application of a watershed model to evaluate ... - Semantic Scholar
APPLICATION OF A WATERSHED MODEL TO EVALUATE MANAGEMENT EFFECTS ON POINT AND NONPOINT SOURCE POLLUTION C. Santhi, J. G. Arnold, J. R. Williams, L. M. Hauck, W. A. Dugas
ABSTRACT. A Total Maximum Daily Load (TMDL) program has been initiated in the North Bosque River Watershed in Texas, USA, where point and nonpoint sources of pollution are of a concern. The Soil and Water Assessment Tool (SWAT), which had been validated for flow and sediment and nutrient transport, was applied to quantify the effects of Best Management Practices (BMPs) related to dairy manure management and municipal wastewater treatment plant effluent. Results are presented for the period from 1960 through 1998 for three sites along the North Bosque River. Results are presented as annual time–weighted concentrations (average of the daily load divided by daily flow over a year) and annual flow–weighted concentrations (total cumulative load divided by total cumulative flow over a year). The wastewater treatment plant BMPs resulted in greater improvement in time–weighted instream soluble phosphorus concentrations than dairy BMPs. On the other hand, dairy BMPs made greater differences in flow–weighted concentrations. This study showed that SWAT could be a useful tool for studying the effects of alternative management scenarios for pollution control from point and nonpoint sources in large watersheds. Keywords.Watershed management, Water quality, Nonpoint source pollution, Point source pollution, Dairy manure management, Municipal wastewater treatment plants, Best management practices, Total maximum daily load.
A
recent United States Environmental Protection Agency (USEPA) Water Quality Inventory (1997) reports that water quality goals are not being met in more than one–third of the rivers, lakes, and estuaries of the U.S. The USEPA’s Clean Water Act (CWA) Program recommends states to identify impaired water bodies (CWA section 303(d) list) and develop a watershed plan for each, including the development of a Total Maximum Daily Load (TMDL), to address the impairment (USEPA, 1997). A TMDL is an estimate of the maximum pollution load a water body can receive from point and nonpoint sources and still maintain the specified standards (USEPA, 1991). The TMDL process involves identification of possible measures (Best Management Practices––BMPs) to reduce the excess load from controllable contributing sources and to bring water bodies into compliance. The Texas Natural Resource Conservation Commission (TNRCC), in cooperation with the Texas State Soil and Water
Article was submitted for review in January 2001; approved for publication by the Soil & Water Division of ASAE in August 2001. The authors are C. Santhi, ASAE Member, Post–Doctoral Research Associate, Jimmy R. Williams, Research Scientist, and William A. Dugas, Professor and Resident Director, Blackland Research and Extension Center, Texas Agricultural Experiment Station, Texas A&M University System, Temple, Texas; Jeffrey G. Arnold, ASAE Member, Agricultural Engineer, Grassland Soil and Water Research Laboratory, USDA–ARS, Temple, Texas; and Larry M. Hauck, Assistant Director, Texas Institute for Applied Environmental Research, Tarleton State University, Stephensville, Texas. Corresponding author: C. Santhi, Blackland Research and Extension Center, Texas Agricultural Experiment Station, Texas A&M University System, Temple, TX 76502; phone: 254–774–6141; fax: 254–774–6001; e–mail: [email protected].
Conservation Board (TSSWCB), is implementing TMDL projects in Texas. A TMDL is being developed in the North Bosque River Watershed of North Central Texas (fig. 1), where phosphorus from dairy manure application is a concern (TNRCC, 1998; TNRCC and TSSWCB, 1999). Instream water quality monitoring has shown manure application to pasture or cropland to be a likely potential nonpoint source of pollution (McFarland and Hauck, 1995; 1999a). McFarland and Hauck (1999a) showed a strong positive correlation between instream phosphorus loadings from a watershed and the number of cows, and total acreage of manure application fields. Other possible nutrient sources in the North Bosque River Watershed are croplands, urban runoff, and effluents from municipal wastewater treatment plants (WWTPs) (table 1). Water quality simulation models can be useful in the TMDL development process. They can assess the present level of pollution where observed data are inadequate to give a complete picture, determine allowable load to receiving waters where there are complex interactions between different pollution sources, and allocate pollutant loads by dividing them among the identified point and nonpoint pollution sources. Unlike plot and field studies where results from specific treatments may be directly comparable to those from control conditions, the effectiveness of BMPs on a watershed scale is not easily identifiable. Determination of BMP effectiveness is particularly complex if the watershed has mixed land uses that generate nonpoint sources of pollution and numerous point sources of pollution (Mostaghimi et al., 1989). Long–term monitoring may be used to study the effective–
Transactions of the ASAE Vol. 44(6): 1559–1570
E 2001 American Society of Agricultural Engineers ISSN 0001–2351
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METHODOLOGY SWAT MODEL DESCRIPTION The watershed loading/water quality model, Soil Water Assessment Tool (SWAT) (Arnold et al., 1998), developed by the United States Department of Agriculture–Agricultural Research Service (USDA–ARS), was used in this study. TMDL guidelines provide criteria for selecting a model for use in TMDL applications (TNRCC, 1999). SWAT is a physically based simulation model, operating on a daily time step. It has the ability to predict changes in nutrient loadings from different management conditions (nonpoint and point sources), is freely available for public use (www.brc.tamus.e du/swat), and has been integrated into USEPA’s modeling framework, Better Assessment Science Integrating Point and Nonpoint Sources (BASINS). Hence, SWAT was selected for this study. SWAT was developed to predict the impact of management practices on water, sediment and agricultural chemical yields in large ungauged basins. To satisfy the objective, the model is physically based, uses readily available inputs, and is capable of simulating long periods for computing the effects of management changes. SWAT has capability for routing runoff and chemicals through streams and reservoirs, adding flows, and input measured data from point sources such as municipal wastewater treatment plants. Major components of the model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides, and agricultural management.
Figure 1. Bosque River Watershed in Texas.
Table 1. Percentage of soluble phosphorus loading contributed by different sources at Hico and Valley Mills in the Bosque River Watershed (McFarland and Hauck, 1999b). Soluble phosphorus loading (%) Source of soluble phosphorus
Hico
Valley Mills
Pasture Range and forest Cropland Urban Dairy waste application fields Point source (WWTP)
8 13 2 4 61 12
9 23 7 7 45 9
ness of BMPs, but that is very expensive. Models, however, may be used to estimate long–term effects of individual BMPs and can account for long–term weather variability. Information regarding watershed–level impacts of BMPs on water quality is imperative to determine whether public resources are to be used to cost–share BMP implementation in a TMDL development process. Watershed–scale studies on effectiveness of pre– and post–implementation of BMPs have been reported through field observation and monitoring studies (Walker and Graczyk, 1993; Park et al., 1994; Griffin, 1995; Edwards et al., 1996). The objective of this article is to demonstrate the utility of a model in the TMDL development process for estimating phosphorus loadings and concentrations under existing and projected conditions of the watershed and to analyze the effectiveness of various BMPs related to dairy manure management and WWTPs in the North Bosque River Watershed. This study provides information to decision–makers on the most feasible and cost effective BMPs. This methodology is applicable to similar TMDL/water quality projects in other parts of the United States.
