Modeling and observing nutrient dynamics in Puget Sound PRISM “inc.” Jan Newton, WA Ecology and UW UW: Al Devol, Kate Edwards, Steve Emerson, Miles Logsdon, Mitsuhiro Kawase, Jeff Richey, Mark Warner WA Ecology: Skip Albertson, Rick Reynolds KC-DNR: Bruce Nairn, Randy Shuman

Lo nutrientHi oxygen Phytoplankton present Hi nutrientLo oxygen No phytoplankton Phytoplankton present No phytoplankton { CO 2 + H 2 O  C(H 2 O) + O 2 } sunlight nutrients

Lo nutrientHi oxygen Phytoplankton present Hi nutrientLo oxygen No phytoplankton 3 common problems in oceanography: { CO 2 + H 2 O  C(H 2 O) + O 2 } sunlight nutrients Nutrient concentration: what does it really tell us? Advection vs. growth: how do you differentiate? µ = delta P / [P * time] we can’t easily measure it!

riverocean What is an estuary ??

Grays Harbor Willapa Bay Strait of Juan de Fuca Puget Sound NE Pacific Ocean Strait of Georgia

Re Puget Sound Dynamic and diverse Scales of variation: –temporal –spatial Boundary conditions: –ocean, river, atmosphere Drivers of change: –Climate –Humans Investigative tools: –Monitoring – Observing – Time-Series –Models, Experimentation

Pacific climate variability, Steven Hare, UW, 1999

ENSO during the last decade NOAA

April 1999 April 1998 Ocean properties are not “constant” m 10 m Smith et al Depth (m) Distance from shore (km) The depth of the thermocline was much deeper following El Niňo than La Niňa. This affects not only the temperature but also the nutrients available at the surface. In fact, we did find more phytoplankton on the coast during summer of 1999 than 1998.

Effect of a drought on river flow… USGS, 2000

Lo nutrientHi oxygen Phytoplankton present Hi nutrientLo oxygen No phytoplankton { CO 2 + H 2 O  C(H 2 O) + O 2 } Add “new” nutrients from human activity: fertilized lawns, sewers, leaking septic tanks, animals, etc. So what do humans do?? Lo oxygen can get lower !!! * sunlight nutrients

Lo nutrientHi oxygen Phytoplankton present Hi nutrientLo oxygen No phytoplankton { CO 2 + H 2 O  C(H 2 O) + O 2 } Add “new” nutrients from human activity: fertilized lawns, sewers, leaking septic tanks, animals, etc. So what do humans do?? Lo oxygen can get lower !!! * sunlight nutrients

Overarching Goal “Through a strongly interacting combination of direct observations and computer models representing physical, chemical, and biological processes in Puget Sound, provide a record of Puget Sound water properties, as well as model now-casts and projections. The information will be used to develop a mechanistic understanding of the Sound’s dynamics, how human actions and climate influence these (e.g., “what-if scenarios”), and how, in turn, water properties influence marine resources and ecosystem health (linkage with other PRISM elements).”

Key questions Understanding plankton dynamics in a temperate fjord: - What physical dynamics of water mass variation most influence stratification, and what is the phytoplankton response? - How important is nitrate versus ammonium in controlling phytoplankton production? - What controls light availability for phytoplankton in the euphotic zone? Assessing ecosystem integrity: - Do salmon have food they need to survive? Is timing ok and what affects that? - What food-web shifts (e.g., macrozoops vs. gelatinous) affect fish etc survival? - How does an invasive species with certain growth/grazing characteristics impact food-web? Understanding perturbation impacts (e.g., climate, human): - How does productivity differ with ENSO and PDO stages? - How does flushing differ with ENSO and PDO stages? - Do land-use practices affect water properties and phytoplankton?

