Analysis of PL-566 Reservoir Production Responses Along a Nutrient Loading Gradient

Transcription

1 Analysis of PL-566 Reservoir Production Responses Along a Nutrient Loading Gradient Richard L. Kiesling, Anne M.S. McFarland, and Larry M. Hauck TR0108 August 2001 Texas Institute for Applied Environmental Research Tarleton State University Box T0410 Tarleton Station Stephenville, Texas Fax

2 Analysis of PL-566 Reservoir Production Responses Along a Nutrient Loading Gradient Acknowledgements The United States Department of Agriculture Lake Waco-Bosque River Initiative supported this study with additional funding provided by the State of Texas. The authors wish to acknowledge the support and dedicated work of the many field personnel and laboratory chemists involved with the monitoring program. Special thanks are due Dr. Owen Lind and Dr. Laura Dávalos-Lind of Baylor University for their participation in the project. Mention of trade names or equipment manufacturers does not represent endorsement of these products or manufacturers by TIAER. Authors Richard Kiesling, Research Scientist, TIAER, kiesling@tiaer.tarleton.edu Anne McFarland, Research Scientist, TIAER, mcfarla@tiaer.tarleton.edu Larry Hauck, Assistant Director of Environmental Science, TIAER, hauck@tiaer.tarleton.edu USDA

3 Contents Chapter 1 Introduction Background Study Area Characteristics Chapter 2 Methods of Data Collection and Analysis General Data Collection Procedures: Tributaries General Data Collection Procedures: Reservoirs Data Management Procedures Time Frame Seasonality Diurnal Variation Left-Censored Data Derived Water Quality Variables Chapter 3 Summary of Previous Water Quality Analysis Summary of Comparisons Within and Between Sites Water Quality Assessment from Previous Reports Conclusions of Previous Reports Chapter 4 Updated Water Quality Analysis Microwatershed Tributary Sites Microwatershed Reservoir Sites Chapter 5 Nutrient Contributions to PL-566 Reservoirs Nutrient Loadings Regression Analyses Chapter 6 Mass Balance Models Application of the Two-Step Model Results of the Two-Step Model Alternative Modeling Approaches Chapter 7 Conclusion Reference List

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5 Tables Table 1 Morphometric data and subwatershed characteristics for nine PL-566 reservoirs Table 2 General sampling history for PL-566 reservoirs March 1991 through December Table 3 Description, abbreviations, and units of water quality constituents measured at sites Table 4 Analysis methods and method detection limits for water quality constituents Table 5 Summary of water quality analysis for microwatershed tributary sites Table 6 Summary of water quality analysis for PL-566 reservoir sites Table 7 Reservoir characteristics and annual loads for six PL-566 reservoirs sampled through Table 8 Vollenweider (1976) and Clasen (1980) model parameters and predicted total-p Table 9 PL-566 stepwise regression results of monthly CHLA values with nutrient concentrations

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7 Figures Figure 1 Bosque River watershed with the upper North Bosque River subwatershed Figure 2 Drainage area of nine PL-566 reservoirs within the upper North Bosque River watershed Figure 3 Rank order of annualized flow-weighted tributary total-p concentration by reservoir site Figure 4 Annual mean summer CHLA as a function of mean summer in-reservoir PO 4 -P Figure 5 Annual mean summer CHLA as a function of mean summer in-reservoir total-p Figure 6 Annual mean summer CHLA as a function of mean summer in-reservoir total-n Figure 7 Annual mean summer CHLA as a function of PO 4 -P from P-limited PL-566 reservoirs Figure 8 Annual mean summer CHLA as a function of total-p from P-limited PL-566 reservoirs Figure 9 Annual mean summer CHLA as a function of DIN from N-limited PL-566 reservoirs Figure 10 Annual mean summer CHLA as a function of total-n from N-limited PL-566 reservoirs Figure 11 Annual mean summer total-p concentration as a function of predicted total-p concentration.. 39 Figure 12 Annual mean summer CHLA concentration as a function of predicted total-p concentration.. 40 Figure 13 Annual mean summer CHLA concentration as a function of annual areal load of PO 4 -P Figure 14 Annual mean summer CHLA concentration as a function of annual volumetric load of PO 4 -P. 42 Figure 15 Annual mean summer CHLA concentration as a function of annual areal load of total-p Figure 16 Annual mean summer CHLA concentration as a function of annual volumetric load of total-p 43 7

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9 CHAPTER 1 Introduction Background Small, shallow reservoirs with intermittent inflow present water quality professionals with numerous factors to consider when devising management strategies. As reservoirs, these small impoundments are subject to rapid hydrologic changes that produce extreme variation in a number of physical and chemical limnological parameters over a short period of time (Kimmel et al., 1990). For example, total suspended solids (TSS) concentrations may increase rapidly, causing transparency and light penetration to suddenly decrease in the reservoir. Mass movement of water associated with advective currents may also dominate the mixing of water in these small reservoirs during periods of significant inflow. In addition to these sudden changes in physical properties, large amounts of organic carbon and particulate and dissolved nutrients may also be transported into the reservoir during rainfall events because of relatively short travel times and the limited opportunity for in-channel deposition or assimilation of particulates and dissolved nutrients. In addition to the complex and variable physical and chemical environment associated with small reservoirs, as shallow lakes, these reservoirs are also prone to individualistic biological responses. Several case studies reported in the literature have documented the development of unique macrophyte or phytoplankton plant communities associated with complex physical and chemical changes (see Sheffer, 1998, for a review). The general mechanisms contributing to these unique development sequences are well known. The difficulty lies in understanding how these mechanisms combine to produce the observed biologic development patterns. The physical and chemical changes produced by pulses of inflow mediate the biological community responses by affecting the magnitude of interaction between the phytoplankton and macrophytes present in the system. Low light levels associated with increases in TSS have the potential to alter the depth at which light limitation occurs for algae and submerged plants. High nutrient concentrations in the water and the sediments can stimulate the growth of algae or macrophytes and alter the balance between these two components of the primary producer community. Additionally, higher trophic levels such as fish can have even stronger effects on the community-level responses of the primary producers in the these reservoirs (Drenner, 1999; Vanni et al., 1997; Sheffer, 1998) by changing the physical and chemical environment in the reservoir. Water quality models are a traditional tool used to predict how the physical and biological components of reservoir ecosystems will respond to changes in water quality management. Water resource management relies on the ability of water quality models to simulate how these complex interactions will alter reservoir characteristics. As water quality management programs change and new policies are developed, some analysis of the expected benefits produced by these changes is often required. However, the unique characteristics of shallow, productive reservoirs present special problems for water quality models, especially for models that were designed to track the less dynamic behavior of natural lakes. 9

