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HomeMy WebLinkAboutDWQ-2024-004536Project Orientation & Technical Support Document Overview Science Panel Meeting | May 18, 2023 Note: all information and data presented are considered draft, in-process material ULWQS Ongoing and Upcoming Activities •Technical Support Document ▪Theme: link management goals to response conditions to nutrients ▪Reference analysis, stressor-response analysis, literature evidence •SP NNC Recommendation ▪Theme: translate TSD into protective nutrient ranges, with uncertainty & report to SC •SC Charge Questions Responses ▪Theme: describe the complex limnology of Utah Lake ▪Draft 1 completed previously ð look toward updating responses as new studies are completed •Implementation Planning ▪Theme: Determine how NNC will be achieved with the “levers” available ▪Takes into account source partitioning, cost & feasibility, timelines Note: all information and data presented are considered draft, in-process material Available Lines of Evidence Lines of evidence will be used differently across portions of the project Note: all information and data presented are considered draft, in-process material Technical Support Document: Reference Technical Support Document: S-R Analysis Technical Support Document: Literature SC Charge Questions Responses Implementation Planning ULWQS Studies Empirical S-R Modeling Watershed mechanistic model Lake mechanistic model Literature Pathway to Criteria (ULWQS Technical Framework) •SP recommendation ð SC recommendation ð Utah Lake Authority endorsement •Regulatory process ð multiple other groups involved with the process ▪DWQ Water Quality Standards workgroup ▪Legislature ▪EPA Note: all information and data presented are considered draft, in-process material •Provide the technical basis for the development of numeric nutrient criteria (NNC) to protect designated uses •Recreation •Aquatic Life •Others (Agriculture, Downstream) •Conduct analyses to support multiple lines of evidence in the NNC framework Purpose of the Technical Support Document Note: all information and data presented are considered draft, in-process material Lines of Evidence 1.Reference-based ▪Results from paleolimnological studies ▪Utah Lake Nutrient Model prediction/extrapolation of reference conditions 2.Stressor-response analysis ▪Utah Lake Nutrient Model output ▪Statistical models 3.Scientific literature ▪Scientific studies of comparable/related lake ecosystems ▪Support/supplement other lines of evidence Note: all information and data presented are considered draft, in-process material Weight of Evidence •Ranges of nutrients deemed protective of uses across lines of evidence •How to distill these lines of evidence into a recommendation? ▪Statistical distributions of endpoints ▪Interpret endpoints in the context of their uncertainty ð weigh lines against each other by their relevance, strength, and reliability Note: all information and data presented are considered draft, in-process material Questions and Discussion Note: all information and data presented are considered draft, in-process material Stressor-Response Analysis Updates Science Panel Meeting | May 18, 2023 Note: all information and data presented are considered draft, in-process material •Discuss details of more complex decisions o Period of analysis o Lake Regions o Phytoplankton o Zooplankton •Present initial draft of S-R models •Discuss Tt recommendations for S-R next steps Agenda Note: all information and data presented are considered draft, in-process material Period of Analysis •Recap: tradeoffs for using all available data vs. recent data ▪All available data: larger sample sizes and potentially larger gradient ▪Recent data: consistent analytical methods, more intensive sampling ▪Only relevant for constituents collected over a long period of time •Goal for S-R analysis is not to characterize recent condition of the lake but rather how the lake behaves Note: all information and data presented are considered draft, in-process material Period of Analysis •History of TP analytical methods ▪Pre-2000: method details unknown ▪2000: change to EPA 365.1, hot plate digestion, Alchem instrument, MRL = 0.020 mg/L ▪2009: change to Lachat instrument ▪2011: change to autoclave digestion, MRL = 0.003 mg/L (2 calibration curves) ▪2016: J flag created to report values between MDL and MRL •Other constituents have had more consistent methods over time, and we have fewer issues with samples < detection limit in Utah Lake Note: all information and data presented are considered draft, in-process material Period of Analysis •Distributions pre- and post-2015 are not substantially different •Very few TP non- detects in either period (n = 3) •TN was not sampled extensively prior to 2015 Note: all information and data presented are considered draft, in-process material Period of Analysis Exploratory visualization of bivariate relationships: Note: all information and data presented are considered draft, in-process material •Tt recommendation: include entire period of record •SP discussion and support/non-support for this recommendation? Decision Point: Period of Analysis Note: all information and data presented are considered draft, in-process material Aggregation Considerations •Utah Lake assessed according to the state’s CALM ▪Uses must be met in all sampled locations ▪Suggest analyzing S-R relationships at the site scale ð identify most sensitive areas of the lake ▪Could aggregate up to a waterbody-scale, but would need to demonstrate the lake- wide condition is protective of conditions at every site. More complicated approach Considering Multiple Regions •Utah Lake currently has two Assessment Units: main basin and Provo Bay •May consider Goshen Bay as a third region by demonstrating unique hydrology •Suggested approach: ▪Define boundary between Goshen Bay/Main basin by TDS differences ▪Run S-R analyses with the three regions, and see if the relationships differ ▪If relationships do not differ ð combine and set a single threshold ▪If relationships do differ ð analyze each region separately and set multiple thresholds Lake Regions: Goshen Bay •Sampling locations suggest a logical “break” to define Goshen Bay •We don’t observe a difference in TDS in overall range (may not hold day- to-day) •Suggest keeping Goshen Bay included with main basin Note: all information and data presented are considered draft, in-process material •Tt recommendations: o Run analysis at the site scale o Consider Goshen Bay a part of the main basin •SP discussion and support/non-support for these recommendations? Decision Points: Lake Regions Note: all information and data presented are considered draft, in-process material Zooplankton •Intent of S-R relationship: quantify the level of primary production associated with zooplankton community that supports aquatic life use •Follow-up since last subgroup meeting: ▪Discussion of zooplankton national model with Lester Yuan (EPA) ▪Review of David Richard’s MIBI provisional report, follow-up email to Theron ▪Discussion of Zhang et al. 2022 with Mike Brett ▪Follow-up with June Sucker Recovery Program on zooplankton conditions that support fish restoration efforts Note: all information and data presented are considered draft, in-process material Zooplankton National Model (USEPA 2021, 2022) •Metric of interest: relationship between total zooplankton biomass and chlorophyll ▪Relationship has a steeper slope at low chlorophyll ▪Trophic transfer becomes less efficient at higher chlorophyll and slope “levels off” ▪Choose a slope threshold that protects against decoupling •Spoke with Lester Yuan about applying this model for Utah Lake •Could run the model at multiple slope thresholds and generate a range of chlorophyll targets