Empirical Input-Output Modeling of Narragansett Bay Stratification and Hypoxia Dan Codiga Graduate School of Oceanography University of Rhode Island October 15, 2007 Workshop Funded by NOAA Coastal Hypoxia Research Program

Today’s Presentation Overview of timeseries-based, empirical input-output approach Overview of timeseries-based, empirical input-output approach Modeling status and preliminary results: stratification hindcasts Modeling status and preliminary results: stratification hindcasts Description of observed stratification, and hypoxic events, based on buoy timeseries Description of observed stratification, and hypoxic events, based on buoy timeseries To model well, “know thy target” To model well, “know thy target” Comments on nowcast/forecast potential Comments on nowcast/forecast potential

Stratification “Density stratification”: Vertical layering “Density stratification”: Vertical layering Denser water underlies less-dense water Denser water underlies less-dense water forced by the strong influence of gravity forced by the strong influence of gravity Two main influences on density: Two main influences on density: Temperature (warmer water is less dense) Temperature (warmer water is less dense) Salinity (fresher water is less dense) Salinity (fresher water is less dense) Up Less dense water More dense water Air Seafloor

Stratification influence on hypoxia Stratification impedes vertical mixing Stratification impedes vertical mixing Isolates deep water from surface Isolates deep water from surface Cuts off replenishment of Oxygen to depth Cuts off replenishment of Oxygen to depth Facilitates hypoxia development Facilitates hypoxia development Less dense More dense Air Seafloor Hypoxia Air Seafloor Strong stratification limits vertical mixing Weak stratification Deep water aerated Vertical mixing Oxygen

Empirical input-output approach Timeseries-based Timeseries-based Output: Target variable Output: Target variable Stratification, or Dissolved oxygen Stratification, or Dissolved oxygen Inputs: Candidate predictors Inputs: Candidate predictors Based on processes thought to be important Based on processes thought to be important Techniques: Techniques: Multiple Linear Regressions (with cross-terms) Multiple Linear Regressions (with cross-terms) Zero-lag Zero-lag Optimally-lagged Optimally-lagged Neural networks Neural networks Static (zero-lag) Static (zero-lag) Dynamic (incorporates lags) Dynamic (incorporates lags)

Processes that strengthen stratification Delivery of fresh water near surface Delivery of fresh water near surface Riverine inflow – seasonal cycle; storm events Riverine inflow – seasonal cycle; storm events Solar heating near surface Solar heating near surface Seasonal cycle; relatively uniform across bay Seasonal cycle; relatively uniform across bay Estuarine circulation Estuarine circulation Brings salty water up-Bay at depth Brings salty water up-Bay at depth Sends fresher water down-Bay near surface Sends fresher water down-Bay near surface “Straining” by down-estuary wind “Straining” by down-estuary wind

Wind Straining: Strengthens OR weakens stratification Up (Lines of constant density) Down-estuary wind Before: Weaker vertical stratification More dense Less dense After: Stronger vertical stratification

Wind Straining: Strengthens OR weakens stratification Up-estuary wind Before: Stronger vertical stratification After: Weaker vertical stratification

Processes that weaken stratification Vertical mixing – mechanical overturning Vertical mixing – mechanical overturning Tidal currents – spring/neap cycle variations Tidal currents – spring/neap cycle variations Wind – storm-dependent Wind – storm-dependent Wind straining: Up-estuary wind Wind straining: Up-estuary wind

Stratification hindcasts, to date S = function(R, T, WM, WS) S = function(R, T, WM, WS) All variables 25-hr low-passed, 12-hr subsampled All variables 25-hr low-passed, 12-hr subsampled S = stratification =  S = stratification =  R = river discharge R = river discharge T = tidal mixing energy T = tidal mixing energy Proportional to tidal range  cubed Proportional to tidal range  cubed WM = wind mixing energy WM = wind mixing energy Proportional to windspeed  cubed Proportional to windspeed  cubed WS = wind straining WS = wind straining Proportional to along-estuary wind component Proportional to along-estuary wind component Omitted: Heat flux; estuarine circulation; lags Omitted: Heat flux; estuarine circulation; lags

Example: Bullocks Reach 2001 Multiple Linear Regression Observed stratification Modeled stratification variance Rivers (R): 48.2% Rivers and Wind Straining (R+WS): 50% Rivers and Wind Mixing (R+WM): 57% Rivers, Wind Straining, Wind Mixing, Tidal Mixing (R+WS+WM+T):65%

Neural network variance Rivers (R): 61% Rivers and Wind Straining (R+WS): 69% Rivers and Wind Mixing (R+WM): 69% Rivers, Wind Straining, Wind Mixing, Tidal Mixing (R+WS+WM+T):77%

