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Wind creates a natural bubble curtain mitigating porpoise avoidance during offshore pile driving

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  • BioConsult SH

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During offshore wind farms construction, abundance in harbour porpoise (Phocoena phocoena) is known to be negatively affected. From 2011 to 2013, extensive passive acoustic monitoring was conducted during research projects accompanying the construction of two wind farms in the German North Sea. Using C-PODs, we studied the effect ranges of pile driving disturbance on acoustic porpoise detections to test how these may change with different wind speeds. We found that disturbance radii highly depended on the prevailing wind speed during construction with further reaching effects at lower wind speed. Disturbance effects reached to 16 km at wind speed of 2m/s and to 10km at wind speed of 5m/s. With increasing wind speed, more air bubbles in the upper water layer may lead to greater mitigation of piling noise, thereby reducing disturbance radii for porpoises. Alternatively, increasing wind may increase high frequency noise due to sediment movement in the water, which could decrease the signal to noise ratio of piling noise leading to a decrease in porpoises' behavioural reactions. Our results indicate that wind speed and possibly background noise are important factors when assessing disturbance effects of anthropogenic noise on marine mammals.
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Wind creates a natural bubble curtain mitigating porpoise avoidance during offshore
pile driving
Anne-Cécile Dragon, Miriam J. Brandt, Ansgar Diederichs, and Georg Nehls
Citation: Proceedings of Meetings on Acoustics 27, 070022 (2016); doi: 10.1121/2.0000421
View online: http://dx.doi.org/10.1121/2.0000421
View Table of Contents: http://asa.scitation.org/toc/pma/27/1
Published by the Acoustical Society of America
Volume 27 http://acousticalsociety.org/
Fourth International Conference on
the Effects of Noise on Aquatic Life
Dublin, Ireland
10-16 July 2016
Wind creates a natural bubble curtain mitigating
porpoise avoidance during offshore pile driving
Anne-Cécile Dragon, Miriam J. Brandt, Ansgar Diederichs and Georg Nehls
BioConsult SH, 25813 Husum, Germany, ac.dragon@bioconsult-sh.de; m.brandt@bioconsult-sh.de;
a.diederichs@bioconsult-sh.de; g.nehls@bioconsult-sh.de
During offshore wind farms construction, abundance in harbour porpoise (Phocoena phocoena) is known
to be negatively affected. From 2011 to 2013, extensive passive acoustic monitoring was conducted
during research projects accompanying the construction of two wind farms in the German North Sea.
Using C-PODs, we studied the effect ranges of pile driving disturbance on acoustic porpoise detections to
test how these may change with different wind speeds. We found that disturbance radii highly depended
on the prevailing wind speed during construction with further reaching effects at lower wind speed.
Disturbance effects reached to 16 km at wind speed of 2m/s and to 10km at wind speed of 5m/s. With
increasing wind speed, more air bubbles in the upper water layer may lead to greater mitigation of piling
noise, thereby reducing disturbance radii for porpoises. Alternatively, increasing wind may increase high
frequency noise due to sediment movement in the water, which could decrease the signal to noise ratio of
piling noise leading to a decrease in porpoises’ behavioural reactions. Our results indicate that wind
speed and possibly background noise are important factors when assessing disturbance effects of
anthropogenic noise on marine mammals.
Published by the Acoustical Society of America
© 2017 Acoustical Society of America [DOI: 10.1121/2.0000421]
Proceedings of Meetings on Acoustics, Vol. 27, 070022 (2017)
Page 1
1. INTRODUCTION
In Europe, offshore wind energy is rapidly developing as an alternative energy source to
nuclear power and fossil fuels (Kumar et al., 2016; Szulecki et al., 2015). During offshore wind
farm construction, large steel foundations are usually driven into the sea floor by noise-intense
hydraulic hammers (Stevens et al., 2015). In the German North Sea, several research projects
have been conducted to develop and test new noise mitigation methods and to evaluate the
regulatory framework for conducting environmental impact assessments (Diederichs et al., 2008;
Tougaard et al., 2009; Dähne et al., 2013 among others).
Marine mammal species possess a sensitive underwater hearing system and recent studies
demonstrated that pile-driving noise affects seals’ and cetaceans’ natural behaviour (Hastie et al.,
2015; Madsen et al., 2006; Russell et al., 2016). The harbour porpoise (Phocoena phocoena)
reproduces in the North Sea (Reid et al., 2003) and is protected under the EU habitats directive
92/43/EEC. Depending on echolocation for orientation and foraging (Madsen et al., 2006;
Wisniewska et al., 2016), porpoise response to pile driving noise can result in behavioral and or
physiological effects, relevant for the conservation of the species. During pile driving, some of
the energy exerted on the pile is transmitted into the water column as noise. Depending on
received levels, sound can have behavioural or physiological effects on marine mammals.
Previous studies on the effects of offshore wind farm construction on harbour porpoises used
passive acoustic monitoring devices (e.g. Cetacean and Porpoise Detectors C-PODs, Chelonia
Ltd) that continuously record harbour porpoise echolocation, i.e. clicking activity. Passive
acoustic devices allow comparing porpoise detections during the construction period to those of
a preconstruction and/or post-construction period at high temporal resolution. Porpoise
detections were shown to decrease significantly during piling up to 20 km around wind farm
construction sites (Tougaard et al., 2009b; Brandt et al., 2011; Dähne et al., 2013). In the absence
of noise mitigation during pile driving, negative effects lasted up to two days within close
vicinity of the foundations (Brandt et al., 2011b; Rose et al., 2014; Tougaard et al., 2009b).
Brandt et al. (2016) shows in a recent study that the spatiotemporal effect ranges of piling can
differ widely between wind farm projects which cannot only be explained by differences in
sound levels emitted. Furthermore, several studies analysed the distribution and behaviour of
porpoises in relation to piling noise levels and tried to identify the noise level at which porpoise
detections or abundance during piling significantly decreased compared to a given baseline
period before or after piling. The onset of a behavioural reaction during pile driving (change in
detection rates, density or observable behaviour) was estimated to occur at noise levels between
140 and 152 dB (Brandt et al., 2016; Dähne et al., 2013; Diederichs et al., 2008; Rose et al.,
2014).