Note: all information and data presented are considered draft, in-process material Richards MIBI •38 zooplankton metrics suggested •Diversity and biomass suggested as primary indicators, but rationale is unclear •Would need to identify thresholds for metric(s) chosen that represent zooplankton conditions supportive of aquatic life use Note: all information and data presented are considered draft, in-process material Zhang et al. 2022 •Zooplankton production:biomass ratio (P/B) ▪Physiological control during cold periods ▪Resource control during warm periods ▪Underpinning: food web energy transfer •Would need to convert zooplankton counts to biomass via literature-derived relationships •Can compare P/B ratios in Utah Lake against other lakes in the study to inform thresholds Note: all information and data presented are considered draft, in-process material Landom et al. June Sucker Report •Larger-bodied Cladocera (Daphnia, also Ceriodaphnia) are indicators of zooplankton community supportive of a healthy fish community •Could use cladoceran relative biomass as a metric of interest •Zooplankton community also correlated with carp biomass – potential confounding factor of top-down controls Note: all information and data presented are considered draft, in-process material Zooplankton •Potential metrics and lines of evidence ▪Slope of zooplankton vs. phytoplankton biomass (EPA national model) ▪Metrics identified in Richards MIBI – which one(s), thresholds? ▪Zooplankton P/B (Zhang et al. 2022) ▪Cladoceran relative abundance (June Sucker Report) •Link metric(s) to chlorophyll concentrations ▪Strong relationship could indicate support for bottom-up controls linked to nutrients ▪Weak relationship could indicate support for top-down controls or other confounding factors Note: all information and data presented are considered draft, in-process material •What are the metrics of interest? •How might we identify thresholds for the metrics of interest? •How do we weigh the value of zooplankton analysis against other lines of evidence? Decision Points: Zooplankton Note: all information and data presented are considered draft, in-process material Phytoplankton •Spatial components of sampling regime ▪Two depths: surface “scum” and surface composite (suggest changing “scum” to surface grab for future iterations) ▪Sampling locations: open water, beach, and marina •How to interpret spatial differences? ▪Assessment methodology: uses must be met in all areas of the lake ð cannot ignore beach and marina sites ▪Should scum samples be interpreted the same way as composite samples? ▪Suggest a “translator” approach for open water vs. nearshore samples: –To support the use in beaches/marinas, what must the open water condition be? –Can run other S-R models for open water sites to translate to nutrient concentrationsNote: all information and data presented are considered draft, in-process material Phytoplankton: Cyanobacteria Cell Count •“Scum” samples generally higher than composite samples •Distributions similar across site types for composite samples •Open water generally lower than beach/marina for scum samples, but smaller sample size Note: all information and data presented are considered draft, in-process material Phytoplankton: Cyanobacteria Biovolume •“Scum” samples generally higher than composite samples •Distributions similar across site types for composite samples •Open water generally lower than beach/marina for scum samples Note: all information and data presented are considered draft, in-process material Phytoplankton: Microcystin •“Scum” samples generally higher than composite samples •Marina samples tend to have higher toxin concentrations than other site types Note: all information and data presented are considered draft, in-process material Phytoplankton: Cyanobacteria Cell Count •When paired samples are available, scum sample always exceeds composite sample (not surprising) •Samples almost exclusively restricted to beach and marina samples Note: all information and data presented are considered draft, in-process material Phytoplankton: Cyanobacteria Biovolume •When paired samples are available, scum sample always exceeds composite sample in beach and marina samples (not surprising) •In main basin open water sites, relationship straddles the 1:1 line Note: all information and data presented are considered draft, in-process material Phytoplankton: Microcystin •When paired samples are available, scum sample always exceeds composite sample (not surprising) •Samples almost exclusively restricted to beach and marina samples Note: all information and data presented are considered draft, in-process material Open Water vs. Nearshore Translator •To protect conditions in the nearshore, what do open water conditions need to be? •For cyano biovolume and cell count at beaches and marinas, we don’t observe a systematic bias between site types when samples are matched in time •For microcystin, beach and marina samples tend to be higher than open water sites •Recommendation: if translator shows a systematic bias, use this to adjust open water targets to protect nearshore. If no systematic bias, treat sites as consistent with one another Note: all information and data presented are considered draft, in-process material •Should composite and scum samples be treated similarly? If not, how should they be handled? •Tt recommends developing an open water to nearshore translator. SP discussion and support/non-support for this recommendation? Decision Points: Phytoplankton Note: all information and data presented are considered draft, in-process material Stressor-Response Analysis •Intent: establish the statistical relationship between stressors and responses ▪Responses linked to management goals, designated use support ▪What stressor level would relate to a response level that supports the use? ▪Incorporate estimates of statistical uncertainty to provide protectiveness ▪Confounding factors can be incorporated as covariates or contribute to statistical uncertainty •S-R analyses pertain to water column conditions ▪Relationships chosen based on causal linkages ▪Relationships independent of e.g., nutrient sources Note: all information and data presented are considered draft, in-process material General S-R Approach •Dataset filtered for April-September, surface samples •Dataset currently includes no time period restrictions, scum samples included •Each site-date observation is a single sample in the analysis •Tested whether a linear regression was the appropriate fit ▪If analysis fit assumptions of linear regression, recommend proceeding ▪If analysis does not fit assumptions, recommend a different approach (e.g., quantile) •Tested statistical significance: ▪Stressor variable ▪Main basin vs. Provo Bay slope ▪Main basin vs. Provo Bay intercept Note: all information and data presented are considered draft, in-process material 30-Day Mean DO vs. Chlorophyll •Utah Lake rarely observed exceedances, except in Provo Bay •Chlorophyll was not a significant predictor of 30-day mean DO •Recommendation: this S-R model is not a promising indicator for generating chlorophyll target Note: all information and data presented are considered draft, in-process material 7-day mean DO vs. Chlorophyll Note: all information and data presented are considered draft, in-process material •Utah Lake rarely observed exceedances, except in Provo Bay •Provo Bay had a negative relationship, but sample size was too small •Recommendation: this S-R model is not a promising indicator for generating chlorophyll target 1-day minimum DO vs. Chlorophyll Note: all information and data presented are considered draft, in-process material •Exceedances observed in Provo Bay •Significant negative relationship •Provo Bay had a significantly