1. Introduction
Excessive nutrients have created numerous negative ecological effects. For instance, excess nutrient loading into the reservoirs in the Trinity River Basin (TRB) has led to eutrophication, depletion of dissolved oxygen, excess algal growth, and fish tissue contamination [
1]. More than 10 river segments in the TRB watershed, including some in reservoirs, are classified under 2000 Clean Water Act list for water quality impairment by point and nonpoint sources by the Texas Commission on Environmental Quality (TCEQ). Therefore, the TRB’s top priority has been focused on protecting water quality.
The TRB extends from northwest of Fort Worth to the Gulf of Mexico near Houston, Texas. The Upper TRB (
Figure 1) including the Dallas-Fort Worth Metro is the most populated and heavily urbanized area in Texas. More than 90% of the regional municipal water supply is from reservoirs. The North Central Texas Council of Governments (NCTCOG) forecasted the future population increase and urban expansion in the Dallas-Fort Worth Metropolitan area and surrounding 10 Counties through the year 2060 [
2]. NCTCOG [
2] estimated that total population will increase from 6.3 million as in 2010 to over 9.1 million in 2030, and eventually to 13.0 million in 2060, more than doubling during the 50 years of period. To meet water supply needs for this increasing population, it has been estimated that 2960 million cubic meters in 2030 and 4070 million cubic meters in 2060 will be needed, which requires 2343 million cubic meters more water (2.4 times larger) than historical usage in 2006 (1727 million cubic meter).
Incoming sediment loads are trapped in the reservoirs reducing their capacity, and nutrients loads have the effect of degrading water quality. Sediment surveys of Texas reservoirs and monitoring of reservoir water quality have raised concerns of water degradation and reducing capacity of reservoirs [
1]. With increasing regional water demands and public concern, a number of studies have been conducted in the TRB. The Soil and Water Assessment Tool (SWAT) [
3] was used to predict current and future water quality and to evaluate the impacts on urban increase and Best Management Practices (BMPs). There have been modeling works for some watersheds previously [
4,
5,
6]. TNRCC [
7] point out that nonpoint source pollution in agricultural streams was one of the major water quality issues. Simulation of the impacts of BMPs on nutrient loading was conducted in Berthold [
1] for the watersheds of five major reservoirs: Bridgeport, Eagle Mountain, Benbrook, Cedar Creek, and Richland-Chambers (
Figure 1) using SWAT. Berthold [
1] reported that primary causes were suspected to be urbanization and suggested implementation of BMPs for storm water, nutrient controls, and agricultural land. The Bridgeport and part of Eagle Mountain watersheds was studied in reference [
4]. They focus on evaluating the impacts of water quality management plans at the farm level (reduced sediment and nutrient loadings up to 99%) and at two watershed outlet locations (reduced sediment and nutrient loadings by only 1% to 2%) using SWAT. They reasoned that the small impacts at the watershed level compared to the farm level were due to the water quality management plan implementation area was very small compared to total watershed area. Debele
et al. [
8] conducted SWAT simulation for the upper streams of Cedar Creek Reservoir and Eagle Mountain Reservoir in the TRB (
Figure 1) with the focus of enhancing SWAT evapotranspiration and overland flow routing modules. Lee
et al. [
9] has reported the Eagle Mountain watershed study in which SWAT simulated the annual Total Nitrogen (TN) yield of 1055 metric ton and annual Total Phosphorous (TP) yield of 173 metric ton to the lake. However, entire watershed-wide overview and modeling works for all watersheds (some were not included in previous studies) were needed for comprehensive understanding of the current status and future assessment for this area including projected population. Wang
et al. [
10] have conducted flow and sediment modeling for all the watersheds with the major reservoirs in the Upper TRB (
Figure 1) using SWAT. The calibrated SWAT performed reasonably well for streamflow and sediment loads. The effects of upland ponds were further evaluated in reference [
10] through a pond removal scenario. Nutrient loads and the future urbanization impact are not yet explored by reference [
10]. This study was built upon previously conducted work by Wang
et al. [
10] and continued the modeling effort for nutrient loads in the watersheds. The objectives of this study are to assess nutrient loads to major reservoirs in the entire Upper TRB and to predict the effects of point source elimination and urbanization on nutrient loads through scenario analyses.
Figure 1.
The locations of major reservoirs and USGS (US Geological Survey) gage stations in the Upper Trinity River Basin (adopted from reference [
10]).
