the columbia river estuary and plume: natural variability, anthropogenic change and physical habitat...
TRANSCRIPT
The Columbia River estuary and plume:
Natural variability, anthropogenic change and physical habitat for salmon
PhD Candidate: Michela Burla
Research Advisor: Antonio M. BaptistaCenter 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
Climate variabilityand change
1800s
1930s-70s
Late1800s -
3,200-10,500 m3s-1
2001: 1,800 m3s-1
1996: 24,500 m3s-1
Salmon in the ecology, economy and culture of the Pacific NW
4
85% of Oregonians want salmon to be saved:
35% part of NW heritage36% measure of region’s environmental health15% commodity value
(The Oregonian, Dec 1997)
Columbia River Basin Salmon
5
Habitat degradation from mining, logging,
irrigation
Damdevelopment
(Lichatowich, 1999)
Salmon catch in the Columbia River, 1866-1994
Salmon recovery strategies in the CR
6
Technological fixes and hatchery production
Continuum of marine, estuarine, and riverine habitats critical to
preserve the diversity of salmon life histories
Production view Population view
Paradigm shift
(Lichatowich, 1999; Bottom et al, 2005, 2008; Fresh et al, 2005; NPPC 1997, 1998, 2009)
CORIE/SATURN: A coastal-margin observatory for the CR estuary-plume-shelf
Goal: to deliver quantifiably reliable environmental information, at the right time and in the right form to the right users.
Can complex models that simulate the physical environment provide credible and useful answers to the decision makers ?
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
Observation network
ELCIRC SELFE
Modeling system Information management
Research Objectives
8
Q1a:To what extent is the CORIE/SATURN modeling system capable to reproduce known dynamics of the CR plume?
Q1b: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?
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
Research Objectives
9
Q2: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?
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
Research Objectives
10
Q3: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?
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
Research Objectives
11
Q4: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?
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
Outline
12
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
Part I
13
Courtesy NOAA
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
Known patterns of variability of the CR plume
14
Classical view
Summer Winter
Barnes et al, 1972
Two winter plume patterns in response to wind (Hickey et al, 1998)
- Thicker, northward, coastally attached
- Thin, west to northwestward
Rapid changes in plume orientation and shape resulting from wind reversals (Fiedler and Laurs, 1990; Hickey et al, 1998)
Frequent summer bi-directional plume (Garcia Berdeal et al, 2002; Hickey et al, 2005)
Interannual variability associated with variability in river discharge and wind forcing (Thomas and Weatherbee, 2006)
A numerical exploration of CR plume variability
15
Q1a: To what extent is the CORIE/SATURN modeling system capable to reproduce known dynamics of the CR plume?
Q1b: 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
Seasonal and monthly climatologiesand anomalies of surface S
Integrative plume metrics
EOF analysis
1999-2006 simulation database(SELFE)
Model-obs and inter-model comparisons
Ability to represent known dynamics
Suite of skill scores
Conditional distributions of modeled salinity
Plume variability: River forcing
16
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Riv
er
dis
cha
rge
(m3s-
1)
1999
2000
2001
2002
2003
2004
2005
2006
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
Plume variability: wind forcing
17Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
1999
2000
2001
2002
2003
2004
2005
2006
Win
d (m
s-1)
Seasonal-Winter downwelling -favorable winds to the north-Summer upwelling- favorable 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
Plume variability:seasonal climatologies
18
Climatologies of the surface S, generated from our 8-year simulations, are consistent with the known prevailing seasonal patterns
Winter Summer
DB14
Plume variability:monthly climatologies and anomalies
19DB14
Plume variability: plume metrics
20
Multi-year simulations of the 3D salinity field
Integrating over space
Salinity cutoff = 28 psu
Area of the surface plume
Plumevolume
Plumelocation
(centroid)
model output@ 15 min intervals
Plume area
Plumeaverage depth
1999
Plume variability: volume
21
Delayed response to increases in CR discharge
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.