lower intercept but not slope •Recommendation: generate chlorophyll targets using this linear regression, consider separate intercept term for Provo Bay Daily max pH vs. Chlorophyll Note: all information and data presented are considered draft, in-process material •Positive relationship observed in main basin, negative relationship observed in Provo Bay (but, n = 6) •Overall R2 = 0.52 •Recommendation: generate chlorophyll targets using this linear regression, either for just main basin or combine Provo Bay given small sample size Microcystin vs. Cyano. Biovolume Note: all information and data presented are considered draft, in-process material •Microcystin significantly positively related to cyanobacteria biovolume •Provo Bay did not have a significantly different slope or intercept •Overall R2 = 0.26 •Recommendation: generate cyano. biovolume targets using this linear regression, combine main basin and Provo Bay Microcystin vs. Cyano. Cell Count Note: all information and data presented are considered draft, in-process material •Microcystin significantly positively related to cyanobacteria cell count •Provo Bay did not have a significantly different slope or intercept •Overall R2 = 0.25 •Recommendation: generate cyano. cell targets using this linear regression and compare to existing cell count threshold, combine main basin and Provo Bay Microcystin vs. Chlorophyll Note: all information and data presented are considered draft, in-process material •Microcystin not significantly related to chlorophyll •More limited sample size than cyano metrics (n = 37), linkage is less direct than for cyano. metrics •Likely want to compare this output to the EPA national model for microcystin. Are these models consistent? •Recommendation: this S-R model is not a promising indicator for generating chlorophyll target Cyano. Biovolume vs. Chlorophyll Note: all information and data presented are considered draft, in-process material •Significantly positive relationship •Provo Bay did not have a significantly different slope or intercept •Overall R2 = 0.25 •Recommendation: generate chlorophyll targets using this linear regression and biovolume targets from microcystin model, combine main basin and Provo Bay Cyanobacteria Cell Count vs. Chlorophyll Note: all information and data presented are considered draft, in-process material •Significantly positive relationship •Provo Bay had a significantly different intercept but not slope •Overall R2 = 0.30 •Recommendation: generate chlorophyll targets using this linear regression, use different intercept for Provo Bay Cyano. Relative Biovolume vs. Chlorophyll Note: all information and data presented are considered draft, in-process material •Very noisy relationship •Y axis is 0-1, not continuous ð linear regression not the best approach, likely want something like a logistic regression •But, a different statistical approach would likely not illuminate a pattern. Likely other drivers of cyano relative abundance not captured here •Recommendation: this S-R model is not a promising indicator for generating chlorophyll target Secchi Depth vs. Chl, TN, TP Note: all information and data presented are considered draft, in-process material •Significantly negative relationship, but noisy (R2 = 0.05- 0.13) ▪Chlorophyll pictured here ▪TN and TP have similar relationships •Likely have confounding influence of suspended sediment •Do we have a Secchi depth threshold in mind that would support aquatic life & recreation? •Recommendation: discussion needed Cyano. Biovolume vs. TN Note: all information and data presented are considered draft, in-process material •Relationship not significant •Recommendation: this S-R model is not a promising indicator for generating a TN target Cyano. Biovolume vs. TP Note: all information and data presented are considered draft, in-process material •Significant positive relationship •Provo Bay did not have a significantly different slope or intercept •N:P ratio may help explain variability •Quantile regression may be considered given wedge-shaped relationship •Recommendation: Use this S-R relationship to derive TP target, combine main basin and Provo Bay, potentially switch to quantile regression Cyano. Cell Count vs. TN Note: all information and data presented are considered draft, in-process material •Significant positive relationship, but noisy (R2 = 0.03) •Provo Bay did not have a significantly different slope or intercept •Quantile regression may be considered given wedge-shaped relationship •Recommendation: Use this S-R relationship to derive TN target but acknowledge uncertainty, combine main basin and Provo Bay, potentially switch to quantile regression Cyano. Cell Count vs. TP Note: all information and data presented are considered draft, in-process material •Significant positive relationship, but noisy (R2 = 0.11) •Provo Bay had a significantly different intercept but not slope •Quantile regression may be considered given wedge-shaped relationship •Recommendation: Use this S-R relationship to derive TP target but acknowledge uncertainty, use different intercept for Provo Bay, potentially switch to quantile regression Chlorophyll vs. TN Note: all information and data presented are considered draft, in-process material •Significant positive relationship •Provo Bay had a significantly different slope and intercept •Quantile regression may be considered given wedge-shaped relationship •N:P ratio may help explain variability •Recommendation: Use this S-R relationship to derive TN target, use different slope and intercept for Provo Bay, use quantile regression Chlorophyll vs. TP Note: all information and data presented are considered draft, in-process material •Significant positive relationship •Provo Bay did not have a significantly different slope or intercept •Quantile regression may be considered given wedge-shaped relationship •N:P ratio may help explain variability •Recommendation: Use this S-R relationship to derive TP target, combine main basin and Provo Bay, use quantile regression Outstanding S-R analysis •National Models ▪Microcystin: compare distributions in Utah Lake with distributions in national model. If consistent, can run national model to generate potential chlorophyll targets ▪Zooplankton: run for a range of input settings to generate potential chlorophyll targets ▪TN: compare observations in Utah Lake with national relationship ▪TP: compare observations in Utah Lake with national relationship •Lake Visitation •Public Perception Note: all information and data presented are considered draft, in-process material •Choose confidence/credible intervals and prediction intervals/quantiles to represent quantification of uncertainty and protectiveness General S-R Guidance Needed for Next Steps Note: all information and data presented are considered draft, in-process material Questions and Discussion Note: all information and data presented are considered draft, in-process material Reference Conditions Analysis Science Panel Meeting | May 19, 2023 Note: all information and data presented are considered draft, in-process material •Provide the technical basis for the development of numeric nutrient criteria (NNC) to protect designated uses •Conduct analyses to support multiple lines of evidence in the NNC framework Purpose of the Technical Support Document Note: all information and data presented are considered draft, in-process material Lines of Evidence 1.Reference-based ▪Results from paleolimnological studies ▪Utah Lake Nutrient Model prediction/extrapolation of reference conditions 2.Stressor-response analysis ▪Utah Lake Nutrient Model output ▪Statistical models 3.Scientific literature ▪Scientific studies of comparable/related lake ecosystems ▪Support/supplement other lines of evidence Note: all