Figure 1.
The locations of major reservoirs and USGS (US Geological Survey) gage stations in the Upper Trinity River Basin (adopted from reference [
10]).
3. Results and Discussion
The results for nutrient calibration and validation are summarized in
Table 5. Based on days with available measured nutrient data, the average daily TN ranged from 94.0 kg/day (Joe Pool) to 29,864 kg/day (Bridgeport) while simulated corresponding values ranged from 85.0 (Joe Pool) to 32,352 kg/day (Bridgeport) with prediction error ranging from −16.3% (Ray Hubbard) to 8.9% (Lewisville, 08053000). Observed average daily TP from days with available data ranged from 7.0 kg/day (Joe Pool) to 38,159 kg/day (Bridgeport), while the corresponding modeled TP ranged from 7.3 kg/day (Joe Pool) to 36,001 kg/day (Bridgeport) with prediction error ranging from −23.6% (Ray Roberts) to 80.6% (Richland-Chambers). The model prediction errors are mostly within a difference of 10%, with some large under or over estimations including Lewisville, Ray Roberts, and Richland-Chambers.
Table 5.
Model results for nutrients in watersheds in the Upper Trinity River Basin.
Table 5.
Model results for nutrients in watersheds in the Upper Trinity River Basin.
Gauge Station ID | Calibration/Validation | Period | TN (kg/day) | Error (%) | TP (kg/Day) | Error (%) |
---|
Observed | Modeled | Observed | Modeled |
---|
Joe Pool |
0849700 | Calibration | 1985–1992/3 | 94.0 | 85.0 | −9.5 | 7.0 | 7.3 | 5.4 |
Validation | 1992/5–2007 | 106.7 | 110.0 | 3.0 | 13.8 | 15.2 | 10.2 |
Lavon |
08058900 | Calibration | 1981–1995 | 621.5 | 640.3 | 3.0 | 139.7 | 131.9 | −5.6 |
Lewisville |
08052700 | Calibration | 1971–1997 | 2039.1 | 1940.9 | 4.8 | 225.5 | 227.8 | 1.1 |
08053000 | Calibration | 1981–1989 | 4190.2 | 3819.2 | 8.9 | 216.1 | 237.4 | 9.9 |
Validation | 1990–1997 | 2091.1 | 2084.4 | 0.3 | 261.2 | 217.8 | −16.6 |
Ray Roberts |
08050840 | Calibration | 1992–1995 | 3199.3 | 3220.5 | 0.7 | 1453.6 | 1,110.4 | −23.6 |
Ray Hubbard |
08061750 | Validation | 1981–1992 | 4010.8 | 3355.8 | −16.3 | 817.2 | 763.2 | −6.6 |
Bridgeport |
08042800 1 | Calibration | 1970–2007 | 29,864.0 | 32,352.0 | 8.3 | 38,159.0 | 36,001.0 | −5.7 |
Richland-Chambers |
08064100 | Calibration | 1984–1995 | 5129.3 | 4991.0 | −2.7 | 443.0 | 800.0 | 80.6 |
Benbrook 2 |
Cedar Creek 3 |
Eagle Mountain 3 |
Model results for Cedar Creek and Eagle Mountain watershed were presented in a different way as nutrient data at multiple major tributaries at the two watersheds from the Tarrant Region Water District (TRWD) monitoring stations were compared. Narasimhan
et al. [
6] illustrated the result of model performance for TN and TP at 10 monitoring sites in the Cedar Creek watershed. Their study showed data comparison between observed and modeled from 1989 to 2002 and found out statistically significant correlation (
r2) of 0.7 and 0.8 for TN and TP, respectively. We compared water quality data collected by TRWD (1989–2002) in each major tributary (Kings, Cedar, Lacy, North Twin, South Twin, Lynn, Clear, Caney and Prairie) for TN and TP in the Cedar Creek watershed. The percent errors ranged from −26% to 28%, except for TN at the Lynn site (43% error) where only five samples were available (see reference [
6] for more detail).
Figure 5 shows model performance for TN and TP in Eagle Mountain watershed by median, 25th and 75th percentile for observation and estimation in each major tributary (Ash, Derrett, Dosier, Walnut and West Fork 4688). Some sites showed disagreement between observed and measured data such as Derrett with percent error of 104% for TN and 217% for TP and Walnut (−90% error for TN and TP). The remaining three sites, including the West Fork 4688 site located at the end of the main channel before the lake entrance, agreed relatively well with percent errors ranging from −3% to 41%.