30-psu plume varied, in average-flow years, within a range comparable to the 20-110 km3 estimated in Hickey et al (1998)
Plu
me
vo
lum
e (
km3)
1999
2000
2001
2002
2003
2004
2005
2006
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov DecR
ive
r d
isch
arg
e (m
3 s
-1)
DB14
Plume variability: average depth
22
06
1218
2002 Plume average depth (m)
Wind (ms-1)
The ratio of plume volume to its surface area (average depth) in the simulations captures the prompt response of the plume to wind reversals
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
DB14
Plume variability: depth and orientation
23
Time series of plume depth at the northward inner shelf location consistent with the two basic winter structures observed in (Hickey et al, 1998)
Agreement with observed shallow summertime plumes and deeper wintertime plumes
Summer plume consistently present at ogi01 in 1999 (high discharge and consistent southward winds)
Occasional appearances at ogi01 if low flows and frequent wind reversals
Bi-directionality observed by Hickey et al. (2005) for the CR summer plume may apply at times to the winter plume as well.
Episodic winter plume reversals confirmed in CORIE/SATURN observations
Plume variability: EOF analysis
24
Plume variability: EOF analysis - winter
25
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
EOF1: 57%CR plume when separated from the coast and oriented northward of the mouth
EOF2: 18%CR plume to the north but hugging the coast.
Plume variability: EOF analysis - winter
26
Plume variability: EOF analysis - summer
27
Evaluation of model skills: methods
Duplicative realizations of circulation database:o DB14 – SELFE (upwind )o DB11 – ELCIRC (ELM)o DB13 – SELFE (ELM)
Skill scoreso RMSEo 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
Distributions of modeled salinity conditional on the value of the observed salinity
28
Evaluation of model skills: scores
• RMSE is in most cases substantially reduced in DB14, except at deeper stations (at 5 and 20 m depth at the three RISE buoys)
• MB is consistently negative for DB11, and markedly larger, in absolute value, than the bias in DB14 (except at deeper stations) excessive freshness in ELCIRC simulations
• Larger biases at depth in DB14 are due to the use of terrain-following coordinates
• Despite the clear overall superiority of SELFE in DB14, ρMO reveals instances where DB14 simulations perform worse than DB11 in reproducing variability in observed salinity
• DB11: variability in modeled salinity is generally distinctively higher than the variability in observed salinity (σM>σO);DB14: σO>σM
• Consistently higher Brier skill scores for DB14 than for DB13: improvement in adopting an upwind method in place of ELM to solve the transport equation
29
Evaluation of model skills:conditional distributions
30
OGI011999
OGI012001
RISEN-1 m2004
RISES-1 m2004
OGI011999
OGI012001
RISES-1 m2004
RISEN-1 m2004
DB14 (SELFE)
DB11 (ELCIRC)
10th and 90th
25th and 75th
50th (median)
Percentiles:
Part I: Summary of findings
31
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
Correctly reproduced known patterns of variability.
Interannual variability around climatological seasonal conditions in agreement with the results of T&W (2006).
Integrative metrics proved valuable in capturing the evolution of the CR plume in its response to variability in river and wind forcing.
Differential influence of the CR plume on the Washington shelf across the years with potential implications on productivity.
8-year EOF analysis confirmed the two basic winter structures observed in 1990-91 (Hickey et al, 1998), indicating generality of the result.
First two EOF modes clearly related to the two key forcing mechanisms of seasonal and inter-annual variability of the CR plume
Part I: Summary of findings
32
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
Prevalent bi-directionality of summer plume regardless of interannual variability.
Short-term bi-directional plumes, previously observed or modeled only in summer, can occasionally develop also in winter as a result of episodically strong upwelling-favorable winds.
Confirmed overall superiority of SELFE in the multi-year DB14 simulations (small RMSE and bias) and excessive freshness of DB11 simulations (ELCIRC) .
DB14, 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
No one score is adequate by itself to fully evaluate the skill of a model
33
Photo courtesy E. Keeley
Q2: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?
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
Part II
34
Photo courtesy E. Keeley
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
Ocean environments in Pacific salmon survival
Both freshwater and ocean environments contribute substantially to egg-to-adult salmon mortality
For the ocean phase of salmon life history, most mortality occurs within the first few weeks or months of ocean residence
Effort in the last decade into understanding the relationship between Pacific salmon production and climate variability patterns, such as ENSO and PDO
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
35
A role for the Columbia River plume?