Regarding noise mitigation, a number of studies have demonstrated that bubble curtains
effectively attenuate pile-driving noise (Diederichs et al., 2014; Lucke et al., 2011; Nehls et al.,
2016; Würsig et al., 2000). Noise attenuation in marine waters is positively influenced by wind
conditions due to increased air-bubbles in the water column (Dol et al., 2012; Farmer and
Lemon, 1984; Mandal et al., 2016; Mathias et al., 2016; Thiele and Schellstede, 1980;
Wanninkhof, 1992). We expect avoidance distances by porpoises during pile driving to therefore
depend on the prevailing wind speed, with further reaching effect radii at lower wind speed. In
this study, the null hypothesis is “no change in porpoise detection rate between high and low
wind conditions”. This hypothesis is tested by investigating wind characteristics and the fine-
scale effects of pile driving on porpoise detections.
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2. MATERIAL & METHODS
i. Data Collection & Preparation
The present study looks at construction effects on harbour porpoises from two wind farms
that were built using tripod piles in the German North Sea between 2011 and 2013. Figure 1
presents Trianel Offshore Windpark Borkum, phase 1 (BWII) and Global Tech I (GTI). In this
study, data of porpoise occurrence were available from passive acoustic monitoring devices (C-
POD, Chelonia Limited) that record porpoise echolocation clicks. At GTI, C-PODs were
deployed at 14 positions and at BWII, C-PODs were deployed at 19 positions. C-PODs are
located in the water column 5-10 m above the sea floor. The C-POD position is fixed at the sea
floor with a mooring system and kept in the water column by a buoy (see details in Brandt et al.,
2016).
Data was analysed for the periods of pile driving activities, i.e. from September 2011 to May
2012 at BWII and from October 2012 to December 2013 at GTI. Data screening was thoroughly
conducted as C-PODs also record tonal signals originating from other sources than porpoise
echolocation activity (e.g. sonar, sediment suspension, waves). To focus only on porpoise clicks,
a strict exclusion criterion was applied to only keep the data when the number of “non-porpoise”
clicks was under the threshold of 100,000 clicks per hour. In addition, the variable number of
“non-porpoise” clicks was included in the statistical analyses to control for potential impacts of
background noise on porpoise detection by C-PODs (see details in Brandt et al., 2016). Finally,
wind speed, wind direction and sea-surface temperature values were gathered from NOAA open
source databases (http://www.esrl.noaa.gov/psd/) and extracted to match the spatiotemporal
resolution of C-POD data. Sediment type has been derived from EMODnet data
(http://www.emodnet-seabedhabitats.eu/).
In order to describe the short-term effects of pile driving on porpoise activity at a small
spatial scale we used the parameter detection positive hours (DPH) as indicator for porpoise
presence. DPH describes whether or not a porpoise click-train was recorded and identified during
a given hour and is thus a binary variable (with the values 0 or 1). Porpoise detection data were
merged with pile driving data, wind characteristics and the other georeferenced environmental
information (i.e. sea surface temperature, sediment type). For the present analyses we only
selected data collected during the hours when piling activity occurred at one of the two wind
farms and only from C-POD-positions up to a maximum distance of 60 km from a piling site.
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Figure 1. Map of the study area depicting all wind farms operating, under construction or for which
construction has been approved in the German North Sea. The two wind farms studies Global Tech 1 (GTI)
and Trianel Windpark Borkum (BWII) are illustrated in orange and mint green respectively. POD stations
are indicated by dark green dots.
ii. Statistical Analyses
First, we combined all data from both wind farms to investigate the overall effects of wind
speed on avoidance radii during piling (dataset hereafter referred to as the combined dataset).
Second, we ran “project-specific models” in order to look potential differences in the effects of
wind on avoidance radii between the two study areas. Dealing with biological processes, we
expected the input (environmental covariates) and output (residuals) time series of statistical
models to display temporal autocorrelation. Considering the model residuals, previous
investigations showed that significant autocorrelation originated from the DPH response variable
and not from environmental covariates.
Preliminary analyses were conducted to investigate different ways of taking autocorrelation
into account and determine the most parsimonious autocorrelation patterns to be taken into
account in further analyses. The definition of a differenced covariate (DPH at t-1) acting as an
auto-regressive component of the first order (Bestley et al., 2010) was found to significantly
reduce the autocorrelation pattern in the combined dataset as well as in each of the two wind
farm project-specific datasets. With the software R (R Core Team, 2015), the function bam
(Wood and Wood, 2015) was used because of its fast-computing ability of large datasets. The
bam function also allow less time-consuming analyses for generalized additive models (GAM,
Wood et al., 2015). C-POD position was included as a random effect to take into account the
geographical location, hence geographically-related characteristics. We ran GAMs including
year, day of year, hour of day, wind direction, sea surface temperature, noise clicks recorded by
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the C-POD and sediment type. Finally, GAMs included the interaction of distance with wind
speed in order to test whether the distance where porpoise detections start to decline is related to
wind speed. Wind speed could affect noise propagation under water as well as background noise
level. The selection of the optimal model is based on the Akaike Information Criterion (AIC,
Akaike, 1974) and on a graphical investigation of the autocorrelation (ACF) and partial
autocorrelation (PACF) functions of model residuals.
3. RESULTS
iii. Wind characteristics
Figure 2 illustrates the frequency of wind speed values for the complete study period and
when piling activity occurred at both wind farms. During piling at GTI, seasonal wind speed
average values vary between 6.4 m/s in summer and 9.4 m/s in winter with a yearly mean wind
speed of 7.0 m/s (standard deviation = 2.9 m/s). During piling at BWII, seasonal wind speed
average values vary between 8.0 m/s in spring and 8.9 m/s in autumn with a yearly mean wind
speed of 6.3 m/s (sd = 2.4 m/s). Seasonal variations are more important at GTI than at BWII but
average wind speed during piling is higher for BWII, potentially due to the lack of piling during
summer for this wind farm.
