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Home My WebLink AboutDWQ-2024-004512Sediment Overview Science Panel Meeting | March 23, 2023 Purpose of Today’s Discussion •Review studies conducted on Utah Lake sediment nutrient cycling •Contextualize importance of sediment characterization in NNC development Context: How will sediment data be used? •All information from the Utah Lake Water Quality Study could be used for: Responding to charge questions Informing the Utah Lake Nutrient Model Informing Technical Support Document development Informing implementation planning •Specifically, sediment research will be used for: Responding to sediment-related charge questions Informing the sediment-relevant parts of the ULNM (e.g., sediment diagenesis, settling) Informing potential timescales for implementation planning Key Studies to Date •Sediment fluxes and equilibrium P concentration study •Littoral sediment study •P binding study •TSSD Limnocorral study •Carbon, Nitrogen, and Phosphorus (CNP) study •Brett mass balance analysis •Other studies from the literature CNP Study (Tetra Tech) •Literature review of nutrient-relevant processes and pools in Utah Lake Sediment-related sources included: –Merrell 2015 (thesis) –Randall 2017 (thesis) –Wang et al. 2017 –Abu Hmeidan et al. 2018 –Hogsett et al. 2019 –Randall et al. 2019 –Reidhead 2019 (thesis) –Goel et al. 2020 (report) •Conceptual model Includes sediment pools and fluxes across the sediment-water interface •External mass balance Did not involve sediments •Internal mass balance SedFlux model CNP Study: SedFlux Model •Purpose: model nutrient fluxes & sediment oxygen demand across the sediment- water interface •Adapted from original work for QUAL2K and WASP (DiToro 2001) •Straightforward application of the current model setup Input data for Utah Lake, reaction network parameters from Su and von Stackelberg (2020) No calibration Should defer to results of EFDC/WASP and field observations when available TP Main Basin: 0.01-1 mg/L Provo Bay: 0.05-1 mg/L PP 0-1 mg/L Phosphorus model Sediment Water Outflow TP 23-84 tons/yr TP Main Basin: 280-1730 mg/kg Provo Bay: 465-1900 mg/kg Phytoplankton 0.7-2 % P TDP 0.003-1 mg/L DOP 0-0.18 mg/L PO43+≈ SRP Main Basin: 0.01-0.85 mg/L Provo Bay: 0.02-4 mg/L PIP Zooplankton 0.5-1.6 % P0.05-160,000 µg/L (small) 50-1,600 µg/L (large) Fish 1-4.5 % P 0.1-4.5 kg/acre BD fraction Fe/Mn compounds 49.1±1.8% (41-61%) HCl fraction CaPO4 or acid-soluble organic P 38.6±2.1% (25-47%) NH4Cl, NaOH, and residual fractions Loosely bound, exchangeable and organic P, refractory P 12.4% Confidence Very high High Medium Low Very low Porewater TDPMain Basin: 1.48 mg/L (0.26-10.82) Provo Bay: 3.85 mg/L (0.40-6.78) Dashed boxes are derived from Randall et al. 2019 (PLoS ONE) External TP Loading Inflow sources (streams, WWTPs, drains, springs, groundwater, precipitation) 152-298 tons/yr Atmospheric Deposition 31-45 tons/yr TDP Release 1.7-1.9 ±0.7-4.0 tons/d SRP Release -4.5-27.2 tons/d Periphyton Negligible Macrophytes 0.2-0.6 % P Macroinvertebrates 5.3-17.0 mg/g dry weight Uptake 0.1-100 ng/(L*h)Uptake 0.17-480 µg/(ind.*d) Uptake not possible to calculate Excretion, decomp: negligible (periphyton), 0-496 µg/(g dry weight*h) (macroinvertebrates) Uptake negligible Excretion 0.01-1,000 µg/(ind.*d) Excretion, Decomp. 0.1-100 ng/(L*h) Excretion Carp: 51.1-117 tons/yr Uptake not possible to calculate Excretion, decomp. not possible to calculate Uptake not possible to calculate PP settling 192-1,230 tons/y 0-83 tons/d PP resuspension 173-257 tons/y 0-82 tons/d Literature- derived values TN 0-3000 mg/kg TN Main Basin: 0.04-12.4 mg/LProvo Bay: 0.7-12.4 mg/L PN 0-0.50 mg/L Nitrogen model Sediment Water Outflow TDN 367 tons/yr150-6,847 kg/d TDN 0.29-5.32 mg/L DON 0-11.9 (from TKN) 0-1.9 mg/L (from TDN) DIN0.01-7.5 mg/L NO2-+ NO3- 0.001-5.2 mg/L NH3 + NH4+ 0.003-5.0 mg/L Confidence Very high High Medium Low Very low External TN Loading Inflow sources (streams, WWTPs, drains, springs, groundwater, precipitation) 2022-2542 tons/yr Atmospheric Deposition 218-249 tons/yr Phytoplankton 5-9 % N Zooplankton 5-14 % N 0.5-1,400,000 µg/L (small) 500-14,000 µg/L (large) Fish8-12 % N 0.8-20 kg/ac Excretion, Decomp. 10-10,000 ng/(L*h) TIN Release -3.8-554.4 tons/d Ammonia Release -12.7-554.4 tons/d Macrophytes 0.8-1.3 % of dry mass Macroinvertebrates 42.7-141.2 mg/g dry weight Porewater TDN 0-16 mg/L Atmospheric N2 Denitrification, anammox 1,372 tons/yr Water column N fixation 0-4.65 µg/(L*h) Benthic N fixation 0.1-1.0 tons/h Uptake 10-10,000 ng/(L*h) Literature-derived values Uptake 1.2-2,160 µg/(ind.*d) Excretion 0.01-10,000 µg/(ind.*d) Excretion Carp: 496-1,140 tons/yr Uptake not possible to calculate Uptake not possible to calculate Excretion, decomp. not possible to calculate Periphyton Negligible Excretion, decomp: negligible (periphyton), 0-168 µg/(g dry weight*h) (macroinvertebrates) Uptake negligible Uptake not possible to calculate PN settling 0-130 tons/d PN resuspension0-129 tons/d SedFlux: NH4+, NO3-, and SRP •Fluxes across sediment-water interface NH4+ and SRP: positive flux to the water column NO3-: positive flux to the water column in summer, negative flux to the sediment in spring & fall •Model predicted higher flux rates under high organic matter supply •Model predicted more variable rates when water column was shallow SedFlux: SOD •Model predicted higher flux rates under high organic matter supply and deeper water column •Modeled