information and data presented are considered draft, in-process material Reference-Based Analysis •Typical approach elsewhere: ▪Identify comparable systems in locations with minimal human impact (e.g., land use, shoreline) ▪Example application: Herlihy and Sobota 2013 for NLA lakes ▪Can set thresholds based on an upper percentile of reference condition Note: all information and data presented are considered draft, in-process material Reference-Based Analysis •BUT, Utah Lake has few, if any, comparable systems •Look to other methods to evaluate applicable reference conditions ▪What did Utah Lake look like pre-EuroAmerican settlement? ▪What would Utah Lake look like if the watershed was minimally impacted by humans? Note: all information and data presented are considered draft, in-process material Reference-Based Analysis •Intended to set a “floor” & add context for how the lake has changed over time •Paleolimnological reconstruction of past conditions ▪Quantify pre-settlement nutrient conditions and how they have changed over time ▪Multiple studies, SC charge question responses •Model-based prediction ▪Watershed model run under a “reference conditions” scenario ð watershed nutrient loading ▪Watershed conditions then used as boundary conditions for the lake model Note: all information and data presented are considered draft, in-process material Paleolimnological Studies •ULWQS paleo study (PI Brahney, including King 2019, Devey 2021) •Other recent theses/dissertations ▪Macharia 2012 ▪Tate 2019 ▪Williams 2021 •Historical studies ▪Brimhall 1972 ▪Bolland 1974 ▪Sonerholm 1974 ▪Brotherson 1981 ▪Javakul et al. 1983 Note: all information and data presented are considered draft, in-process material Using paleo evidence in the TSD •Lines of evidence span qualitative, semi-quantitative, quantitative ▪Qualitative: community composition ▪Semi-quantitative: oligo/meso/eutrophic conditions ▪Quantitative: sediment nutrient concentrations, isotope values •Some cores have reliable dating, some do not •Combine paleo lines of evidence to form holistic picture of reference conditions Note: all information and data presented are considered draft, in-process material Benthic Diatoms (Bolland 1974, Jakuval et al. 1983, Brahney et al. 2021) •Benthic & epiphytic diatoms dominated in pre-industrial conditions •Increasing prevalence of planktonic diatoms approaching present day •Supports evidence for clearer water conditions pre-settlement Note: all information and data presented are considered draft, in-process material Bolland 1974 Planktonic Diatom Community •Conditions shift from oligo-mesotrophic to eutrophic diatom taxa from deep to shallow core (Bolland 1974) •Pollution-tolerant species increase moving up-core (Brahney et al. 2021) •Supports evidence for a shift from oligo-mesotrophic to eutrophic conditions Note: all information and data presented are considered draft, in-process material Bolland 1974 Phytopigments (King 2019) •Increase in diatom production around 1890 •Transition from diatoms to cyanobacteria and green algae production in recent decades •Support evidence for a shift from oligo-mesotrophic conditions to eutrophic conditions Note: all information and data presented are considered draft, in-process material King 2019 Phytopigments (King 2019) •Greater chlorophyll a degradation rates post-1890 •Highest chlorophyll a concentrations near the surface (subject to degradation) •Decreases in production around 1950, consistent with timeline of wastewater treatment plants in 1950s •Support evidence for a shift from oligo-mesotrophic conditions to eutrophic conditions Note: all information and data presented are considered draft, in-process material King 2019 eDNA (King 2019) •eDNA records show an increasing abundance of cyanobacteria post- 1900 and representation of hardstem bulrush pre-1900 •Support evidence for a shift from oligo-mesotrophic conditions to eutrophic conditions Note: all information and data presented are considered draft, in-process material King 2019 Other taxa •Macrophytes ▪Paleo evidence points to a historical macrophyte-dominated clear-water state ▪Present-day macrophyte restoration dependent on several factors, not just nutrient regime (carp, non-algal turbidity, algal production, hysteresis) ▪King et al. 2023 found a self-stabilizing submerged macrophyte community would require water clarity consistent with chlorophyll < 18 µg/L and Secchi depth ~ 1 m •Zooplankton ▪Increase in cladocerans post-1890 ▪Decrease in ostracods post-1890 Note: all information and data presented are considered draft, in-process material Nutrient Concentrations in Sediments •P concentrations increase in cores from 20 cm to present (Brimhall 1972, Bolland 1974) •Exchangeable and Ca-bound P increase through time – fractions indicative of water column P (Devey 2021) •Fe-bound P and Al-bound P relatively stable through time (Devey 2021) •Support a shift to more eutrophic conditions from preindustrial times to present Note: all information and data presented are considered draft, in-process material Devey 2021 Nutrient isotopes •δ15N isotope values increase through time post- 1900 (Macharia 2012, King 2019) •δ15N positively correlated with %N in sediment bulk organic matter (R2 = 0.79, Macharia 2012) •Suggest greater prevalence of wastewater- derived N to present (consistent with pop. growth) •Wastewater delivery and treatment timeline: ▪Provo sewers were built in 1908, treatment plant in 1956 ▪Orem sewers were built in 1945, treatment plant in 1959 Note: all information and data presented are considered draft, in-process material Goshen Bay core, King 2019 Inferring water column nutrients from sediments •Sediments store integrated information about system conditions •Sediment nutrient concentrations are proportional to water column concentrations, but not a direct 1:1 relationship •Converting sediment concentrations to water column concentrations could be difficult in Utah Lake ▪Alkalinity, P speciation, equilibration, water levels, etc. ▪Many calibration efforts needed to arrive at a reliable water column conversion ▪An approach more likely to be successful would be to calculate relative differences over time •Subgroup may choose to pursue a mass balance approach (use Mike Brett’s approach or similar?) Note: all information and data presented are considered draft, in-process material Limitations •Additional changes have co-occurred alongside nutrient regime changes ▪Carp introduction ▪Hydrologic changes ▪Climate change •Emphasizes the need to evaluate lines of evidence holistically ▪Do paleo lines of evidence agree (direction, magnitude)? ▪Reference scenario for mechanistic model will mimic present-day, minimally disturbed condition Note: all information and data presented are considered draft, in-process material •Paleo subgroup planning to meet to discuss ULWQS paleo study •June SP meeting: get into analysis specifics, synthesizing info sources Plans for Finalizing Paleo Analysis of Reference Conditions Note: all information and data presented are considered draft, in-process material Questions and Discussion Watershed Model Reference Scenario •“Reference” could be interpreted in different ways ▪Remove human-influenced land cover ▪Remove human-influenced hydrologic changes ▪Remove all traces of human impact ▪Extreme example: simulate Lake Bonneville! ð need to draw the line somewhere •What is the goal of this scenario? ▪Utah Lake doesn’t have comparable systems to consider as reference ▪Develop simulation of comparable system with minimal anthropogenic nutrient inputs ▪What would Utah Lake look like under minimal human influence? ▪Recall: serves as a “floor,” not as a realistic expectation Note: all information and data presented are considered draft, in-process material