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Figure 2. Frequency of wind speed (m/s) all-year round and during piling activity at GTI wind farm (left)
and BWII wind farm (right).
iv. Wind speed reduces porpoise response to piling
Figure 3 illustrates DPH during piling from the GAM outputs over the complete dataset
including the interaction between wind speed and distance to the piling site. The interaction of
wind speed with distance was highly significant showing that decreases in DPH occurred at
larger distances from construction sites when wind speed was lower during piling. In addition,
the increase of DPH occurs not only with increasing distance to the piling site but also increasing
wind speed values. DPH rates reach a maximum at about 14 m/s. With no wind, the disturbance
radius, i.e. the area where porpoise detections are below the overall average (isoline 0) as
estimated from the model outputs, is about 17 km. At wind speed of about 2 m/s, DPH reaches
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the overall average at about 16 km. At wind speed of 8 m/s, the global average is reached at
about 5 km. This points to large differences in effect ranges of piling depending on wind speed.
Figure 3. GAM outputs over the combined dataset showing the interaction of wind speed (m/s) with distance
to piling site on DPH. Shown are the predicted absolute values for DPH with 95% confidence intervals
(dotted lines). Histograms on the x- and y-axes illustrate data availability
Similarly, Figure 4 illustrates the effect of the interaction between wind speed and distance to
piling site on porpoise detections during piling separately for each of the two wind farms. The
interaction of wind speed with distance was highly significant showing that decreases in DPH
occurred at larger distances from construction sites when wind speed was lower during piling.
With no wind, the radius of the area where porpoise detections are below the overall average as
estimated from the model outputs is about 16 km for both GTI and BWII wind farms. At GTI,
the disturbance radii during piling range from 16 km at 2 m/s wind speed to 5 km at 8 m/s wind
speed. At BWII, the disturbance radii during piling range from 16 km at 2 m/s wind speed to 10
km at 8 m/s wind speed.
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Figure 4. GAM outputs showing the effect of distance (km) and wind speed (m/s) on DPH during the hours
of piling (hour relative to piling = 0). Top: dataset of the GTI wind farm and bottom: dataset of the BWII
one. The predicted absolute values for DPH are shown with 95% confidence intervals (dotted lines).
4. DISCUSSION
When analysing porpoise detections during offshore pile driving, we find increasing porpoise
detections with increasing distance from piling for the combined dataset as well as for both
wind farm specific datasets. As we have controlled for false positive porpoise detection and
wind effect on C-POD by using a strict exclusion criteria and including the noise click
variable into the analyses (see details in Material & Methods), we can clearly state that
increasing porpoise detections correspond to increasing acoustic porpoise activities. During
piling activities, an increase in porpoise detection rate can thus be directly interpreted as a
decreased disturbance effect on harbour porpoises. Furthermore, our results reveal that
porpoise detection rates during offshore pile driving increase with increasing wind speed at
both wind farms. Depending on wind speed, the radii of avoidance estimated from the model
outputs range from 5 to 17 km, the maximum of which is of the same order of magnitude
than effect ranges for unmitigated pile driving in the literature (Brandt et al., 2011a; Dähne et
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al., 2013; Tougaard et al., 2006). Given differences in wind speed between seasons and
projects, it is expected to observe varying effect range estimates between different wind farm
projects. In a recent study, Brandt et al. (2016) found differing effects on porpoises between
wind farms that stem partially from local environmental characteristics (e.g. sediment type,
wind conditions). In comparison to the wind farm BWII, there was for instance more
variability in wind speed during the construction period at GTI and larger differences in
disturbance radii between low- and high-wind speed conditions.
Considering distance to construction site, our results show that further reaching effects are
observed at lower wind speed. This is likely related to noise during piling travelling further at
low wind speed, thus leading to a further reaching deterrence effect on porpoises. Conversely
during high wind conditions, a decrease in porpoise detection rate occurs up to nearer
distances from construction site, indicating a lower deterrence effect on porpoises during
piling. Numerous studies have found wind conditions and sea state to affect noise
propagation (e.g. (Jones et al., 2009; Thiele and Schellstede, 1980). In terms of physics, two
main potential processes may account for this wind effect on piling disturbance on porpoises:
with increasing wind speed, (i) there are more air bubbles in the upper water layer and (ii)
there is increased sediment movement in the water. First, wind is known to increase the
natural aeration of the water column by leading to more air-bubbles especially in the upper
water layer. With increasing wind speed, this “natural bubble-curtain” may lead to greater
noise mitigation, thereby reducing disturbance radii for porpoises. Heinis et al. (2015) also
show evidence for noise to travel further at lower wind speed due to stronger reflection of
noise by a smooth water surface. Although roughness and foam effects are driven by surface
wind speed (Hong and Shin, 2013), the surface roughness is difficult to model as it does not
linearly correlate with local wind and wave conditions (Yang et al., 2013). Further work
could be thus implemented to separate the effects of surface roughness from wind speed or
investigate how wave breaking acts as distinct phenomenon from bubble entrainment in the
upper layers of the water column. Second, high-wind conditions are associated with
increased waves breaking and sediment movements, responsible for increased ambient noise
especially at higher frequencies. Increasing wind, through high frequency noise due to
sediment movement, could decrease the signal to noise ratio of piling noise. A lower signal-
to-noise ratio would make piling noise more difficult for porpoises to differentiate from other
noises or would cause them to perceive it as less disturbing, potentially leading to a decrease
in porpoises’ behavioural reactions.
In addition to differences in sound propagation at varying wind speed, variations in
porpoises’ hearing could contribute to explaining why disturbance radii decrease at higher
wind speed. Other studies confirm this interpretation that showed higher sea state to
especially mitigates frequencies above 1 kHz where porpoise hearing is more sensitive (Jones
et al., 2009). Besides pile driving, harbour porpoises may also respond to other
anthropogenic noise sources such as shipping activities that are continuously occurring in the
North Sea. Piling and preparation noises (e.g. shipping) in times of high- background noise
may disturb less the porpoises than when noises occur in a quieter environment. The
magnitude with which those physical and biological processes interact and how much each of
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Proceedings of Meetings on Acoustics, Vol. 27, 070022 (2017)
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these contributes to explain the decreased disturbance effects at higher wind speed remain to
be investigated in more details.