Figure 5.
Median, 25th and 75th percentile of measured and predicted TN (a) and TP (b) at monitoring sites throughout the Eagle Mountain. Measured data were provided by Tarrant Region Water District (TRWD) from the 1991 to 2004 tributary study. The first bar in each column indicates measured data and the second bar indicates the SWAT estimation. The “n” indicates the number of samples.
Figure 5.
Median, 25th and 75th percentile of measured and predicted TN (a) and TP (b) at monitoring sites throughout the Eagle Mountain. Measured data were provided by Tarrant Region Water District (TRWD) from the 1991 to 2004 tributary study. The first bar in each column indicates measured data and the second bar indicates the SWAT estimation. The “n” indicates the number of samples.
The calibrated SWAT model was used to predict nutrient losses from upland and nutrient loads into reservoirs. The ten study watersheds ranged in size from 580 km
2 (Joe Pool) to 5157 km
2 (Richland-Chambers). Nutrient losses and loads into reservoirs by watershed size are illustrated in
Figure 6. The study watersheds vary widely in their simulated nutrient loss and transport, reflecting the variability among watersheds. For example, annual overland TN rates ranged from 42 kg/km
2 for Bridgeport to 936 kg/km
2 for Ray Hubbard. Annual TP rates ranged from 26 kg/km
2 for Bridgeport to 206 kg/km
2 for Ray Hubbard (
Figure 6). Normalized nutrient loads into the receiving reservoirs by watershed size indicated that the Lavon and Ray Hubbard watersheds discharge the highest TN rates (1340 and 1200 kg/km
2) to the lakes and those watersheds again discharges extremely large TP (106 and 179 kg/km
2) compared to the other watersheds. The remaining watersheds had discharge rates ranging from 40 to 1000 kg/km
2 for TN and from 26 to 67 kg/km
2 for TP (
Figure 6).
Figure 6.
SWAT simulated nutrient loads by area for each watershed in the Upper Trinity River Basin. (a): Total nitrogen; (b): Total phosphorus.
Figure 6.
SWAT simulated nutrient loads by area for each watershed in the Upper Trinity River Basin. (a): Total nitrogen; (b): Total phosphorus.
Half of the Ray Hubbard watershed is developed, and WWTPs serving urban areas within the watershed appear to contribute a large portion of the nutrient loads reaching the lake. According to scenario analysis, the elimination of these WWTPs reduced reservoir loading of TN by 20% and TP by 8% (
Table 6). Point sources appear to contribute substantially to Lake Lavon’s nutrient loads as evidenced by the point source elimination scenario, which reduces TN loads by 56% and TP loads by 24% (
Table 6). Lewisville and Benbrook also see great nutrient loads reduction with the point source elimination scenario, which reduce TN by 27% and 23%, TP by 14% and 9%, respectively, for the two watersheds (
Table 6).
The impacts of projected population growth in the Upper TRB on nutrient loads also varied among watersheds. Increased urbanization caused changes in overland TN losses ranging from a decrease of 3% (Ray Hubbard) to an increase of 24% (Benbrook). Projected urbanization increased TP losses from upland areas by 3% (Ray Hubbard) to 111% (Benbrook), except for Richland-Chambers where total phosphorus losses decreased by 1% (
Table 7). This decrease was due to the fact that pastureland and cropland dominate the Richland-Chambers watershed and are large contributors to nutrient loading. Therefore, nutrient loads in this watershed decreased with urbanization due to proportional decreases in pastureland and cropland. Most of the Benbrook watershed is in Parker and Johnson counties. The two counties are expected to have 284% and 257% population increase from 2000 to 2030, respectively [
2]. Urban area in Benbrook was projected to expand from 9% based on 2001 NLCD (
Figure 2) to 26% of the total watershed area based on the relationship between the fraction of counties that is urban and population density established in reference [
15]. This resulted in 288 km
2 total urban area in 2030 from the 2001’s 99 km
2, a total increase of 191% in urban area (
Table 7). The model predicted the greatest change in nutrient loads, with TN increasing 23% and TP increasing 111%.
Table 6.
Simulated baseline annual nutrient loads into each lake and percent reduction with point source elimination scenario.
Table 6.
Simulated baseline annual nutrient loads into each lake and percent reduction with point source elimination scenario.