Uses and benefits The information will be used –for teaching at various levels –to promote and aid research –to help define effective regional planning Public benefit includes: –Resource and habitat protection (e.g., clean water, fish, shellfish) –Waste/pollution planning and allocation –Puget Sound quality maintenance

Climate variation impacts Remote sensing Modeling Observations Partnerships/ Monitoring Virtual Puget Sound

Climate variation impacts Remote sensing Modeling Observations Partnerships/ Monitoring Virtual Puget Sound

Ships & Buoys

Climate variation impacts Remote sensing Modeling Observations Partnerships/ Monitoring Virtual Puget Sound

Marine Water Quality Index Ships & Buoys

Climate variation impacts Remote sensing Modeling Observations Partnerships/ Monitoring Virtual Puget Sound

Marine Water Quality Index Remote sensing Ships & Buoys

Climate variation impacts Remote sensing Modeling Observations Partnerships/ Monitoring Virtual Puget Sound

Marine Water Quality Index El Niño vs La Niña Remote sensing Ships & Buoys

Climate variation impacts Remote sensing Modeling Observations Partnerships/ Monitoring Virtual Puget Sound

Marine Water Quality Index El Niño vs La Niña Remote sensing Ships & Buoys Aquatic biogeochemical cycling model

Marine Water Quality Index El Niño vs La Niña Remote sensing Ships & Buoys Aquatic biogeochemical cycling model

Climate variation impacts Remote sensing Modeling Observations Partnerships/ Monitoring Virtual Puget Sound

Observing Nutrient Dynamics PRISM Observations PRISM-sponsored cruises Partnership with WA Ecology and King Co DNR monitoring (PSAMP) JEMS: Joint Effort to Monitor the Strait, co-sponsored by MEHP, et al. ORCA: Ocean Remote Chemical-optical Analyzer, initial sponsorship EPA/NASA, also WA SG, KC-DNR

Annual June and Dec. cruises; 10 so far Greater Puget Sound including Straits Synoptic hydrographic, chemical, and biological data Input for models, student theses, regional assessments PRISM cruises

Student training and involvement –UG and G; majors and non-majors Data collection on synoptic basis –verification for models –time-series at solstices Involvement of larger community –media, K-12, other marine programs, local governments Value of a PRISM cruise?

PRISM cruise participation: UW Undergraduates - 34 persons, 60 trips (41%) –Oceanography - 30 –Other Majors - 4 [UW Tacoma, Biochemistry, Computer Sci, Fisheries] UW Grad Students- 21 persons, 23 trips (16%) –Oceanography - 11 –Other Majors - 10 [Chem, Geol, Appl Math, Biol, Genetics, Sci Ed, Foriegn] WA State Dept. Ecology - 8 persons, 20 trips UW Faculty - 4 persons, 13 trips King County DNR - 4 persons, 5 trips US Coast Guard Techs - 6 persons Congressional Staff - 6 persons Media - 4 persons Totals : 94 persons, 146 trips UW Staff - 3 persons 57% student labor CORE - 2 persons NOAA/PMEL - 1 person Ocean Inquiry Project - 1 person High School Teacher - 1 person Data after 7 cruises:

PRISM Observations: Hood Canal Oxygen and Ammonium

JEMS line Joint Effort to Monitor the Strait (JEMS) King County MEHP PRISM Ecology NOAA Friday Harbor Labs

JEMS visits the three stations monthly. Data collection began September 1999 and is ongoing. sensor profiles - Temperature - Salinity - Density - Oxygen - Chlorophyll a bottle samples (0, 30, 80, 140 m) - Oxygen - Nutrients - Chlorophyll a net tows - Plankton - Larvae

Temperature With 2 1/2 years of data we can begin to study the interannual variation of water properties passing through the Strait and into the Puget Sound. Determining the inter- annual variation of water properties in the Strait is necessary for understanding variation in San Juans and Puget Sound.

Salinity Local: High salinity at depth on US side. Low salinity at surface on Canadian side. Annual: Low salinity water mixes down during winter. High salinity water enters during summer. Interannual: drought is easily observed.

Temperature Salinity Compare Sept 2000 with Sept 2001 Q1: What effects can we expect from climate variation ??

How did the environment vary in 2000 vs. 2001? Air Temperature: No apparent difference Sunlight: No apparent difference Upwelling: No apparent difference River Discharge: Drought, Fall 2000 Increased flow, Fall 2001 Skagit River Discharge

fresher, warmer water from Sound and San Juans flowing out colder, salty water from Pacific Ocean flowing in North Canada South U.S.A.