SOD was higher than measured SOD by an order of magnitude modeled rates likely unrealistic •SOD was not particularly sensitive to reaction network parameters •SOD was sensitive to: Water column DO concentration (accurate) Settling rate of POC (inaccurate?) •Hypotheses… Sediment dilutes incoming POC Frequent resuspension does SOD become BOD? SedFlux may not capture important factors driving SOD SedFlux: Comparisons to field observations •SRP, NH4+, NO3-comparable to other studies •SOD substantially higher than other studies Rate (g m-2 d-1) Main Basin Tetra Tech CNP Hogsett et al. 2019 Goel et al. 2020 Provo Bay Tetra Tech CNP Hogsett et al. 2019 Goel et al. 2020 SRP Flux 0.006-0.20 -0.004-0.071 -0.0024 ± 0.0042 0.005-0.17 0.01 -0.012 ±0.0097 NH4+Flux 0.03-1.23 -0.033-0.141 -0.0098 ± 0.0034 0.005-0.89 1.442 -0.017 ±0.01 NO3-Flux -0.01-0.01 -0.008-0.08 ---0.13-0.009 0 -- SOD 4.90-14.38 0.9-2.04 2.97 1.91-14.58 4.61 0.05 Sediment-Related Charge Questions •What are current sediment equilibrium P concentrations (EPC) throughout the lake? What effect will reducing inputs have on water column concentrations? If so, what is the expected lag time for lake recovery after nutrient inputs have been reduced? •What is the sediment oxygen demand of, and nutrient releases from, sediments in Utah Lake under current conditions? •Does lake stratification [weather patterns] play a result in anoxia and phosphorus release into the water column? Can this be tied to HAB formation? What are current sediment equilibrium P concentrations (EPC) throughout the lake? What effect will reducing inputs have on water column concentrations? If so, what is the expected lag time for lake recovery after nutrient inputs have been reduced?•EPC: water column P conc. at which there is no net exchange with sediments •Goel et al. 2020: 0.27 mg/L in main basin, 0.86 mg/L in Provo Bay •Controlled batch experiments would more precisely identify EPC (P binding study) •Expect some degree of enhanced sediment loading following water column reductions until equilibrium is reached mass balance analysis by M. Brett What is the sediment oxygen demand of, and nutrient releases from, sediments in Utah Lake under current conditions? •4 studies have addressed this topic Hogsett et al. 2019 Randall et al. 2019 Goel et al. 2020 Tetra Tech 2021 (CNP) •Sediments are a net sink for total nutrients •Bioavailable forms of N and P are released from the sediments •Rates are spatially variable Does lake stratification [weather patterns] play a result in anoxia and phosphorus release into the water column? Can this be tied to HAB formation? •Evidence of transient thermal stratification, no persistent seasonal stratification •Thus, do not expect hypolimnetic DO depletion and nutrient accumulation •Possible that local zones of anoxia do form •Some sediment P is bound to redox-sensitive iron compounds •Frequent wind-driven mixing brings surface sediments into contact with the water column (Tetra Tech 2021) Questions and Discussion Mitch Hogsett, PE, PhD SOD, Nutrient Fluxes, and Sediment Characteristics Topics 1.SOD and WC Respiration (dark conditions) 2.Sediment and WC Nutrient Dynamics 3.Sediment Mineralogy 4.Sediment P-Speciation Sediment Oxygen Demand Sediment Oxygen Demand SOD and WC Respiration SOD20 and SOD13 Temp SOD SOD20 SOD20 SOD13 SOD13 Site (C)(g/m2/d)(g/m2/d)%difference (g/m2/d)%difference 1 17.1 -4.61 -5.76 125%-3.36 73% 2 23.5 -1.42 -1.08 76%-0.63 45% 3 22.5 -1.49 -1.23 82%-0.72 48% 4 18.3 -2.04 -2.33 114%-1.36 67% 5 22 -1.67 -1.43 86%-0.84 50% 6 19.1 -1.03 -1.10 107%-0.64 63% 7 23 -1.06 -0.84 79%-0.49 46% 8 22.9 -0.9 -0.72 80%-0.42 47% Nutrient Dynamics Sediment Nutrient Fluxes 1.5 !"# $!∗&'( )*+"#$%& ,*+"'!$%& $-./ )01 $-.2 ,)+"($%& )*+"#$%& = 0.014 !/ $!∗&'( DIN WC Nutrient Rates Annual sediment fluxes estimates All site average annual load •1,500 tons P/year, 7,500 tons N/year Utah Lake proper average annual load •950 tons P/year, 4,750 tons N/year Utah Lake proper SOD13 annual load •520 tons P/year,2,612 tons N/year Sediment TS and VS Sediment Minerology Sediment P-Speciation Questions? Technical Support Document Overview Science Panel Meeting | March 23, 2023 •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 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 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 Stressor-Response Analysis •Output from the Utah Lake Nutrient model (current and reduced nutrient loading) •In-lake monitoring data for water quality variables •Application of EPA’s Ambient Water Quality Criteria nutrient models Stressor-Response Analysis •a Use Assessment Endpoint Stressor Response Empirical S-R Data Available Mechanistic Model Output Recreation, Aquatic Life, Agriculture, Drinking Water Algal toxins Chlorophyll a Microcystin concentration Yes No Recreation, Aquatic Life, Agriculture, Drinking Water Algal toxins Cyanobacterial abundance Microcystin concentration Yes No Recreation Algal blooms Chlorophyll a Cyanobacterial abundance Yes Yes Recreation, Aquatic Life pH Chlorophyll a pH Yes Yes Recreation Lake visitation Chlorophyll a Annual visitation Yes No Recreation Lake visitation Cyanobacterial abundance Annual visitation Yes No Recreation Lake visitation Kd, Secchi depth Annual visitation Yes No Recreation Public perception Chlorophyll a Public perception User perception No Recreation Public perception Cyanobacteria abundance Public perception User perception No Recreation Public perception