Summary: preliminary modeling results, stratification hindcasts Best agreement with target (~60-70% variance captured): Bullock Reach Best agreement with target (~60-70% variance captured): Bullock Reach Decreasing order of importance: River; Wind mixing; Wind straining; Tidal mixing Decreasing order of importance: River; Wind mixing; Wind straining; Tidal mixing Moving southward, agreement decreases to ~20-40% at southernmost buoys Moving southward, agreement decreases to ~20-40% at southernmost buoys Suggests importance of processes the model does not include: Suggests importance of processes the model does not include: Lags; estuarine circulation; &/ heat flux Lags; estuarine circulation; &/ heat flux

Characteristics of individual sites Conimicut (~40-50%), N. Prudence (~30- 50%): Conimicut (~40-50%), N. Prudence (~30- 50%): Wind (straining and mixing) and tide increased in importance w/r/t river Wind (straining and mixing) and tide increased in importance w/r/t river Poppasquash (~25-40%) Poppasquash (~25-40%) Wind straining increases in importance Wind straining increases in importance Greenwich Bay (~40-50%) Greenwich Bay (~40-50%) Dominated by river as at Bullocks Dominated by river as at Bullocks Mt View, Quonset Point (~15-35%) Mt View, Quonset Point (~15-35%) Weakly influenced by river Weakly influenced by river

Narragansett Bay Stratification Buoy monitoring program Buoy monitoring program Measure near- surface and near-bottom Measure near- surface and near-bottom Temperature, Salinity, and D.O. Temperature, Salinity, and D.O. Samples each 15 minutes Samples each 15 minutes Deployed spring to fall Deployed spring to fall BR CP NP GB MV PP Providence (Narragansett) QP

Example Timeseries: Bullocks Reach 2001 Stratification Temperature Salinity River runoff Tidal Height

Hypoxia associated with stratification Stratification Surface D.O. Deep D.O. -- Hypoxia

Stratification: Temperature or Salinity? Surface salinity most important Surface salinity most important Deep salinity is relatively constant in time Deep salinity is relatively constant in time Temperature relatively less important Temperature relatively less important contributes generally 1-3 units at all sites contributes generally 1-3 units at all sites most important at southern sites most important at southern sites Rarely more than half the total stratification Rarely more than half the total stratification Strongest in Spring, usually vanished by Sept Strongest in Spring, usually vanished by Sept

North-South Gradation in Stratification SiteTypical Peak ( sigma units [kg / m 3 ] ) BR CP4-68 NP PP MV1-23 [GB ] NORTH Stronger Strat SOUTH Weaker Strat

Hypoxic “event” finder algorithm Define “event” start Define “event” start Dissolved oxygen (15-minute data) below 2.9 mg/l for at least one day Dissolved oxygen (15-minute data) below 2.9 mg/l for at least one day Define “event” end Define “event” end Above 2.9 mg/l for at least one day Above 2.9 mg/l for at least one day Results similar to those presented earlier by Heather Stoffel Results similar to those presented earlier by Heather Stoffel Bullocks: 4-6 events per year Bullocks: 4-6 events per year North Prudence: 2-5 events per year North Prudence: 2-5 events per year

Anatomy of hypoxic event

Quantifying spring-neap phase

Event Start-Times w/r/t Spring-Neap tidal phase NEAP SPRING

Event End-Times NEAP SPRING

Feasibility of forecasting stratification If tidal mixing was the best candidate predictor, we could forecast years in advance based on tidal cycles If tidal mixing was the best candidate predictor, we could forecast years in advance based on tidal cycles Instead, river flow is the best candidate Instead, river flow is the best candidate Forecasting at timescales of days to weeks is then limited by our ability to forecast river runoff due to storms Forecasting at timescales of days to weeks is then limited by our ability to forecast river runoff due to storms Forecasting at seasonal timescales is similarly limited by our ability to forecast wet/dry years Forecasting at seasonal timescales is similarly limited by our ability to forecast wet/dry years

Modeling Hypoxia Similar technique as for stratification Similar technique as for stratification Model output: deep dissolved Oxygen Model output: deep dissolved Oxygen Model inputs: Model inputs: Stratification Stratification Nutrient fluxes Nutrient fluxes Solar input Solar input Chlorophyll Chlorophyll Similar limitations for feasibility of forecasting on day/week & seasonal timescales Similar limitations for feasibility of forecasting on day/week & seasonal timescales

Over the past 8 years, when summer hypoxia was most intense, Northern-bay abundance of juvenile Winter Flounder was lowest Over the past 8 years, when summer hypoxia was most intense, Northern-bay abundance of juvenile Winter Flounder was lowest Why does hypoxia matter? 2. More important example: May impede Winter Flounder recovery