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THE STUDY AREA The Bosque River Watershed (fig. 1) has a drainage area of 4,277 km2, and has four tributaries, North Bosque River, Middle Bosque River, South Bosque River, and Hog Creek, that drain into Lake Waco, which is the source of drinking water for the city of Waco. Land use in this watershed is mostly range and pasture with some cropland in the southern portion. There are about 100 dairies operating in the watershed, primarily upstream of Hico (fig. 1) with a total herd size of about 40,450. Manure produced by these animals is applied on dairy manure application fields that cover about 94.5 km2. DATA SOURCES AND MANAGEMENT PRACTICES The Geographic Resource Assessment Support System– Geographic Information System (GRASS–GIS) interface of the SWAT model (Srinivasan and Arnold, 1994) was used to develop input files. This interface delineates the watershed into subbasins based on topography. Delineated subbasins, land use, soil, and waste application field (WAF) maps were overlaid to identify the manure–application and non–application areas in each subbasin. WAF management: Sudan grass, coastal bermuda grass, sudan grass with winter wheat in rotation, sorghum hay (forage), and sorghum hay–winter wheat in rotation were simulated on WAFs. Annual manure application rate on WAFs was determined based on the number of animals and annual amount of manure generated per animal (ASAE, 1988). Manure nutrient concentrations were taken from Gassman (1997). Non–WAF management: Fertilizer was applied to improved pastures to maintain productivity and three to four forage harvests were simulated each year. No fertilizer was
TRANSACTIONS OF THE ASAE
applied to rangeland, but forage was harvested three to four times to simulate grazing. Typical fertilizer rates and tillage practices for corn, wheat, and grain sorghum were used. Urban land had both pervious (70%) and impervious lands (30%). Pervious areas represent areas such as lawns and parks and they were simulated with irrigation and fertilizer management. Impervious areas represent built up areas and streets were simulated with street sweeping. Flows and nutrient loadings from eight municipal WWTPs in the watershed (McFarland and Hauck, 1999b) were input into the SWAT–GIS interface as point sources. MODEL CALIBRATION AND VALIDATION Measured water quality data were used for calibrating and validating the model for flow, sediment, and organic and mineral nitrogen and phosphorus on a monthly basis from 1993 through 1998 depending on the monitoring data available at several locations in the watershed (Santhi et al., 2001). A wide variation of streamflow conditions were monitored during the model calibration period, including the second and third largest streamflows recorded in the North Bosque River and a period of moderate drought in 1996. The statistical measures such as coefficient of determination (R2) and Nash–Suttcliffe simulation efficiency (ENS) (Nash and Suttcliffe, 1970) were used to evaluate model prediction. The R2 value is an indicator of strength of relationship between the observed and simulated values. Nash–Suttcliffe simulation efficiency (ENS) indicates how well the plot of observed versus simulated value fits the 1:1 line. If the R2 and ENS values are less than or very close to zero, the model prediction is considered ’unacceptable or poor.’ If the values are one, then the model prediction is ’perfect’. Ramanarayanan et al. (1997) suggested that model prediction is acceptable or satisfactory, if R2 >0.6 and ENS >0.5. The statistical measures indicate that monthly simulated flow, sediment, and nutrient loadings were close to the observed values during the calibration and validation periods at Hico and Valley Mills (table 2). In this article, soluble phosphorus (sol P) refers to the water soluble P predicted by SWAT, which was assumed to be closely equivalent to soluble reactive P (or orthophosphate P), a common laboratory analyte of water quality sampling. FLOW–WEIGHTED AND TIME–WEIGHTED CONCENTRATIONS A primary response to the nutrient enrichment in the North Bosque River is an overabundance of attached algae (periphyton) and suspended algae (TIAER, 1999). Algal communities were determined to be limited by P rather than nitrogen, and subsequent analyses of data showed a stronger
statistical relationship of algal biomass to sol P (measured as soluble reactive phosphorus) than to total P (TIAER, 1999). As a consequence of these findings, and as periphyton response to nutrient fluxes and loadings are not completely understood, many water quality models including SWAT do not include the periphyton component. Due to the complexity involved in periphyton simulation, a conservative approach was selected considering two methods of sol P concentrations in this study. They are (a) flow–weighted (FWT), and (b) time–weighted (TWT) concentrations. n
FWT =
∑(PO 4) i *10 6
i =1
(1)
n
∑Q i
i =1
where FWT = flow–weighted concentration of sol P in mg/m3 (ppb) PO4 = sol P load in kg/d Q = surface runoff volume in m3/d i = day of observation n = number of days of observations. 6
10 * TWT
=
n
(PO 4 ) i
i =1
Qi
∑
(2)
n
where TWT = time–weighted concentration in mg/m3 (ppb) PO4 = sol P load in kg/d Q = surface runoff volume in m3/d PO4/Q = the concentration i = day of observation n = number of days of observations. FWT concentration represents a volume–weighted concentration based on total loading and flow volume over a period, and TWT concentration represents a time–weighted average concentration using daily concentrations over a period. SWAT was able to accurately predict FWT and TWT sol P concentrations at Hico and Valley Mills (fig. 2). The TWT concentration reflects the day to day exposure of periphyton and the FWT concentration more accurately reflects the exposure of periphyton to storm loadings. Thus, TWT concentration is more responsive to continuous point sources of sol P than to episodic nonpoint source contributions. In contrast, the FWT concentration is more responsive
Table 2. Summary of monthly calibration and validation results at Hico and Valley Mills (Santhi et al., 2001). Hico Valley Mills Calibration (1993–97)[a] Model output Flow (mm) Sediment (t/ha) Organic N (kg/ha) Organic P (kg/ha) Mineral N (kg/ha) Soluble P (kg/ha) [a]
Validation (1998)[a]
Calibration (1996–1997)[a]
Validation (1998)[a]
R2
ENS
R2
ENS
R2
ENS
R2
ENS
0.80 0.81 0.61 0.71 0.64 0.60
0.79 0.80 0.58 0.70 0.59 0.59
0.92 0.98 0.92 0.95 0.89 0.83
0.87 0.70 0.73 0.72 0.75 0.53
0.89 0.87 0.60 0.61 0.72 0.66
0.83 0.69 0.57 0.59 –0.08 0.53
0.80 0.95 0.71 0.80 0.72 0.93
0.62 0.23 0.43 0.39 0.64 0.81
R2 = Coefficient of determination; ENS = Nash–Sutcliffe Efficiency (Nash and Sutcliffe, 1970).
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ÓÓ ÕÕ ÚÚ ÖÖ ÓÓ ÕÕ ÚÚ ÖÖ ÓÓ ÕÕ ÚÚÔÔ ÖÖ ÒÒ ÓÓ ÕÕ ÚÚ ÖÖ ÔÔ ÒÒ ŠŠ ÚÚ ÓÓ ÕÕ ÚÚ ÖÖ ÓÓ ÕÕ ÚÚÔÔ ÒÒ ŠŠ ÚÚ ÖÖ Ü Ú ÖÖ Ü Ú
Sol P Concentration (ppb)
250 200 150 100
50
0
ÛÛ
FWT – Observed
Hico
FWT – Simulated
Valley Mills
TWT – Observed
TWT – Simulated
Figure 2. Observed and simulated time–weighted (TWT) and flow– weighted (FWT) soluble phosphorus (sol P) concentrations at Hico and Valley Mills.
to the episodic nonpoint sources of sol P that are larger in total loading for this system than point source loadings. An objective of the TMDL development process was to obtain reductions in both FWT and TWT sol P concentrations to ensure reductions in periphyton and suspended algal biomass. Hence, the BMP results are discussed for both concentrations. BMP SCENARIOS The calibrated SWAT model was used to study the long–term effects of BMPs by simulating the watershed hydrology using daily historical weather information for 1960 to 1998. Long–term simulation helps to capture and remove the major ’stochastic uncertainty’ imposed by weather, predominantly rainfall in this case. BMPs examined to estimate the effect of reducing the sol P at different locations in the watershed included control measures used with agricultural nonpoint sources and with municipal WWTPs. Inputs associated with each BMP scenario are described below: (a) Existing Conditions To simulate the existing conditions, the model used present values for dairy herd size (40,450), WAF areas (94.5 km2), average manure application rate (13 t/ha/yr), average effluent discharge volume of 1997–98 from WWTPs with current median concentrations for nutrients, urban and cropland areas, and existing agricultural management practices (table 3). Initial sol P concentration in the soil of WAFs Table 3. Assumptions used in existing and future conditions. BMP assumptions Existing Future Dairy herd size Manure application rate Manure application area (km2) P diet reduction in animal feed (%) Excess manure haul off Sol P concentration in manure application area (ppm) 8 Existing WWTPs– flows & P limit concentration (mg/l) 3 Additional WWTPs–flows & P limit concentration (mg/l) Urbanland (km2) Cropland (km2)
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40,450 13 t/ha/yr 94.5 No No
67,000 46 t/ha/yr 44.5 No No
250 1997–98 Median No No 99 725
250 Year 2020 Median Year 2020 1 128 725