Kd, Secchi depth Public perception User perception No Aquatic Life DO Chlorophyll a DO Yes Yes Aquatic Life Food resources Chlorophyll a Zooplankton:Phytoplankton National Model No Aquatic Life Food resources Chlorophyll a Proportion cyanobacteria Yes Yes Aquatic Life Light Chlorophyll a Kd, Secchi depth Yes Yes Criteria Setting TN & TP Chlorophyll a Yes Yes Criteria Setting TN & TP Cyanobacterial abundance Yes Yes Criteria Setting TN & TP Kd, Secchi depth Yes Yes Primary Datasets Water Chemistry Grab data (surface and integrated) Multiple parameters Many sites around the lake Primary data providers: DWQ & WFWQC Continuous Buoy Data Surface data DO, pH, temperature, turbidity, chlorophyll & phycocyanin fluorescence 4 sites Data provider: DWQ •Phytoplankton Surface composite and surface “scum” Phytoplankton taxa abundance, toxins Many sites around the lake Data providers: DWQ & WFWQC •Zooplankton Zooplankton tows, presumably surface Zooplankton taxa abundance Many sites around the lake Data providers: USU, WFWQC, BYU General Stressor-Response Approach •Statistical Models Linear regression Quantile regression Logistic regression •Assign a threshold for the response •Account for uncertainty and protectiveness Confidence/credible interval Prediction interval/quantile Purpose of Today’s Discussion •Highlight data availability for S-R relationships of interest •Make decisions about aggregation approach •Set stage for future feedback on statistical approach, management- relevant decisions Review of Stressor-Response Relationships of Interest What you will see today: Previous data analysis, for context Un-aggregated S-R visualizations for compiled dataset inform aggregation and statistical approach Microcystin vs. Chlorophyll and Cyanobacteria •Previous analysis: + correlation between cyano cell count and microcystin •Linear regression and/or logistic regression may be appropriate •Threshold: 8 µg/L (EPA 2019 recommended recreational criteria) Microcystin vs. Chlorophyll and Cyanobacteria Several factors explained variability in cyano. and microcystin Surface “scum” vs. composite Location within the lake Microcystin vs. Chlorophyll and Cyanobacteria •National model: links chlorophyll, cyanobacteria, microcystin from NLA data •Location-specific settings: ecoregion, lake max. depth •Note: Utah Lake morphometry is at the edge of the distribution of NLA lakes  site-specific analysis may be more informative Cyanobacteria vs. Chlorophyll •May use this relationship to relate cyanobacteria abundance thresholds to a potential chlorophyll target (see EPA 2019, Ravenscroft SC presentation 2021) Cell count: potential thresholds of 20,000, 40,000, 100,000 cells/mL Biovolume Relative abundance EPA 2019 reviews state and international guidelines/action levels, WHO guidance pH vs. Chlorophyll •Expect to see diel pH cycles aligning with photosynthesis (+) and respiration (-) •Utah Lake has high alkalinity  typically see exceedances at 9 rather than 6.5 •pH criteria is a “not to exceed” •Exceedances of pH criteria occur at all sites, most consistently at Provo Bay (red) pH vs. Chlorophyll •Linking pH with chlorophyll Best pH data will come from continuous sondes Best chlorophyll data will come from grab data •Need to link parameters in space and time think about aggregation approach DO vs. Chlorophyll •DO has 3 criteria measures Daily minimum (5 and 3 mg/L) 7-day mean (4 and 6 mg/L) 30-day mean (5.5 mg/L) •Some exceedances across sites, most in Provo Bay (red) DO vs. Chlorophyll •Linking DO with chlorophyll Best DO data will come from continuous sondes Best chlorophyll data will come from grab data •Need to link parameters in space and time think about aggregation approach Zooplankton vs. Chlorophyll: National Model •Analyzes the trophic relationship between phytoplankton and zooplankton Reflects efficiency of energy transfer between trophic positions A tight linkage between chlorophyll and zooplankton is observed at lower chlorophyll Relationship becomes decoupled at higher chlorophyll •To identify a chlorophyll target, select a slope threshold that protects against the trophic decoupling Zooplankton vs. Chlorophyll •Several zooplankton data for Utah Lake USU WFWQC BYU •Need to make sense of how to combine (different methods, taxa output) •What are the metrics of interest? Total, certain taxa? For any metrics, will need to be able to link to protection of designated use •Could update the national model with Utah Lake data, or use the national model conceptual setup to inform site-specific model Public Perception vs. Chlorophyll, Cyanobacteria, Clarity User perception survey is underway Annual Visitation vs. Chlorophyll, Cyanobacteria, Clarity •Utah DNR keeps record of visitation of Utah Lake State Park •Imperfect metric; does not capture total visitation to the lake •Additional data from user perception survey? •Can link visitation data to water quality data Monthly totals for visitation, subset for growing season? Growing season chlorophyll, cyanobacteria, clarity Clarity vs. Chlorophyll •Secchi depth negatively related to chlorophyll •Variability in this relationship high proportion of non-algal turbidity •Need to establish threshold for protective Secchi depth value Chlorophyll vs. TN and TP •Once chlorophyll target(s) identified, can link chlorophyll to nutrients •Positive relationship, variability could be due to nutrient limitation and non- bioavailable nutrient pools •Unequal variance may be