, ignoring these factors. Therefore, the use of, for example, data obtained during the first flush (subject to high phosphorous concentration) for regression may not be very accurate for predicting the phosphorus load for the remaining year. However, such an approach would present the user with a worst-case scenario. Moreover, the export coefficient model is simple, transparent, cheap, widely used by consultants, and well-understood.
4 Conclusion and Recommendations for Further Research The accuracy of the complex SWAT model and the rather simple (but practical) export coefficient approach
Table 14 Comparison between observed and predicted phosphorous loads for the sub-basins during wet months Month
Load type
Load (kg/year)
Relative error
Kiga
Rendan
Keshar
Sangan
December
Observed Predicted
339 324
883 799
946 1,067
1,594 1,925
−9 %
January
Observed Predicted
1,801 185
2,852 1,167
4,559 3,079
8,996 7,726
33 %
February
Observed Predicted
2,876 1,007
4,650 2,780
8,136 6,267
11,512 9,642
27 %
March
Observed Predicted
1,975 776
2,353 1,085
6,477 5,414
8,627 7,773
22 %
2122, Page 16 of 17
to simulate the TP load in the relatively steep Kan River watershed have been assessed. The comparison reveals that the SWAT model can give detailed and better results. The export coefficient method has less flexibility. The required number of coefficients for accurate estimations is high. Discharge, TSS, and phosphorous were calibrated using the SWAT model. The model underestimated the TP load in spring, while overestimated it in fall. The SWAT model requires more input data than simple models such as the export coefficient one. However, it is superior in estimating the impact of the TMDL for each pollutant source such as soil erosion and cattle grazing. For example, the SWAT model can predict the total load of phosphorus accurately by 7 %. Four suggested coefficient sets were obtained from literature to examine the general applicability of the export coefficient method. Several landscape features and watershed characteristics were ignored by the export coefficient method, which, therefore, predicted pollutant loads only roughly. Steep areas proved particularly challenging for the simple export coefficient approach. The authors recommend further studies covering rather complex self-purification processes, environmental protection practices such as wastewater treatment plant impacts, sediment stripping processes, and contour farming and cattle grazing reduction strategies. In contrast to the export coefficient approach, which can still be used as a practical screening tool, the SWAT model should be applied as a basis for the proposed further research studies. Acknowledgments Measurements were undertaken at the Institute of Water and Energy at Sharif University of Technology. The authors gratefully appreciate the guidance received by Masoud Tajrishi.
References Abbaspour, K. (2012). C.SWAT-CUP 2012. SWAT calibration and uncertainty programs—A user manual. Arnold, J. G., Srinivasan, R., Muttiah, R. S., & Willimas, J. R. (1998). Large area hydrologic modeling and assessment part I: model development. Journal of American Water Resources Association, 34(1), 73–89. Arnold, J. G., Srinivasan, R. S., & Allen, P. M. (2000). Regional estimation of base flow and groundwater recharge in the upper Mississippi basin. Journal of Hydrology, 227(1–4), 1037–1052.