was set at 250 ppm based on soil tests in a subwatershed in the North Bosque River (Gassman, 1997; A. N. Sharpley, USDA–ARS, Durant, Okla., and L. M. Hauck, TIAER, Stephenville, Tex., personal communication, 1998). (b) Future Conditions For the future condition scenario, the model used the projected conditions of the watershed in the year 2020. These future conditions included the projected dairy herd size (67,000), manure application in WAFs at the crop nitrogen requirement rate (N rate at 46 t/ha/yr), WAF areas required for a N rate requirement, maximum permitted discharge volumes from WWTPs with median concentrations for nutrients, urban area increased by 30% to reflect the projected population growth in 2020, and cropland area at current levels (table 3). Three additional WWTPs with 1 mg/l concentration of total P were input into the model as point sources along the North Bosque River to account for possible industrial future growth outside existing communities. Initial sol P concentration in the soil of all WAFs was set at 250 ppm to reflect application areas that had received manure in previous years. This scenario reflects the watershed under a maximum growth condition scenario. (c) Dairy BMPs Three dairy BMPs were examined. They are: S Haul Off: All solid dairy manure was hauled out of the watershed and only liquid manure was applied. The liquid manure from dairy waste lagoons was applied over 12% of the waste application area calculated at N rate. In open area dairy lots, nutrients collected in the lagoon from the liquid manure equals 12% of the nutrients collected from the solid manure (Gassman, 1997). Hence, 12% of the waste application area was chosen in this scenario. Initial sol P concentration in WAFs was set at 250 ppm in the liquid manure applied area and the remaining WAF area was set at a lower concentration (5 ppm). All other conditions remained the same as that of the future condition scenario. S Crop P Requirement Rate (P rate): In general, crop growth requires uptake of more N than P. Thus, if manure is applied at a rate to satisfy the crop N demand, there is application of P beyond crop requirements (Edwards et al., 1996). Hence, in this scenario, manure was applied at crop P requirement rate (6.35 t/ha/yr) over an area computed using projected herd size. About 13% of the WAFs (a ratio of N rate area to P rate area) were set with an initial soil sol P concentration of 250 ppm to reflect the application areas that had received manure in previous years. The remaining WAF area was set with an initial soil sol P concentration of a regular pasture/rangeland (5 ppm). Supplemental commercial fertilizer for N was added to maintain crop growth. All other conditions remained the same as that of the future condition scenario. S P Diet Reduction in Animal Feed: P is a critical nutrient in dairy cow diets. Recent studies have indicated that lactating cow health, reproduction, and milk production can be maintained in this region at P dietary levels of 0.4% (Keplinger, 1999). The average dairy cow diet in this region is in the range of 0.52 to 0.53% P. Keplinger (1999) suggests reductions in dairy diet P to 0.4% in the region will reduce the manure P content by 29%. In this scenario,
TRANSACTIONS OF THE ASAE
P content of dairy manure was reduced by 29%. All other watershed conditions remained the same as in the future condition scenario. (d) WWTP BMPs To study the impacts of WWTP BMPs, concentrations of total P (sol P and organic P) in WWTP effluents were evaluated at 0.5, 1.0, and 2.0 mg/l. (e) Combined Dairy and WWTP BMPs The impacts of dairy and WWTP BMPs were studied individually in the beginning of the project. The North Bosque River showed greater benefits in flow–weighted concentration for dairy BMPs due to greater reduction in episodic loadings. The river showed greater benefits in time–weighted concentration for WWTP BMPs due to greater reduction in continuous loadings from WWTPs. Due to the differing response of the river to dairy and WWTP BMPs as indicated by the FWT and TWT concentrations, the objective of the TMDL was aimed at obtaining reductions in both concentrations. Hence, a combination of dairy and WWTP BMPs were developed and evaluated to assess their collective effectiveness in achieving the water quality goals. Three combined dairy and WWTP BMPs (Scenarios I, II, III) were evaluated (table 4). Scenario I was a modification of the existing condition scenario with additional conditions imposed on manure application rate (P rate), P diet reduction in animal feed, and 1 mg/l limit of total P in WWTPs. Scenario II was a modification over the future scenario with manure applied at P rate, P diet reduction, and 1 mg/l P limit on WWTPs. Scenario III was a modification of Scenario II with an added restriction imposed on manure by limiting WAFs to the area currently specified in dairy permits (94.5 km2), and excess manure, after application at a P–rate on permitted WAFs, was hauled out of the watershed. BMP ANALYSES Results at three sites, Stephenville, Meridian, and Valley Mills, along the North Bosque River representing various upstream combinations of land use and pollution sources were investigated (fig. 1). The Stephenville WWTP has a permitted effluent discharge of 11, 340 m3/d and is the major WWTP in this watershed. The watershed along the North Bosque River at Stephenville has a drainage area of 321 km2 and 10% of this area contains WAFs. Meridian (1570 km2) is about halfway downstream. Valley Mills (2997 km2) is located immediately upstream of Lake Waco.
Sol P concentrations are shown as probability exceedance plots to analyze the effectiveness of BMPs. In these exceedance plots, the annual time or flow–weighted sol P concentrations for the simulation period (1960 through 1998) are ranked and plotted against their associated exceedance probability values. These plots are also useful in identifying the relevant BMPs to achieve the desired water quality concentration. The targets (or goals) of the TMDL were determined by the state regulatory agency and stakeholders to be in terms of an annual average sol P concentration to be measured from monitoring at key locations along the North Bosque River. Consequently, daily model results were used to estimate annual TWT and FWT concentrations. Similarly, mean TWT and FWT sol P concentrations (computed for the simulation period of 39 years) are used to estimate the percentage of reduction in sol P concentration for each BMP with reference to the future condition scenario.
RESULTS AND DISCUSSION DAIRY BMPS Mean TWT sol P concentrations were decreased by about 12% at Stephenville, 45% at Meridian, and 56% at Valley Mills in the existing condition scenario as compared to the future condition scenario (fig. 3a). Mean FWT concentrations were decreased by 22, 29, and 28% for the same locations (fig. 3b). These reductions were predominantly caused by projected increases in dairy cows and WWTP discharges in the future scenario. TWT concentration curves of the dairy BMPs (Haul off, P rate, and P diet reduction BMPs) were closer to each other (fig. 4) and showed lesser deviations from future scenario than the FWT concentration curves (fig. 5). These observations are in agreement with percentage reductions in mean TWT and FWT concentrations (fig. 3). Mean TWT concentrations showed reductions ranging from 1 to 12% (fig. 3a) while mean FWT concentrations showed reductions ranging from 7 to 60% for dairy BMPs (fig. 3b). Benefits of dairy BMPs in reducing episodic loadings (nonpoint sources) of sol P were better reflected by the FWT concentrations compared to the TWT concentrations. WWTP BMPS Reductions of TWT sol P concentrations due to WWTP BMPs as compared to the future condition were much greater than FWT concentrations (figs. 6 and 7). The higher concentrations at Stephenville were due to the discharges
Scenario
Table 4. Assumptions used in dairy and wastewater treatment plant and combined BMP scenarios. WWTP WWTP Dairy manure Reduced P Type of BMP flow period P conc. limit application rate in diet
Existing Future Haul off P rate P diet WWTP–0.5 mg/l WWTP–1.0 mg/l WWTP–2.0 mg/l Scenario I Scenario II Scenario III
Existing conditions Future conditions Dairy Dairy Dairy WWTP WWTP WWTP Combined Combined Combined
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1997–98 (actual) 2020 (projected) 2020 2020 2020 2020 2020 2020 1997–98 2020 2020
Median conc. Median conc. Median conc. Median conc. Median conc. 0.5 mg/l 1.0 mg/l 2.0 mg/l 1 mg/l 1 mg/l 1 mg/l
Between N and P rate N rate N rate P rate N rate N rate N rate N rate P rate P rate P rate
No No No No Yes No No No Yes Yes Yes
Haul off manure No No Yes No No No No No No No Yes
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Reduction of sol P concentration (%)
100
(1303)
90
(211)
(93)
Existing Haul off
Ú ! " Ú ÚÚ Ú # Ú ÚÚ Ú Ú Ú ÚÚ Ú Ú ÚÚ Ú $ Ú ÚÚ Ú Ù ÙŽŽ ÚÚ ÙÙ ÙÙŸŸ Ú ÙÙ Ÿ ŽŽ ŸŸ ŽÚ %
80
Prate
70
P diet reduction
60
WWTP–0.5mg/l
50
WWTP–1mg/l
40
WWTP–2mg/l
30
Scenario I
20
Scenario II
10
Scenario III
0
Stephenville
Meridian
Valley Mills
Locations a) Time Weighted
Reduction of sol P concentration (%)
100
(669)
(228)
(137)
90 80 70 60 50 40 30 20 10 0
5 5 6 * ’ / 44 7 Ú ’ / 44 ’ / ’ /44 44. )) ’ " -Ú / 44 1 )) ’ " -Ú / 44 4 )) ’-"Ú /44 ))
Existing
. .22&& $$3 00 ++ . 11 00 22 && $$ 3 Ú . ++ 11 00 22 && $$ 3 Ú . Ÿ ++ 11 && Ú00 ++ .22 Ÿ $$ 113 00 22 && $$ 3 Ú (( ++ . Ÿ ** , ÚÚ 11 00 22 && $$ 3 Ú Ÿ (( Ž ++ . ** , ÚÚ 11 00 22 && $$ 3 .22&& ŸÚÚ (( ŽÚ00 ++ ** ,$$ 113 Stephenville
Meridian
Locations
Haul off Prate P diet reduction WWTP–0.5mg/l WWTP–1mg/l WWTP–2mg/l Scenario I Scenario II Scenario III
Valley Mills
b) Flow Weighted Figure 3. Percentage reductions of time–weighted and flow–weighted sol P concentrations from future condition scenario for existing, dairy, wastewater treatment plant and combined BMPS at three locations on North Bosque River (mean sol P concentration for the future condition scenario in parenthesis).
