The Columbia River estuary and plume Natural variability

The Columbia River estuary and plume: Natural variability, anthropogenic change and physical habitat for salmon Ph. D Candidate: Michela Burla Research Advisor: Antonio M. Baptista Center for Coastal Margin Observation and Prediction, OHSU Committee: Edmundo Casillas, NOAA Fisheries Daniel L. Bottom, NOAA Fisheries Tawnya Peterson, CMOP, OHSU

The Columbia River 3, 200 -10, 500 m 3 s-1 2001: 1, 800 m 3 s-1 1996: 24, 500 m 3 s-1 1800 s 1930 s-70 s Climate variability and change Late 1800 s 3

Salmon in the ecology, economy and culture of the Pacific NW 85% of Oregonians want salmon to be saved: 35% part of NW heritage 36% measure of region’s environmental health 15% commodity value (The Oregonian, Dec 1997) 4

Columbia River Basin Salmon catch in the Columbia River, 1866 -1994 Habitat degradation from mining, logging, irrigation Dam development (Lichatowich, 1999) 5

Salmon recovery strategies in the CR Production view Population view Technological fixes and hatchery production Continuum of marine, estuarine, and riverine habitats critical to preserve the diversity of salmon life histories Paradigm shift (Lichatowich, 1999; Bottom et al, 2005, 2008; Fresh et al, 2005; NPPC 1997, 1998, 2009) 6

CORIE/SATURN: A coastal-margin observatory for the CR estuary-plume-shelf Observation network Modeling system Information management ELCIRC SELFE Ø Goal: to deliver quantifiably reliable environmental information, at the right time and in the right form to the right users. Ø Can complex models that simulate the physical environment provide credible and useful answers to the decision makers ? Ø Opportunity and challenge: can high-resolution numerical models address the time scales relevant to investigate the impact of anthropogenic activities in the context of natural variability and climate change? 7

Research Objectives Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary Q 1 a: To what extent is the CORIE/SATURN modeling system capable to reproduce known dynamics of the CR plume? Q 1 b: Can multi-year simulation databases of circulation further our understanding of the seasonal and inter-annual variability of the plume in its response to river, ocean and atmospheric forcings? IV. Future work: residence times in the CR estuary V. Conclusions 8

Research Objectives Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary Q 2: Does the CR plume play a role in the survival of juvenile salmon migrating from the Columbia River to the ocean? Through what mechanisms? Do inter-annual variability and climate and ocean regimes modulate that role? IV. Future work: residence times in the CR estuary V. Conclusions 9

Research Objectives Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary Q 3: Can we use the high-resolution modeling capabilities of CORIE/SATURN to investigate the impact of natural variability and anthropogenic change on physical habitat opportunity for salmon in the CR estuary? V. Conclusions 10

Research Objectives Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary V. Conclusions Q 4: How does variability in river, ocean and atmospheric forcings modify migration paths and residence times in the CR estuary and plume, potentially affecting survival success for outmigrating juvenile salmon? 11

Outline Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary V. Conclusions 12

Part I Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary Courtesy NOAA V. Conclusions 13

Known patterns of variability of the CR plume Classical view Ø Two winter plume patterns in response to wind (Hickey et al, 1998) - Thicker, northward, coastally attached - Thin, west to northwestward Ø Rapid changes in plume orientation and shape resulting from wind reversals (Fiedler and Laurs, 1990; Hickey et al, 1998) Summer Winter Barnes et al, 1972 Ø Frequent summer bi-directional plume (Garcia Berdeal et al, 2002; Hickey et al, 2005) Ø Interannual variability associated with variability in river discharge and wind forcing (Thomas and Weatherbee, 2006) 14

A numerical exploration of CR plume variability Q 1 a: To what extent is the CORIE/SATURN modeling system capable to reproduce known dynamics of the CR plume? Q 1 b: Can multi-year simulation databases of circulation further our understanding of the seasonal and inter-annual variability of the plume in its response to river, ocean and atmospheric forcings? Analysis of plume variability | Evaluation of model skills 1999 -2006 simulation database (SELFE) Model-obs and inter-model comparisons Seasonal and monthly climatologies and anomalies of surface S Ability to represent known dynamics Integrative plume metrics EOF analysis Suite of skill scores Conditional distributions of modeled salinity 15