suited for quantile regression Chlorophyll vs. TN and TP •EPA National Models include chlorophyll-nutrient models •Takes into account other pools of P and N (blue line) vs. phytoplankton (black line) •Lake-specific settings: ecoregion, lake max. depth, DOC, turbidity Cyanobacteria vs. TN and TP •Previous Analysis Quantiles: 0.1, 0.25, 0.5, 0.75, 0.9 Logistic regression w/ threshold of 100,000 cells/mL Clarity vs. TN and TP •Secchi depth negatively related to TP and TN •Variability in this relationship high proportion of non-algal turbidity •Need to establish threshold for protective Secchi depth value Decision Points for Today •Seasonal aggregation •Depth considerations •Period of interest •Spatial aggregation Decision Points: Aggregation •Growing/recreation season April-September Statistical metric: mean, geometric mean, median? •Depths to represent surface Suggest ≤ 1 m and composite surface Decision Points: Aggregation •Period of interest Use all available data? Use data only from x years ago to present? Decision Points: Aggregation •Extent: break lake into regions? Utah Lake currently has two Assessment Units: main basin and Provo Bay Add Goshen Bay as a separate zone? May lead to different targets for each region  consider management implications How to combine spatial and temporal aggregation? –Aggregate all sites for a given date, then aggregate to growing season –Aggregate each site to growing season, then aggregate across sites –Aggregate all samples across sites and dates for a growing season Decision Point: Statistical Approach, Uncertainty, Protectiveness •Statistical approach: Tetra Tech will propose a draft set of analyses for SP feedback •Uncertainty and Protectiveness: Tetra Tech will pose choices to SP Linear regression: choose prediction interval, confidence interval Quantile regression: choose quantile Logistic regression: choose probability National models (Bayesian): Credible interval, aka certainty level •Also need to coordinate this with DWQ standards staff Evaluating Numeric Targets •Magnitude “the maximum amount of the contaminant that may be present in a water body that supports the designated use” This value is most readily identified from analyses •Frequency “the number of times the contaminant may be present above the magnitude over the specified period (duration)” Examples: not to be exceeded, x exceedances in a season, x exceedances in y years •Duration “the period over which the magnitude is calculated“ Examples: grab (single date), seasonal central tendency •Some parameters already have these defined (e.g., microcystin, DO, pH) Questions and Discussion SEDIMENT DIAGENESIS THE MISSING LINK External Loads Sediment Demands and Releases Prepared by James L. Martin July, 2016 SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE •Sediment diagenesis results in oxygen demands and nutrient releases SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE •Sediment diagenesis results in oxygen demands and nutrient releases –a major sink of oxygen in aquatic environments •Leads to hypoxia –Hypoxic or Dead zones are becoming more common in estuarine and coastal environments and have, as reported in Science (Diaz and Rosenberg, 2008), spread exponentially since the 1960s and resulting in serious consequences for ecosystem functioning –As of 2008 (Diaz and Rosenberg 2008), dead zones have been reported from more than 400 systems, affecting a total area of more than 245,000 square kilometers. SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE •Sediment diagenesis results in oxygen demands and nutrient releases –a major source of nutrients in aquatic environments •Leads to eutrophication •Impacts nutrient criteria development SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE •So, how is it determined? –1) GUESS (e.g. model calibration) 0 1 2 3 4 5 6 7 00.511.522.533.5 River Mile DO C o n c e n t r a t i o n ( m g / l ) Average Minimum Maximum Without SOD With SOD SOD BOD Reaeration A REALLY BAD IDEA!! SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE •So, how is it determined? –2) MEASURE •How many measurements? •Where and When? THIS IS EXPENSIVE! SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE •So, how is it determined? –2) MEASURE (Core method) •How many measurements? •Where and When?THIS IS EXPENSIVE! University of Maryland Center for Environmental Science, 2006 SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE •So, how is it determined? –2) MEASURE •ALSO: how do we relate these measurements to external loads? •Sediment Diagenesis is driven by organic fluxes from the water column, which are ultimately derived from external loads CAUSE EFFECT THE MISSING LINK SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE •Ex. For wasteload allocations assume it does not change in response to load changes (i.e., use measured values)? Your permit will be based on assuming SOD will not change! That’s ridiculous, I will see you in Court you %^$@!!