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Weather-induced episodic mixing events in lake ecosystems are often unpredictable, and their impacts are therefore poorly known. The impacts can be short-lived, including changes in water temperature and stratification, but long-lasting effects on the lake’s biology may also occur. In this study we used automated water quality monitoring (AWQM) data from 8 boreal lakes to examine how the episodic weather-induced mixing events influenced thermal structure, hypolimnetic dissolved oxygen (DO), fluorometric chlorophyll estimates (Chl-a), and lake metabolism and how these events varied in frequency and magnitude in lakes with different characteristics. Rise in wind speed alone had an effect on the lakes with the weakest thermal stability, but a decrease in air temperature together with strong wind induced mixing events in all lakes. The return period of these mixing events varied widely (from 20 to 92 d) and was dependent on the magnitude of change in weather. In lakes with strong stability, thermal structure and hypolimnetic DO concentration were only slightly affected. Weather-induced mixing in the upper water column diluted the surface water Chl-a repeatedly, whereas seasonal maximum occurred in late summer on each lake. Although Finnish lakes have been characterized with stable stratification during summer, we observed many substantial mixing events of relatively short return periods relevant to both chemical and biological properties of the lakes.
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Inland Waters (2016) 6, pp.523–534
© International Society of Limnology 2016
DOI: 10.5268/IW-6.4.886
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Article
Authors personal copy
Response of boreal lakes to episodic weather-induced events
Jonna Kuha,1* Lauri Arvola,2 Paul C. Hanson,3 Jussi Huotari,2 Timo Huttula,4 Janne Juntunen,4 Marko
Järvinen,4 Kari Kallio,5 Mirva Ketola,6 Kirsi Kuoppamäki,6 Ahti Lepistö,5 Annalea Lohila,7 Riku Paavola,8
Jussi Vuorenmaa,5 Luke Winslow,9 and Juha Karjalainen1
1 University of Jyväskylä, Jyväskylä, Finland
2 Lammi Biological Station, University of Helsinki, Lammi, Finland
3 University of Wisconsin, Madison, WI, USA
4 Finnish Environment Institute, Jyväskylä, Finland
5 Finnish Environment Institute, Helsinki, Finland
6 University of Helsinki, Lahti, Finland
7 Finnish Meteorological Institute, Atmospheric Composition Research, Helsinki, Finland
8 Oulanka Research Station, University of Oulu, Kuusamo, Finland
9 US Geological Survey, Center for Integrated Data Analytics, Middleton, WI, USA
* Corresponding author: jonna.kuha@jyu.
Received 8 May 2015; accepted 27 July 2016; published 2 November 2016
Abstract
Weather-induced episodic mixing events in lake ecosystems are often unpredictable, and their impacts are therefore
poorly known. The impacts can be short-lived, including changes in water temperature and stratication, but long-last-
ing effects on the lake’s biology may also occur. In this study we used automated water quality monitoring (AWQM)
data from 8 boreal lakes to examine how the episodic weather-induced mixing events inuenced thermal structure,
hypolimnetic dissolved oxygen (DO), uorometric chlorophyll estimates (Chl-a), and lake metabolism and how these
events varied in frequency and magnitude in lakes with different characteristics. Rise in wind speed alone had an effect
on the lakes with the weakest thermal stability, but a decrease in air temperature together with strong wind induced
mixing events in all lakes. The return period of these mixing events varied widely (from 20 to 92 d) and was dependent
on the magnitude of change in weather. In lakes with strong stability, thermal structure and hypolimnetic DO concentra-
tion were only slightly affected. Weather-induced mixing in the upper water column diluted the surface water Chl-a
repeatedly, whereas seasonal maximum occurred in late summer on each lake. Although Finnish lakes have been char-
acterized with stable stratication during summer, we observed many substantial mixing events of relatively short
return periods relevant to both chemical and biological properties of the lakes.