from the major WWTP. Mean TWT concentrations showed reductions ranging from 21% to 78% for WWTP BMPs (fig. 3a) and mean FWT concentrations showed reductions ranging from 4% to 50% (fig. 3b). The results of WWTP BMPs showed greater benefits in reducing the continuous loadings from WWTPs as opposed to the total loadings and hence, they are better reflected in TWT concentrations. COMBINED BMPS The combined BMPs showed reductions ranging from 56% to 75% in mean TWT concentrations (fig. 3a) and 39% to 65% in mean FWT concentrations (fig. 3b). The combined BMP scenarios showed almost similar trends at Stephenville for both TWT and FWT concentrations (figs. 8 and 9). All three scenarios showed about 60% reduction from the future condition scenario and there were only minor differences between BMPs (fig. 3). Scenario I showed the largest
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reductions for TWT and FWT concentrations at Stephenville, and for TWT concentrations at Meridian and Valley Mills. Scenarios I and III showed similar reductions at Meridian and Valley Mills for FWT concentrations (fig. 3b). The manure hauled off from dairy WAFs compensated for increase in sol P concentrations of WWTPs under permitted flow (future) conditions in Scenario III (table 4). Hence, the percentage reductions ware close to that of Scenario I. However, the TWT sol P concentrations showed reduced benefits for Scenario III compared to Scenario I at Meridian and Valley Mills (fig. 3a), because of the increased discharges from WWTPs for the future growth conditions in Scenario III. Scenario II showed reduced benefits at all three sites for both concentrations. Scenario I showed highest benefits (lowest sol P concentrations) among the three combined BMP scenarios.
TRANSACTIONS OF THE ASAE
Sol P concentration (ppb)
Stephenville 2000 1600 1200 800 400 0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
0.7
0.8
0.9
1
0.7
0.8
0.9
1
Exceedance probability Meridian Sol P concentration (ppb)
400 300 200 100 0 0
0.1
0.2
0.3 0.4 0.5 0.6 Exceedance probability Valley Mills
Sol P Concentration (ppb)
300 250 200 150 100 50 0 0
0.1
0.2
0.3
0.4
0.5
0.6
Exceedance probability Existing
Future
Haul off
Prate
Pdiet reduction
Figure 4. Exceedance probability of time–weighted sol P concentration for dairy BMPs at three locations on the North Bosque River.
CONCLUSIONS A modeling approach was used to quantify the long–term effects of dairy, wastewater treatment plant (WWTP), and combinations of dairy and WWTP BMPs in the Bosque River Watershed in Texas. Mean time–weighted (TWT) soluble phosphorus (sol P) concentrations showed reductions ranging from 1% to 12% for dairy BMPs, while mean flow– weighted (FWT) sol P concentrations showed reductions ranging from 7% to 60%(fig. 3) at three sites on the North Bosque River. For WWTP BMPs, mean TWT sol P concentrations showed reductions ranging from 21% to 78% and mean FWT concentrations showed reductions ranging
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from 4% to 50%. The combined BMP scenarios showed reductions ranging from 56% to 75% in mean TWT concentrations and 39% to 65% in mean FWT concentrations. These results revealed that benefits of dairy BMPs in reducing episodic loadings of sol P are better reflected by the FWT concentrations compared to the TWT concentrations, and WWTP BMPs showed greater benefits in reducing the continuous loadings as opposed to the total loadings and hence, they are better reflected in the TWT concentrations. Modeling assessment methods used in this study are relevant and adaptable to similar TMDL/water quality projects in other parts of the United States.
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Stephenville
Sol P concentration (ppb)
2000 1600 1200 800 400 0 0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Exceedance probability Meridian
Sol P concentration (ppb)
400 300 200 100 0 0
0.1
0.2
0.3 0.4 0.5 0.6 Exceedance probability
0.7
0.8
0.9
1
0.7
0.8
0.9
1
Valley Mills Sol P Concentration (ppb)
300 250 200 150 100 50 0 0
0.1
0.2
0.3
0.4
0.5
0.6
Exceedance probability Existing
Future
Haul off
Prate
Pdiet reduction
Figure 5. Exceedance probability of flow–weighted sol P concentration for dairy BMPs at three locations on the North Bosque River.
ACKKNOWLEDGEMENTS The authors acknowledge USDA for providing funding for this research work through Texas Institute for Applied Environmental Research, Stephenville, Texas.
REFERENCES Arnold, J. G., R. Srinivasan, R. S. Muttiah, and J. R. Williams. 1998. Large area hydrologic modeling and assessment. Part I: Model development. J. Am. Water Resources Assoc. 34(1): 73?89.
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ASAE Standards, 35th Ed. 1988. ASAE D384.1. Manure production and characteristics. ASAE standard, ASAE data. St. Joseph, Mich.: ASAE. Edwards, D. R., T. C. Daniel, H. D. Scott, J. F. Murdoch, M. J. Habiger, and H. M. Burks. 1996. Stream quality impacts of best management practices in a Northwestern Arkansas Basin. Water Resources Bull. 32(3): 499–509. Gassman, P. W. 1997. The national pilot program integrated modeling system: Environmental future condition scenario assumptions and results for the APEX model. Livestock Series Report 9, CARD Publ.
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Figure 6. Exceedance probability of time–weighted sol P concentration for wastewater treatment plant BMPs at three locations on the North Bosque River.
Griffin, C. B. 1995. Uncertainty analysis of BMP effectiveness for controlling nitrogen from urban nonpoint sources. Water Resources Bull. 31(6): 1041–1050. Keplinger, K. O. 1999. Cost savings and environmental benefits of dietary P reductions for dairy cows in the Bosque River Watershed. PR99–09. Stephenville, Tex.: Texas Inst. for Applied Environmental Research (TIAER), Tarleton State Univ.
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McFarland, A. M. S., and L. M. Hauck. 1995. Livestock and the environment: Scientific underpinnings for policy analysis. PR95–01. Stephenville, Tex.: Texas Inst. for Applied Environmental Research (TIAER), Tarleton State Univ. _____. 1999a. Relating agricultural land uses to in–stream stormwater quality. J. Environ. Quality 28: 836–844.
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Stephenville Sol Pconcentration (ppb)
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Figure 7. Exceedance probability of flow–weighted sol P concentration for wastewater treatment plant BMPs at three locations on the North Bosque River.
_____. 1999b. Existing nutrient sources and contributions to the Bosque River Watershed. PR99–11. Stephenville, Tex.: Texas Inst. for Applied Environmental Research (TIAER), Tarleton State Univ. Mostaghimi, S., U. S. Tim, P. W. McClellen, J. C. Carr, R. K. Byler, T. A. Dillaha, V. O. Shanholtz, and J. R. Pratt. 1989. Watershed/water quality monitoring for evaluating BMP
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effectiveness: Nomini creek watershed. Pre–BMP evaluation final report, Report no. N–P1–8906. Richmond, Va.: Virginia Dept. Conservation and Historic Resources, Div. Soil and Water Conservation. Nash, J. E., and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models: Part I. A discussion of principles. J. Hydrol. 10(3): 282–290.
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Stephenville
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Figure 8. Exceedance probability of time–weighted sol P concentration for combined BMPs at three locations on the North Bosque River.
Park, S. W., S. Mostaghimi, R. A. Cooke, and P. W. McClellan. 1994. BMP impacts on watershed runoff, sediment and nutrient yields. Water Resources Bull. 30(6): 1011–1022. Ramanarayanan, T. S., J. R. Williams, W. A. Dugas, L. M. Hauck, and A. M. S. McFarland. 1997. Using APEX to identify alternative practices for animal waste management: Part II. Model application. ASAE Paper 97–2209. St. Joseph, Mich.: ASAE. Santhi, C., J. G. Arnold, J. R. Williams, W. A. Dugas, R. Srinivasan, and L. M. Hauck. 2001. Validation of the SWAT model on a
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large river basin with point and nonpoint sources. J. Am. Water Resources Assoc. 37(5): 1169–1188. Srinivasan, R., and J. G. Arnold. 1994. Integration of basin–scale water quality model with GIS. Water Resources Bull. (30)3: 453–462. TIAER. 1999. Summary of stream bioassay results: 1997–1998. Stephenville, Tex.: Texas Inst. for Applied Environmental Research (TIAER), Tarleton State Univ.
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Figure 9. Exceedance probability of flow–weighted sol P concentration for combined BMPs at three locations on the North Bosque River.
TNRCC. 1998. Section I: 1998 Clean Water Act Section 303(d) List and schedule for development of TMDLs. Austin, Texas: TNRCC. Available at: www.tnrcc.state.tx.us/admin/topdoc/sfr/058/index.html. _____. 1999. Developing total maximum daily load projects in Texas: A guide for lead organizations. TNRCC. Available at: www.tnrcc.state.tx.us/admin/topdoc/gi/250/fulldoc.pdf. TNRCC and TSSWCB. 1999. Texas nonpoint source pollution assessment report and management program. SFR–68/99. Tex.: TNRCC and TSSWCB.