10 Analysis of PL-566 Production Responses Along a Nutrient Loading Gradient Wetzel (1990) has summarized how different the hydrology and ecology of reservoirs is from that of natural lakes. His summary suggests there are many reasons to question the application of lake models to reservoirs. Reservoirs are characterized by a high degree of variation in inflow, outflow, and water level relative to natural lakes. Hydrologic variation complicates and in some cases even violates lake model assumptions regarding residence time and biogeochemical cycling. Models based on statistical relationships derived from data sets that under-represent small reservoirs (e.g. Vollenweider, 1976; van Straten, 1986) may not be able to track reservoir responses to alternative management scenarios. The North Bosque River watershed of north central Texas contains a number of small floodwater detention reservoirs routinely monitored over the past few years by the Texas Institute for Applied Environmental Research (TIAER). Six of these reservoirs were actively monitored from September of 1993 through December of The land uses in the watersheds above these reservoirs are agricultural, but vary greatly in the intensity of agricultural practices, which results in a large gradient of nutrient loadings to the reservoirs. The primary objective of this report is to evaluate whether simple predictive models of algal biomass response to nutrient loading are able to accurately predict the range of responses observed in these small reservoirs, which are found in many areas of the country. Future reports will assess the specific nature of the biological responses including changes in phytoplankton community composition to nutrient loading or other water quality characteristics that vary between these reservoirs. Study Area Characteristics The headwaters of the North Bosque River, often referred to as the upper North Bosque River (UNBR) watershed, are located almost entirely within Erath county, the number one milk producing county in the state (USDA-ARS, 2001). The upper North Bosque River watershed encompasses about 93,200 ha (230,000 acres) and is primarily a rural area. The City of Stephenville (population about 15,000) is located in the upper third of the watershed, while a small portion of the City of Dublin (population about 3,500) is located along the western edge of the watershed. The watershed terminates at the City of Hico just inside the Hamilton county line (Figure 1). As of January 1995, there were 94 dairies located in the UNBR watershed with a combined milking herd size of nearly 34,000 cows (McFarland and Hauck, 1997). While dairying is the dominant agricultural practice in the watershed, other significant agricultural practices include the production of peanuts, range cattle, pecans, peaches, and forage hay (Dallas Morning News, 1993). As part of the State of Texas review of nonpoint source pollution, the Texas Natural Resource Conservation Commission (TNRCC) and the Texas State Soil and Water Conservation Board (TSSWCB) identified the North Bosque River in central Texas as nonpoint source impaired, because of concentrated animal feeding operations (TNRCC & TSSWCB, 1999). These reported findings have been supported by analysis of land use specific export coefficients (McFarland and Hauck, 1999, 2001). In an effort to document and investigate the responses of the North Bosque River watershed to these elevated nonpoint source nutrient loadings, TIAER has monitored water quality in the upper North Bosque River watershed since early 1991 (Nelson et al., 1992; Hauck et al., 1994; McFarland and Hauck, 1995a). Part of this monitoring program has entailed sampling several flood retardation reservoirs. A total of nine reservoirs with drainage basins reflecting a range of different land uses have been monitored for significant periods of time since mid These impoundments were constructed by the U.S. Department of Agriculture Soil 10

11 Chapter 1 Introduction Conservation Service (SCS, now the Natural Resources Conservation Service, NRCS) during the 1950s and 60s under Public Law 566 (PL-566). Figure 1 Bosque River watershed with the upper North Bosque River subwatershed outlined in bold. There are 40 of these flood retardation reservoirs in the study area and they are prominent hydrologic features in the watershed. The individual storage capacity of these reservoirs is generally less than 200 acre-feet (247,000 m 3 ) at pool elevation (elevation of the principal outlet), although temporary flood storage is typically up to 10 times larger. Reservoir surface area at normal pool elevation is typically 20 to 40 acres giving mean depths in the range 5 to 10 feet (Table 1). Collectively, these relatively small reservoirs have a significant impact on the hydrology of area streams and rivers. Approximately 56 percent of the total drainage area in the upper North Bosque River drains through PL-566 reservoirs (McFarland and Hauck, 1995a). 11