5. CONCLUSION
Pile driving during the construction of offshore wind farms is known to affect acoustic
porpoise detections and porpoise abundance around construction sites. Considering varying wind
conditions, we investigated the spatial effects of pile driving on porpoise detections. We
demonstrated that the spatial extent of porpoise disturbance during offshore pile driving is
influenced by the prevailing wind conditions with further reaching disturbance radii at low wind
speed. Porpoise detections increase with wind speed values and distance to piling site. Our
results suggest that porpoise disturbance not only depends on piling noise levels but also on how
this noise is naturally attenuated in the environment. They also raise the question of how
porpoises perceive the noise of piling activities with varying environmental background noise.
There are pronounced changes in sound propagation and noise perception at different weather
conditions (e.g. low wind speed) that so far have been seldom considered when assessing the
effects of anthropogenic noises on acoustic-reliant marine mammals.
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REFERENCES
Akaike, H. (1974). “A new look at the statistical model identification,” IEEE Trans. Autom.
Control, 19, 716723. doi:10.1109/TAC.1974.1100705
Bestley, S., Patterson, T. A., Hindell, M. A., and Gunn, J. S. (2010). “Predicting feeding success
in a migratory predator: integrating telemetry, environment, and modeling techniques,”
Ecology, 91, 23732384.
Brandt, M. J., Diederichs, A., Betke, K., and Nehls, G. (2011). “Responses of harbour porpoises
to pile driving at the Horns Rev II offshore wind farm in the Danish North Sea,” Mar.
Ecol. Prog. Ser., 421, 205216.
Brandt, M. J., Diederichs, A., Betke, K., and Nehls, G. (2011). “Effects of offshore pile driving
on harbor porpoises (Phocoena phocoena),” Adv. Exp. Med. Biol., 730, 281284.
Brandt, M. J., Dragon, A.-C., Diederich, A., Schubert, A., Kosarev, V., Nehls, G., Wahl, V., et al.
(2016). Effects of offshore pile driving on harbour porpoise abundance in the German
Bight 2009 -2013 BioConsult-SH.
Dähne, M., Gilles, A., Lucke, K., Peschko, V., Adler, S., Krügel, K., Sundermeyer, J., et al.
(2013). “Effects of pile-driving on harbour porpoises (Phocoena phocoena) at the first
offshore wind farm in Germany,” Environ. Res. Lett., 8, 25002.
Diederichs, A., Hennig, V., and Nehls, G. (2008). Investigations of the bird collision risk and the
responses of harbour porpoises in the offshore wind farms Horns Rev, North Sea, and
Nysted, Baltic Sea, in Denmark Part II: Harbour porpoises (Abschlussbericht), Husum
(DEU): Universität Hamburg & BioConsult SH.
Diederichs, A., Pehlke, H., Nehls, G., Bellmann, M., Gerke, P., Oldeland, J., Grunau, C., et al.
(2014). Entwicklung und Erprobung des Großen Blasenschleiers zur Minderung der
A-C. Dragon et al.
Wind, natural noise mitigation and porpoises
Proceedings of Meetings on Acoustics, Vol. 27, 070022 (2017)
Page 11
Hydroschallemissionen bei Offshore-Rammarbeiten, OWP Borkum West II:
Baumonitoring und Forschungsprojekt HYDROSCHALL-OFF BW II (Schlussbericht),
BioConsult SH, p. 250.
Dol, H., Ainslie, M. A., Colin, M., and Janmaat, J. (2012). “Simulation of an underwater acoustic
communication channel characterized by wind-generated surface waves and bubbles,”
70054. doi:10.1121/1.4772935
Farmer, D. M., and Lemon, D. D. (1984). “The Influence of Bubbles on Ambient Noise in the
Ocean at High Wind Speeds,” J. Phys. Oceanogr., 14, 17621778. doi:10.1175/1520-
0485(1984)014<1762:TIOBOA>2.0.CO;2
Hastie, G. D., Russell, D. J. F., McConnell, B., Moss, S., Thompson, D., and Janik, V. M. (2015).
“Sound exposure in harbour seals during the installation of an offshore wind farm:
predictions of auditory damage,” (A. Punt, Ed.) J. Appl. Ecol., 52, 631640.
doi:10.1111/1365-2664.12403
Heinis, F., de Jong, C. A. F., and Rijkswaterstaat Underwater Sound Working Group (2015).
“Framework for assessing ecological and cumulative effects of offshore wind farms - Part
B: Description and assessment of the cumulative effects of implementing the Roadmap
for Offshore Wind Power.,”
Hong, S., and Shin, I. (2013). “Wind Speed Retrieval Based on Sea Surface Roughness
Measurements from Spaceborne Microwave Radiometers,” J. Appl. Meteorol. Climatol.,
52, 507516. doi:10.1175/JAMC-D-11-0209.1
Jones, A., Sendt, J., Duncan, A. J., Clarke, P. A., and Maggi, A. (2009). “Modelling the acoustic
reflection loss at the rough ocean surface,” Australian Acoustical Society, Adelaide,
Australia. Presented at the conference: ACOUSTICS 2009.
A-C. Dragon et al.
Wind, natural noise mitigation and porpoises
Proceedings of Meetings on Acoustics, Vol. 27, 070022 (2017)
Page 12
Kumar, Y., Ringenberg, J., Depuru, S. S., Devabhaktuni, V. K., Lee, J. W., Nikolaidis, E.,
Andersen, B., et al. (2016). “Wind energy: Trends and enabling technologies,” Renew.
Sustain. Energy Rev., 53, 209224. doi:10.1016/j.rser.2015.07.200
Lucke, K., Lepper, P. A., Blanchet, M.-A., and Siebert, U. (2011). “The use of an air bubble
curtain to reduce the received sound levels for harbor porpoises (Phocoena phocoena),” J.