Key words: automated water quality monitoring, chlorophyll a, episodic events, hypolimnetic oxygen, lakes,
production, stability
Introduction
The dynamics of freshwater lakes are nonlinear
(Carpenter et al. 2011) and variable on both spatial and
temporal scales (Levin 1992, Heini et al. 2014), which
leaves the detection of many short-term physical and
biological processes outside the limits of traditional
water quality monitoring (Kratz et al. 2006). Abrupt
changes in lake ecosystems are often driven by weather-
induced episodic events (Jennings et al. 2012, Klug et al.
2012, Crockford et al. 2014), and therefore modern tools,
including automated water quality monitoring (AWQM),
are needed to understand these changes (Benson et al.
2009, Kallio et al. 2010, Hamilton et al. 2014).
Because lakes provide numerous important
ecosystem services, such as drinking water supply,
sheries, and recreation (Aylward et al. 2005, Kratz et
al. 2006), it is important to understand their response to
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DOI: 10.5268/IW-6.4.886
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Kuha et al.
© International Society of Limnology 2016
weather-induced episodic events, which are likely to
become more severe in the future (Dokulil 2013, IPCC
2014). Future climate scenarios predict that the North
Atlantic weather system will become more unstable
with heavier storms (Hov et al. 2013). In northern
Europe, a longer open-water season and stratied period
are also expected, increasing exposure of lakes to
external forcing, which in turn may alter their internal
dynamics (Huttula et al. 1992, Bergström et al. 2011,
Forsius et al. 2013).
A strict seasonality, with full turnover in spring and
autumn (Lewis 1983), characterizes dimictic boreal lakes
and causes dramatic changes in their physical, chemical,
and biological parameters (Bengtsson 1996, Pulkkanen
2013). During the stratied season, intermediate distur-
bances (from minutes to days with their own seasonality)
can be important to lake ecosystem functioning (Padisák
1993, Flöder and Sommer 1999). During summer,
recurring low pressure systems with cool air and high
wind speed can cause mixing in lakes by lowering the
water-column stability and deepening the epilimnion
(Spigel and Imberger 1987). A partial or complete
overturn in stormy weather (Soranno et al. 1997) will
introduce hypolimnetic water into the epilimnion
(Jennings et al. 2012). Mixing can inject oxygen and heat
into the deeper water layers, and the resulting nutrient
upwelling from the hypolimnion can cause sudden algal
blooms (Kallio 1994, Soranno et al. 1997).
In Finland, weather in summer is characterized as a
variation between the eastern high and low pressure
systems travelling across the country in a southwest to
northeast direction. These systems, occurring periodically
and lasting typically from 3 to 5 days, vary in their
temperature and wind conditions (Heino 1994). Thermal
stratication in summer is a typical phenomenon in most
Finnish lakes (Kuusisto 1981), and therefore any major
changes in temperature and dissolved oxygen (DO) strat-
ication may affect their productivity by changing the
nutrient availability and, subsequently, the biotic activity
in the lake, as has been shown elsewhere (e.g., Charlton
1980, Nõges et al. 2011, van de Bogert et al. 2012).
In this study we used comprehensive on-site meteor-
ological and AWQM data from 8 boreal lakes in Finland
and combined manually collected low-frequency data to
study the response of the lakes to weather-induced
mixing events. The study included a simultaneous
monitoring period in all study lakes. Specically, we
concentrated on the stratied summer period and aimed
to quantify the frequency of the mixing events and
changes they caused in water column stability, hypolim-
netic DO, uorometric chlorophyll a (Chl-a) estimates,
and (case wise) metabolism estimates. In addition, we
aimed to determine the essential drivers of the events
and quantify their magnitude.
Methods
Study lakes
For the multi-lake comparison, AWQM and discrete
datasets were combined from 8 Finnish lakes represent-
ing areas from northern (68°N) to southern (61°N)
Finland: Pallasjärvi, Yli-Kitka, Konnevesi, Jyväsjärvi,
Päijänne, Vesijärvi, Vanajavesi, and Pyhäjärvi (Fig. 1).