12 Analysis of PL-566 Production Responses Along a Nutrient Loading Gradient Table 1 Morphometric data and subwatershed characteristics for nine PL-566 reservoirs sampled by TIAER within the upper North Bosque River watershed. Site Reservoir Drainage Area (DA) in Acres Upstream Tributary Site Tributary DA in Acres Tributary / Reservoir DA Ratio Reservoir Capacity Volume (ac-ft) Reservoir Pool Volume (ac-ft) Reservoir Pool Surface Area (ac) Maximum Depth (ft) AL na a na na GC na na na IC IC NF NF NF NF SC SC SF SF SF SF SP SP GB GB GB GB a. na indicates not applicable. Upstream tributary sites were not monitored above PL-566 reservoirs AL030 and GC020. Besides acting as flood retardation structures, these reservoirs also reduce downstream sediment and nutrient loads by increasing water residence times and net particulate settling. When water levels are below the principal spillway, as is often the case, small runoff events may be totally captured by these reservoirs without any release (McFarland and Hauck, 1995b). This feature is particularly important because almost all of the creeks in the upper North Bosque River watershed are intermittent during the summer and early fall. For many of the PL-566 structures, the tributaries are seasonally intermittent. In extreme cases, stream flow is limited to a short runoff period following storm events. Consequently, sampling these intermittent stream systems is often very difficult because inflow patterns are very unpredictable. Runoff is dependent on antecedent and current rainfall conditions leading to the production of episodic stream flow. In this variable environment, the perennial PL-566 reservoirs become indicators of storm water quality because they integrate much of the impact from storm water runoff into their systems. PL-566 reservoirs are very common in Texas. Although originally constructed to retard surface runoff, these water bodies have developed a full range of residence aquatic life including a well-developed plankton (McFarland and Hauck, 1997) and benthic macroinvertebrate (Coan and Hauck, 1996) communities. In the UNBR watershed, PL-566 reservoirs are the first perennial water bodies to receive and assimilate nonpoint source runoff from upstream agricultural land uses associated with intensive animal husbandry (McFarland and Hauck, 1997, 1995b; Hauck et al., 1994). By virtue of their close proximity to dairies and manure application fields in the upper reaches of the UNBR watershed, PL-566 impoundments are susceptible to high levels of nutrient enrichment. In a limited number of cases, PL-566 watersheds (referred to as microwatersheds) have apparently responded to these high levels of nutrient loading by producing equally high levels of algal biomass. Observed algal biomass production is so high in some UNBR PL-566 reservoirs, they have been classified as hypereutrophic (McFarland and Hauck, 1997). As dysfunctional ecosystems, these overly productive reservoirs represent a challenge to water resource managers. Meeting this challenge requires the development, testing, and implementation of appropriate assessment tools including predictive models of reservoir productivity. Historically, the reservoirs monitored by TIAER are located in different subwatersheds of the upper North Bosque River watershed (Figure 2). These reservoirs include AL030 on Alarm 12

13 Chapter 1 Introduction Creek, GC020 on Green Creek, GB030 on Goose Branch, IC030 on Indian Creek, NF030 on the North Fork, SC030 on Sims Creek, SF030 and SF060 on the South Fork, and SP030 on Spring Creek. Each site is labeled using a five-digit alphanumeric format common to all TIAER sampling sites. The first two digits specify the tributary or river where the site is located, while the last three digits indicate the relative location of the site on the North Bosque River. Lower numeric values indicate sites nearer the headwaters, while larger numeric values indicate sites further downstream. Sampling has been discontinued on the Alarm Creek (AL030) and Green Creek (GC020) reservoirs as well as on SF060 located on the South Fork of the UNBR, leaving six reservoirs sampled through the end of our study period: GB030, IC030, NF030, SC030, SF030, SP030. Data from all nine reservoirs are used in the current analysis when appropriate, but the analysis of nutrient loading was limited to the six reservoirs listed in Table 2. Figure 2 Drainage area of nine PL-566 reservoirs within the upper North Bosque River watershed sampled by TIAER.. 13

14 Analysis of PL-566 Production Responses Along a Nutrient Loading Gradient Table 2 General sampling history for PL-566 reservoirs March 1991 through December Each x indicates the number of depths sampled in a given month, while C indicates samples that were composited across depths prior to laboratory analyses. By late 1998, persistent drought conditions had limited the number of samples that could be taken with depth at GB030 and SF030. Month Year GB030 IC030 NF030 SC030 SF030 SP030 Mar 1991 xxx xxx Apr xxxc xxxc May xxxc xxxc June xxx xxx July xxx xxx Aug xxx xxx Sept xxx xxx Oct xxx xxx Nov xxx xxx Dec xxx xxx Jan 1992 xxx xxx Feb x xxx Mar xxx xxx Apr xxx xxx May xxx xxx June xxx xxx July xxx xxx Aug Sept xxx xxx Oct xxx xxx Nov xxx xxx Dec xxx xxx Jan 1993 xxx xxx Feb xxx xxx Mar xxx xxx Apr xxx xxx May xxx xxx June xxx xxx July xxx xxx Aug xxx xxx xxx xxx xxx Sept xxx xxx xxx xxx xxx Oct xxx xxx xxx xxx xxx Nov xxx xxx xxx xxx xxx Dec xxx xxx xxx xxx xxx Jan 1994 xxx xxx xxx xxx xxx Feb xxx xxx xxx xxx xxx Mar xxx xxx xxx xxx xxx Apr xxx xxx xxx xxx xxx May xxx xxx xxx xxx xxx June xxx xxx xxx xxx xxx July xxx xxx xxx xxx xxx Aug xxx xxx xxx xxx xxx Sept xxx xxx xxx xxx xxx Oct xxx xxx xxx xxx xxx Nov xxx xxx xxx xxx xxx Dec xxx xxx xxx xxx xxx Jan 1995 xxx xxx xxx xxx xxx Feb xxx xxx xxx xxx xxx Mar xxx xxx xxx xxx xxx 14