Acoust. Soc. Am., , doi: 10.1121/1.3626123. doi:10.1121/1.3626123
Madsen, P. T., Wahlberg, M., Tougaard, J., Lucke, K., and Tyack, P. L. (2006). “Wind turbine
underwater noise and marine mammals: implications of current knowledge and data
needs,” Mar. Ecol. Prog. Ser., 309, 279295.
Mandal, A. K., Misra, S., Ojha, T., Dash, M. K., and Obaidat, M. S. (2016). “Effects of Wind-
Induced Near-Surface Bubble Plumes on the Performance of Underwater Wireless
Acoustic Sensor Networks,” IEEE Sens. J., 16, 40924099.
doi:10.1109/JSEN.2015.2443012
Mathias, D., Gervaise, C., and Di Iorio, L. (2016). “Wind dependence of ambient noise in a
biologically rich coastal area,” J. Acoust. Soc. Am., 139, 839850.
doi:10.1121/1.4941917
Nehls, G., Rose, A., Diederichs, A., Bellmann, M., and Pehlke, H. (2016). “Noise Mitigation
During Pile Driving Efficiently Reduces Disturbance of Marine Mammals,” Eff. Noise
Aquat. Life II, Springer New York, New York, NY, Vol. 875, pp. 755762.
R Core Team (2015). R: A Language and Environment for Statistical Computing, R Foundation
for Statistical Computing, Vienna, Austria. Retrieved from http://www.R-project.org/
Reid, J. B., Evans, P. G., and Northridge, S. P. (2003). Atlas of cetacean distribution in north-west
European waters, Joint Nature Conservation Committee.
A-C. Dragon et al.
Wind, natural noise mitigation and porpoises
Proceedings of Meetings on Acoustics, Vol. 27, 070022 (2017)
Page 13
Rose, A., Diederichs, A., Nehls, G., Brandt, M. J., Witte, S., Höschle, C., Dorsch, M., et al.
(2014). Offshore Test Site alpha ventus; Expert Report: Marine Mammals IfAÖ,
Bioconsult SH.
Russell, D. J. F., Hastie, G. D., Thompson, D., Janik, V. M., Hammond, P. S., Scott-Hayward, L.
A. S., Matthiopoulos, J., et al. (2016). “Avoidance of wind farms by harbour seals is
limited to pile driving activities,” J. Appl. Ecol., , doi: 10.1111/1365-2664.12678.
doi:10.1111/1365-2664.12678
Stevens, R. F., Soosainathan, L., Rahim, A., Saue, M., Gilbert, R., Senanayake, A. I., Gerkus, H.,
et al. (2015). “Design Procedures for Marine Renewable Energy Foundations,” Offshore
Technology Conference, doi:10.4043/25960-MS. doi:10.4043/25960-MS
Szulecki, K., Fischer, S., Gullberg, A. T., and Sartor, O. (2015). “Giving shape to the energy
Union Evolution, national expectations and implications for EU energy and cliamte
governance,” Berlin, Germany. . Presented at the The 2020 Strategy Experience: Lessons
for Regional Cooperation, EU Governance and Investment.
Thiele, R., and Schellstede, G. (1980). Standardwerte zur Ausbreitungsdämpfung in der Nordsee
FWG-Bericht.
Tougaard, J., Carstensen, J., Teilmann, J., Skov, H., and Rasmussen, P. (2009). “Pile driving zone
of responsiveness extends beyond 20 km for harbor porpoises (Phocoena phocoena
(L)),” J. Acoust. Soc. Am., 126, 1114. doi:10.1121/1.3132523
Tougaard, J., Carstensen, J., Wisz, M. S., Jespersen, M., Teilmann, J., Bech, N. I., and Skov, H.
(2006). Harbour porpoises on Horns Reef. Effects of the Horns Reef Wind Farm. Final
Report to Vattenfall A/S. NERI Commissioned Report. 111 p.
A-C. Dragon et al.
Wind, natural noise mitigation and porpoises
Proceedings of Meetings on Acoustics, Vol. 27, 070022 (2017)
Page 14
Tougaard, J., Henriksen, O. D., and Miller, L. A. (2009). “Underwater noise from three types of
offshore wind turbines: Estimation of impact zones for harbor porpoises and harbor
seals,” J. Acoust. Soc. Am., 125, 37663773. doi:10.1121/1.3117444
Wanninkhof, R. (1992). “Relationship between wind speed and gas exchange over the ocean,” J.
Geophys. Res., 97, 7373. doi:10.1029/92JC00188
Wisniewska, D. M., Johnson, M., Teilmann, J., Rojano-Doñate, L., Shearer, J., Sveegaard, S.,
Miller, L. A., et al. (2016). “Ultra-High Foraging Rates of Harbor Porpoises Make Them
Vulnerable to Anthropogenic Disturbance,” Curr. Biol., 26, 14411446.
doi:10.1016/j.cub.2016.03.069
Wood, S. N., Goude, Y., and Shaw, S. (2015). “Generalized additive models for large data sets,”
J. R. Stat. Soc. Ser. C Appl. Stat., 64, 139155. doi:10.1111/rssc.12068
Wood, S., and Wood, M. S. (2015). “Package ‘mgcv,’” R Package Version,.
Würsig, B., Greene, C. R., and Jefferson, T. A. (2000). “Development of an air bubble curtain to
reduce underwater noise of percussive piling,” Mar. Environ. Res., 49, 7993.
doi:10.1016/S0141-1136(99)00050-1
Yang, D., Meneveau, C., and Shen, L. (2013). “Dynamic modelling of sea-surface roughness for
large-eddy simulation of wind over ocean wavefield,” J. Fluid Mech., 726, 6299.
doi:10.1017/jfm.2013.215
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... Porpoise detections in the vicinity of the construction site started to decline several hours before piling, although not to the extent found during piling. The most likely explanation, in our opinion, is an increase in construction-related activities, such as an increase in shipping traffic in combination with enhanced sound transmission during the calm weather conditions during which piling activities occur (Dragon et al. 2016). This could contribute to porpoise deterrence, and a recent study suggests that porpoises may react to shipping activity at distances over 1 km (Dyndo et al. 2015). ...