+@! SEDIMENT OXYGEN DEMAND AND NUTRIENT RELEASE •So, how is it determined? –3) MODEL (e.g. QUAL2K, CE-QUAL- ICM, WASP routines) From Chapra Pelletier, 2003. QUAL2K User documentation Di Toro, D. M. 2001. Sediment Flux Modeling, Wiley-Interscience, New York, New York. 624 pp. MODEL OVERVIEW Sediment Diagenesis See: Martin and Wool, 2014, “WASP Sediment Diagenesis Routines: Model Theory and User's Guide” Particulate Organics (C, N, P) Oxygen Dissolved Materials (N,P,CH4,H2S, etc.) Sediment Diagenesis Surface Area Solids Concentration in Layer 1 (kg/L) Solids Concentration in Layer 2 (kg/L) Thickness (assumed = 2 cm) H2 =Thickness (≈= 10 cm) Diffusion (T-20) d12 2 DKL = (H /2) θ Burial velocity to inactive sediments (m/day) Particle Mixing Reactor Diffusion: internal computation Particle Mixing Benthic Stress The rate of mixing of the sediment by macrobenthos (bioturbation, w12) is estimated by an apparent particle diffusion coefficient (Dp), temperature corrected that varies with the biomass of the benthos. Assuming that the mass of the benthos is proportional to the labile carbon in the sediment ( , or POC, in oxygen equivalents in layer 2 in G class 1), ( 20),1*12 2,/2 tTPOC P POC R CwDHC Θ −= where is a particle mixing coefficient and CPOC,R is a reference POC concentration. (Note POC is in units of oxygen equivalents). Particle Mixing Benthic Stress An additional impact is that if anoxia occurs for periods of time, the benthic population is ultimately reduced or eliminated, so that bioturbuation is consequently reduced or eliminated. To include this effect, Di Toro (2001) computes the stress that low dissolved oxygen conditions (benthic stress, S) imposes on the population assuming that the stress accumulates as , ,2[ (0)] P P tt tMDtt s MD KS SSkSt KO t ∆∆ ∆ ++∂−=−+ ≈∂+ where ks = decay constant for benthic stress, KM,Dp = particle mixing half-saturation concentration for oxygen As [O2(0)] approaches zero, then (1-ksS) approaches zero, so that the particle mixing coefficient is similarly reduced, as ()*12 12 1 ttsw w kS ∆+= − The stress is continued at the minimum value for the year to conform with the observation that once the benthic population has been reduced by low dissolved oxygen, it does not recover until the next year (Di Toro 2001). Reactor Inputs Constants Value Units Description SA SA 2832.68 m2 Surface area of sediments (computed) m1 m1 0.5 kg/L Solids concentration in layer 1 m2 m2 0.5 kg/L Solids concentration in layer 2 Dd Dd 2.50E-03 m2/d Diffusion coefficient between layers 1 and 2 ΘDd ThtaDd 1.08 none Temperature correction factor for Dd H2 H2 0.1 m Thickness of layer 2 w2 w2 6.85E-06 m/d Burial velocity for layer 2 to inactive sediments Dp Dp 6.00E-05 m2/day Diffusion coefficient fo particle mixing ΘDp ThtaDp 1.117 none Temperature correction factor for Dp POC1,R POC1R 0.1 mgC/g Reference POC1 concentration for Dp measurement ks kBEN_STR 0.03 1/day First-order decay coefficient for accumulated benthic stress KM,Dp KM_O2_Dp 4 mgO/L M-M half-saturation constant for oxygen wrt benthos Fluxes IN JPOM JPOM=vsACPOM vs=settling velocity, A=area, CPOM =POM concentration JPOM JPOC JPON JPOP G1 G2 G3 G1 G2 G3G1G2G3 G classes represent reactivity: G1=labile, G2=refractory, G3=inert G Class Input JPOM fPON1 fPON1 0.65 Fraction of PON that is labile fPON2 fPON2 0.25 Fraction of PON that is refractory fPOP1 fPOP1 0.65 Fraction of POP that is labile fPOP2 fPOP2 0.2 Fraction of POP that is refractory fPOC1 fPOC1 0.65 Fraction of POC that is labile fPOC2 fPOC2 0.2 Fraction of POC that is refractory JPOM JPOC JPON JPOP G1 G2 G3 G1 G2 G3G1G2G3 Mass Balance (for each POM and G class) JPOM 2 1 ()()tt t tt tt tt tt tt tt tt ttTTpT pT L dT dT T T T T HC HC fCfC KfCfC C CC Jtt ++ ++ + + ++ +− =− − − − − + −+ 22 22 12 22 11 12 22 11 22 2 1 2 2 ()ω κω∆∆ ∆∆ ∆ ∆ ∆∆ ∆ ∆∆ bioturbation diffusion diagenesis burial influx s = surface transfer rate; SOD/[O2(0)], where SOD=SOD rate and O2(0) is the overlying water concentration fd1 = fraction dissolved in layer 1 fd2 = fraction dissolved in layer 2 fp1 = fraction particulate in layer 1 fp2 = fraction particulate in layer 2 CT1t+∆t = total concentration in layer 1 at time t+∆t CT2t+∆t = total concentration in layer 2 at time t+∆t CT2t = total concentration in layer 2 at time t CdOt+∆t = concentration in overlying water column KL12 = mass transfer coefficient via diffusion ω12 = particle mixing coefficient between layers 1 and 2 ω2 = sedimentation velocity for layer 2 JT1t+∆t = source term for total chemical in layer 1 at time t+∆t JT2t+∆t = source term for total chemical in layer 2 at time t+∆t κ12 = square of reaction velocity in layer 1 Diagenesis in Layer 2 Diagenesis Input JPOM 2 1 ()()tt t tt tt tt tt tt tt tt ttTTpT pT L dT dT T T T T HC HC fCfC KfCfC C CC Jtt ++ ++ + + ++ +− =− − − − − + −+ 22 22 12 22 11 12 22 11 22 2 1 2 2 ()ω κω∆∆ ∆∆ ∆ ∆ ∆∆ ∆ ∆∆ bioturbation diffusion diagenesis burial influx Diagenesis in Layer 2 kPON1 kdiaPON1 0.035 1/day First order diagenesis (decay) rate constant for PON1 ΘPON1 ThtaPON1 1.1 none Temperature correction factor for kdiaPON1 kPON2 kdiaPON2 0.0018 1/day First order diagenesis (decay) rate constant for PON2 ΘPON2 ThtaPON2 1.15 none Temperature correction factor for kdiaPON2 kPON3 kdiaPON3 0 1/day First order diagenesis (decay) rate constant for PON3 ΘPON3 ThtaPON3 1.17 none Temperature correction factor for kdiaPON3 kPOP1 kdiaPOP1 0.035 1/day First order diagenesis (decay) rate constant for POP1 ΘPOP1 ThtaPOP1 1.1 none Temperature correction factor for kdiaPOP1 kPOP2 kdiaPOP2 0.0018 1/day First order diagenesis (decay) rate constant for POP2 ΘPOP2 ThtaPOP2 1.15 none Temperature correction factor for kdiaPOP2 kPOP3 kdiaPOP3 0 1/day First order diagenesis (decay) rate constant for POP3 ΘPOP3 ThtaPOP3 1.17 none Temperature correction factor for kdiaPOP3 kPOC1 kdiaPOC1 0.035 1/day First order diagenesis (decay) rate constant for POC1 ΘPOC1 ThtaPOC1 1.1 none Temperature correction factor for kdiaPOC1 