Cross-Channel Density Gradient North South Warmer fresher water drives stronger density gradient during Sep 2001 than in Sep 2000

Geostrophic Velocity High River Flow Large Cross- Channel Gradient Increased Geostrophic Out Flow Decreased Residence Time 2000 drought had consequences…

Partnership: Ecology PSAMP monitoring Analysis of monitoring data identified South Puget Sound as an area susceptible to eutrophication Led to focused study on South Sound nutrient sensitivity (SPASM) Coordination of SPASM and PRISM modeling/observ. Q2: Where is Puget Sound most sensitive to nutrient loading and are affects being seen ??

~3000 ~2000 n=19 n=5 x 80 n=30 n=8 Primary Production (mg C m -2 d -1 ) > > > > Newton et al., 2001

32/ 79 28/78 13/17 10/16 15/20 9/14 4/11 11/ / 51 % increase in integrated / surface prod’n 5-15 / >10-30 >15-25 / >30-50 >25-35 / >50-70 >35 / >70 Newton et al., 2001

Hood Canal South Sound Central Basin Effect of added nutrients: Newton et al., 2001

July , 2000 enhancement October 15-21, 2000Sept. 20- Oct. 2, 2000 no enhancementsurface enhancement Sigma-t Chl ug/l O2 mg/l enhancement no enhancement surface enhancement depth (m) primary productivity (mg C m -3 d -1 ) Effect of nutrient addition on phytoplankton productivity blue = ambient production red = spiked with NH 4 & PO 4 Carr Inlet, WA Ecology Newton and Reynolds, 2002 ORCA website

July , 2000 enhancement October 15-21, 2000Sept. 20- Oct. 2, 2000 no enhancementsurface enhancement Sigma-t Chl ug/l O2 mg/l enhancement no enhancement surface enhancement depth (m) primary productivity (mg C m -3 d -1 ) Effect of nutrient addition on phytoplankton productivity blue = ambient production red = spiked with NH 4 & PO 4 Carr Inlet, WA Ecology Newton and Reynolds, 2002 ORCA website

Partnership: KC- DNR’s WWTP siting Region’s growth is requiring greater capacity to treat wastewater. New WWTP proposed. KC MOSS study to site marine outfall and assess potential impacts Coordinated modeling and observ. effort with PRISM Marine outfall zones with depth contours Q3: “What if” we built a new outfall in Central Puget Sound ??

Modeling Nutrient Dynamics PRISM Models POM model: Princeton Ocean model, hydrodynamics ABC model: Aquatic Biogeochemical Cycling

ABC Model

What is an Aquatic Biogeochemical Cycling Model and why develop one for PRISM? Describes the dynamics of nutrients, plankton, and organic material in a water column; this has defining importance for water quality, food for higher trophic levels, and change impact projections. Water quality models commonly in use take more of a curve-fitting approach, are composed of antiquated coding, and do not support teaching as well. The model is an essential tool for exploring the fundamentals of biogeochemical cycling in Puget Sound, for use in planning or ”what-if” scenarios, and for use in teaching and communication.

ABC model development Identified the need Design box and wire Mathematically define transfer processes Develop model architecture and code Create GUI Test (Ocean 506b) Interface with hydrodynamic model

ABC model development Phytoplankton –Reynolds, Newton Zooplankton –Gentleman, Leising Nutrients/organics –Devol Oxygen –Warner Hydrodynamics –Kawase, Albertson, Nairn Light –Reynolds Model coding –Davis –Serper Model architecture –Logsdon Model implementation –Nairn Model integration –Averill –Nairn –Logsdon

Aquatic Biogeochemical Cycling Model: Features Under active development (UW, WDOE, KCDNR) Simulates three-dimensional concentrations of chemical and biological entities: Dissolved oxygen and nutrients (NO 3, PO 4, NH 4 ) Phytoplankton biomass (three types) Zooplankton biomass (three types) Particulate and dissolved organic matter (C, N, P) Externally forced by hydrodynamics and sunlight Designed to interface with a variety of circulation models including POM, linkage to MM-5 and SWIM Spatially explicit model based on published equations for biological and chemical reactions