Plume variability: River forcing Seasonal - Sustained peaks during the spring snowmelt freshet - More episodic peaks generated by winter storms - Flows decreasing through the summer into the fall Interannual - Intensity of winter storms and timing and intensity of the freshet (though reduced by flow regulation) - Highest flows of winter and spring 1999, followed by 2000 - 2001 drought 16

Plume variability: wind forcing Seasonal - Winter downwelling favorable winds to the north - Summer upwellingfavorable winds to the south - Stronger wind stress during winter storms Interannual - Intensity of winter storms and timing of spring transition - E. g. strongly enhanced downwelling of Feb 1999 - Weak northward winds and reversals of Feb 2003 - Upwelling winds of Feb 2005 and 2006 - Late spring transition of 2000 and 2005 17

Plume variability: seasonal climatologies Winter Summer Climatologies of the surface S, generated from our 8 -year simulations, are consistent with the known prevailing seasonal patterns DB 14 18

Plume variability: monthly climatologies and anomalies DB 14 19

Plume variability: plume metrics Multi-year simulations of the 3 D salinity field Area of the surface plume Integrating over space Salinity cutoff = 28 psu Plume volume Plume average depth model output @ 15 min intervals Plume area 1999 Plume location (centroid) 20

Plume variability: volume Ø Delayed response to increases in CR discharge Ø Largest volumes formed following the freshet season of 1999 and 2000, with seasonally larger volumes characterizing, in most years, the stormy winter season and the spring. Ø 30 -psu plume varied, in average-flow years, within a range comparable to the 20 -110 km 3 estimated in Hickey et al (1998) DB 14 21

Plume variability: average depth 2002 Plume average depth (m) 18 12 6 0 Wind (ms-1) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec The ratio of plume volume to its surface area (average depth) in the simulations captures the prompt response of the plume to wind reversals DB 14 22

Plume variability: depth and orientation Ø Time series of plume depth at the northward inner shelf location consistent with the two basic winter structures observed in (Hickey et al, 1998) Ø Agreement with observed shallow summertime plumes and deeper wintertime plumes Ø Summer plume consistently present at ogi 01 in 1999 (high discharge and consistent southward winds) Ø Occasional appearances at ogi 01 if low flows and frequent wind reversals Ø Bi-directionality observed by Hickey et al. (2005) for the CR summer plume may apply at times to the winter plume as well. Ø Episodic winter plume reversals confirmed in CORIE/SATURN observations 23

Plume variability: EOF analysis 24

Plume variability: EOF analysis - winter Our analysis: Winter months: Nov-March of all years 1999 -2006 Hickey et al, 1998: EOF analysis of 1 m salinity survey data, October 25 -November 28, 1990 EOF 1: 57% CR plume when separated from the coast and oriented northward of the mouth EOF 2: 18% CR plume to the north but hugging the coast. 25

Plume variability: EOF analysis - winter 26

Plume variability: EOF analysis - summer 27

Evaluation of model skills: methods Ø Duplicative realizations of circulation database: o DB 14 – SELFE (upwind ) o DB 11 – ELCIRC (ELM) o DB 13 – SELFE (ELM) Ø Skill scores o RMSE o Brier skill score= 1 -MSE/MSEref o Correlation skill score, ρMO o (Unconditional) model bias: MB=(E(M)-E(O))/ σO o Normalized standard deviation for the model predictions, σM/σO Ø Distributions of modeled salinity conditional on the value of the observed 28 salinity

Evaluation of model skills: scores • • • RMSE is in most cases substantially reduced in DB 14, except at deeper stations (at 5 and 20 m depth at the three RISE buoys) MB is consistently negative for DB 11, and markedly larger, in absolute value, than the bias in DB 14 (except at deeper stations) excessive freshness in ELCIRC simulations Larger biases at depth in DB 14 are due to the use of terrain-following coordinates Despite the clear overall superiority of SELFE in DB 14, ρMO reveals instances where DB 14 simulations perform worse than DB 11 in reproducing variability in observed salinity DB 11: variability in modeled salinity is generally distinctively higher than the variability in observed salinity (σM>σO); DB 14: σO>σM Consistently higher Brier skill scores for DB 14 than for DB 13: improvement in adopting an upwind method in place of ELM to solve the transport equation 29

Evaluation of model skills: conditional distributions DB 14 (SELFE) RISEN -1 m 2004 OGI 01 1999 OGI 01 2001 RISES -1 m 2004 DB 11 (ELCIRC) RISEN -1 m 2004 OGI 01 1999 OGI 01 2001 RISES -1 m 2004 Percentiles: 10 th and 90 th 25 th and 75 th 50 th (median) 30