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We investigated the disturbance effects of offshore windfarm construction on harbour porpoises using acoustic porpoise monitoring data and noise measurements during construction of the first seven large-scale offshore wind farms in the German Bight between 2010 and 2013. At six wind farms active noise mitigation systems (NMS) were applied during most piling events, one was constructed without. Based on GAM analyses, we describe a clear gradient in the decline of porpoise detections after piling, depending on noise level and distance to piling. Declines were found at sound levels exceeding 143 dB re 1 μPa²s (SEL05) and up to 17 km from piling. When only considering piling events with NMS, the maximum effect distance was 14 km. Compared to 24–48 h before piling, porpoise detections declined more strongly during unmitigated piling events at all distances. At 10–15 km from the pilings declines were around 50 % for unmitigated pilings but only 17 % when NMS were applied. Up to ~2 km from the construction site porpoise detections declined several hours before the start of piling and were reduced for about 1–2 days after piling, while at the maximum effect distance avoidance was only found during the hours of piling. The application of first generation NMS thus reduced the effect range of pile driving and led to a lower decline of porpoise detections over all distances. However, NMS were still under development and did not always work with equal efficiency. As NMS have further developed since, future investigations are expected to show further reduction of disturbance effects.
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The “Energy Union” can be seen as the most significant policy idea that seeks to reform European energy governance, policy and regional cooperation. However, so far the concept is mostly an empty box in which every stakeholder tries to put whatever is on the top of their priority list. This paper tries to structure the discussion by first showing the roots and evolution of the “Energy Union” concept in the EU, focusing on proposals by D. Tusk, J-C. Juncker and the European Commission. It then provides a comparative analysis of four country cases representing different energy mixes and energy policy directions: Germany, France, Poland and Norway. Having analysed the different interests and standpoints we move on to exploring the possible scenarios for the future of EU energy policy, emphasizing the potential impact of “Energy Union’s” governance mechanism which can reach far beyond what is expected and provide welcome coherence in Europe’s energy and climate policy.
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This study analyses the effects of the construction of eight offshore wind farms within the German North Sea between 2009 and 2013 on harbour porpoises (Phocoena phocoena). It combines porpoise monitoring data from passive acoustic monitoring using Porpoise Detectors (POD data 2010-2013) and aerial surveys (2009-2013) with data on noise levels and other piling characteristics. These data were analysed in detail in connection to pile driving activities, most of which occurred with application of noise mitigation techniques in order to reduce disturbance effects. Prior to investigating piling effects on porpoises, baseline analyses were conducted to identify the seasonal distribution of harbour porpoises in different geographic subareas. Daily POD data and aerial survey data highlighted similar seasonal patterns with higher densities in spring and summer. Highest porpoise occurrence was found next to the SAC Sylt Outer Reef in the northeast of the German Bight in early summer. Another high density area occurred near the SAC Borkum Reef Ground in the southwest almost year round, which is in line with previous findings. In addition to porpoise monitoring data, noise measurements from the seven wind farms con-structed between 2010 and 2013 were combined and noise levels extrapolated where measure-ments did not exist. Analyses of these measured noise levels revealed that there was high variability within each wind farm. Median noise levels during noise mitigated piling were about 10 dB lower than those measured during unmitigated piling. However, several noise levels measured during noise mitigated piling were as high as those during unmitigated piling and there was a high variability in these measurements within projects ranging over about 20 dB. This high variability probably results from differences in the combination of noise mitigation systems and how well a particular system worked at the time. It may also result from several environmental factors such as water depth, sediment and wind speed that all affect sound propagation. Probably due to these reasons, there was also no clear difference in noise levels between foundation types. The present study shows that noise mitigation systems used during this study were still under development and thus did not always work consistently well. Establishing the relationship of noise levels to porpoise responses is crucial for environmental im-pact assessments based on noise prognosis for specific projects. Non-parametric analyses revealed a clear gradient in how much porpoise detections declined at different noise level classes: Compared to a baseline period 25-48 h before piling, porpoise detections declined by over 90 % at noise levels above 170 dB, but only by about 25 % at noise levels between 145 and 150 dB. Below 145 dB this decline was smaller than 20 % and may thus not clearly be related to noise emitted by the piling process. Based on the complete POD-dataset analysed at an hourly resolution using GAM techniques and controlling for other environmental variables, we also found a clear gradient in the decline of porpoise detections during piling with noise level. While a decline in porpoise detections was found at noise levels above 143 dB SEL05, not all porpoises left the noise impacted area at that noise level. In further analyses, distance from piling was used as a proxy for noise to analyse detailed effect ranges. This was done because it increased the sample size (noise data did not exist for each POD-position and each piling) and model fit using distance instead of noise improved. Analyses pooling all available POD-data yielded an effect range up to 17 km when analysed with General Additive Models (GAM). Non-parametric analyses revealed significant declines in porpoise detections during piling when compared to 25-48 h before in up to 20-30 km, but only in up to 10-15 km was this decline at least 20 %. With increasing distances to the construction site, the magnitude of decline during piling clearly decreased. When noise mitigation was considered within this GAM model, the estimated effect range of 14 km during noise mitigated piling was lower compared to the complete dataset (17 km) or unmitigated piling (between 20 and 34 km). Caution is required when interpreting these results because of the relatively low dataset for unmitigated piling events. Nevertheless, it shows that noise mitigation effectively reduced porpoise disturbance. This reduction in disturbance may be less than would usually be expected under properly working noise mitigation (when effects may be expected to only reach up to about 5 km). This is probably related to the high variability in noise level measurements due to the fact that noise mitigation systems were still under development and did not always work reliable at that