Biogeochemical Systems Model rPONrPOP lPOC lPON lPOP DOC DON DOP O2O2 NO 3 NH 4 PO 4 Z1  ic Z2 mac Z3 gel P1 flag P2 dia P3 nan rPOC 19 state variables 48 transfer processes

State Variables: P1, P2, P3: dPi/dt = ps P-O2 – pr O2-P – hg P-Z – pe P-DOM – pd P-r,lPOM – cs P-out growth – respiration – grazing – exudation – cell death – cell sinking Z1, Z2, Z3: dZi/dt = – zr O2-Z – zd Z-DOM – ze Z-r,lPOM – zp Z-out – zm Z-r,lPOM + zg [ P,Z,r,lDOM]- Z – cg Z-Z + zs Z-Z – respiration – exudation – egestion – predation – mortality + grazing – carnivory + swimming

NH 4 : dNH 4 /dt = ne P-NH4 + nx Z-NH4 + bm [DOM,r,lPOM]-NH4 – nu NH4-P – ni NH4-NO3 phytopk excretion + zoopk excretion + bacterial remineralization – nutrient uptake – nitrification NO 3 : dNO 3 /dt = ni NH4-NO3 - nu NO3-P nitrification – nutrient uptake {airborne deposition, precipitation at surface}

Biogeochemical Systems Model rPONrPOP lPOC lPON lPOP DOC DON DOP O2O2 NO 3 NH 4 PO 4 Z1  ic Z2 mac Z3 gel P1 flag P2 dia P3 nan rPOC

Transfer Processes: ps: photosynthesis (16) ps = Pi  o i e RiT min { rll, rnu N, rnu P } where:  o i = maximal growth rate for Pi =  o i Tbase * e -Ri*Tbase  o i Tbase = maximal growth rate for Pi at Tbase Tbase = base temperature Ri = temperature growth coefficient for Pi T = temperature (input) rll, rnu N, rnu P = resource limitation factors: relative degree of growth limitation due to light, nutrient uptake for N, or for P

Resource limitation factors: rll= 1 - e –E k i /E target theory for photosynthesis E k i = light saturation coefficient for Pi E = light (input) rnu N = rnu NH4 + rnu NO3 rnu P = rnu PO4 rnu NH4 = NH 4 Monod function K i NH4 + NH 4 rnu NO3 = NO 3 * K i NH4 ammonium K i NO3 + NO 3 K i NH4 + NH 4 inhibition term K i [nutr] = half saturation constant for Pi on nutrient [NO 3, NH 4, PO 4 ]

Nitrate [µM] Irradiance [µmol m -2 s -1 ] Temperature [°C] Phytoplankton specific-growth rate, d -1 R. Reynolds

Biogeochemical Systems Model rPONrPOP lPOC lPON lPOP DOC DON DOP O2O2 NO 3 NH 4 PO 4 Z1  ic Z2 mac Z3 gel P1 flag P2 dia P3 nan rPOC

nu: nutrient uptake (13a, b, c) from NO 3, NH 4, PO 4 to Pi nu NH4-P = ps / stoich(C:N) 2 * rnu NH4 rnu NH4 + rnu NO3 nu NO3-P = ps / stoich(C:N) 1 * rnu NO3 rnu NH4 + rnu NO3 nu NO3-O2 = nu NO3-P / stoich(N:O) 1 (to account for O 2 produced during assimilative nitrate reduction) nu PO4-P = ps / stoich(C:P) 1 where: rnu NH4 = NH 4 K i NH4 + NH 4 rnu NO3 = NO 3 * K i NH4 K i NO3 + NO 3 K i NH4 + NH 4 K i [nutr] = half saturation constant for Pi on nutrient [NO 3, NH 4, PO 4 ]