15 Chapter 1 Introduction Table 2 General sampling history for PL-566 reservoirs March 1991 through December (continued) Each x indicates the number of depths sampled in a given month, while C indicates samples that were composited across depths prior to laboratory analyses. By late 1998, persistent drought conditions had limited the number of samples that could be taken with depth at GB030 and SF030. Month Year GB030 IC030 NF030 SC030 SF030 SP030 Apr xxx xxx xxx xxx xxx May xxx xxx xxx xxx xxx June xxx xxx xxx xxx xxx July xxx xxx xxx xxx xxx Aug xxx xxx xxx xxx xxx Sept xxx xxx xxx xxx xxx Oct xxx xxx xxx xxx xxx Nov xxx xxx xxx xxx xxx Dec xxx xxx xxx xxx xxx Jan 1996 xxx xxx xxx xxx xxx Feb xxx xxx xxx xxx xxx Mar xxx xxx xxx xxx xxx Apr xxx xxx xxx xxx xxx May xxx xxx xxx xxx xxx June xxx xxx xxx xxx xxx July xxx xxx xxx xxx xxx Aug xxx xxx xxx xxx xxx Sept xxx xxx xxx xxx xxx xxx Oct xxx xxx xxx xxx xxx xxx Nov xxx xxx xxx xxx xxx xxx Dec xxx xxx xxx xxx xxx xxx Jan 1997 xxx xxx xxx xxx xxx xxx Feb xxx xxx xxx xxx xxx xxx Mar xxx xxx xxx xxx xxx xxx Apr xxx xxx xxx xxx xxx xxx May xxx xxx xxx xxx xxx xxx June xxx xxx xxx xxx xxx xxx July xxx xxx xxx xxx xxx xxx Aug xxx xxx xxx xxx xxx xxx Sept xxx xxx xxx xxx xxx xxx Oct xxx xxx xxx xxx xxx xxx Nov xxx xxx xxx xxx xxx xxx Dec xxx xxx xxx xxx xxx xxx Jan 1998 xxx xxx xxx xxx xxx xxx Feb xxx xxx xxx xxx xxx xxx Mar xxx xxx xxx xxx xxx xxx Apr xxx xxx xxx xxx xxx xxx May xxx xxx xxx xxx xxx xxx June xxx xxx xxx xxx xxx xxx July xxx xxx xxx xxx xxx xxx Aug xxx xxx xxx xxx xxx xxx Sept xxx xxx xxx xxx xxx xxx Oct xx xx xxx xxx xxx xxx Nov xxx xxx xx xxx x xxx Dec x xxx xxx xxx x xxx 15

16 Analysis of PL-566 Production Responses Along a Nutrient Loading Gradient Each of the reservoir sites had associated tributary monitoring sites. Grab samples as well as automated storm samples exist for ten upstream sites associated with six of the reservoirs in this study (Figure 2). Three sites were located in the Goose Branch watershed (GB020, GB025, GB040) above reservoir GB030, three in the North Fork (NF005, NF009, NF020) above reservoir NF030, and one site each in Indian Creek (IC020) above reservoir IC030, Sims Creek (SC020) above SC030, South Fork (SF020) above reservoir SF030, and Spring Creek (SP020) above reservoir SP030. Automated storm flow sampling was discontinued for sites in Indian, Sims, and Spring Creek watersheds at the end of February1998, but bi-weekly grabs samples were collected through the end of the report period. The general physical characteristics of these six reservoirs as well as Alarm Creek (AL030) and Greens Creek (GC020) reservoirs are presented in Table 1 as outlined from the original reservoir design specifications and construction documentation by the SCS and as reported in McFarland and Hauck (1997) with the addition of GB030. The maximum depth was estimated from sampling data collected near the spillway outlet at each reservoir. The drainage area associated with each reservoir was delineated using a geographic information system (GIS) topography layer (Figure 2 and Table 1). 16

17 CHAPTER 2 Methods of Data Collection and Analysis General Data Collection Procedures: Tributaries Sampling methods, sampling frequency, and site descriptions for all of the PL-566 tributary sites (referred to as microwatershed sites) have been previously reported in the semiannual water quality report for the study period (e.g., Pearson and McFarland, 1999). Grab samples as well as automated storm samples exist for the ten upstream sites associated with the six reservoirs in this study. Three sites are located in the Goose Branch watershed, three in the North Fork, and one site each in Indian Creek, Sims Creek, South Fork, and Spring Creek watersheds. In general, water level data and stage-discharge relationships were used in conjunction with storm water and grab sample constituent analysis to calculate daily, monthly, and annual loads to the PL-566 reservoirs (see Table 1). In most cases, discharge was calculated by combining site-specific water level data with the stage-discharge equation for the site. The resulting flow data were then combined with constituent concentrations from storm and grab samples to calculate cumulative nutrient loading at each tributary site listed in Table 1. A more detailed description of the methodology used to make these calculations has been published in a TIAER report by McFarland and Hauck (1999). Where discharge data were available, load estimates were calculated through November of Not all load estimates available for the tributary sites listed in Table 1 were used in the application of reservoir models. Differences in the period of record and other considerations limited the application of reservoir nutrient loading models to the six sites listed in Table 2. General Data Collection Procedures: Reservoirs Monthly water quality samples were collected at the deepest point near the principal spillway of each reservoir. Samples were taken 1 foot (0.3 meters) below the surface, mid-depth, and 1 foot (0.3 meters) above the bottom of the reservoir. Grab samples were retrieved at all three depths using an alpha-style horizontal sampler and transferred to clean, polyethylene containers. A separate water sample was taken at the top depth for analysis of chlorophyll-α (CHLA). Dissolved oxygen, ph, specific conductance, and water temperature were measured in situ using Hydrolab field sampling equipment. Secchi disc depth (ZSD) was also routinely measured at each reservoir. All monitoring efforts between May 1993 and December 1998 were conducted under an approved quality assurance project plan (QAPP) or its amendments. A variety of physical and chemical constituents were measured at each site. A general outline of the water quality constituents measured, the abbreviations used in this report, and the units of measurements is provided in Table 3. The methods of analysis and the method detection limit (MDL), for the study period, are listed in Table 4. 17