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USEPA. 1991. Guidance for water quality–based decisions in the TMDL process. EPA 440/4–91–001. Washington, D.C.: U.S. EPA, Office of Water. Available at: www.epa.gov/OWOW/tmdl/decisions. _____. 1997. National water quality inventory: 1996 report to United States Congress. EPA841–R–97–005. Washington, D.C.: U.S. EPA. Walker, J. F., and D. J. Graczyk. 1993. Preliminary evaluation of effects of BMPs in the Black Earth Creek, Wisconsin, Priority Watershed. Water Sci. Technol. 28: 539–548.
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ABSTRACT. A Total Maximum Daily Load (TMDL) program has been initiated in the North Bosque River Watershed in Texas, USA, where point and nonpoint sources of pollution are of a concern. The Soil and Water Assessment Tool (SWAT), which had been validated for flow and sediment and nutrient transport, was applied to quantify the effects of Best Management Practices (BMPs) related to dairy manure management and municipal wastewater treatment plant effluent. Results are presented for the period from 1960 through 1998 for three sites along the North Bosque River. Results are presented as annual time–weighted concentrations (average of the daily load divided by daily flow over a year) and annual flow–weighted concentrations (total cumulative load divided by total cumulative flow over a year). The wastewater treatment plant BMPs resulted in greater improvement in time–weighted instream soluble phosphorus concentrations than dairy BMPs. On the other hand, dairy BMPs made greater differences in flow–weighted concentrations. This study showed that SWAT could be a useful tool for studying the effects of alternative management scenarios for pollution control from point and nonpoint sources in large watersheds. Keywords.Watershed management, Water quality, Nonpoint source pollution, Point source pollution, Dairy manure management, Municipal wastewater treatment plants, Best management practices, Total maximum daily load.
A
recent United States Environmental Protection Agency (USEPA) Water Quality Inventory (1997) reports that water quality goals are not being met in more than one–third of the rivers, lakes, and estuaries of the U.S. The USEPA’s Clean Water Act (CWA) Program recommends states to identify impaired water bodies (CWA section 303(d) list) and develop a watershed plan for each, including the development of a Total Maximum Daily Load (TMDL), to address the impairment (USEPA, 1997). A TMDL is an estimate of the maximum pollution load a water body can receive from point and nonpoint sources and still maintain the specified standards (USEPA, 1991). The TMDL process involves identification of possible measures (Best Management Practices––BMPs) to reduce the excess load from controllable contributing sources and to bring water bodies into compliance. The Texas Natural Resource Conservation Commission (TNRCC), in cooperation with the Texas State Soil and Water
Article was submitted for review in January 2001; approved for publication by the Soil & Water Division of ASAE in August 2001. The authors are C. Santhi, ASAE Member, Post–Doctoral Research Associate, Jimmy R. Williams, Research Scientist, and William A. Dugas, Professor and Resident Director, Blackland Research and Extension Center, Texas Agricultural Experiment Station, Texas A&M University System, Temple, Texas; Jeffrey G. Arnold, ASAE Member, Agricultural Engineer, Grassland Soil and Water Research Laboratory, USDA–ARS, Temple, Texas; and Larry M. Hauck, Assistant Director, Texas Institute for Applied Environmental Research, Tarleton State University, Stephensville, Texas. Corresponding author: C. Santhi, Blackland Research and Extension Center, Texas Agricultural Experiment Station, Texas A&M University System, Temple, TX 76502; phone: 254–774–6141; fax: 254–774–6001; e–mail: [email protected].
Conservation Board (TSSWCB), is implementing TMDL projects in Texas. A TMDL is being developed in the North Bosque River Watershed of North Central Texas (fig. 1), where phosphorus from dairy manure application is a concern (TNRCC, 1998; TNRCC and TSSWCB, 1999). Instream water quality monitoring has shown manure application to pasture or cropland to be a likely potential nonpoint source of pollution (McFarland and Hauck, 1995; 1999a). McFarland and Hauck (1999a) showed a strong positive correlation between instream phosphorus loadings from a watershed and the number of cows, and total acreage of manure application fields. Other possible nutrient sources in the North Bosque River Watershed are croplands, urban runoff, and effluents from municipal wastewater treatment plants (WWTPs) (table 1). Water quality simulation models can be useful in the TMDL development process. They can assess the present level of pollution where observed data are inadequate to give a complete picture, determine allowable load to receiving waters where there are complex interactions between different pollution sources, and allocate pollutant loads by dividing them among the identified point and nonpoint pollution sources. Unlike plot and field studies where results from specific treatments may be directly comparable to those from control conditions, the effectiveness of BMPs on a watershed scale is not easily identifiable. Determination of BMP effectiveness is particularly complex if the watershed has mixed land uses that generate nonpoint sources of pollution and numerous point sources of pollution (Mostaghimi et al., 1989). Long–term monitoring may be used to study the effective–
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E 2001 American Society of Agricultural Engineers ISSN 0001–2351
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METHODOLOGY SWAT MODEL DESCRIPTION The watershed loading/water quality model, Soil Water Assessment Tool (SWAT) (Arnold et al., 1998), developed by the United States Department of Agriculture–Agricultural Research Service (USDA–ARS), was used in this study. TMDL guidelines provide criteria for selecting a model for use in TMDL applications (TNRCC, 1999). SWAT is a physically based simulation model, operating on a daily time step. It has the ability to predict changes in nutrient loadings from different management conditions (nonpoint and point sources), is freely available for public use (www.brc.tamus.e du/swat), and has been integrated into USEPA’s modeling framework, Better Assessment Science Integrating Point and Nonpoint Sources (BASINS). Hence, SWAT was selected for this study. SWAT was developed to predict the impact of management practices on water, sediment and agricultural chemical yields in large ungauged basins. To satisfy the objective, the model is physically based, uses readily available inputs, and is capable of simulating long periods for computing the effects of management changes. SWAT has capability for routing runoff and chemicals through streams and reservoirs, adding flows, and input measured data from point sources such as municipal wastewater treatment plants. Major components of the model include hydrology, weather, erosion, soil temperature, crop growth, nutrients, pesticides, and agricultural management.
Figure 1. Bosque River Watershed in Texas.
Table 1. Percentage of soluble phosphorus loading contributed by different sources at Hico and Valley Mills in the Bosque River Watershed (McFarland and Hauck, 1999b). Soluble phosphorus loading (%) Source of soluble phosphorus
Hico
Valley Mills
Pasture Range and forest Cropland Urban Dairy waste application fields Point source (WWTP)
8 13 2 4 61 12
9 23 7 7 45 9
ness of BMPs, but that is very expensive. Models, however, may be used to estimate long–term effects of individual BMPs and can account for long–term weather variability. Information regarding watershed–level impacts of BMPs on water quality is imperative to determine whether public resources are to be used to cost–share BMP implementation in a TMDL development process. Watershed–scale studies on effectiveness of pre– and post–implementation of BMPs have been reported through field observation and monitoring studies (Walker and Graczyk, 1993; Park et al., 1994; Griffin, 1995; Edwards et al., 1996). The objective of this article is to demonstrate the utility of a model in the TMDL development process for estimating phosphorus loadings and concentrations under existing and projected conditions of the watershed and to analyze the effectiveness of various BMPs related to dairy manure management and WWTPs in the North Bosque River Watershed. This study provides information to decision–makers on the most feasible and cost effective BMPs. This methodology is applicable to similar TMDL/water quality projects in other parts of the United States.