Proposed Approach 1.Run watershed model in “reference mode” 2.Extract watershed nutrient loading & hydrology to be passed to lake model 3.Run lake model with reference watershed inputs (i.e., new boundary condition) 4.Extract water column nutrient and primary production data Note: all information and data presented are considered draft, in-process material Proposed Watershed Model Reference Scenario •Changed elements ▪Land cover based on pre-EuroAmerican settlement vegetation from LANDFIRE, accounting for natural wildfires ▪Water withdrawals (i.e., for irrigation, public water supply) and releases removed ▪Irrigation removed (no agricultural land) ▪Point source discharges removed (no developed land) ▪Septic systems removed (no developed land) •Maintained elements ▪Dams (e.g., Deer Creek Reservoir, Jordan River outflow) and stream hydraulics (i.e., channel geometry) ▪Subbasin boundaries, drainage divides, and stream routing ▪Weather conditions ▪Calibrated parameter values for natural land covers (e.g., forest)Note: all information and data presented are considered draft, in-process material Questions and Discussion Utah Lake WQ Model Update May 19, 2023 Topics •Background on Individual Models ▪EFDC ▪SWAN ▪WASP •How the Models Work Together •Model Hydrodynamic Performance •Model Water Quality •Hydrodynamic model •EFDC solves the equations of mass and momentum transport •EFDC is a 2-D/3-D orthogonal curvilinear grid •EFDC also provides solutions for salinity, temperature, and conservative tracers with full density feedback to handle stratified conditions Environmental Fluid Dynamics Model (EFDC) Hydrodynamics Dynamics (E, u, v, w, mixing)Dye Temperature Salinity Near Field Plume Drifter •Wave Model •Wind-generated waves in water bodies (coastal areas, lakes). Simulating WAves Nearshores (SWAN) Wind Wind z x Periphyton Biomass D : C : N : P : Chl IP IN Phytoplankton Biomass Group 3 D : C : N : P : Si: Chl DOGroup 2 D : C : N : P : Si: ChlGroup 1 D : C : N : P : Si : Chl TIC H2CO3 –HCO3-–CO32- Total Alkalinity Particulate Detrital OM SiPNCD Dissolved OM Si P N CBOD1 CBOD2 CBOD3 Inorganic Nutrients NO3PO4SiO2NH4 pH atmosphere uptake excretion Inorganic Solids S3S1S2 ox i d a t i o n ox i d a t i o n ni t r i f i c a t i o n photosynthesis and respiration death dissolution mineralization sorption Water Quality Analysis Simulation Program (WASP) EFDC SWAN •Grid cell volumes •Velocities •Temperatures •ISS WASP Hydrodynamics Hydro Linkage Water Quality Outputs •Grid cell volumes •Velocities •Temperatures •ISS •Shear stress Outputs •Nutrient concentrations •Algae biomass •BOD •Dissolved oxygen Modeling Framework (How Models Work Together) Linkage Between EFDC-SWAN Model Setup Overview Computational grid Model Bathymetry Meteorological Data •NCDC NOAA WBAN 24174 (Provo Municipal Airport, UT) was used as a primary station for: ▪Air Temperature ▪Relative Humidity ▪Altimeter Pressure ▪Wind Speed and Direction •KPVU (MesoWest), Buoy Stations used as supplemental Stations •PRISM data used for precipitation. NASA’s NLDAS data was used for Solar Radiation and NARR was used for Cloud Cover. 11 12UTAH Lake Hydrodynamic and Water Quality Modeling KPVU UTLAK Provo Airport (KPUV) Utah Lake Near Mosida (UTLAK) Wind Buoy 4917390 Buoy 4917450 4917390 4917450 Hydrodynamic Calibration WSE and Current Velocity 2019 ADCP Deployment Significant Wave Height WQX-4917390 Calibration Statistics WSE and Significant Wave Height Mean Median 5 %tile 95 %tile Mean Median 5 %tile 95 %tile Hs 0.08 0.04 0.01 0.40 0.07 0.03 0.00 0.38 0.75 0.04 0.06 0.47 0.93 Mean Abs Err RMS Err Norm RMS Err Index of AgrmtParameterMeasuredSimulatedR2 Mean Median 5 %tile 95 %tile Mean Median 5 %tile 95 %tile WSE -1.09 -1.10 -2.00 -0.14 -1.05 -1.07 -1.95 -0.11 0.99 0.05 0.07 0.03 1.00 Parameter Measured Simulated R2 Mean Abs Err RMS Err Norm RMS Err Index of Agrmt Temperature Temperature Temperature Temperature Temperature Temperature Calibration Statistics Temperature Number Obs Mean Median 5 %tile 95 %tile Mean Median 5 %tile 95 %tile WQX-4917520 WTEMP 42 16.94 19.36 4.52 25.58 18.03 19.36 6.01 25.64 0.96 1.31 1.71 0.07 0.98 WQX-4917310 WTEMP 43 17.35 19.41 4.22 24.69 17.60 18.59 5.76 25.65 0.95 1.33 1.59 0.07 0.98 WQX-4917320 WTEMP 33 17.16 18.94 3.98 26.26 17.16 18.59 5.60 25.14 0.97 1.26 1.40 0.06 0.99 WQX-4917370 WTEMP 41 16.73 18.13 5.32 25.06 17.78 18.95 5.79 25.54 0.97 1.15 1.50 0.07 0.99 WQX-4917390 WTEMP 43 16.75 17.75 5.77 24.54 17.70 18.66 5.55 25.79 0.96 1.26 1.59 0.07 0.98 WQX-4917770 WTEMP 42 17.33 18.70 6.31 24.91 17.48 18.36 6.03 24.68 0.93 1.24 1.65 0.07 0.98 WQX-4917450 WTEMP 36 17.26 18.67 6.03 24.16 16.92 17.32 6.26 24.33 0.92 1.27 1.69 0.08 0.98 WQX-4917500 WTEMP 42 16.95 18.48 5.50 25.10 17.50 18.68 5.98 24.65 0.97 1.06 1.32 0.06 0.99 WQX-4917600 WTEMP 36 16.30 17.78 4.72 25.70 16.79 17.28 6.08 24.70 0.95 1.35 1.54 0.07 0.98 RMS Err Norm RMS Err Index of AgrmtStationParameter Measured Simulated R2 Mean Abs Err Draft Water Quality Sediment Transport (inorganics) •Sediment size classes (hydrometer tests UDWQ) •2 Cohesive •1 Non-Cohesive •Transport parameters incipient motion •Critical Shear estimates (UDWQ) [0.38 –2.81]Pa •Wave Shear estimates (Utah Lake WQ Study, 2020) [~0.17] Pa Clay Silt Sand 0.005 mm 0.062 mm 2 mm South Lake 0-5 85.4 14.6 0.0 South Lake 5-15 79.4 20.6 0.0 South Lake 15-30 90.6 9.4 0.0 Provo buoy 0-5 58.2 41.8 0.0 Provo buoy 5-15 87.0 13.0 0.0 Provo Buoy 15-30 73.0 27.0 0.0 North Lake 0-5 73.0 27.0 0.0 North Lake 5-15 72.7 27.3 0.0 North Lake 15-30 77.1 22.9 0.0 Provo Bay 0-5 69.2 10.2 20.6 Provo Bay 5-15 48.3 13.5 38.2 Provo Bay 15-30 45.9 26.5 27.6 Goshen 0-5 82.2 17.8 0.0 Goshen 5-15 78.3 21.7 0.0 Goshen 15-30 77.1 22.9 0.0 Averaged 73.2 21.1 Calculated Percent Content Sediment Transport: Shear Stress Sediment Transport: Shear Stress & TSS TSS (draft) TSS (draft) TSS (draft) TSS (draft) TSS (draft) Sediment Diagenesis and P-Binding •Sediment CNP contents and nutrient fluxes •Littoral Sediment Study (2022). CNP content & fluxes •Hogsett et al. (2018). Fluxes •P-Binding •Partition coefficients from P-Binding study Kd [20 –130] L/Kg Chlorophyll a (draft) Chlorophyll a (draft) Chlorophyll a (draft) Chlorophyll a (draft) Chlorophyll a (draft) DISSOLVED OXYGEN Dissolved Oxygen (draft) Dissolved Oxygen (draft) Dissolved Oxygen (draft) Dissolved Oxygen (draft) Dissolved Oxygen (draft) TOTAL NITROGEN Total Nitrogen (draft) Total Nitrogen (draft) Total Nitrogen (draft) Total Nitrogen (draft) Total Nitrogen (draft) TOTAL PHOSPHORUS Total Phosphorus (draft) Total Phosphorus (draft) Total Phosphorus (draft) Total Phosphorus (draft) Total Phosphorus (draft) May 19, 2023 Maddie Keefer, Afshin Shabani, Cole Blasko, Jon Butcher, Kevin Kratt (Tetra Tech) Utah Lake Watershed Model Update Note: all information and data presented are considered draft, in-process material 2 Agenda Note: all information and data presented are considered draft, in-process material Watershed Model Overview Hydrology Calibration Recap Water Balance Analysis Water Quality Calibration Process/Progress Update Watershed Model Overview 3Note: all information and data presented are considered draft, in-process material Note: all information and data presented are considered draft, in-process material Watershed Model Applications •Quantifying nutrient load contributions to Utah Lake by source •Simulating impact of management actions (e.g., permit limits, BMPs) on nutrient loading to Utah Lake •Evaluating alternative watershed conditions (e.g., climate, land use) 4 Example TP relative source contribution chart (Bear  Creek watershed, Colorado) Example reservoir chlorophyll‐a response to nutrient  load reductions (Bear Creek watershed, Colorado) Note: all information and data presented are considered draft, in-process