18 Analysis of PL-566 Reservoir Production Responses Along a Nutrient Loading Gradient Table 3 Description, abbreviations, and units of water quality constituents measured at sites in the Bosque River watershed. Constituent Abbreviation Units Description Ammonia-nitrogen NH 3 -N mg/l Inorganic form of nitrogen that is readily soluble and available for plant uptake. Elevated levels are toxic to many fish species. Chlorophyll-α CHLA µgm/l Indicator of phytoplankton biomass. Dissolved oxygen DO mg/l Indicator of the amount of oxygen available in the water for biological and chemical reactions. Nitrite-nitrogen + nitratenitrogen NO 2 -N + NO 3 -N mg/l Inorganic forms of nitrogen. Products in the conversion of N from the ammonia form under aerobic conditions. Orthophosphate-phosphorus PO 4 -P mg/l Inorganic form of phosphorus that is readily soluble and available for plant uptake. Soluble reactive phosphorus (SRP) is another term for this constituent. Hydrogen ion activity Total Kjeldahl nitrogen ph standard units Measures the hydrogen ion activity in a water sample as an indication of acidity/baseness. TKN mg/l Organic and ammonia forms of nitrogen are included in TKN. Total phosphorus total-p mg/l Represents both organic and inorganic forms of phosphorus. Table 4 Analysis methods and method detection limits for water quality constituents. Constituent Method Estimated MDL a Field Measurements Conductivity SM b 2510B 10 µmhos/cm Dissolved oxygen EPA c mg/l ph EPA units Water temperature EPA C Secchi depth TNRCC d not applicable Laboratory Measurements Ammonia-nitrogen EPA mg/l Chlorophyll-α SM 10200H 1-25 µg/l Nitrite-nitrogen + nitrate-nitrogen EPA mg/l Total Kjeldahl nitrogen EPA mg/l Orthophosphate-phosphorus EPA mg/l Total phosphorus EPA mg/l Total suspended solids EPA mg/l a. MDLs are periodically updated by TIAER s laboratory. A range is presented if more than one MDL was used over the monitoring period (01 Sep Dec 99). b. SM refers to Standard Methods for the Examination of Water and Wastewater, 18th Edition (APHA, 1992). c. EPA refers to Methods of Chemical Analysis of Water and Wastes (USEPA, 1983). d.tnrcc refers to TNRCC Surface Water Quality Monitoring Procedures Manual, (TNRCC, 1999). Data Management Procedures Time Frame Water quality monitoring by TIAER in the UNBR watershed has occurred under a number of different projects and, thus, sampling frequency and constituents sampled have varied from site to site over time. For example, routine monthly grab sampling was initiated at NF030 and SF030 in March 1991, while sampling at IC030, SC030, and SP030 was not initiated until August 1993 (Table 2). GB030 sampling started in May of Due to shifting priorities with different projects, some gaps appear in the database, but a consistent sampling history exists, 18

19 Chapter 2 Methods of Data Collection and Analysis between August 1993 and December 1998, for the six reservoirs listed in Table 2 with the exception of GB030, which was included in the study because of its high levels of algal biomass. Most water quality constituents were measured consistently at each of the sites in Table 2 throughout this period. Some exceptions in the database include data for TKN and total-p. TKN and total-p were measured consistently only at sites IC030, NF030, SF030, and SP030 after August Measurements of TKN and total-p were initiated at SC030 and SF060 in January 1995 and at GB030 when it was added as a site. No measurements of TKN and total-p were taken at GC020, and only a limited number of measurements of TKN or total-p were taken at AL030. Previous comparisons between sites for TKN and total-p were evaluated using data collected between August 1993 and August 1995 (e.g., McFarland and Hauck, 1997). For this time frame, only sites IC030, NF030, SF030, and SP030 were included in the TKN and total-p analyses, although within site comparisons were conducted for sites SC030 and SF060 for these constituents (McFarland and Hauck, 1997). Seasonality To account for seasonal variability in water quality, a summer and winter season were recognized. In a past study, four seasons were used in the evaluation (spring, summer, fall, and winter; McFarland and Hauck, 1995a). For the most recent study by McFarland and Hauck (1997), shifts in the timing of spring warm-up and cooling temperatures in the fall from year to year made it difficult to clearly define four seasons over the study period. However, two seasons seemed very pronounced and most analyses were stratified by season into winter (November through April) and summer (May through October) periods. This grouping, based on water temperature, has been preserved in this report for the independent evaluation of water quality data relative to TNRCC screening criteria. However, in the analysis of algal biomass response to nutrient loading the summer season has been shifted by one month to cover the months April through September. The purpose of this shift is to better approximate the algal growing season by including the critical spring growth phase common to reservoirs in central Texas (e.g., Sterner, 1994). Seasonality defined by the phenology of the algae differs somewhat from the seasonality defined by weather, including water temperature (Marshall and Peters, 1989). Diurnal Variation Diurnal variation was not specifically addressed in this study or in previous reports on PL-566 reservoirs. To minimize complications due to diurnal variations in reservoir water quality, an attempt was made to collect all monthly grab samples at approximately the same time of day. Monthly sampling generally occurred over a two-day period with samples collected at four reservoirs each day to minimize travel time and distances between sites when eight reservoirs were being sampled. Once the number of reservoirs was reduced to six, the sampling was completed in less than six hours. Most samples were collected between 9 am and 3 pm. Although diurnal variations are expected to influence some water quality constituents, particularly DO and ph, no attempts were made to adjust any of the data for this factor. Left-Censored Data Left-censored data refers to values less than the laboratory method detection limit (MDL). The MDLs associated with each constituent (Table 4) are reported on a semiannual basis in our 19