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THE STUDY AREA The Bosque River Watershed (fig. 1) has a drainage area of 4,277 km2, and has four tributaries, North Bosque River, Middle Bosque River, South Bosque River, and Hog Creek, that drain into Lake Waco, which is the source of drinking water for the city of Waco. Land use in this watershed is mostly range and pasture with some cropland in the southern portion. There are about 100 dairies operating in the watershed, primarily upstream of Hico (fig. 1) with a total herd size of about 40,450. Manure produced by these animals is applied on dairy manure application fields that cover about 94.5 km2. DATA SOURCES AND MANAGEMENT PRACTICES The Geographic Resource Assessment Support System– Geographic Information System (GRASS–GIS) interface of the SWAT model (Srinivasan and Arnold, 1994) was used to develop input files. This interface delineates the watershed into subbasins based on topography. Delineated subbasins, land use, soil, and waste application field (WAF) maps were overlaid to identify the manure–application and non–application areas in each subbasin. WAF management: Sudan grass, coastal bermuda grass, sudan grass with winter wheat in rotation, sorghum hay (forage), and sorghum hay–winter wheat in rotation were simulated on WAFs. Annual manure application rate on WAFs was determined based on the number of animals and annual amount of manure generated per animal (ASAE, 1988). Manure nutrient concentrations were taken from Gassman (1997). Non–WAF management: Fertilizer was applied to improved pastures to maintain productivity and three to four forage harvests were simulated each year. No fertilizer was
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applied to rangeland, but forage was harvested three to four times to simulate grazing. Typical fertilizer rates and tillage practices for corn, wheat, and grain sorghum were used. Urban land had both pervious (70%) and impervious lands (30%). Pervious areas represent areas such as lawns and parks and they were simulated with irrigation and fertilizer management. Impervious areas represent built up areas and streets were simulated with street sweeping. Flows and nutrient loadings from eight municipal WWTPs in the watershed (McFarland and Hauck, 1999b) were input into the SWAT–GIS interface as point sources. MODEL CALIBRATION AND VALIDATION Measured water quality data were used for calibrating and validating the model for flow, sediment, and organic and mineral nitrogen and phosphorus on a monthly basis from 1993 through 1998 depending on the monitoring data available at several locations in the watershed (Santhi et al., 2001). A wide variation of streamflow conditions were monitored during the model calibration period, including the second and third largest streamflows recorded in the North Bosque River and a period of moderate drought in 1996. The statistical measures such as coefficient of determination (R2) and Nash–Suttcliffe simulation efficiency (ENS) (Nash and Suttcliffe, 1970) were used to evaluate model prediction. The R2 value is an indicator of strength of relationship between the observed and simulated values. Nash–Suttcliffe simulation efficiency (ENS) indicates how well the plot of observed versus simulated value fits the 1:1 line. If the R2 and ENS values are less than or very close to zero, the model prediction is considered ’unacceptable or poor.’ If the values are one, then the model prediction is ’perfect’. Ramanarayanan et al. (1997) suggested that model prediction is acceptable or satisfactory, if R2 >0.6 and ENS >0.5. The statistical measures indicate that monthly simulated flow, sediment, and nutrient loadings were close to the observed values during the calibration and validation periods at Hico and Valley Mills (table 2). In this article, soluble phosphorus (sol P) refers to the water soluble P predicted by SWAT, which was assumed to be closely equivalent to soluble reactive P (or orthophosphate P), a common laboratory analyte of water quality sampling. FLOW–WEIGHTED AND TIME–WEIGHTED CONCENTRATIONS A primary response to the nutrient enrichment in the North Bosque River is an overabundance of attached algae (periphyton) and suspended algae (TIAER, 1999). Algal communities were determined to be limited by P rather than nitrogen, and subsequent analyses of data showed a stronger
statistical relationship of algal biomass to sol P (measured as soluble reactive phosphorus) than to total P (TIAER, 1999). As a consequence of these findings, and as periphyton response to nutrient fluxes and loadings are not completely understood, many water quality models including SWAT do not include the periphyton component. Due to the complexity involved in periphyton simulation, a conservative approach was selected considering two methods of sol P concentrations in this study. They are (a) flow–weighted (FWT), and (b) time–weighted (TWT) concentrations. n
FWT =
∑(PO 4) i *10 6
i =1
(1)
n
∑Q i
i =1
where FWT = flow–weighted concentration of sol P in mg/m3 (ppb) PO4 = sol P load in kg/d Q = surface runoff volume in m3/d i = day of observation n = number of days of observations. 6
10 * TWT
=
n
(PO 4 ) i
i =1
Qi
∑
(2)
n
where TWT = time–weighted concentration in mg/m3 (ppb) PO4 = sol P load in kg/d Q = surface runoff volume in m3/d PO4/Q = the concentration i = day of observation n = number of days of observations. FWT concentration represents a volume–weighted concentration based on total loading and flow volume over a period, and TWT concentration represents a time–weighted average concentration using daily concentrations over a period. SWAT was able to accurately predict FWT and TWT sol P concentrations at Hico and Valley Mills (fig. 2). The TWT concentration reflects the day to day exposure of periphyton and the FWT concentration more accurately reflects the exposure of periphyton to storm loadings. Thus, TWT concentration is more responsive to continuous point sources of sol P than to episodic nonpoint source contributions. In contrast, the FWT concentration is more responsive
Table 2. Summary of monthly calibration and validation results at Hico and Valley Mills (Santhi et al., 2001). Hico Valley Mills Calibration (1993–97)[a] Model output Flow (mm) Sediment (t/ha) Organic N (kg/ha) Organic P (kg/ha) Mineral N (kg/ha) Soluble P (kg/ha) [a]
Validation (1998)[a]
Calibration (1996–1997)[a]
Validation (1998)[a]
R2
ENS
R2
ENS
R2
ENS
R2
ENS
0.80 0.81 0.61 0.71 0.64 0.60
0.79 0.80 0.58 0.70 0.59 0.59
0.92 0.98 0.92 0.95 0.89 0.83
0.87 0.70 0.73 0.72 0.75 0.53
0.89 0.87 0.60 0.61 0.72 0.66
0.83 0.69 0.57 0.59 –0.08 0.53
0.80 0.95 0.71 0.80 0.72 0.93
0.62 0.23 0.43 0.39 0.64 0.81
R2 = Coefficient of determination; ENS = Nash–Sutcliffe Efficiency (Nash and Sutcliffe, 1970).
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ÓÓ ÕÕ ÚÚ ÖÖ ÓÓ ÕÕ ÚÚ ÖÖ ÓÓ ÕÕ ÚÚÔÔ ÖÖ ÒÒ ÓÓ ÕÕ ÚÚ ÖÖ ÔÔ ÒÒ ŠŠ ÚÚ ÓÓ ÕÕ ÚÚ ÖÖ ÓÓ ÕÕ ÚÚÔÔ ÒÒ ŠŠ ÚÚ ÖÖ Ü Ú ÖÖ Ü Ú
Sol P Concentration (ppb)
250 200 150 100
50
0
ÛÛ
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Hico
FWT – Simulated
Valley Mills
TWT – Observed
TWT – Simulated
Figure 2. Observed and simulated time–weighted (TWT) and flow– weighted (FWT) soluble phosphorus (sol P) concentrations at Hico and Valley Mills.
to the episodic nonpoint sources of sol P that are larger in total loading for this system than point source loadings. An objective of the TMDL development process was to obtain reductions in both FWT and TWT sol P concentrations to ensure reductions in periphyton and suspended algal biomass. Hence, the BMP results are discussed for both concentrations. BMP SCENARIOS The calibrated SWAT model was used to study the long–term effects of BMPs by simulating the watershed hydrology using daily historical weather information for 1960 to 1998. Long–term simulation helps to capture and remove the major ’stochastic uncertainty’ imposed by weather, predominantly rainfall in this case. BMPs examined to estimate the effect of reducing the sol P at different locations in the watershed included control measures used with agricultural nonpoint sources and with municipal WWTPs. Inputs associated with each BMP scenario are described below: (a) Existing Conditions To simulate the existing conditions, the model used present values for dairy herd size (40,450), WAF areas (94.5 km2), average manure application rate (13 t/ha/yr), average effluent discharge volume of 1997–98 from WWTPs with current median concentrations for nutrients, urban and cropland areas, and existing agricultural management practices (table 3). Initial sol P concentration in the soil of WAFs Table 3. Assumptions used in existing and future conditions. BMP assumptions Existing Future Dairy herd size Manure application rate Manure application area (km2) P diet reduction in animal feed (%) Excess manure haul off Sol P concentration in manure application area (ppm) 8 Existing WWTPs– flows & P limit concentration (mg/l) 3 Additional WWTPs–flows & P limit concentration (mg/l) Urbanland (km2) Cropland (km2)
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40,450 13 t/ha/yr 94.5 No No
67,000 46 t/ha/yr 44.5 No No
250 1997–98 Median No No 99 725
250 Year 2020 Median Year 2020 1 128 725