kPOC2 kdiaPOC2 0.0018 1/day First order diagenesis (decay) rate constant for POC2 ΘPOC2 ThtaPOC2 1.15 none Temperature correction factor for kdiaPOC2 kPOC3 kdiaPOC3 0 1/day First order diagenesis (decay) rate constant for POC3 ΘPOC3 ThtaPOC3 1.17 none Temperature correction factor for kdiaPOC3 Ammonia JPOM 2 1 Diffusion (dissolved) Bioturbation (particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of PON -Nitrification Ammonia Input 4111 41 41 1 ;11NHdp NH NH SffSS π ππ==++ 4221 42 42 1 ;11NHdp NH NH SffSS π ππ==++ Partitioning where S1 and S2 are solids concentrations in layer 1 and 2 and is a partition coefficient T NH tt O NH d NH T nitrification f f f Cs − += − 2 204,1 4 1 4 ,1 κθ ∆ JPOM 2 1 Diffusion (dissolved) Bioturbation(particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of PON -nitrification 2,0 2,0 4, 2 O NH O OfOK=+ 44 4,1 4 NHNHt NH NH KfCK=+ s=surface transfer/diffusion rate with water column, κNH4= reaction velocity, =temperature coefficient, O2,0=dissolved oxygen concentration in the overlying water column, and KNH4,O2 =half-saturation concentration of dissolved oxygen in the nitrification reaction, CNH4= ammonia concentration from the previous time step, KNH4 = half-saturation concentration of ammonia in the nitrification reaction Ammonia Input κNH3 KappaN H3F 1.31E-01m/d First nitrification step (NH3→NO2) reaction velocity, fresh water κNH3 KappaN H3S 1.31E-01m/d First nitrification step (NH3→NO2) reaction velocity, salt water ΘNH3 ThtaNH3 1.123none Temperature correction factor for κNH3 KM,NH3 KM_NH 3 0.728mg/L M-M half-saturation constant for ammonia in NH3→NO2 KM,O2,NH3 KM_O2_ NH3 0.37mg/L M-M half-saturation constant for oxygen in NH3→NO2 KdNH3 KdNH3 1L/kg NH3 distribution (partition) coefficient (both Layer 1 and Layer 2) Note impact of salinity! JPOM 2 1 Diffusion (dissolved) Bioturbation(particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of PON -nitrification SALTND 1 ppt Salinity above which salt water nitrification and denitrification rates apply Nitrite JPOM 2 1 Diffusion (dissolved) Diffusion (dissolved) Burial (particulate) +Nitrification to NO2 – Reaction (Nitrification) to NO3 Notes: •assumed all dissolved; •reaction rate modified by oxygen in overlaying water and temperature •rate not impacted by salinity, •Annamox not considered Sedimentation (Burial) Nitrite Input JP OM 2 1 Diffusion (dissolved) Diffusion (dissolved) Burial (particulate) +Nitrification to NO2 – Reaction (Nitrification) to NO3 κN02 KappaNO2F 100m/d Second nitrification step (NO2→NO3) reaction velocity, fresh water κN02 KappaNO2S 100m/d Second nitrification step (NO2→NO3) reaction velocity, salt water ΘNO2 ThtaNO2 1.123none Temperature correction factor for κNO2KM,O2,NO2 KM_O2_NO2 0.37mg/L M-M half-saturation constant for oxygen in NO2→NO3 Nitrate JPOM 2 1 Diffusion (dissolved) Diffusion (dissolved) Sedimentation (Burial) +Nitrification to NO3 Notes: •assumed all dissolved; •reaction rate modified by oxygen in overlaying water and temperature •denitrification rate impacted by salinity, •Annamox not considered -Denitrification -Denitrification Sedimentation (Burial) Nitrate JP OM 2 1 Diffusion (dissolved) Diffusion (dissolved) Sedimentation (Burial) +Nitrification to NO3-Denitrification -Denitrification Sedimentation (Burial) κN03,1 KappaNO3_1F 0.1m/d Denitrification reaction velocity in layer 1, fresh water κN03,1 KappaNO3_1S 0.1m/d Denitrification reaction velocity in layer 1, salt water ΘNO3 ThtaNO3 1.08none Temperature correction factor for κNO3 in both layer 1 and 2 Sulfides (Salt water only) JPOM 2 1 Diffusion (dissolved) Bioturbation (particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of POC (in oxygen units and corrected for denitrification) -decomposition of particulate sulfide and dissolved sulfide Sulfide Input Partitioning where S1 and S2 are solids concentrations in layer 1 and 2 and are partition coefficients for layer 1 and 2 JPOM 2 1 Diffusion (dissolved) Bioturbation(particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of PON -decay s=surface transfer/diffusion rate with water column, κ = reaction velocities for particulate or dissolved form, O2,0=dissolved oxygen, and KMHS,O2=half-saturation concentration of dissolved oxygen in the reaction, CH2S= sulfide concentration ,1 1 11 ,1 1 ,1 1 1 ;11 HS dp HS HS SffSS π ππ==++ ,2 2 21 ,2 2 ,2 2 1 ;11 HS dp HS HS SffSS π ππ==++ 2,0 ,2 O MHS O OfK=DHS PHS tt O d p HS decay f f f Css +=−+ 22, 2 ,1 , 2 ,11 1 2 ,1 κκ ∆ Sulfide Input JPOM 2 1 Diffusion (dissolved) Bioturbation(particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of PON -decay kHS1 KappaHSD_1 0.2 m/d Dissolved sulfide oxidation reaction velocity in layer 1 kHS2 KappaHSP_1 0.4 m/d Particulate sulfide oxidation reaction velocity in layer 1 ThtaHS ThtaHS 1.079 none Temperature coefficient for sulfide oxidation KM_O2_H S KM_O2_HS 4 mgO2/l Sulfide oxidation normalization constant KdHS1 KdHS1 100 L/kg Sulfide partition coefficient in layer 1 KdHS2 KdHS2 100 L/kg Sulfide partition coefficient in layer 2 Methane (Fresh water only) JPOM 2 1 Diffusion (dissolved) + flux from carbon diagenesis: Maximum methane production related to diagenesis of POC (in oxygen units and corrected for denitrification); that is remaining carbon diagenesis is converted to carbon dioxide and methane Diffusion (dissolved)-oxidation Methane Solubility: Gas production JPO M 2 1 Diffusion (dissolved) + flux from carbon diagenesis: Maximum methane