Does the CR plume influence salmon survival?What we know
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)
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)
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)
Survival of outmigrating juvenile salmon varies at time scales consistent with changes in the CR plume
36
How does intraseasonal variability in salmon survival relate to variability in the physical plume
environment simulated by CORIE/SATURN?
Smolt-to-adult return rates (SARs)
37
PIT taggingBargingMigration
Through the estuary
Upstream migration to spawn
2-4 Years at sea
Adult detection
SAR = # Adults
# Juveniles
The correlation analysis
38
May 1999
The CR plume:a fast-changing hydrodynamic feature
Correlation analysis between daily values of SARs and plume metrics.
Since we could only roughly estimate time of ocean entry, we explored the cross-correlations at different time lags.
Analysis performed using anomalies from the 4-year climatologies
Non-parametric method to account for autocorrelation in testing significance of cross-correlations
DB14
Steelhead
39
1999 2001 2002 2003
Favorable large-scale ocean conditionsPoor large-scale ocean conditions
1999
lag (days)
Cro
ss-c
orre
latio
n co
effic
ien
t
DB14
Monthly PDO index
1900 1920 1940 1960 1980 2000
Chinook
40
1999 2001 2002 2003
Favorable large-scale ocean conditionsPoor large-scale ocean conditions
DB14
Strengths and uncertainties
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.
41
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.
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 (16-40% 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 within-season variability are correctly captured .
Part II: Summary of findings
42
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
Lagged cross-correlations suggested that steelhead benefited from the plume environment at a narrow window of time around their ocean entry.
Contribution of plume conditions to the overall variability in steelhead survival became modest when large-scale ocean conditions turned unfavorable.
Daily variability of the plume did not affect survival of Chinook salmon.
Differential response between the two species is consistent with observed and previously reported behavioral characteristics
H: Steelhead mainly use the plume to move quickly away from coastal predation and for a more direct migration to ocean habitats.
43
Courtesy J. Burke
Q3: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?
Succeeded in using the high-quality CORIE/SATURN simulations of plume dynamics to develop a biological hypothesis
Part III
44
Courtesy J. Burke
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
Physical Habitat Opportunity
45
Water depth:10cm d 2m
Water velocity:v 30cm/s
Salinity:0 s 5 psu
Temperature:0 T 19 oC
Estuarine PHO metrics
Sub-yearling ocean-type
salmon
Habitat Opportunity =
Hours of PHO per week @ each grid point
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).
Long-term simulation
databases of circulation , u, S, T
LongLong--term term simulation simulation
databases of databases of circulationcirculation , u, S, T, u, S, T
Long-term simulation
databases of circulation , u, S, T
LongLong--term term simulation simulation
databases of databases of circulationcirculation , u, S, T, u, S, T
Physical Habitat Opportunity
46
PHO accumulated per week over a specified region (hours*m2)
Averaged PHO within the inundated area (hours/week)
Water depth:10cm d 2m
Water velocity:v 30cm/s
Salinity:0 s 5 psu
Temperature:0 T 19 oC
Estuarine PHO metrics
Sub-yearling ocean-type
salmon
Long-term simulation
databases of circulation , u, S, T
LongLong--term term simulation simulation
databases of databases of circulationcirculation , u, S, T, u, S, T
Long-term simulation
databases of circulation , u, S, T
LongLong--term term simulation simulation
databases of databases of circulationcirculation , u, S, T, u, S, T
River flow (m3 s-1)
Habit
at
opport
unit
y
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 regions
47
Mouth Middle estuary Tidal freshwater
Peripheral bays
Baker
Youngs
Grays
Cathlamet
Interannual variability and anthropogenic change
48
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
Anthropogenic change
Interannual variability
1999-2006 simulation database
Time series of weekly PHO climatologies and anomalies
Catalogue of anomaly maps
Water depth in the modern lower estuary
49
1999-2006climatology(109 h*m2)
1999
2000
2001
2002
2003
2004
An
om
aly
(re
lati
ve to
19
99
-20
06 c
lima
tolo
gy)
2005
2006
Week 42 – neap tide
Week 43 – spring tide