Biogeochemical Systems Model rPONrPOP lPOC lPON lPOP DOC DON DOP O2O2 NO 3 NH 4 PO 4 Z1  ic Z2 mac Z3 gel P1 flag P2 dia P3 nan rPOC

hg: herbivorous grazing (1-9) from Pi to Zi; i = 1-3; j = 1-3 hg P-Z = P1g + P2g + P3g Pig = Zj * Imax* max (B - Co, 0) *  j Pi * O 2. K iA + B A K iO2 + O 2 where:I max = maximal ingestion rate = I maxTbase * e -fz(T)*Tbase I maxTbase = maximal ingestion rate at Tbase Tbase = base temperature Co = feeding threshold level, below which no grazing occurs  j = preference for prey type, j=1-8: 1=P1, 2=P2, 3=P3, 4=Z1, 5=Z2, 6=Z3, 7=lPOM, 8=rPOM K iA = half-saturation constant for total food K iO2 = half saturation constant for Zi on O 2 A = total food available =  1*P1 +  2*P2 +  3*P3 +  4*Z1 +  5*Z2 +  6*Z3 +  7*lPOM +  8*rPOM B = total food = P1 + P2 + P3 + Z1 + Z2 + Z3 + lPOM + rPOM

Can run in Chemostat mode

1-cell ABC model output, constant light, no mixing

But want multi-cell resolution with hydrodynamics Run coupled ABC-POM Test in Budd Inlet Previous EFDC model runs and field data

Model Inputs SW Rad [W m -2 ] Air Temp [°C] Rel. Humidity [%] River Flow [m 3 s -1 ]

Date Depth [m] Temperature at oceanic boundary (DNA001) °C Boundary Conditions

Surface Bottom Temperature [°C] Data comparisons

Northing (km) 19 Depth (m) 0 14 degrees C Longitudinal section of temperature in Budd Inlet

mmoles phyto C /m 3 Plan view of phytoplankton conc. in Budd Inlet Easting (km) Northing (km)

Aquatic Biogeochemical Cycling Model: Applications Primary applications are to assess: –dynamics of phytoplankton blooms (eutrophic’n, HABs) –dynamics of dissolved oxygen and water quality –sensitivity to changes, both human (e.g., WWTP, climate change) and natural (e.g., ENSO, regime shift) Suitable for both marine and freshwater systems Supports linkages; will provide output to –nearshore sediment-biological model –higher trophic level models (e.g., salmon!) Same tool can be used for teaching, basic research, applied research, and planning decisions.

Aquatic Biogeochemical Cycling Model: Status Coded in C++ by Computer Science Honors UG User-friendly web interface (GUI) allows easy model runs, storing coefficients 1-cell model and web interface used and tested in graduate-level class Spring, 2000 Coupled ABC to POM; testing coupled model in Budd Inlet against other model output and field data Soon to be able to run coupled model from web Working on visualization schemes for sections, time- series, and animations

Goals for achieving VPS Internal to ABC: –Sediment module ABC needs directly: –POM (hydrodynamics) DSHVM (river input) MM-5 (weather forcings) ABC can support: –Sediment/toxics transport and fate –Nearshore processes (NearPRISM) –Upper trophic levels (e.g., fish management) –HABs

MEPS: “A Partnership for Modeling the Marine Environment of Puget Sound, Washington” NOPP / PRISM Kawase et al. Develop, maintain and operate a system of simulation models of Puget Sound’s circulation and ecosystem, a data management system for oceanographic data and model results, and an effective delivery interface for the model results and observational data for research, education and policy formulation.

MIXED Model/measurement Integration eXperiment in Estuary Dynamics A PRISM project

Motivation Study coupled ecosystem of south Sound –Biology, chemistry, circulation, runoff, weather –Water quality vulnerability Integrate, validate PRISM models –Preparation for other studies; domain is entire Sound –Virtual Puget Sound, bloodstream Education –Oceanography fieldwork class –Oceanography of Puget Sound class SPASM Carr

MIXED components BioFloat - D’Asaro, Reynolds –O 2, chlorophyll following water parcel: biological productivity Surveys – Reynolds, Newton –O 2, chlorophyll, nutrients, productivity Integrated physical model - Kawase, Edwards Aquatic Biogeochemistry Model – al. ORCA - Devol, Emerson PRISM - Richey et al. Ocean Students

When: next Spring bloom Plots from ORCA, USGS websites; data from NDBC. Streamflow Wind speed (m/s) Air temp. (C)

Conclusions Puget Sound shows strong temporal and spatial variation re nutrient dynamics Region has sensitivity to climate and human perturbations Combined observations and modeling are needed to answer questions