Part I: Summary of findings Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary V. Conclusions Ø Correctly reproduced known patterns of variability. Ø Interannual variability around climatological seasonal conditions in agreement with the results of T&W (2006). Ø Integrative metrics proved valuable in capturing the evolution of the CR plume in its response to variability in river and wind forcing. Ø Differential influence of the CR plume on the Washington shelf across the years with potential implications on productivity. Ø 8 -year EOF analysis confirmed the two basic winter structures observed in 199091 (Hickey et al, 1998), indicating generality of the result. Ø First two EOF modes clearly related to the two key forcing mechanisms of seasonal and inter-annual variability of the CR plume 31

Part I: Summary of findings Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary V. Conclusions Ø Prevalent bi-directionality of summer plume regardless of interannual variability. Ø Short-term bi-directional plumes, previously observed or modeled only in summer, can occasionally develop also in winter as a result of episodically strong upwellingfavorable winds. Ø Confirmed overall superiority of SELFE in the multi-year DB 14 simulations (small RMSE and bias) and excessive freshness of DB 11 simulations (ELCIRC). Ø DB 14, to an extent, achieved better performance in terms of RMSE –even when exhibiting weaker correlation with the observations– by producing results that are conservatively less variable than the corresponding observations Ø No one score is adequate by itself to fully evaluate the skill of a model 32

High quality of CORIE/SATURN simulations provides a rationale for using integrative metrics of CR plume structure to investigate the ecological implications of plume dynamics Q 2: Does the CR plume play a role in the survival of juvenile salmon migrating from the Columbia River to the ocean? Through what mechanisms? Photo courtesy E. Keeley Do inter-annual variability and climate and ocean regimes modulate that role? 33

Part II Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary Photo courtesy E. Keeley V. Conclusions 34

Ocean environments in Pacific salmon survival Ø Both freshwater and ocean environments contribute substantially to egg-to-adult salmon mortality Ø For the ocean phase of salmon life history, most mortality occurs within the first few weeks or months of ocean residence Ø Effort in the last decade into understanding the relationship between Pacific salmon production and climate variability patterns, such as ENSO and PDO Ø Local marine environments (ocean-shelf upwelling, river plumes) may play as large a role in the early marine survival of salmon as the regime shifts operating at broader, regional scales A role for the Columbia River plume? 35

Does the CR plume influence salmon survival? What we know Ø Higher abundance of juvenile salmon in the coastal region off the CR associated with the low-salinity plume waters and frontal zones compared to the surrounding ocean waters (De Robertis et al, 2005) Ø Juvenile salmon do not seem to take advantage of increased zooplankton biomasses at plume fronts, possibly due to their transience or small scale (Morgan et al, 2005) Ø Local conditions in the environments that connect the river migration corridor to the ocean more likely determine rapid change in survival during a migration season than conditions farther away (ocean feeding areas of the gulf of Alaska or Bering Sea) (Scheuerell et al, 2009) Ø Survival of outmigrating juvenile salmon varies at time scales consistent with changes in the CR plume How does intraseasonal variability in salmon survival relate to variability in the physical plume environment simulated by CORIE/SATURN? 36

Smolt-to-adult return rates (SARs) Migration Barging PIT tagging Through the estuary 2 -4 Years at sea SAR = Upstream migration to spawn # Adults # Juveniles Adult detection 37

The correlation analysis The CR plume: a fast-changing hydrodynamic feature May 1999 Ø Correlation analysis between daily values of SARs and plume metrics. Ø Since we could only roughly estimate time of ocean entry, we explored the cross-correlations at different time lags. Ø Analysis performed using anomalies from the 4 -year climatologies Ø Non-parametric method to account for autocorrelation in testing significance of cross-correlations DB 14 38

Cross-correlation coefficient Steelhead Monthly PDO index 1999 1900 1920 1940 1960 1980 2000 lag (days) 1999 2001 Favorable large-scale ocean conditions DB 14 2002 2003 Poor large-scale ocean conditions 39

Chinook 1999 2001 Favorable large-scale ocean conditions DB 14 2002 2003 Poor large-scale ocean conditions 40