20 Analysis of PL-566 Reservoir Production Responses Along a Nutrient Loading Gradient data reports to the Texas Natural Resource Conservation Commission (TNRCC). The following rule was used in dealing with data below the associated MDL: Values determined to be less than the laboratory MDL (i.e., left-censored) were entered into the database as one-half the MDL as recommended by Gilliom and Helsel (1986) and Ward et al. (1988). Constituents with left-censored values included NH 3 -N, NO 2 -N+NO 3 - N, TKN, PO 4 -P, and total-p. Derived Water Quality Variables The following derived water quality variables were included as part of the statistical analysis: Total nitrogen (total-n) = (TKN) + (NO 3 -N) + (NO 2 -N), Organic-nitrogen (organic-n) = (TKN) - (NH 3 -N), Dissolved inorganic-nitrogen (DIN) = (NH 3 -N) + (NO 2 -N) + (NO 3 -N), and Total suspended solids derived from turbidity estimates (TSS d ). Since sampling at the various reservoir sites was initiated for different studies, either TSS or turbidity were measured at each site as an indicator of water clarity, although never both during a given month (McFarland and Hauck, 1997). Before 1995, TSS was generally measured at IC030, NF030, SF030, and SP030, while turbidity was generally measured at AL030, GC020, SC030, and SF060. To make use of these data in a general comparison between reservoir sites, a regression relationship between TSS and turbidity was developed by McFarland and Hauck (1997). The following equation was developed and used to convert turbidity values to TSS values for reservoir sites: 1) TSS d = exp( ( ln( turbidity) )) where R 2 =0.68 TSS d = derived TSS value in mg/l and turbidity = turbidity measurement in NTU. This equation was developed using 324 samples collected primarily at stream sites representing both storm and base flow samples. While this equation does not explain all the variability between turbidity and TSS measurements, it does provide a conversion factor, which allows a general comparison of water clarity between reservoirs. Laboratory analyses were adjusted in 1995 to include TSS as part of the water quality analysis for all reservoir samples. 20

21 CHAPTER 3 Summary of Previous Water Quality Analysis McFarland and Hauck (1997) completed a statistical analysis of water quality data collected through 1995 from eight of the PL-566 reservoirs. Of the nine reservoirs listed in Table 1, only GB030 was excluded from their analysis. Significant differences within and between sites were seen in a number of physical and chemical parameters. The most important result uncovered in McFarland and Hauck (1997) was the significant relationship between land-use patterns in the reservoir watersheds and the average in-reservoir concentration of nutrients such as PO 4 - P. Land use was also able to predict the average algal biomass in these reservoirs. These results suggest that a cause and effect relationship might exist between nutrients derived from specific land-use patterns and increases in algal biomass. Summary of Comparisons Within and Between Sites McFarland and Hauck (1997) documented poorer water quality at sites AL030, IC030, NF030, and SF060 for all measured constituents. Sites GC020, SC030, SF030, and SP030 were found to generally reflect conditions of higher water quality than GC020, SC030, SF030, and SP030 for CHLA, TKN, and total-n during both seasons. Summer conductivity and TSS d values followed the same pattern. Less significant relationships were observed during both seasons for ZSD, PO 4 -P, and total-p, although limited data were available for the analysis of total-p. Water temperature was the only physical factor that failed to show a trend towards significant differences between sites. Based on McFarland and Hauck (1997), reservoir depth, land use in the watershed, and season were the main factors producing statistically significant differences in water quality between sites. Deeper reservoirs generally had lower summer DO values at the middle and bottom depths than shallower reservoirs. Values for ph were also generally lower at the bottom depth of deeper reservoirs than shallower reservoirs. NH 3 -N and DIN values also increased with depth in some of the deep, productive reservoirs. For example, a dramatic increase in NH 3 -N and DIN values with depth was observed at IC030 when DO concentrations were low. Despite similar reservoir morphometry and DO and ph values at depth, no pattern of increasing NH 3 - N or DIN was found for SC030. These contradictory observations suggest that an interreservoir study of in-reservoir algal productivity and in-reservoir sediment-oxygen demand is necessary to understand this phenomenon. Trends in some water quality parameters corresponded to differences in land use above the respective reservoir sites. Reservoirs having watersheds with large amounts of land devoted to dairy waste application fields (WAFs) or with high numbers of dairy cows per acre were much more likely to have high PO 4 -P concentrations, as well as elevated algal biomass measured as CHLA. Unlike the analysis of other water quality parameters, these relationships were not heavily influenced by season. Average PO 4 -P concentrations were positively correlated with the proportional area of WAFs, as well as intensive agriculture with little 21