Water Air Soil Pollut (2014) 225:2122 ASTER GDEM (2014). ASTER GDEM, Japan space system. http://gdem.ersdac.jspacesystems.or.jp/search.jsp (accessed on 14 April 2014). Bai, J., Ouyang, H., Xiao, R., Gao, J., Gao, H., Cui, B., & Huang, L. (2010). Spatial variability of soil carbon, nitrogen, and phosphorus content and storage in an alpine wetland in the Qinghai–Tibet Plateau, China. Australian Journal of Soil Research, 48(8), 730–736. Blake, L., Mercik, S., Koerschens, M., Moskal, S., Poulton, P. R., Goulding, K. W. T., Weigel, A., & Powlson, D. S. (2000). Phosphorus content in soil, uptake by plants and balance in three European long-term field experiments. Nutrient Cycling in Agroecosystems, 56(3), 263–275. Bosch, N., Allan, J. D., Dolam, D. M., Han, H., & Richards, R. P. (2011). Application of the Soil and Water Assessment Tool for six watershed of Lake Erie: Model parameterization and calibration. Journal of Great Lakes Research, 37, 263–271. Bowes, M. J., Hilton, J., Irons, G. P., & Hornby, D. D. (2005). The relative contribution of sewage and diffuse phosphorous sources in the River Avon catchment, southern England: Implications for nutrient management. Science of the Total Environment, 344(1–3), 67–81. Buck, O., Niyogi, D. K., & Townsend, C. R. (2004). Scaledependence of land use effects on water quality of streams in agricultural catchments. Environmental Pollution, 130(2), 287–299. Calabi-Floody, M., Velasquez, G., Gianfreda, L., Saggar, S., Bolan, N., Rumpel, C., & Luz Mora, M. (2012). Improving bioavailability of phosphorous from cattle dung by using phosphatase immobilized on natural clay and nanoclay. Chemosphere, 89(66), 648–655. Carpenter, S. R., Caraco, N. F., Correll, D. L., Howarth, R. W., Sharply, A. N., & Smith, V. H. (1998). Nonpoint pollution on surface waters with phosphorus and nitrogen. Ecological Application, 8, 559–568. Cooper, A. B., & Thomson, C. E. (1988). Nitrogen and phosphorous in streamwaetrs from adjacent pasture, pine and native forest catchemnts. New Zeland Journal of Marine and Freshwater Research, 22, 279–291. Dils, R. M., Heathwaite, A. L., Anderson, M. G., & Brooks, S. M. (1996). Phosphorous fractionation in hillslope hydrological pathways contributing to agricultural runoff. In Advances in Hillslope processes, volume 1 (pp. 229–251). Chichester: Wiley & sons Ltd. Ding, X., Shen, Z., Hong, Q., Yang, Z., Wu, X., & Liu, R. (2010). Development and test of the export coefficient model in the Upper Reach of the Yangtze River. Journal of Hydrology, 383, 233–244. Dixon, B., & Earls, J. (2012). Effects of urbanization on streamflow using SWAT with real and simulated meteorological data. Applied Geology, 35(1–2), 174–190. Eshtooki, P. (2012). Chitgar artificial urban lake, second phase: Lake water quality. CAL-STR-R-GEN-EN-DD001A, 2012. Eveborn, D., Kong, D., & Gustafsoon, J. P. (2012). Wastewater treatment by soil infiltration: Long-term phosphorus removal. Journal of Contaminant Hydrology, 140–141, 24–33. Goonetilleke, A., Thomas, E., Ginn, S., & Gilbert, D. (2005). Understanding the role of land use in urban stormwater quality management. Environmental Management, 74, 31– 42.
Water Air Soil Pollut (2014) 225:2122 Gou, H., Hu, Q., & Jiang, T. (2008). Annual and seasonal stream flow responses to climate and land-cover changes in the Poyang Lake basin, China. Journal of Hydrology, 335(1–4), 106–122. Hartcher, M. G., & Post, D. A. (2008). The impact of improved landuse cover on the range of modelled sediment yield from two sub-catchments of the Mae Chaem, Thailand. Mathematics and Computers in Simulation, 78(2–3), 367– 378. Hoare, R. A. (1982). Nitrogen and phosphorous in the Ngongotaha stream. New Zealand Journal of Marine and Freshwater Research, 16(3–4), 339–349. Hodge, T.A., & Armstrong, L.J. (1993). New techniques for modelling the management of stormwater quality impacts, ed W. James. Lewis Publishers, Boca Raton, Florida. pp. 201-214. Ierodiaconou, D., Laurenson, L., Leblanc, M., Stagnitti, F., Duff, G., Salzman, S., & Versace, V. (2005). The consequence of land use change on nutrient exports: a regional scale assessment in south-west