production related to diagenesis of POC (in oxygen units and corrected for denitrification); that is remaining carbon diagenesis is converted to carbon dioxide and methane Diffusion (dissolved) -oxidation 0 20 40 60 80 100 120 140 160 180 0 5 10 15 20 25 30 35 40 CH 4 S a t u r a t i o n ( m g O 2 / L ) Temperature (oC) Di Toro Eq. 10.51 Conv. from Yamamoto et al. (20-T)OCH4,SAT H C = 100 1+ 1.02410  where Ho is the depth of the water column over the sediment. Methane may be oxidized, producing sediment oxygen demand, or exchanged with the water column in either gaseous or dissolved form. Methane Inputs JPOM 2 1 Diffusion (dissolved) + flux from carbon diagenesis: Maximum methane production related to diagenesis of POC (in oxygen units and corrected for denitrification); that is remaining carbon diagenesis is converted to carbon dioxide and methane Diffusion (dissolved) -oxidation κCH4 KappaCH4 0.7 m/d Methane oxidation reaction velocity ΘCH4 ThtaCH4 1.079 none Temperature correction factor for κCH4 Solution Procedures •Solution may be –steady-state –time variable •Assume two layers, a “thin” (oxic, depending on overlying water) upper layer and anaerobic layer –assume that layer 1 can be considered at steady-state in relation to layer 2 (matrix solution) Fluxes to water column •Computed based on surface transfer rate (s) ()11 tt tt d T dO J sfC C ∆∆++= − •So, the computation of SOD requires an iterative solution ,2 12[ ( )] LO D SODKsH Oo = = = •That depends upon the SOD and overlying oxygen concentration Computation of SOD •Start with an initial estimate of the SOD •Solve layer 1 and 2 equations for ammonia, nitrate, sulfide and methane –Solve for the ammonia flux by establishing the chemical specific conditions –Compute the oxygen consumed by nitrification (NCOD) –Solve for the nitrate flux by establishing the chemical specific conditions –Compute methane (fresh water) or sulfide (salt water) oxidation •For salt water, compute sulfide reaction terms and compute SOD due to hydrogen sulfide •For fresh water, compute methane flux by establishing the chemical specific –Compare computed and saturation concentrations and correct –Calculate the CSOD due to methane –Compute the total CSOD due to sulfides or methane –Compute flux terms –Compute the total SOD due to the sulfide or methane, adding term for NCOD –Refine the estimate of SOD. A root finding method is used to make the new estimate •Go to step (2) if no convergence 2 20 2,1 2 2 2,1 T NO tt NO no O NO NSOD a f Cs κθ ∆ − +=12 0.5(32)1.1414noa gO gN −= = 2230.5NO O NO −−+→ ()2 2 T-20, ,1 , ,1 O 2 ,1 fHS D D HS P P tt HS H S ffCSOD Cs κ κθ ∆++= 4 max 1(1 ( ))CH cCSOD CSOD Sech Hλ= − 42HSNHNOSOD CSOD CSOD CSOD=++ Salt water Fresh water 442CHNHNOSOD CSOD CSOD CSOD=++ 44 2 204,1 4 1 ,1 T NH tt NH no O NH d NH NSOD a f f f Cs κθ ∆ − += 4 2 221.5 2NH O H NO H O ++−+ →++ 11.5(32)3.4314noa gO gN −= = Other Variables •Once the SOD and s are known (computed), then other model variables, not impacting SOD, may be computed –Phosphates –Silica Phosphates JPOM 2 1 Diffusion (dissolved) Bioturbation (particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of POP Phosphate Input Partitioning where S1 and S2 are solids concentrations in layer 1 and 2 and is a partition coefficient JPOM 2 1 Diffusion (dissolved) Bioturbation(particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of POP 4,1 1,1 ,1 4,1 1 4,1 1 1 ;11 POdp PO PO SffSS π ππ==++ 4,2 2,2 ,1 4,2 2 4,2 2 1 ;11 POdp PO PO SffSS π ππ==++ For layer 1, the aerobic layer, if the oxygen concentration in the overlying water column exceeds a critical concentration (O2CRIT, specified in input) then the partition coefficient is increased to represent the trapping of phosphates, or sorption onto iron oxyhydroxide. If the dissolved oxygen is below the critical value, then the sorption coefficient in layer 1 goes to zero. Phosphate Input JPOM 2 1 Diffusion (dissolved) Bioturbation(particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of PON -nitrification KdPO4,2 KdPO42 20L/kg PO4 distribution (partition) coefficient under anaerobic conditions (layer 2) ∆KdPO4,1 dKdPO41F 20none Incremental distribution coefficient under aerobic conditions, fresh water ∆KdPO4,1 dKdPO41S 20none Incremental distribution coefficient under aerobic conditions, salt water O2crit,PO4 O2critPO4 2mgO/L Critical oxygen conc. where Kd begins to decrease due to low DO Silica JPOM 2 1 Diffusion (dissolved) Bioturbation (particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of PSi -decay Silica Input Partitioning where S1 and S2 are solids concentrations in layer 1 and 2 and is a partition coefficient JPOM 2 1 Diffusion (dissolved) Bioturbation(particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of PSi -dissolution ,1 1,1 ,1 ,1 1 ,1 1 1 ;11 Sidp Si Si SffSS π ππ==++ ,2 2,2 ,1 ,2 2 ,2 2 1 ;11 Sidp Si Si SffSS π ππ==++ For layer 1, the aerobic layer, if the oxygen concentration in the overlying water column exceeds a critical concentration (O2CRITSI, specified in input) then the partition coefficient is increased to represent the trapping of silica, or sorption onto iron oxyhydroxide. If the dissolved oxygen is below the critical value, then the sorption coefficient in layer 1 goes to zero as in (Di Toro 2001, Eq. 7.18) ( 20)3 ,2 , T SiSid Si m PSi PkfPKκΘ−=+ PSi = the biogenic silica diagenesis flux to which detrital silica was added; Km,PSi=half saturation constant; kSi= rate of silica dissolution; Silica Input