was set at 250 ppm based on soil tests in a subwatershed in the North Bosque River (Gassman, 1997; A. N. Sharpley, USDA–ARS, Durant, Okla., and L. M. Hauck, TIAER, Stephenville, Tex., personal communication, 1998). (b) Future Conditions For the future condition scenario, the model used the projected conditions of the watershed in the year 2020. These future conditions included the projected dairy herd size (67,000), manure application in WAFs at the crop nitrogen requirement rate (N rate at 46 t/ha/yr), WAF areas required for a N rate requirement, maximum permitted discharge volumes from WWTPs with median concentrations for nutrients, urban area increased by 30% to reflect the projected population growth in 2020, and cropland area at current levels (table 3). Three additional WWTPs with 1 mg/l concentration of total P were input into the model as point sources along the North Bosque River to account for possible industrial future growth outside existing communities. Initial sol P concentration in the soil of all WAFs was set at 250 ppm to reflect application areas that had received manure in previous years. This scenario reflects the watershed under a maximum growth condition scenario. (c) Dairy BMPs Three dairy BMPs were examined. They are: S Haul Off: All solid dairy manure was hauled out of the watershed and only liquid manure was applied. The liquid manure from dairy waste lagoons was applied over 12% of the waste application area calculated at N rate. In open area dairy lots, nutrients collected in the lagoon from the liquid manure equals 12% of the nutrients collected from the solid manure (Gassman, 1997). Hence, 12% of the waste application area was chosen in this scenario. Initial sol P concentration in WAFs was set at 250 ppm in the liquid manure applied area and the remaining WAF area was set at a lower concentration (5 ppm). All other conditions remained the same as that of the future condition scenario. S Crop P Requirement Rate (P rate): In general, crop growth requires uptake of more N than P. Thus, if manure is applied at a rate to satisfy the crop N demand, there is application of P beyond crop requirements (Edwards et al., 1996). Hence, in this scenario, manure was applied at crop P requirement rate (6.35 t/ha/yr) over an area computed using projected herd size. About 13% of the WAFs (a ratio of N rate area to P rate area) were set with an initial soil sol P concentration of 250 ppm to reflect the application areas that had received manure in previous years. The remaining WAF area was set with an initial soil sol P concentration of a regular pasture/rangeland (5 ppm). Supplemental commercial fertilizer for N was added to maintain crop growth. All other conditions remained the same as that of the future condition scenario. S P Diet Reduction in Animal Feed: P is a critical nutrient in dairy cow diets. Recent studies have indicated that lactating cow health, reproduction, and milk production can be maintained in this region at P dietary levels of 0.4% (Keplinger, 1999). The average dairy cow diet in this region is in the range of 0.52 to 0.53% P. Keplinger (1999) suggests reductions in dairy diet P to 0.4% in the region will reduce the manure P content by 29%. In this scenario,
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P content of dairy manure was reduced by 29%. All other watershed conditions remained the same as in the future condition scenario. (d) WWTP BMPs To study the impacts of WWTP BMPs, concentrations of total P (sol P and organic P) in WWTP effluents were evaluated at 0.5, 1.0, and 2.0 mg/l. (e) Combined Dairy and WWTP BMPs The impacts of dairy and WWTP BMPs were studied individually in the beginning of the project. The North Bosque River showed greater benefits in flow–weighted concentration for dairy BMPs due to greater reduction in episodic loadings. The river showed greater benefits in time–weighted concentration for WWTP BMPs due to greater reduction in continuous loadings from WWTPs. Due to the differing response of the river to dairy and WWTP BMPs as indicated by the FWT and TWT concentrations, the objective of the TMDL was aimed at obtaining reductions in both concentrations. Hence, a combination of dairy and WWTP BMPs were developed and evaluated to assess their collective effectiveness in achieving the water quality goals. Three combined dairy and WWTP BMPs (Scenarios I, II, III) were evaluated (table 4). Scenario I was a modification of the existing condition scenario with additional conditions imposed on manure application rate (P rate), P diet reduction in animal feed, and 1 mg/l limit of total P in WWTPs. Scenario II was a modification over the future scenario with manure applied at P rate, P diet reduction, and 1 mg/l P limit on WWTPs. Scenario III was a modification of Scenario II with an added restriction imposed on manure by limiting WAFs to the area currently specified in dairy permits (94.5 km2), and excess manure, after application at a P–rate on permitted WAFs, was hauled out of the watershed. BMP ANALYSES Results at three sites, Stephenville, Meridian, and Valley Mills, along the North Bosque River representing various upstream combinations of land use and pollution sources were investigated (fig. 1). The Stephenville WWTP has a permitted effluent discharge of 11, 340 m3/d and is the major WWTP in this watershed. The watershed along the North Bosque River at Stephenville has a drainage area of 321 km2 and 10% of this area contains WAFs. Meridian (1570 km2) is about halfway downstream. Valley Mills (2997 km2) is located immediately upstream of Lake Waco.
Sol P concentrations are shown as probability exceedance plots to analyze the effectiveness of BMPs. In these exceedance plots, the annual time or flow–weighted sol P concentrations for the simulation period (1960 through 1998) are ranked and plotted against their associated exceedance probability values. These plots are also useful in identifying the relevant BMPs to achieve the desired water quality concentration. The targets (or goals) of the TMDL were determined by the state regulatory agency and stakeholders to be in terms of an annual average sol P concentration to be measured from monitoring at key locations along the North Bosque River. Consequently, daily model results were used to estimate annual TWT and FWT concentrations. Similarly, mean TWT and FWT sol P concentrations (computed for the simulation period of 39 years) are used to estimate the percentage of reduction in sol P concentration for each BMP with reference to the future condition scenario.
RESULTS AND DISCUSSION DAIRY BMPS Mean TWT sol P concentrations were decreased by about 12% at Stephenville, 45% at Meridian, and 56% at Valley Mills in the existing condition scenario as compared to the future condition scenario (fig. 3a). Mean FWT concentrations were decreased by 22, 29, and 28% for the same locations (fig. 3b). These reductions were predominantly caused by projected increases in dairy cows and WWTP discharges in the future scenario. TWT concentration curves of the dairy BMPs (Haul off, P rate, and P diet reduction BMPs) were closer to each other (fig. 4) and showed lesser deviations from future scenario than the FWT concentration curves (fig. 5). These observations are in agreement with percentage reductions in mean TWT and FWT concentrations (fig. 3). Mean TWT concentrations showed reductions ranging from 1 to 12% (fig. 3a) while mean FWT concentrations showed reductions ranging from 7 to 60% for dairy BMPs (fig. 3b). Benefits of dairy BMPs in reducing episodic loadings (nonpoint sources) of sol P were better reflected by the FWT concentrations compared to the TWT concentrations. WWTP BMPS Reductions of TWT sol P concentrations due to WWTP BMPs as compared to the future condition were much greater than FWT concentrations (figs. 6 and 7). The higher concentrations at Stephenville were due to the discharges
Scenario
Table 4. Assumptions used in dairy and wastewater treatment plant and combined BMP scenarios. WWTP WWTP Dairy manure Reduced P Type of BMP flow period P conc. limit application rate in diet
Existing Future Haul off P rate P diet WWTP–0.5 mg/l WWTP–1.0 mg/l WWTP–2.0 mg/l Scenario I Scenario II Scenario III
Existing conditions Future conditions Dairy Dairy Dairy WWTP WWTP WWTP Combined Combined Combined
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1997–98 (actual) 2020 (projected) 2020 2020 2020 2020 2020 2020 1997–98 2020 2020
Median conc. Median conc. Median conc. Median conc. Median conc. 0.5 mg/l 1.0 mg/l 2.0 mg/l 1 mg/l 1 mg/l 1 mg/l
Between N and P rate N rate N rate P rate N rate N rate N rate N rate P rate P rate P rate
No No No No Yes No No No Yes Yes Yes
Haul off manure No No Yes No No No No No No No Yes
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Reduction of sol P concentration (%)
100
(1303)
90
(211)
(93)
Existing Haul off
Ú ! " Ú ÚÚ Ú # Ú ÚÚ Ú Ú Ú ÚÚ Ú Ú ÚÚ Ú $ Ú ÚÚ Ú Ù ÙŽŽ ÚÚ ÙÙ ÙÙŸŸ Ú ÙÙ Ÿ ŽŽ ŸŸ ŽÚ %
80
Prate
70
P diet reduction
60
WWTP–0.5mg/l
50
WWTP–1mg/l
40
WWTP–2mg/l
30
Scenario I
20
Scenario II
10
Scenario III
0
Stephenville
Meridian
Valley Mills
Locations a) Time Weighted
Reduction of sol P concentration (%)
100
(669)
(228)
(137)
90 80 70 60 50 40 30 20 10 0
5 5 6 * ’ / 44 7 Ú ’ / 44 ’ / ’ /44 44. )) ’ " -Ú / 44 1 )) ’ " -Ú / 44 4 )) ’-"Ú /44 ))
Existing
. .22&& $$3 00 ++ . 11 00 22 && $$ 3 Ú . ++ 11 00 22 && $$ 3 Ú . Ÿ ++ 11 && Ú00 ++ .22 Ÿ $$ 113 00 22 && $$ 3 Ú (( ++ . Ÿ ** , ÚÚ 11 00 22 && $$ 3 Ú Ÿ (( Ž ++ . ** , ÚÚ 11 00 22 && $$ 3 .22&& ŸÚÚ (( ŽÚ00 ++ ** ,$$ 113 Stephenville
Meridian
Locations
Haul off Prate P diet reduction WWTP–0.5mg/l WWTP–1mg/l WWTP–2mg/l Scenario I Scenario II Scenario III
Valley Mills
b) Flow Weighted Figure 3. Percentage reductions of time–weighted and flow–weighted sol P concentrations from future condition scenario for existing, dairy, wastewater treatment plant and combined BMPS at three locations on North Bosque River (mean sol P concentration for the future condition scenario in parenthesis).