Victoria, Australia. Journal of Environmental Management, 74, 305–316. Johnes, P. J. (1996). Evaluation and management of the impact of land use change on the nitrogen and phosphorous load delivered to surface waters: The export coefficient modelling approach. Journal of Hydrology, 183(3–4), 323–349. Kibena L., & Gumindoga N.W. (2013). Assessing the relationship between water quality parameters and changes in landuse patterns in the Upper Manyame River, Zimbabwe. Physics and Chemistry of the Earth, PARTS A/B/C (in press). Laurent, F., & Ruelland, D. (2011). Assessing impacts of alternative land use and agricultural practices on nitrate pollution at the catchment scale. Journal of Hydrology, 409, 440–450. Lee, S. W., Hwang, S. J., Lee, S. B., Hwang, H. S., & Sung, H. C. (2009). Landscape ecological approach to the relationships of land use patterns in watersheds to water quality characteristics. Landscape and Urban Planning, 92(2), 80–89. Lou, Y., & Zhang, M. (2009). Management-oriented sensitivity analysis for pesticide transport in watershed-scale water quality modeling using SWAT. Environmental Pollution, 157, 3370–3378. May, L., House, W. A., Bowes, M., & McEvoy, J. (2001). Seasonal export of phosphorous from a lowland catchment: upper River Cherwell in Oxfordshire England. The Science of the Total Environment, 269, 117–130. Moriasi, D. N., Arnold, J. G., Van Liew, M. W., Bingner, R. L., Harmel, R. D., & Veith, T. L. (2007). Model evaluation guidelines for systematic quantification of accuracy in watershed simulations. American Society of Agricultural and Biological Engineers, 50(3), 885–900.
Page 17 of 17, 2122 Munkundan, R., Pradhanang, S. M., Schneiderman, E. M., Pierson, D. C., Vanandhi, A., Zion, M. S., Matonse, A. H., Lounsbury, D. G., & Steenhuis, T. S. (2013). Suspended sediment source areas and future climate impact on soil erosion and sediment yield in a New York City water supply watershed, USA. Geomorphology, 183, 110–119. Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models. Journal of Hydrology, 10(3), 282–290. Novotny, V. (1999). Integrating diffuse/nonpoint pollution control and water body restoration into watershed management. Journal of the American Water Resources Association, 35, 717–727. Omernik, J.M. (1976). The influence of land use on stream nutrient level. Report EPA 00/3-76-014,Oregon. Poor, C. J., & McDonnell, J. J. (2007). The effects of land use on stream nitrate dynamics. Journal of Hydrology, 332(1–2), 54–68. Saunders, D. L., & Kalff, J. (2001). Nitrogen retention in wetlands, lakes and rivers. Hydrobiologia, 443, 205–212. Scholz, M. (2010). Wetland systems—Storm water management control. Berlin: Springer Verlag. Seeboonruang, U. (2012). A statistical assessment of the impact of land uses on surface water quality indexes. Journal of Environmental Management, 101, 134–142. Shang, X., Wang, X., Zhang, D., Chen, W., Chen, X., & Kong, H. (2012). An improved SWAT-based computational framework for identifying critical source areas for agricultural pollution at the lake basin scale. Ecological Modelling, 226, 1–10. Shrestha, S., Kazama, F., Newham, L. T. H., Babel, M. S., Clemente, R. S., Ishidaira, H., Nishida, K., & Sakamoto, Y. (2008). Catchment scale modelling of point source and non-point source pollution loads using pollutant export coefficients determined from long-term in-stream monitoring data. Journal of Hydro-Environment Research, 2(3), 134–147. Singh, J., Knapp, H. V., Arnold, J. G., & Demissie, M. (2005). Hydrological modelling of the Iroquois River watershed using HSFP and SWAT. Journal of the American Water Resources Association, 41, 343–360. Sliva, L., & Williams, D. D. (2001). Buffer zone versus whole catchment approaches to studying land use impact on river water quality. Water Resources, 35(14), 3462–3472. Tehran Municipilary’s Consultant. (2010). Revision in comprehensive plan of Tehran’s surface water management (2nd ed.). Tehran: Mahab Ghods. Worrall, F., & Burt, T. P. (1999). The impact of the land use change on water quality at the catchment scale: The use export coefficient and structural models. Journal of the Hydrology, 221(1–2), 75–90.