22 Analysis of PL-566 Reservoir Production Responses Anong a Nutrient Loading Gradient seasonal effect. The proportion of land area with soils from hydrologic group C was also an important correlate of PO 4 -P concentrations. However, there were no significant correlations between soil group C and any of the land-use categories. Average CHLA also varied as a function of land use. Season had a stronger effect on the relative strength of the relationship between CHLA and the proportional agricultural land use. Conversely, the extent of rangeland in the watershed was negatively correlated with CHLA algal biomass. Relationships between increased in-stream and reservoir nutrient concentrations and elevated reservoir CHLA levels were not explored by McFarland and Hauck, but they are an important biological consideration in lentic aquatic environments and the focus of this report. Resourcebased phytoplankton growth models predict a cause and effect relationship between the availability of limiting nutrients and algal biomass (Tilman, 1982; Grover, 1997). If phosphorus is the limiting nutrient in these PL-566 reservoirs (Dávalos-Lind and Lind, 1999), increases in phosphorus supply to the reservoirs are predicted to increase algal biomass. This prediction provides the foundation for a statistical analysis of the relationship between nutrient loadings from the surrounding watersheds and algal biomass in the reservoirs measured as CHLA. Water Quality Assessment from Previous Reports The initial assessment of reservoir water quality performed by McFarland and Hauck (1997) for data through 1995 was based on TNRCC screening levels and criteria from 1994 (TNRCC, 1994). Using these criteria, several potential water quality problems were identified for some of the reservoirs listed in Table 1. For DO, none of the reservoir sites indicated average values lower than the 5.0 mg/l assessment criterion. However, mean ph levels at the surface of reservoirs IC030, NF030, and SF060, during the summer, exceeded the upper criterion level of 9.0, while measurements of potential concern were also observed at GC020 and SP030. Average CHLA values at reservoirs AL030, IC030, NF030, and SF060 also exceeded the screening level of 30 µg/l during both the summer and winter seasons. Based on the percent of samples exceeding the screening level, measurements of concern were also indicated at SC030 and SF030 for CHLA, during the summer. Data from GB030 were not included in this analysis because sampling did not commence at this reservoir until early Nutrients were highly variable in their relationships with the screening criteria. For nitrogen constituents, average values for NH 3 -N, NO 3 -N, and total-n did not exceed the screening criteria. In contrast to nitrogen, average PO 4 -P values at AL040, IC030, NF030, and SF060 exceeded the TNRCC s 1994 screening level of 0.1 mg/l during both the summer and winter seasons. Total-P levels were evaluated only at sites IC030, NF030, SF060, and SP030 due to limited data. All measurements of total-p at IC030 and NF030 exceeded the screening level of 0.2 mg/l. The nitrogen to phosphorus ratio was also evaluated as an indicator of potential nutrient limitation. Total-N to total-p ratios were evaluated based on guidelines presented by Thomann and Mueller (1987) to determine whether nitrogen or phosphorus was more limiting to plant and algae growth. Of the four reservoirs for which total-p data were available, SP030 appeared to be phosphorus limited according to this criterion. This conclusion is consistent with the result of algal growth potential bioassays performed in 1997 and 1998 (Dávalos-Lind and Lind, 1999). NF030 and SF060 exhibited a summer N-to-P ratio indicative of potential dominance by blue-green algae (Cyanobacteria). Algal growth bioassays conducted in NF030 in 1997 and 1998 indicated the reservoir was nitrogen limited 22

23 Chapter 3 Summary of Previous Water Quality Analysis (Dávalos-Lind and Lind, 1999), consistent with the hypothesis that this reservoir had a high potential for developing significant blue-green algae populations. Trophic state indices (TSIs) were used as indicators of productivity in each reservoir. TSI values during the summer indicated that reservoirs AL030, IC030, NF030, and SF060 were more productive than reservoirs GC020, SC030, SF030, and SP030. All three TSI indices for IC030, SF060, and NF030 indicated hypereutrophic production levels during both the summer and winter. Conclusions of Previous Reports The prominence of PL-566 structures in the upper North Bosque River watershed makes them an important feature in evaluating overall water quality within the watershed. Of the eight PL-566 reservoirs evaluated by McFarland and Hauck (1997), at least four of these reservoirs (AL030, IC030, NF030, and SF060) were negatively impacted by accelerated eutrophication. Nitrogen, rather than phosphorus, was identified as the potential limiting nutrient in these impacted reservoirs based upon total-n to total-p ratios. Dairy WAFs appeared to be the dominant source of these nutrient loadings based on relationships of land use and soil characteristics with reservoir water quality. 23

24 Analysis of PL-566 Reservoir Production Responses Anong a Nutrient Loading Gradient 24

25 CHAPTER 4 Updated Water Quality Analysis The initial assessment of water quality performed by McFarland and Hauck (1997) covered data collected by TIAER through 1995 and included comparisons based on TNRCC screening levels from 1994 (TNRCC, 1994). We repeated the analysis of water quality data by expanding the time frame of the analysis to include all data collected from the six PL-566 reservoirs sampled through 1998 (Table 2). Water quality data collected between July of 1993 and June of 1998 were compared to the 1998 TNRCC screening levels using the TNRCC methodology (TNRCC, 1998). Details of our application of the TNRCC screening methodology may be found in Pearson and McFarland (1999). Microwatershed Tributary Sites Tributary water quality data were compared to State of Texas water quality criteria or TNRCC screening levels as of 1998 (Table 5). Monitoring data for DO, ph, NO 2 -N + NO 3 -N, NH 3 -N, PO 4 -P, and total-p were all evaluated for the frequency with which they exceeded the appropriate screening level or criterion. Following the TNRCC standard guidance, the frequency of these exceedences was used to classify tributaries into three categories: fully supporting, partially supporting, or not supporting water quality standards. Table 5 Summary of water quality analysis for microwatershed tributary sites. Shaded values indicate not supporting or concern with greater than 25 percent of samples in exceedance of TNRCC criteria or screening levels. Constituent and Associated Screening Level or Criteria Size Sample Type DO <2.0 mg/l ph <6.5 or >9.0 NO 2 -N+ NO 3 -N >3.1 mg/l NH 3 -N >0.3 mg/l PO 4 -P >1.4 mg/l Total-P >1.6 mg/l %WAF GB020 routine 23% 0% 5% 100% 52% 86% 41% n storm flow 16% 79% 69% 87% n GB025 routine 0% 0% 19% 53% 53% 69% 49% n storm flow 4% 61% 63% 83% n GB040 routine 3% 0% 85% 60% 11% 17% 60% n storm flow 72% 88% 58% 73% n