material Watershed Model Applications •Not currently set up to: ▪Evaluate other pollutants (e.g., bacteria, metals) ▪Simulate individual or field-scale BMPs –BMPs must be simulated in aggregate ▪Identifying certain pollution issues (e.g., locations of failing septic systems) 5 Watershed Model Selection •Specific criteria defined related to watershed characteristics and simulation capabilities, source representation, usability, and general platform criteria •Quantitatively ranked 11 modeling platforms •Top rated: Hydrologic Simulation Program - FORTRAN Modeling Process Gather  Data Build  Model Calibrate  Model Assess  Current  Conditions Run  Scenarios 7Note: all information and data presented are considered draft, in-process material 8 Watershed Modeling QAPP •Quality objectives for measured and modeled data •Model framework to support the project goals and objectives •Data collection and acquisition to support model build and calibration •Specification of quality assurance/quality control (QA/QC) activities to assess model performance •Model usability assessment Note: all information and data presented are considered draft, in-process material 9 Model Extent Note: all information and data presented are considered draft, in-process material 10 Model Delineations Note: all information and data presented are considered draft, in-process material Started with  HUC12s and  modified  boundaries to  account for gaging  stations and water  diversions 11 Hydrologic Response Units •Climate, geology, topography, and land use/cover influence runoff and stream water quality; combined features to form model HRUs •Land use: combined NLCD 2016, Utah’s water related land use coverage, post-2016 fire perimeters Note: all information and data presented are considered draft, in-process material 12 Hydrologic Response Units •Impervious HRUs applied coverages for buildings and roads Note: all information and data presented are considered draft, in-process material 13 Hydrologic Response Units •USDA Soil Survey Geographic Database (SSURGO) ▪Hydrologic Soil Group –A/B: higher infiltration –C/D: lower infiltration •USGS 10-meter digital elevation model (DEM) Model Slope Percent of Total  Area Low (<10 degrees)41.22% Medium (10‐30 degrees)43.58% High (>30 degrees)15.20% Note: all information and data presented are considered draft, in-process material 14 Irrigation •Irrigation of agricultural lands and lawns/landscapes represented ▪Reference evapotranspiration (ETo) from Utah Climate Center (e.g., AgWeather sites) ▪Crop coefficients to estimate water demand for crop type ▪Irrigation demand = crop water demand - precipitation Note: all information and data presented are considered draft, in-process material Note: all information and data presented are considered draft, in-process material 15 Diversions and releases •Data obtained from ▪Central Utah Water Conservancy District ▪Provo River Water Users Association ▪Utah Division of Water Rights ▪Annual distribution system reports •Diversion time series represented as withdrawals •Releases represented as external water imports 16 Weather •Hourly time series •13 weather zones •Derived from gridded weather datasets Variable Source Precipitation PRISM Potential  evapotranspiration NLDAS Air temperature NLDAS Wind speed NLDAS Solar radiation NLDAS Dew point temperature NLDAS Cloud cover NARR High: 61 inches Low: 11 inches Note: all information and data presented are considered draft, in-process material 17 Permitted Point Source Discharges Facility Type Payson City WWTP Municipal Salem City WRF Municipal Salem City Corporation Municipal Provo City Corporation Municipal Mona City WWTP Municipal Springville WWTP Municipal Spanish Fork WWTP Municipal Jordanelle WRF Municipal Nephi Rubber Products Industrial Ensign Bickford Company Industrial Payson Power Plant Industrial McWane Ductile – Utah Industrial PacifiCorp Lake Side Power Plant Industrial Midway Fish Hatchery Aquatic Animal Production Facility Springville Fish Hatchery Aquatic Animal Production Facility •Time series based on facility data from: ▪Discharge monitoring report (DMR) data ▪Monthly operating reports (MOR) Note: all information and data presented are considered draft, in-process material Hydrology Calibration Recap 18Note: all information and data presented are considered draft, in-process material Note: all information and data presented are considered draft, in-process material Hydrologic Calibration (complete) •Calibrated to multiple endpoints to ensure robust model: ▪Remotely-sensed snow depth and water storage ▪Actual evapotranspiration ▪Daily, monthly, and cumulative gaged flow •Guided by multiple visuals and statistical metrics related to: ▪Total flow ▪Seasonal/monthly flows ▪High/low flow distribution ▪Nash Sutcliffe efficiency (NSE) coefficients •Daily streamflow records obtained from USGS monitoring sites •Calibration sought to obtain the best overall fit at multiple locations, with priority on larger tributaries to Utah Lake (Spanish Fork, Provo River) Note: all information and data presented are considered draft, in-process material Hydrologic Calibration Sites 20 Site Total  Flow  Relative  Error Monthly  NSE Provo River 10% 0.756 Spanish Fork 7% 0.814 American Fork 32% 0.737 Sixth Water Creek ‐2% 0.597 Salt Creek at Nephi ‐4% 0.994 Diamond Fork ‐4% 0.699 Currant Creek 42% 0.707 Hobble Creek 31%‐0.054 Dry Creek ‐14% 0.582 Summit Creek ‐56%‐0.017 Provo River (USGS Gage ID 10163000) Water Balance Analysis 21Note: all information and data presented are considered draft, in-process material Note: all information and data presented are considered draft, in-process material •Streams in the Utah Lake watershed have significant interactions with deep groundwater ▪Recharge of deep aquifer occurs at foot of mountains, discharges back into streams near lake or direct to the lake •Difficult to simulate in HSPF, which simulates only local shallow groundwater pathways •Focused on this issue as one of the final steps of calibrating hydrology •Compared output from HSPF to USGS groundwater modeling 22 Water Balance QA – Groundwater/Surface Water Interactions Note: all information and data presented are considered draft, in-process material Conceptual Model of Groundwater Recharge and Discharge, Northern Utah Lake (from Cederberg et al., 2009) 23 Water Balance QA – Consistency of HSPF Losses and USGS Groundwater Balances Note: all information and data presented are considered draft, in-process material •Conclusion: HSPF model provides a close match to the USGS groundwater studies – although the physical mechanisms represented in HSPF do not fully match the USGS conceptual model Approximate Annual Average (acre-feet/year) Discharge of Groundwater in the Utah Lake Watershed USGS Groundwater  Modeling Reports HSPF Watershed Model  (v58) 205,000 to 340,000 323,957 Note: all information and data presented are considered draft, in-process material •Comparison of the Utah Lake inflows implied by the recorded lake levels and water balance compared to the flows predicted by HSPF at a monthly time scale •The overall water balance of Utah Lake can be written as: ΔV = Qin – Qout + GW - E + P + W •Rearranged to solve for Qin: Qin = ΔV + Qout – GW + E – P – W 25 Model Comparison to Utah Lake Water Balance •EFDC model runs over calendar years 2015 – 2020 ▪Water balance analysis suggests that Qin is approximately 515,840 AF/yr •HSPF model has almost identical output of flow to the lake at 515,751 AF/yr Note: all information and data presented are considered draft, in-process material Utah Lake Water Balance 26 Component AF/yr Notes ΔV 15,590 ULDB_Stage_Vol_SA workbooks provided by Scott Daly, based on 1/1/05 elevation  of 1366.6 m and 12/31/20 elevation of 1367.4 m, resulting in net volumetric gain  of 1.15E8 m3 converted to AF/yr Qout 137,785 From daily flows at Jordan River Narrows in Jordan_River_Outflow, provided by  Scott Daly.  