time. Considerable improvement has happened since then. Our result that piling noise above 143 dB SEL05 led to disturbance effects in porpoises (even though not all porpoises were affected at these noise levels), supports earlier estimations by NEHLS ET AL. (2016) that properly working sound mitigation, under which 160 dB are not exceeded at a 750 m distance (as intended by the regulatory framework), would lead to a substantial reduction of the area in which porpoises are affected by about 90 %. Project-specific models yielded large differences in effect ranges as well as effect magnitude. De-clines in detection rates during piling in 0-5 km distance were smallest at the wind farm DT with 51 % and largest at BARD with 83 %. This also applies to effect ranges, which for DT were estimated to be 6 km based on GAM models and 0-5 km based on non-parametric statistics. During all other projects significant declines by at least 20 % were found in at least 5-10 km but occurred in up to 20-30 km distance. Such differences between projects cannot be explained by differences in noise levels alone as DT was not significantly quieter than several other projects. Instead it may be linked to a relatively high quality of feeding habitat and a lower motivation of porpoises to leave the noise impacted area, but exact reasons are currently not known. From aerial survey data there was an indication for porpoise densities to be increased during and up to 12 h after piling at distances above 20 km. This effect could not be confirmed by POD-data, which could be related to the smaller spatial coverage of the latter. Elevated densities at distances above 20 km rely on little data, however, and need to be interpreted cautiously. Effect duration after piling was about 20-31 h at the close vicinity of the construction site (up to 2 km) and decreased with increasing distance. Project-specific estimates ranged between 16 and 46 h (when defined as the first local maximum after an initial increase in detection rates), with the exception of DT where effect duration was difficult to define (no local maximum reached). In all wind farm projects, we observed significant decreases in porpoise detections already prior to piling at distances of up to 10 km. This was independent of piling or deterrence measures. The most likely explanation for this are effects by increased shipping activity during preparation works in combination with increased sound propagation at low wind speed. It was found that deterrence effects prior to as well as during piling reached further at lower wind speed indicating that the effects of wind and sea state on sound propagation may be underestimated. There was no indication for the presence of temporal cumulative effects. Only at BWII we found some indication for potential habituation of porpoises to piling. Neither analyses of hourly nor daily POD-data revealed any further indication for habituation. However, without any knowledge of porpoise residency patterns within the German Bight and individual responses to disturbance, this topic remains difficult to address. From analyses of daily POD-data there was some indication for piling effects on porpoise detections to differ between seasons: Piling effects were longer lasting during winter and autumn than during spring and summer. As porpoise density tends to be lower in autumn and winter this effect may be related to longer lasting effects at lower porpoise densities. However, this could not be confirmed when looking at area-specific piling effects. Piling effects were not generally longer lasting in areas of lower porpoise densities. Using results from aerial survey data and POD-data analyses, the PCoD model was applied to es-timate disturbance consequences of wind farm construction on the population level. After explo-ration of the interim PCoD model, several limitations of the model were pointed out that may be improved before providing a realistic estimation for porpoise population trends as a result of dis-turbance. Applying the PCoD model using conservative input parameters for construction effects arising from the present study (increasing the chances for the model to predict a population de-cline), the risk of a decline of 1% of the population in the German Bight is estimated to be below 30 %. The predicted median decline is below the 1 % generally considered as critical for all chosen time periods and varies between 0.9 % for the piling period and 0.2 % for twelve years after piling had finished. There were no indications for such a population decline of harbour porpoises over the five year study period arising from analyses of daily POD data and aerial survey data at a larger scale. Despite extensive construction activities over the study period and an increase in these over time, there was no negative trend in acoustic porpoise detections or densities within any of the subareas studied. In some areas, POD-data even detected a positive trend from 2010 to 2013. On a regional scale, porpoise distribution patterns, as found by aerial survey data, differed between years. These regional changes could partly be related to wind farm construction sites but only within a radius of 20 km around piling. However, there was no evidence for an overall change in distribution patterns at a larger scale within the German Bight over the 5-year study period. Even though clear negative short-term effects (1-2 days in duration) of offshore wind farm con-struction were found on acoustic porpoise detections and densities, there is currently no indication that harbour porpoises within the German Bight are presently negatively affected by wind farm construction at the population level. This is even though sound mitigation techniques were still under development and further improved after this study period.
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The question of how individuals acquire and allocate resources to maximize fitness is central in evolutionary ecology. Basic information on prey selection, search effort, and capture rates are critical for understanding a predator's role in its ecosystem and for predicting its response to natural and anthropogenic disturbance. Yet, for most marine species, foraging interactions cannot be observed directly. The high costs of thermoregulation in water require that small marine mammals have elevated energy intakes compared to similar-sized terrestrial mammals [1]. The combination of high food requirements and their position at the apex of most marine food webs may make small marine mammals particularly vulnerable to changes within the ecosystem [2-4], but the lack of detailed information about their foraging behavior often precludes an informed conservation effort. Here, we use high-resolution movement and prey echo recording tags on five wild harbor porpoises to examine foraging interactions in one of the most metabolically challenged cetacean species. We report that porpoises forage nearly continuously day and night, attempting to capture up to 550 small (3-10 cm) fish prey per hour with a remarkable prey capture success rate of >90%. Porpoises therefore target fish that are smaller than those of commercial interest, but must forage almost continually to meet their metabolic demands with such small prey, leaving little margin for compensation. Thus, for these "aquatic shrews," even a moderate level of anthropogenic disturbance in the busy shallow waters they share with humans may have severe fitness consequences at individual and population levels.