Strengths and uncertainties Ø Our results were robust to the high inter-annual variability in local ocean (plume) conditions, till the regime shift in the large-scale ocean conditions occurred. Ø SARs are a metric that encompasses several stages in the life history of the fish and multiple years: conditions that steelhead encounter in the plume at the time of ocean entry can explain only part of their overall survival (1640% of its variability). Small numbers of returning adults upon which the SARs were based made their estimate fairly imprecise, but we believe that the trends of withinseason variability are correctly captured. Ø Alternative interpretations (e. g. local upwelling, which may affect salmon survival through bottom-up forcing of the marine food web) do not explain the differential response of the two species. 41

Part II: Summary of findings Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary V. Conclusions Ø Lagged cross-correlations suggested that steelhead benefited from the plume environment at a narrow window of time around their ocean entry. Ø Contribution of plume conditions to the overall variability in steelhead survival became modest when large-scale ocean conditions turned unfavorable. Ø Daily variability of the plume did not affect survival of Chinook salmon. Ø Differential response between the two species is consistent with observed and previously reported behavioral characteristics Ø H: Steelhead mainly use the plume to move quickly away from coastal predation and for a more direct migration to ocean habitats. 42

Succeeded in using the high-quality CORIE/SATURN simulations of plume dynamics to develop a biological hypothesis Q 3: Can we use the high-resolution modeling capabilities of CORIE/SATURN to investigate the impact of natural variability and anthropogenic change on physical habitat opportunity for salmon in the CR estuary? Courtesy J. Burke 43

Part III Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary Courtesy J. Burke V. Conclusions 44

Physical Habitat Opportunity = availability of habitat that, based upon physical factors, physiological constraints, and ecological interactions, salmon can access and which salmon can benefit from occupying (Bottom et al, 2005). Estuarine PHO metrics q Water depth: Long -term Long-term simulation databases of of circulation hh, , u, u, S, S, TT 10 cm d 2 m Hours of PHO per week @ each grid point q Water velocity: v 30 cm/s q Salinity: 0 s 5 psu q Temperature: 0 T 19 o. C Sub-yearling ocean-type salmon 45

Physical Habitat Opportunity = availability of habitat that, based upon physical factors, physiological constraints, and ecological interactions, salmon can access and which salmon can benefit from occupying (Bottom et al, 2005). q Water depth: Long -term Long-term simulation databases of of circulation hh, , u, u, S, S, TT 10 cm d 2 m q Water velocity: v 30 cm/s q Salinity: 0 s 5 psu q Temperature: 0 T 19 o. C Sub-yearling ocean-type salmon PHO accumulated per week over a specified region (hours*m 2) Averaged PHO within the inundated area (hours/week) Habitat opportunity Estuarine PHO metrics River flow (m 3 s-1) 46

Estuarine regions Mouth Middle estuary Tidal freshwater Peripheral bays Baker Grays Youngs Cathlamet 47

Interannual variability and anthropogenic change Interannual variability 1999 -2006 simulation database Time series of weekly PHO climatologies and anomalies Catalogue of anomaly maps Anthropogenic change Scenario 1: Predevelopment (1880) bathymetry and flow Scenario 2: Modern dikes in predevelopment scenario Scenario 3: Predevelopment flow over modern bathymetry Scenario 4: Modern (2004) flow over predevelopment bathymetry Scenario 5: Modern flow over modern bathymetry 48

Water depth in the modern lower estuary Ø Influence of tides dominates variability in shallow water (and lowvelocity) habitats in the modern CR lower estuary Ø Differential response to neap and spring tides across lower estuary Week 42 – neap tide Week 43 – spring tide DB 14 49

Water depth in the tidal freshwater region Ø Only more extreme flows have an appreciable, but still modest, impact on PHO in the modern bathymetry Ø Strong historical freshets brought considerable gain in shallow water habitats through access to the floodplain in the predevelopment bathymetry DB 17 DB 14 50

Velocity in the tidal freshwater region Ø In the modern bathymetry, the moderate gain in shallow water habitat, as Q , tends to be canceled out by PHO loss due to velocity constraints Ø In the predevelopment bathymetry, loss in PHO due to increasing velocities stopped for flows higher than 15, 000 m 3 s-1 (inundated floodplain) DB 17 DB 14 51

Ø Simulations confirmed that, by mid. July (and through September), habitat is scarcely available for salmon to rear in the middle estuary because of excessively warm temperatures. Hours Ø Continuous improvements in the quality of the CORIE/SATURN simulations: DB 17 skill in representing temperature changes in the middle estuary has been transformative. Model bias Influence of temperature on PHO week 52