Likely has a small high bias due to groundwater inputs above Narrows. GW 38,708 Direct‐to‐lake GW estimate developed for U. Utah model and adopted for Tt  model, derived from USGS groundwater reports.  May have a high bias if USGS  estimates are from 1990s data. E 588,013 Daily rate (inches) applied to timeseries of surface area from ULDB_Stage_vol_SA workbook.   P 157,999 Daily rate (inches) applied to timeseries of surface area from ULDB_Stage_vol_SA workbook. WWTP 30,090 Timpanogos and Powell Slough WWTP discharges direct to lake as specified in  EFDC model Qin = ΔV + Qout - GW + E – P - WWTP 27 Comparison of Monthly Inflows Note: all information and data presented are considered draft, in-process material There are some seasonal discrepancies, but these are likely mainly due to the lack of seasonal specification of groundwater inflow and wastewater discharges, coupled with the uncertainty in estimating evaporative losses 28 Comparison of Cumulative Inflow Note: all information and data presented are considered draft, in-process material Suggest that the current version of the watershed model is an unbiased estimate of flow to Utah Lake + Progress Update Water Quality Calibration Process Note: all information and data presented are considered draft, in-process material Note: all information and data presented are considered draft, in-process material •Sediment calibration follows the hydrologic calibration and precedes nutrient calibration •Water temperature, DO, and nutrient calibration follows sediment 30 Approach for Watershed Model Calibration Hydrology  Calibration Sediment  Calibration Nutrient  Calibration Note: all information and data presented are considered draft, in-process material •Inputs and observed sediment concentrations have various sources of uncertainty •Calibrate to multiple endpoints to ensure robust model:▪Daily, monthly, and cumulative monitored sediment concentration▪Sediment Source Assessment▪Reach/Sediment Balance •Objective is to represent the overall sediment behavior of the watershed, with knowledge of the morphological characteristics of the stream (i.e., aggrading or degrading behavior)▪Using sediment loading rates that are consistent with available values and providing a reasonable match with instream sediment data 31 Multi-Objective Sediment Calibration Note: all information and data presented are considered draft, in-process material Example of Debris Flows after 2016 Wildfires Note: all information and data presented are considered draft, in-process material Sediment Calibration •Guided by multiple visuals and statistical metrics related to: ▪Sediment loading rates from the landscape ▪Seasonal/monthly TSS concentrations ▪High/low flow distribution ▪Average and median concentration and load error •Observed TSS records obtained from Utah stream monitoring sites •Calibration seeks to obtain the best overall fit at multiple locations, with priority on larger tributaries to Utah Lake (Spanish Fork, Provo River) 33 Note: all information and data presented are considered draft, in-process material 34 Sediment/WQ Calibration Sites Sediment calibration is underway and progressing well. Once sediment calibration is complete and final statistics are calculated, water quality (e.g., water temperature, nutrients) calibration will begin Site TSS  Samples Provo River at Murdock and  Olmstead 79 Provo River at Wildwood 44 Hobble Creek 132 Spanish Fork at Utah Lake 104 Spanish Fork at Moark 71 Diamond Fork 89 Starvation Creek/Upper Soldier  Creek 11 Thistle Creek 21 Currant Creek 46 Note: all information and data presented are considered draft, in-process material Main objective is to obtain acceptable agreement of observed and simulated concentrations, while maintaining the instream water quality parameters within physically realistic bounds and the nonpoint loading rates within the expected ranges from literature 35 Nutrient Calibration Concentration Metrics TN TP Average  Observed  Concentration  (mg/L) 0.531 0.035 Average  Simulated  Concentration  (mg/L) 0.538 0.039 Relative Error 2.1% 12.8% Example Nutrient Calibration Results - Bear Creek, Colorado 36 Steps in Nutrient Calibration Note: all information and data presented are considered draft, in-process material 1.Estimate all model parameters, including land use specific accumulation and depletion/removal rates, washoff rates, and subsurface concentrations 2.Compare simulated nonpoint loading rates with expected range of nonpoint loadings from each land use and adjust loading parameters when necessary to improve agreement and consistency 3.Calibrate instream water temperature 4.Compare simulated and observed instream concentrations at each of the calibration stations 5.Analyze the results of comparisons in steps 3 and 4 to determine appropriate instream and/or nonpoint parameter adjustments, and repeat those steps as needed until calibration targets are achieved; ▪Watershed loadings are adjusted when the instream simulated and observed concentrations are not in full agreement ▪Instream parameters are adjusted within the range determined reasonable 37 Prospective Timeline Note: all information and data presented are considered draft, in-process material Sediment  Calibration  Complete May 2023 Water Quality  Calibration  Complete June 2023 Sensitivity  Analysis + Model  Documentation  June 2023 Questions and Discussion Note: all information and data presented are considered draft, in-process material Quantification of nutrient inputs, sediment storage and release, and long-term recover in Utah Lake Michael Brett Professor of Limnology Department of Civil & Environmental Engineering University of Washington https://health.utah.gov/enviroepi/appletree/HAB/Utah_Lake_2016_1.JPG •Quantifying nutrient fluxes in Utah Lake is important because this lake frequently has toxic cyanobacteria blooms •Cyanobacteria blooms are strongly associated with high phosphorus and nitrogen concentrations •These nutrients can come from natural watershed weathering and biological processes •However, high nutrient concentrations are usually associated with anthropogenic inputs (e.g., from wastewater discharges, agricultural runoff, stormwater, etc.) https://www.inaturalist.org/guide_taxa/707293 Quantification of nutrient inputs, sediment storage and release, and long-term recover in Utah Lake Outline 1.The general mass balance model 2. The first-order TP mass balance for Utah Lake 3. The effect of external nutrient sources on Tplake 4. Transition to a new steady-state 5. Is internal loading included in this model? 