26 Analysis of PL-566 Reservoir Production Responses Along a Nutrient Loading Gradient Table 5 Summary of water quality analysis for microwatershed tributary sites. (continued) Shaded values indicate not supporting or concern with greater than 25 percent of samples in exceedance of TNRCC criteria or screening levels. Constituent and Associated Screening Level or Criteria Size Sample Type DO <2.0 mg/l ph <6.5 or >9.0 NO 2 -N+ NO 3 -N >3.1 mg/l NH 3 -N >0.3 mg/l PO 4 -P >1.4 mg/l Total-P >1.6 mg/l %WAF IC020 routine 2% 0% 22% 13% 3% 3% 17% n storm flow 5% 32% 9% 17% n NF005 routine 0% 0% 5% 27% 36% 41% 42% n storm flow 14% 64% 69% 86% n NF009 routine 5% 0% 1% 13% 1% 4% 3.4% n storm flow 4% 33% 0% 6% n NF020 routine 0% 0% 5% 27% 29% 40% 45% n storm flow 8% 64% 56% 74% n SC020 routine 0% 0% 0% 7% 0% 1% 6% n storm flow 0% 12% 0% 2% n SF020 routine 0% 0% 0% 6% 0% 0% 0% n storm flow 0% 5% 0% 1% n SF050 routine 4% 0% 0% 21% 2% 6% 16% n storm flow 4% 40% 10% 20% n SP020 routine 0% 0% 0% 2% 0% 0% 0% n storm flow 0% 4% 0% 0% n Several tributary sites that drain the watersheds upstream of the PL-566 reservoirs exceeded the 1998 water quality screening criteria adopted by the TNRCC for a number of water quality parameters (Table 5). The Goose Branch tributaries sites (GB020, GB025, and GB040) showed concentrations of concern for NH 3 -N, PO 4 -P, and total-p for grab and/or storm water samples. Two of the three tributaries draining the North Fork microwatershed (NF005, NF020) also showed concern for these same constituents. The subwatersheds associated with the GB020, GB025, NF005, and NF020 tributary sites all had more than 40 percent WAFs. These results are consistent with the previous analysis of existing nutrient sources in the Bosque River watershed (McFarland and Hauck, 1999). McFarland and Hauck identified WAFs as a 26

27 Chapter 4 Updated Water Quality Analysis major source of nutrient export to surface waters with a disproportionately high contribution to total in-stream load relative to their percent of total land area. In contrast to these results, the tributary sites upstream of the Sims Creek (SC030), Spring Creek (SP030), and South Fork (SF030) reservoirs had very few water quality problems. All three of the subwatersheds defined by the tributary sample sites have little (6 percent for SC020) or no WAF acreage (0 percent for SP020 and SF020). The only nutrient water quality concern at any of these sites was in the Sims Creek drainage at SC020. Storm water samples collected at SC020 produced a potential water quality concern for NH 3 -N at this site with more than 10 percent of samples exceeding the screening level. The land use in this subwatershed was dominated by woodlands and range (74 percent) with some pasture (12 percent) and very few WAFs (6 percent). Water quality problems at site NF009 in the North Fork microwatershed were similar to those observed at SC020, only more severe. Storm water samples collected at NF009 failed to meet screening levels for NH 3 -N indicating a water quality concern. In contrast to other subwatersheds in the North Fork drainage, land use in the NF009 subwatershed was over 58 percent woodlands and range; however, there was a significant amount of pasture (27 percent) with a small percentage of WAFs (3 percent). An intermediate decline in water quality was detected at two other tributary sites. Subwatersheds IC020 and SF050, with 17 and 16 percent WAFs respectively, had fewer nutrient water quality standard failures than subwatersheds GB020, GB025, NF005, and NF020, which all have over 40 percent WAFs. Only NH 3 -N in storm water consistently exceeded the TNRCC screening level for IC020 and SF050, although total-p was also a potential concern in storm flow samples at IC020 and SF050. The general pattern that emerges from the exceedence frequency analysis is a linkage between land use and water quality degradation. The result of this analysis of the water quality condition of tributary sites is consistent with previous analyses of land-use contributions to nutrient runoff. Microwatershed Reservoir Sites Water quality data collected from the six PL-566 reservoirs actively sampled through 1998 (Table 2) were compared to State of Texas water quality criteria or TNRCC screening levels as of 1998 (Table 6). As with the tributary sites, monitoring data were evaluated for the frequency with which they exceeded the screening level or criterion. Chlorophyll-α (CHLA), was added to the evaluation of reservoir data. Following the TNRCC standard guidance, the frequency of these exceedences was again used to classify reservoirs into three categories: fully supporting, partially supporting, or not supporting water quality standards. Of the six reservoir sites, Goose Branch (GB030) and North Fork (NF030) reservoirs had the highest exceedence probabilities for CHLA (96 percent and 95 percent) followed closely by Indian Creek reservoir (IC030) at 93 percent. These three reservoirs also failed to support the screening levels for NH 3 -N, PO 4 -P, and total-p, and Goose Branch also failed to support the NO 2 -N + NO 3 -N screening level. The remaining reservoirs on Sims Creek (SC030), South Fork (SF030), and Spring Creek (SO030) had fewer exceedences, but all of these sites had at least one potential water quality concern. Both the Sims Creek and South Fork reservoirs failed to meet the water quality screening levels for CHLA, and total-p was also a potential water quality concern. Spring Creek was the only reservoir with partially supporting CHLA levels, indicating that algal biomass was a potential concern for this reservoir. 27