Influence of tides dominates variability in shallow water (and low-velocity) habitats in the modern CR lower estuary
Differential response to neap and spring tides across lower estuary
DB14
Water depth in the tidal freshwater region
50
1999-2006climatology(109 h*m2)
1999
2000
2001
2002
2003
2004
An
om
aly
(re
lati
ve to
19
99
-20
06 c
lima
tolo
gy)
2005
2006
Only more extreme flows have an appreciable, but still modest, impacton PHO in the modern bathymetry
Strong historical freshets brought considerable gain in shallow water habitats through access to the floodplain in the predevelopment bathymetry
DB14
DB17
Velocity in the tidal freshwater region
51
1999-2006climatology(109 h*m2)
1999
2000
2001
2002
2003
2004
An
om
aly
(re
lati
ve to
19
99
-20
06 c
lima
tolo
gy)
2005
2006
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
In the predevelopment bathymetry, loss in PHO due to increasing velocities stopped for flows higher than 15,000 m3s-1 (inundated floodplain)
DB14
DB17
Influence of temperature on PHO
52
Continuous improvements in the quality of the CORIE/SATURN simulations: DB17 skill in representing temperature changes in the middle estuary has been transformative.
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.
Mod
el b
ias
Hou
rs
week
Influence of salinity intrusion on PHOin the middle estuary
53
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
Modest estimated loss in PHO due to deeper salt intrusion into the modern middle estuary
Order of magnitude not dissimilar from the loss determined by extreme low flows within the natural variability of the modern system
DB17
Part III: Summary of findings
54
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
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
Modest estimated loss due to deeper salt intrusion in the modern middle estuary
How salinity intrusion is changing relative to historical conditions needs to be a focus of further investigation
Confirmed rearing habitat scarcely available in the middle estuary because of excessively warm T by mid-July through September
Spatial connectivity among pockets of habitat opportunity needs to be investigated
Future work
55
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
Q4: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?
Differential response of estuarine regions
56
Preliminary results from ELCIRC simulations (DB11)
Shallow environments and well-connected channels exhibit a differential response to changes in river discharge, both seasonally and interannually
Shallow regions are areas of longer retention
DB11
RTs in the estuary-plume continuum
57
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 wind-driven, and not necessarily linked to the presence of the plume
1999
1999
2001
2001
DB11
Contributions
58
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
Demonstrated the quality of CORIE/SATURN simulations in reproducing known dynamics of the CR plume
Improved our understanding of CR plume variability, in particular by showing:
o that results of a bimodal winter plume and prevalence of a bidirectional plume in summer can be generalized regardless of interannual variability
o episodic winter plume bidirectionality
Evaluated skill of the simulations providing feedback to the model developers
Contributions
59
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
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
Acknowledgments
My committee Joseph Zhang Charles Seaton, Paul Turner,
Ethan VanMatre, Michael Wilkin John Williams, Charles ‘Si’
Simenstand, Doug Marsh Sergey Frolov Barbara Hickey, Ed Dever, Jen
Burke, Mark Scheuerell Sandra Oster Nate Hyde, Aaron Racicot OGI staff: Amy, Nancy, Alison…
60
The PDX Aliens Peter My family Bonnie Gibbs…
Funding support for this research: NOAA Fisheries National Science Foundation
The end
61
Back-up slides
62
Plume variability: location (centroid)
63
N-S relative to the CR mouth Distance from shore
Coastal Upwelling Index Columbia River discharge
DB14
Accounting for the autocorrelation in the data
64
The shape of the ACF and PACF suggested that simple AR1 models were not adequate to describe the plume metrics time series in our study.
We could not assume that frequencies lower than the daily sampling frequency (removed by removing autocorrelation) were unimportant.
Size of SAR dataset and non-stationarity of plume series: potential shortcomings of adjusting hypothesis testing procedure
Non-parametric test
DFTPhase-
randomized IFT2000
surrogatesPlume time
series
Empirical distribution for rCRIT generated ‘resampling’ in the frequency domain
Surrogates preserve the original autocorrelation structure