6. Conclusions The nutrient mass balance in lakes Change in concentration = Inputs – Outputs – Removal The nutrient mass balance in lakes Change in concentration = Inputs – Outputs – Removal It is conventional practice to assume “steady-state” to characterize long-term average conditions The nutrient mass balance in lakes Change in concentration = Inputs – Outputs – Removal Or Inputs = Outputs + Removal 0 Inputs = Outputs + Removal Inputs = Outputs + Removal Inputs = Outputs + Removal Outputs Inputs = Outputs + Removal Mass balance models quantify Net Removal (sediment deposition - sediment release) Sediment accumulation Phosphorus vs. Phytoplankton Biomass 0.1 1 10 100 1000 Ch l o r o p h y l l (µg L -1 ) 1 10 100 1000 Total Phosphorus (µg L-1) y = 0.08x1.5 r2 = 0.91 Jones and Bachmann (1976) •Phosphorus and Nitrogen concentrations are the main determinants of phytoplankton biomass •Cyanobacteria blooms are also strongly associated with high phosphorus and nitrogen concentrations Phosphorus vs. Phytoplankton Biomass 0.1 1 10 100 1000 Ch l o r o p h y l l (µg L -1 ) 1 10 100 1000 Total Phosphorus (µg L-1) y = 0.08x1.5 r2 = 0.91 Jones and Bachmann (1976) •Mass balance tells us that nutrients are either in the water or in the sediments! •Due to Stoichiometric constraints only the nutrients in the water at any given time contribute to phytoplankton biomass! •The nutrients in the water column on average equal the output/advected concentration that regulates phytoplankton biomass Change = Input – Output – Removal V = lake volume Q = water flow into/out of lake C = concentration into/out of lake s = first order loss rate of nutrients 1. The general mass balance model Where the time rate of change is due to the mass flux into the system, mass flux out of the system, plus or minus any mass changes within the system, where V represents the volume of the reactor (or lake in this example, L3), Cout represents the constituent (or nutrient) concentration in the outflow (M/L3), Qin represents the total inflow to the reactor (L3/T), Cin represents the flow-weighted average input concentration (M/L3), Qout represents the total outflow (L3/T), and 𝜎represents a first-order reaction rate for the removal or production of the constituent in the system. In this model, Cout also equals the concentration in the reactor or Clake because the reactor (or natural system) is assumed to be perfectly mixed. L above represents length, and volume or L3 is commonly represented as m3. M represents mass, and T presents time. In this case, we focus on first-order kinetics (e.g., exponential decay and growth) for the sake of simplicity, but these reactions can also be zero-order, second-order or even intermediate-order. Change = Input – Output – Removal If we assume steady-state or long term average conditions 0 Input = Output + Removal V = lake volume Q = water flow into/out of lake C = concentration into/out of lake s = first order loss rate of nutrients where = the lake’s water residence time relative to inflows (i.e., V/Qin) = where = the lake’s water residence time relative to inflows (i.e., V/Qin) = Due to the high evaporative losses in Utah Lake where = the lake’s water residence time relative to inflows (i.e., V/Qin) = Without these high evaporative losses the model predicts a TPlake concentration of 22 µg L-1 where = the lake’s water residence time relative to inflows (i.e., V/Qin) or in term’s of Total Phosphorus (TP) = Lake TP concentrations are determined by the flow weighted input concentration (TPin) The first-order net loss rate to the sediments (s) The amount of time available to be lost to the sediments () 2. The first-order TP mass balance for Utah Lake Lake TP concentrations are determined by the flow weighted input concentration (TPin) The first-order net loss rate to the sediments (s) The amount of time available to be lost to the sediments () 2. The first-order TP mass balance for Utah Lake Without the high evaporative losses the model predicts a TPlake concentration of 22 µg L-1 •Phosphorus removal depends on the water residence time () and the loss rate (s) From: Brett and Benjamin (2008) Freshwater Biology 53: 194–211 N = 305 Based on a tremendous effort originally by LaVere Merritt (who laid a very strong foundation) and subsequently by Kateri SalkGundersen, Scott Daly and various sub-committee members to quantify nutrient inputs from tributaries, WWTPs, Atmospheric Deposition, etc. (and accounting for attenuation between WWTPs and Utah Lake) TPlake = 68 µg L-1 TPin = 343 µg L-1 Qout/Qin = 0.29 = V/Qin = 1.09 yr s = 4.31 yr-1 TP Removal = 1 - (Q*TPout/Q*TPin) = 0.937 •Phosphorus removal depends on the water residence time () and the loss rate (s) •Utah Lake is a very effective sink for phosphorus •The 96th percentile for TP removal relative to these data •This is probably due to reactions with CaCO3in the lake and formation of calcium-P mineral complexes in the sediments From: Brett and Benjamin (2008) Freshwater Biology 53: 194–211 N = 305 ೟ೝ೔್ೠ೟ೌೝ೔೐ೞ ೏ೝೌ೔೙ೞ ೛ೝ೐೎೔೛೔೟ೌ೟೔೚೙ ೈೈ೅ು ೟ೝ೔್ೠ೟ೌೝ೔೐ೞ ೏ೝೌ೔೙ೞ ೛ೝ೐೎೔೛೔೟ೌ೟೔೚೙ ೈೈ೅ು 3. The effect of external nutrient sources on TPlake ೟ೝ೔್ೠ೟ೌೝ೔೐ೞ ೏ೝೌ೔೙ೞ ೛ೝ೐೎೔೛೔೟ೌ೟೔೚೙ ೈೈ೅ು ೟ೝ೔್ೠ೟ೌೝ೔೐ೞ ೏ೝೌ೔೙ೞ ೛ೝ೐೎೔೛೔೟ೌ೟೔೚೙ ೈೈ೅ು Vary WWTP effluent TP concentrations (TPeff) We can use this mass balance approach to predict how the lake’s phosphorus concentration will depend on the input concentrations for the major point sources We can use this mass balance approach to predict how the lake’s phosphorus concentration will depend on the input concentrations for the major point sources Increasing capital and O&M costs, energy use, and greenhouse gas emissions at lower WWTP effluent concentrations Example from Spokane Basin of WA state 0 20,000 40,000 60,000 80,000 100,000 CO 2 Re l e a s e d ( k g d -1 ) 0 20 40 60 80 100 120 In p u t T P ( µ g L -1 ) 1001000 WWTP effluent TP (µg L-1) TPRIVER CO2 Re s e r v o i r A few calculus steps = concentration at time t = new steady-state concentration = initial concentration r = flushing rate = 1/ 4. Transition to a new steady-state Transition to a new steady-state in Utah Lake s = 4.31 yr-1 r = 0.29 yr-1 This model predicts transition to a new steady-state in Utah Lake will be mainly governed by the removal term (s) and will be rapid Transition to a new steady-state in Utah Lake Transition to a new steady-state in Utah Lake 5. Is internal loading included in this model? 5. Is internal loading included in this model? How to estimate internal loading at a lake wide scale? a.Upper Klamath Lake example of internal loading b.Calculate Utah Lake internal loading How to estimate internal loading at a lake wide scale? a.Upper Klamath Lake example of internal loading b.Calculate Utah Lake internal loading c.If internal loading in Utah Lake was 1,500 tonnes P/yr, that would be equivalent to adding ≈ 2,200 μg P/L to the lake water each year above and beyond all external inputs. How to estimate internal loading at a lake wide scale? a.Upper Klamath Lake example of internal loading b.Calculate Utah Lake internal loading c.If internal loading in Utah Lake was 1,500 tonnes P/yr, that would be equivalent to adding ≈ 2,200 μg P/L to the lake water each year above and beyond all external inputs. d.Recall, only the nutrients actually in the water column at any given time support phytoplankton blooms Internal loading comparison with Upper Klamath Lake (in south-central Oregon) TPIN adjusted for evaporation We can use this approach to quantify summer releases of phosphorus from the sediment back to the water column (i.e., “internal loading) for Utah Lake based on long-term monthly average concentrations •This internal loading averages + 24 µg/L •This is equivalent to about 30 tonnes P/yr of internal loading •If a more flexible approach is used this estimate increases by about 50% to 45 tonnes P/yr Interestingly, this internal loading tends to follow the summer phytoplankton bloom, and not vice-versa •This internal loading averages + 24 µg/L •This is equivalent to about 30 tonnes P/yr of internal loading •If a more flexible approach is used this estimate increases by about 50% to 45 tonnes P/yr Interestingly, this internal loading tends to follow the summer phytoplankton bloom, and not vice-versa •A similar pattern is seen in Upper Klamath Lake External Loading Tonnes TP/yr WWTPs 133.4 Tributaries 49.6 Atmos Dep 32.0 Internal Loading 45.0 62% 23% 15% Conclusions: Utah Lake has quite high phosphorus inputs mostly from WWTPs Conversely, the lake is exceptionally effective at removing phosphorus from the water column and sequestering it in the sediments However, lake TP concentrations are still directly linearly related to input concentrations Recovery once external inputs are reduced is predict to be very rapid Internal loading increases summer TP concentrations by about 33 µg L-1