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As part of global efforts to reduce dependence on carbon‐based energy sources there has been a rapid increase in the installation of renewable energy devices. The installation and operation of these devices can result in conflicts with wildlife. In the marine environment, mammals may avoid wind farms that are under construction or operating. Such avoidance may lead to more time spent travelling or displacement from key habitats. A paucity of data on at‐sea movements of marine mammals around wind farms limits our understanding of the nature of their potential impacts. Here, we present the results of a telemetry study on harbour seals Phoca vitulina in The Wash, south‐east England, an area where wind farms are being constructed using impact pile driving. We investigated whether seals avoid wind farms during operation, construction in its entirety, or during piling activity. The study was carried out using historical telemetry data collected prior to any wind farm development and telemetry data collected in 2012 during the construction of one wind farm and the operation of another. Within an operational wind farm, there was a close‐to‐significant increase in seal usage compared to prior to wind farm development. However, the wind farm was at the edge of a large area of increased usage, so the presence of the wind farm was unlikely to be the cause. There was no significant displacement during construction as a whole. However, during piling, seal usage (abundance) was significantly reduced up to 25 km from the piling activity; within 25 km of the centre of the wind farm, there was a 19 to 83% (95% confidence intervals) decrease in usage compared to during breaks in piling, equating to a mean estimated displacement of 440 individuals. This amounts to significant displacement starting from predicted received levels of between 166 and 178 dB re 1 μPa(p‐p). Displacement was limited to piling activity; within 2 h of cessation of pile driving, seals were distributed as per the non‐piling scenario. Synthesis and applications. Our spatial and temporal quantification of avoidance of wind farms by harbour seals is critical to reduce uncertainty and increase robustness in environmental impact assessments of future developments. Specifically, the results will allow policymakers to produce industry guidance on the likelihood of displacement of seals in response to pile driving; the relationship between sound levels and avoidance rates; and the duration of any avoidance, thus allowing far more accurate environmental assessments to be carried out during the consenting process. Further, our results can be used to inform mitigation strategies in terms of both the sound levels likely to cause displacement and what temporal patterns of piling would minimize the magnitude of the energetic impacts of displacement.
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The wind dependence of acoustic spectrum between 100 Hz and 16 kHz is investigated for coastal biologically rich areas. The analysis of 5 months of continuous measurements run in a 10 m deep shallow water environment off Brittany (France) showed that wind dependence of spectral levels is subject to masking by biological sounds. When dealing with raw data, the wind dependence of spectral levels was not significant for frequencies where biological sounds were present (2 to 10 kHz). An algorithm developed by Kinda, Simard, Gervaise, Mars, and Fortier [J. Acoust. Soc. Am. 134(1), 77–87 (2013)] was used to automatically filter out the loud distinctive biological contribution and estimated the ambient noise spectrum. The wind dependence of ambient noise spectrum was always significant after application of this filter. A mixture model for ambient noise spectrum which accounts for the richness of the soundscape is proposed. This model revealed that wind dependence holds once the wind speed was strong enough to produce sounds higher in amplitude than the biological chorus (9 kn at 3 kHz, 11 kn at 8 kHz). For these higher wind speeds, a logarithmic affine law was adequate and its estimated parameters were compatible with previous studies (average slope 27.1 dB per decade of wind speed increase).
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Acoustic monitoring of harbor porpoises (Phocoena phocoena L., 1758) indicated a strongly reduced disturbance by noise emitted by pile driving for offshore wind turbine foundations insulated by a big bubble curtain (BBC). This newly developed noise mitigation system was tested during construction of the offshore wind farm Borkum West II (North Sea). Because porpoise activity strongly corresponded to the sound level, operation of the new system under its most suitable configuration reduced the porpoise disturbance area by ~90%. Hence, for the first time, a positive effect of a noise mitigation system during offshore pile driving on an affected marine mammal species could be demonstrated.
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Sea surface scattering by wind-generated waves and bubbles is regarded to be the main non-platform-related cause of the time variability of shallow acoustic communication channels. Simulations for predicting the quality of acoustic communication links in such channels thus require adequate modelling of these dynamic sea-surface effects. It is known that, for frequencies in the range 1-4 kHz, the main effect of bubbles on sea surface reflection loss is due to refraction, which can be modelled with a modified sound-speed profile accounting for the bubble void fraction in the surface layer. The upward refraction induced by the bubble cloud then effectively acts as a catalyst for increasing the rough-surface scattering. In the present work, it is shown that, for frequencies in the range 4-8 kHz, bubble extinction also provides a significant contribution to the surface loss, including both the effects of bubble scattering and absorption. As this is the frequency band adopted in the EDA-RACUN project, in which the reported research has been conducted, both bubble refraction and extinction effects should be modelled for acoustic channel simulations in RACUN. These model-based channel simulations will be performed by applying a ray-tracer, together with a toolbox for generation of rough sea-surface evolutions.
Conference Paper
The Geotechnics Sub-Committee of the American Society of Civil Engineers (ASCE) Coasts, Oceans, Ports, and Rivers Institute (COPRI) Marine Renewable Energy (MRE) Committee is preparing a guide document for marine renewable energy foundations. That guide would use standard design codes for fixed foundations and mooring anchors in API RP 2GEO and DNV. The static method of computing axial pile capacity described in API RP 2GEO (2011) is generally used to compute ultimate compressive and tensile capacities of pipe piles driven to a given penetration. Lateral soil resistance -pile deflection (p-y) data for clays and sands are usually developed using procedures proposed by Matlock (1970) and O'Neill and Murchison (1983), respectively, and outlined in API RP 2GEO (2011). Marine energy foundations are unique in several ways. Axial pile capacity computations are usually based on a reasonable lower bound, in contrast to the soil resistance to driving, which is based on a reasonable upper bound. For structures supporting wind turbines, however, underestimating (or overestimating) the soil stiffness could require a change in turbine operation and a loss of power production. Although the classical API method is recognized as an appropriately conservative design method for offshore pile foundations, a prediction method is more well suited for structures supporting wind turbines, such as the CPT-based methods for predicting pile capacity in granular soils presented in API RP 2GEO (2011). If a prediction method is used to compute the soil resistance to driving, the evaluation of pile drivability may be overly conservative. Ageing in both clay and sand should also be taken into account. Wind turbines are often supported on large diameter monopiles. The applicability of the p-y data for such large diameter piles needs to be verified. Finally, marine renewable energy generated by in-stream hydrokinetics, ocean thermal energy conversion, and wave energy converters may be floating devices usually anchored to the seafloor. There are uncertainties in the design and installation of these anchors, which become critical for large sustained tensile loads that may degrade due to creep and cyclic loading. Copyright © (2015) by the Offshore Technology Conference All rights reserved.