JPOM 2 1 Diffusion (dissolved) Bioturbation(particulate) Diffusion (dissolved)Burial (particulate) Burial (particulate) + Flux due to diagenesis of PSi-dissolution -nitrification ? Presently not in WASP; need to add Inputs to Diagenesis Model •Fluxes (C,N,P, Si; see previous slides) •Rates and constants (see previous slides) •Overlying water column [f(time, space)] –NH4–NO2–NO3–PO4–O2 –Salinity –Available Silica –CH4–Temperature –Salinity Inputs to Diagenesis Model •Initial Conditions –POM for each G-class in Layer 2 •PON(1), PON(2), PON(3) •POP(1), POP(2), POP(3) •POC(1), POC(2), POC(3 –Dissolved concentrations (for layers 1 and 2) •Dissolved NH3 •NO2•NO3•Dissolved PO4 From restart file, discussed later Outputs from Diagenesis Model •Ammonia flux to water column (mg/m2-day) •Nitrite flux to water column (mg/m2-day) •Nitrate flux to water column (mg/m2-day) •PO4 flux to water column (mg/m2-day) •Aqueous Methane flux to water column (gO2/m2-day) •Gas Methane flux to water column (gO2/m2-day) •SOD Sediment Oxygen demand (gO2/m2-day) •Sulfide flux to water column (gO2/m2-day) •Dissolved (available) silica flux to water column What’s Missing •Iron and manganese •multiple layers and ability to simulate impact of scour and sedimentation •Impact of benthic algae •Impacts/simulation of rooted macrophytes •Other stuff? WASP 8 IMPLEMENTATION WASP 8 IMPLEMENTATION: MODEL TIME STEP See: Martin and Wool, 2014, “WASP Sediment Diagenesis Routines: Model Theory and User's Guide” WASP 8 IMPLEMENTATION: MODEL PARAMETERS See: Martin and Wool, 2014, “WASP Sediment Diagenesis Routines: Model Theory and User's Guide” Descriptive Descriptive WASP 8 IMPLEMENTATION: MODEL PARAMETERS See: Martin and Wool, 2014, “WASP Sediment Diagenesis Routines: Model Theory and User's Guide” WC 1 WC 2 SD 1 WASP 8 IMPLEMENTATION: MODEL CONSTANTS See: Martin and Wool, 2014, “WASP Sediment Diagenesis Routines: Model Theory and User's Guide” WASP 8 IMPLEMENTATION: TYPICAL PROCESS Preliminary Calibration and Model Set-Up Run model (including diagenesis model) for extended period (years) with Restart Option Estimate diagenesis Initial Conditions, etc. Check model predictions for quasi-steady and realistic conditions Iterate as necessary QUESTIONS? COMMENTS? Utah Lake Water Quality Study Overview of Utah Lake WASP Model Enhancements and Progress March 24, 2023 Utah Lake Model Enhancements •Overall project objective Develop a predictive model of hydrodynamics and water quality (organic matter, nutrient cycling and phytoplankton dynamics) in Utah Lake •Scope of work included following model enhancements to improve performance of Utah Lake hydrodynamic and water quality models Improved representation of physical processes by implementing a wind-wave model coupled to EFDC Incorporation of sediment diagenesis in all bottom cells of the model domain Incorporation of pH and alkalinity as simulated state variables of the water quality model Improvement of overall model performance, stability and run-time efficiency •Purpose of this presentation is to explain how each of these have been addressed and to summarize current model performance Topics •Background on Individual Models EFDC SWAN WASP •How the Models Work Together •Model Hydrodynamic Performance •Model Enhancements Water Quality –Sediment Diagenesis –pH Other –Sediment Transport –P-Binding –Model efficiency •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 S3S1S2ox 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 Computational grid Simulated transport: WSE Simulated transport: Temperature 4917390 4917450 Simulated transport: Significant Wave Height Simulated transport: Shear Stress Water Quality Model Enhancements Incorporation of sediment diagenesis in all cells Approaches to simulate sediment nutrient fluxes •Descriptive: SOD, nutrient fluxes are user defined •Predictive: Modeled based on sediment organic matter content and settling fluxes of organic matter Basis of sediment diagenesis model Initial conditions •Organic C, N, P concentrations in sediments (g/kg) Sediment nutrient fluxes •SOD, NH3, NO3, PO4 Kinetic rates •POM dissolution •DOM mineralization •Nitrification Basis of sediment diagenesis model Example SOD Incorporation of pH Additional Enhancements Sediment Transport P-Binding Model efficiency Sediment transport •Improvements to simulate wind induced wave impacts in EFDC alone, not enough to impact water quality •Incorporated EFDC code upgrades to send simulated ISS from EFDC to WASP via hydrodynamic file Coordinated effort with Tim Wool Send shear stress and simulate ISS in WASP Directly send ISS to WASP from EFDC •Full impact of wind induced wave shear into sediment simulations •WASP receives ISS concentrations and use them to calculate light extinction •Mechanistic Carbonate system pH Ca balance -budget •Advantages Full representation of inorganic Carbon cycle/buffer Impacts on pH •Disadvantages Not available in WASP Needs development and incorporation into WASP Computationally intensive Extensive data required From: http://ocean.stanford.edu/courses/bomc/chem/lecture_10.pdf P-Binding Mechanistic Approach •Simplified (Proposed) Partition coefficient Simulate P-deposition Assumed equilibrium between dissolved/particulate fraction •Cs (mg/Kg)= K* Cw(mg/L) K (L/Kg) •Advantages Simple. Computationally inexpensive Net impact captured. Settling •Disadvantages Approximation. Independent from pH –Carbon cycle Sediment PO4- settling P-Binding Simplified Approach