Influence of salinity intrusion on PHO in the middle estuary Ø Salt may penetrate deeper in the modern CR system, at times limiting habitat opportunity in Cathlamet Bay and off Grays Bay also at higher flows Ø Modest estimated loss in PHO due to deeper salt intrusion into the modern middle estuary Ø Order of magnitude not dissimilar from the loss determined by extreme low flows within the natural variability of the modern system DB 17 53

Part III: Summary of findings Research Objectives Ø Strategies aimed at re-establishing some connectivity between the river and its floodplain through modification of both flow and bathymetry are necessary to restore access to shallow and low-velocity rearing habitats in the upper estuary I. Seasonal and interannual variability of the CR plume Ø Modest estimated loss due to deeper salt intrusion in the modern middle estuary II. The CR plume and salmon survival Ø How salinity intrusion is changing relative to historical conditions needs to be a focus of further investigation Introduction III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary V. Conclusions Ø Confirmed rearing habitat scarcely available in the middle estuary because of excessively warm T by mid-July through September Ø Spatial connectivity among pockets of habitat opportunity needs to be investigated 54

Future work Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary V. Conclusions Q 4: How does variability in river, ocean and atmospheric forcings modify migration paths and residence times in the CR estuary and plume, potentially affecting survival success for outmigrating juvenile salmon? 55

Differential response of estuarine regions Preliminary results from ELCIRC simulations (DB 11) ØShallow environments and wellconnected channels exhibit a differential response to changes in river discharge, both seasonally and interannually ØShallow regions areas of longer retention DB 11 56

RTs in the estuary-plume continuum Ø While RTs in the estuary are clearly influenced by river flow regimes, dominant processes affecting RTs in the domain extending over the plume region are winddriven, and not necessarily linked to the presence of the plume DB 11 57

Contributions Introduction Research Objectives I. Seasonal and interannual variability of the CR plume Ø Demonstrated the quality of CORIE/SATURN simulations in reproducing known dynamics of the CR plume Ø Improved our understanding of CR plume variability, in particular by showing: o II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary o that results of a bimodal winter plume and prevalence of a bidirectional plume in summer can be generalized regardless of interannual variability episodic winter plume bidirectionality Ø Evaluated skill of the simulations providing feedback to the model developers V. Conclusions 58

Contributions Introduction Research Objectives I. Seasonal and interannual variability of the CR plume II. The CR plume and salmon survival III. Salmon habitat opportunity in the CR estuary IV. Future work: residence times in the CR estuary V. Conclusions Ø Demonstrated how high-resolution numerical models like SELFE and ELCIRC, in the context of CMOs, can be successfully used to: o Formulate hypotheses for the mechanisms that link performance of biological species to their physical environment o Address the temporal scales that are relevant to investigate natural variability and anthropogenic change o Inform salmon recovery strategies in the CR basin - Combination of flow and bathymetry modifications are necessary to restore access to shallow and low-velocity rearing habitats in the upper estuary 59

Acknowledgments q My committee q Joseph Zhang q Charles Seaton, Paul Turner, Ethan Van. Matre, Michael Wilkin q John Williams, Charles ‘Si’ Simenstand, Doug Marsh q Sergey Frolov q Barbara Hickey, Ed Dever, Jen Burke, Mark Scheuerell q Sandra Oster q Nate Hyde, Aaron Racicot q OGI staff: Amy, Nancy, Alison… q q The PDX Aliens Peter My family Bonnie Gibbs… Funding support for this research: q NOAA Fisheries q National Science Foundation 60

The end 61

Back-up slides 62

Plume variability: location (centroid) DB 14 N-S relative to the CR mouth Distance from shore Coastal Upwelling Index Columbia River discharge 63

Accounting for the autocorrelation in the data Ø The shape of the ACF and PACF suggested that simple AR 1 models were not adequate to describe the plume metrics time series in our study. Ø We could not assume that frequencies lower than the daily sampling frequency (removed by removing autocorrelation) were unimportant. Ø Size of SAR dataset and non-stationarity of plume series: potential shortcomings of adjusting hypothesis testing procedure Non-parametric test Empirical distribution for r. CRIT generated ‘resampling’ in the frequency domain Plume time series DFT Phaserandomized IFT 2000 surrogates Surrogates preserve the original autocorrelation structure 64