from the major WWTP. Mean TWT concentrations showed reductions ranging from 21% to 78% for WWTP BMPs (fig. 3a) and mean FWT concentrations showed reductions ranging from 4% to 50% (fig. 3b). The results of WWTP BMPs showed greater benefits in reducing the continuous loadings from WWTPs as opposed to the total loadings and hence, they are better reflected in TWT concentrations. COMBINED BMPS The combined BMPs showed reductions ranging from 56% to 75% in mean TWT concentrations (fig. 3a) and 39% to 65% in mean FWT concentrations (fig. 3b). The combined BMP scenarios showed almost similar trends at Stephenville for both TWT and FWT concentrations (figs. 8 and 9). All three scenarios showed about 60% reduction from the future condition scenario and there were only minor differences between BMPs (fig. 3). Scenario I showed the largest
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reductions for TWT and FWT concentrations at Stephenville, and for TWT concentrations at Meridian and Valley Mills. Scenarios I and III showed similar reductions at Meridian and Valley Mills for FWT concentrations (fig. 3b). The manure hauled off from dairy WAFs compensated for increase in sol P concentrations of WWTPs under permitted flow (future) conditions in Scenario III (table 4). Hence, the percentage reductions ware close to that of Scenario I. However, the TWT sol P concentrations showed reduced benefits for Scenario III compared to Scenario I at Meridian and Valley Mills (fig. 3a), because of the increased discharges from WWTPs for the future growth conditions in Scenario III. Scenario II showed reduced benefits at all three sites for both concentrations. Scenario I showed highest benefits (lowest sol P concentrations) among the three combined BMP scenarios.
TRANSACTIONS OF THE ASAE
Sol P concentration (ppb)
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400 300 200 100 0 0
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Sol P Concentration (ppb)
300 250 200 150 100 50 0 0
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Exceedance probability Existing
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Haul off
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Figure 4. Exceedance probability of time–weighted sol P concentration for dairy BMPs at three locations on the North Bosque River.
CONCLUSIONS A modeling approach was used to quantify the long–term effects of dairy, wastewater treatment plant (WWTP), and combinations of dairy and WWTP BMPs in the Bosque River Watershed in Texas. Mean time–weighted (TWT) soluble phosphorus (sol P) concentrations showed reductions ranging from 1% to 12% for dairy BMPs, while mean flow– weighted (FWT) sol P concentrations showed reductions ranging from 7% to 60%(fig. 3) at three sites on the North Bosque River. For WWTP BMPs, mean TWT sol P concentrations showed reductions ranging from 21% to 78% and mean FWT concentrations showed reductions ranging
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from 4% to 50%. The combined BMP scenarios showed reductions ranging from 56% to 75% in mean TWT concentrations and 39% to 65% in mean FWT concentrations. These results revealed that benefits of dairy BMPs in reducing episodic loadings of sol P are better reflected by the FWT concentrations compared to the TWT concentrations, and WWTP BMPs showed greater benefits in reducing the continuous loadings as opposed to the total loadings and hence, they are better reflected in the TWT concentrations. Modeling assessment methods used in this study are relevant and adaptable to similar TMDL/water quality projects in other parts of the United States.
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Stephenville
Sol P concentration (ppb)
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Exceedance probability Meridian
Sol P concentration (ppb)
400 300 200 100 0 0
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Valley Mills Sol P Concentration (ppb)
300 250 200 150 100 50 0 0
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Exceedance probability Existing
Future
Haul off
Prate
Pdiet reduction
Figure 5. Exceedance probability of flow–weighted sol P concentration for dairy BMPs at three locations on the North Bosque River.
ACKKNOWLEDGEMENTS The authors acknowledge USDA for providing funding for this research work through Texas Institute for Applied Environmental Research, Stephenville, Texas.
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ASAE Standards, 35th Ed. 1988. ASAE D384.1. Manure production and characteristics. ASAE standard, ASAE data. St. Joseph, Mich.: ASAE. Edwards, D. R., T. C. Daniel, H. D. Scott, J. F. Murdoch, M. J. Habiger, and H. M. Burks. 1996. Stream quality impacts of best management practices in a Northwestern Arkansas Basin. Water Resources Bull. 32(3): 499–509. Gassman, P. W. 1997. The national pilot program integrated modeling system: Environmental future condition scenario assumptions and results for the APEX model. Livestock Series Report 9, CARD Publ.
TRANSACTIONS OF THE ASAE
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Figure 6. Exceedance probability of time–weighted sol P concentration for wastewater treatment plant BMPs at three locations on the North Bosque River.
Griffin, C. B. 1995. Uncertainty analysis of BMP effectiveness for controlling nitrogen from urban nonpoint sources. Water Resources Bull. 31(6): 1041–1050. Keplinger, K. O. 1999. Cost savings and environmental benefits of dietary P reductions for dairy cows in the Bosque River Watershed. PR99–09. Stephenville, Tex.: Texas Inst. for Applied Environmental Research (TIAER), Tarleton State Univ.
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McFarland, A. M. S., and L. M. Hauck. 1995. Livestock and the environment: Scientific underpinnings for policy analysis. PR95–01. Stephenville, Tex.: Texas Inst. for Applied Environmental Research (TIAER), Tarleton State Univ. _____. 1999a. Relating agricultural land uses to in–stream stormwater quality. J. Environ. Quality 28: 836–844.
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Stephenville Sol Pconcentration (ppb)
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Figure 7. Exceedance probability of flow–weighted sol P concentration for wastewater treatment plant BMPs at three locations on the North Bosque River.
_____. 1999b. Existing nutrient sources and contributions to the Bosque River Watershed. PR99–11. Stephenville, Tex.: Texas Inst. for Applied Environmental Research (TIAER), Tarleton State Univ. Mostaghimi, S., U. S. Tim, P. W. McClellen, J. C. Carr, R. K. Byler, T. A. Dillaha, V. O. Shanholtz, and J. R. Pratt. 1989. Watershed/water quality monitoring for evaluating BMP
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effectiveness: Nomini creek watershed. Pre–BMP evaluation final report, Report no. N–P1–8906. Richmond, Va.: Virginia Dept. Conservation and Historic Resources, Div. Soil and Water Conservation. Nash, J. E., and J. V. Sutcliffe. 1970. River flow forecasting through conceptual models: Part I. A discussion of principles. J. Hydrol. 10(3): 282–290.
TRANSACTIONS OF THE ASAE
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Figure 8. Exceedance probability of time–weighted sol P concentration for combined BMPs at three locations on the North Bosque River.
Park, S. W., S. Mostaghimi, R. A. Cooke, and P. W. McClellan. 1994. BMP impacts on watershed runoff, sediment and nutrient yields. Water Resources Bull. 30(6): 1011–1022. Ramanarayanan, T. S., J. R. Williams, W. A. Dugas, L. M. Hauck, and A. M. S. McFarland. 1997. Using APEX to identify alternative practices for animal waste management: Part II. Model application. ASAE Paper 97–2209. St. Joseph, Mich.: ASAE. Santhi, C., J. G. Arnold, J. R. Williams, W. A. Dugas, R. Srinivasan, and L. M. Hauck. 2001. Validation of the SWAT model on a
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large river basin with point and nonpoint sources. J. Am. Water Resources Assoc. 37(5): 1169–1188. Srinivasan, R., and J. G. Arnold. 1994. Integration of basin–scale water quality model with GIS. Water Resources Bull. (30)3: 453–462. TIAER. 1999. Summary of stream bioassay results: 1997–1998. Stephenville, Tex.: Texas Inst. for Applied Environmental Research (TIAER), Tarleton State Univ.
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Figure 9. Exceedance probability of flow–weighted sol P concentration for combined BMPs at three locations on the North Bosque River.
TNRCC. 1998. Section I: 1998 Clean Water Act Section 303(d) List and schedule for development of TMDLs. Austin, Texas: TNRCC. Available at: www.tnrcc.state.tx.us/admin/topdoc/sfr/058/index.html. _____. 1999. Developing total maximum daily load projects in Texas: A guide for lead organizations. TNRCC. Available at: www.tnrcc.state.tx.us/admin/topdoc/gi/250/fulldoc.pdf. TNRCC and TSSWCB. 1999. Texas nonpoint source pollution assessment report and management program. SFR–68/99. Tex.: TNRCC and TSSWCB.
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USEPA. 1991. Guidance for water quality–based decisions in the TMDL process. EPA 440/4–91–001. Washington, D.C.: U.S. EPA, Office of Water. Available at: www.epa.gov/OWOW/tmdl/decisions. _____. 1997. National water quality inventory: 1996 report to United States Congress. EPA841–R–97–005. Washington, D.C.: U.S. EPA. Walker, J. F., and D. J. Graczyk. 1993. Preliminary evaluation of effects of BMPs in the Black Earth Creek, Wisconsin, Priority Watershed. Water Sci. Technol. 28: 539–548.
TRANSACTIONS OF THE ASAE