Watershed Area (km2) | Modeling Period | TN (kg/Year) | Point Source Elimination (%) | TP (kg/Year) | Point Source Elimination (%) |
---|
Joe Pool | 580 | 1986–2007 | 129,885 | −7.3 | 31,339 | −1.0 |
Lavon | 1993 | 1968–2007 | 2,671,500 | −55.7 | 210,750 | −24.2 |
Lewisville | 2520 | 1968–2007 | 2,518,920 | −27.2 | 168,080 | −14.4 |
Ray Roberts | 1790 | 1987–2007 | 646,060 | −5.9 | 103,590 | −6.0 |
Ray Hubbard | 907 | 1968–2007 | 1,088,650 | −20.4 | 162,480 | −7.8 |
Benbrook | 1100 | 1970–2005 | 145,000 | −23.4 | 44,500 | −9.3 |
Bridgeport | 2849 | 1970–2007 | 113,000 | −13.0 | 74,000 | −2.9 |
Richland Chambers | 5157 | 1977–2006 | 4,011,580 | −4.0 | 285,104 | −0.3 |
Cedar Creek | 2600 | 1966–2002 | 1,419,380 | −3.8 | 188,670 | −6.0 |
Eagle Mountain | 2230 | 1971–2004 | 1,057,437 | −1.1 | 173,383 | −1.2 |
Table 7.
Increase of TN and TP by estimated population growth by 2030.
Table 7.
Increase of TN and TP by estimated population growth by 2030.
Watershed | Urban Area Increase (%) | Increase of TN (%) | Increase of TP (%) |
---|
Joe Pool | 17.0 | 16.4 | 37.4 |
Lavon | 9.0 | 9.2 | 14.4 |
Lewisville | 8.0 | 2.9 | 23.5 |
Ray Roberts | 5.0 | 4.7 | 16.6 |
Ray Hubbard | 2.0 | −2.6 | 3.2 |
Benbrook | 190.9 | 23.6 | 111.0 |
Bridgeport | 59.0 | 10.3 | 30.7 |
Richland Chambers | 1.2 | −2.6 | −1.1 |
Cedar Creek | 3.4 | 7.1 | 6.3 |
Eagle Mountain | 8.5 | 16.9 | 3.3 |
The limitation of the urbanization scenario is that the percentage of the urban increase is represented in the model only by sub-watershed, which means the same rate of urban increase was applied to the entire sub-watershed with no particular spatial location within the sub-watershed. Continuous records of measured data for nutrient loads are not available; data are also not up-to-date. The discontinued water-quality monitoring stations pose a challenging task for watershed evaluation.
4. Conclusions
The water quality in numerous reservoirs has been a concern in the Upper TRB including Dallas-Fort Worth Metro area. These 12 reservoirs in ten watersheds are sources of water supply for the area and increased population growth and heavy urbanization are expected in the future. Water quality trends, based on two decades of monitoring, in reservoirs is degrading and a continuation of water quality monitoring and comprehensive modeling was necessary.
Available datasets including GIS data, agricultural operations, WWTPs, USGS gage stations, sediment loadings, and monitored nutrient data were used to calibrate the SWAT model for local conditions and to estimate water quality. The model overviews the current condition in each watershed with an acceptable to good range of statistical model performance evaluation although some of watersheds showed large model errors (TP for Richland-Chambers at 80.6% error). The model prediction errors are mostly acceptable at this large scale, except for two out of five tributaries at the Eagle Mountain watershed and TP in Richland-Chambers.
Normalization for nutrient loadings by watershed area showed that some watersheds such as Lavon and Ray Hubbard generated an extremely high level of nutrients. It should be emphasized that future watershed management needs to focus on those watersheds. Barring potential management changes, such as wastewater treatment, improved crop use efficiency, etc., the urbanization scenario increased TN loads in each watershed ranged from −3% to 24% and increased TP loads ranged from −1% to 111%.
This study provides an overall estimate of the magnitude and spatial distribution of pollutants generated by overland flow as well as the amount actually delivered to the major reservoirs within the TRB. Although the SWAT model has been successfully applied in ungauged basins, thorough model calibration and validation increases the confidence in its applicability. Insufficient data, especially related to point sources and observed data on nutrients makes model calibration and validation a challenging task. Nevertheless, modeling studies using a comprehensive semi-distributed model such as SWAT do help simulate and assess the pollutant generation and transport potential of the landscape.