The lakes are mostly dimictic with the exception of
polymictic Pyhäjärvi. The lakes represent a wide range of
surface area (3–1050 km2) and maximum depth (24–95 m).
Their trophic status varies from oligotrophic to eutrophic
and water colour from clear to humic (Table 1).
Lake Lat Long Ao,
km2
Max
depth,
m
Mean
depth
(z), m
z
√Ao
Mean
Chl-a, µg
L−1
Mean
colour
mg L−1 Pt
Mean total
phosphorus,
µg L−1
Jyväsjärvi 62°15′N 25°47’E 3 25 7.0 4.0 10.8 70 25
Pallasjärvi 68°01′N 24°12’E 17 36 9.0 2.2 2.1 13 5
Vesijärvi 61°15′N 25°47’E 44 42 6.8 1.0 9.6 10 27
Vanajavesi 61°08′N 24°16’E 103 24 7.7 0.8 16.0 50 24
Pyhäjärvi 61°01′N 22°17’E 155 26 5.5 0.4 7.2 17 20
Konnevesi 62°38′N 26°24’E 189 57 10.6 0.8 4.2 25 6
Yli-Kitka 66°07′N 28°39’E 237 41 6.6 0.4 3.9 30 9
Päijänne 62°09′N 25°47’E 1050 95 14.2 0.4 5.9 29 13
Table 1. Location and limnological characteristics of the study lakes. Chlorophyll a (Chl-a), water colour and total phosphorus represent mean
summer (Jun-Aug) values in the epilimnion or the uppermost 1 m layer. Data from HERTTA-database of Finnish Environment Institute (SYKE).
Ao is surface area of the lake .
DOI: 10.5268/IW-6.4.886
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Response of boreal lakes to episodic weather-induced events
Inland Waters (2016) 6, pp.523–534
Data
AWQM data for summer (Jun, Jul, Aug) water tempera-
tures were available from all lakes during 2013 (Table 2).
Water temperature was measured by AWQM at 10 min to
3 h intervals from surface water (1.0 or 1.5 m) and from
selected depths at the same interval, or with discrete
sampling at a 1-4 week interval (Table 2). Water
temperature data were measured with temperature data
loggers or proling systems (Table 3). Similarly, AWQM
data of near-bottom DO concentrations were available
from 4 of the study lakes and discrete data from others
(Table 2). Optical DO sensors were used for data
collection (Table 3). With identical specication,
additional summer AWQM data of water temperature
and DO from other years were available from lakes
Konnevesi, Vesijärvi, and Pyhäjärvi (Table 2). In
summer 2013, automated Chl-a (uorescence) data,
measured with single-wavelength uorometers, were
available from the surface water (1.0 or 1.5 m) from 4 of
the study lakes at a 1–3 h interval (Table 2). The uoro-
metric data were calibrated to Chl-a concentration (µg
L−1, measured in laboratory with ethanol extraction) by
using site-specic calibration equations (linear
regression). In lakes where cyanobacteria are known to
make a major contribution to chlorophyll, multiple linear
regressions were used in calibration, accounting for both
Chl-a and phycocyanin (PC) uorescence, measured
with PC uorometers simultaneously with chlorophyll u-
orescence and calibrated with cell counts of cyanobacteria
(Table 3). These uorometric Chl-a estimates consist of Fig. 1. Locations of the study lakes in Finland.
Lake (basin) Depth (m) for
temperature
Depth (m) for DO Depth (m) for
Chl-a (in 2013)
Study years Citation
Jyväsjärvi 1, 2, 5, 7, 10, 15 15 2013 Kuha et al. 2016
Pallasjärvi 1–33 (1 m step) 33* 2013 Lohila et al. 2015
Vesijärvi
(Kajaanselkä)
1, 5, 15, 25 25 1.0 2011, 2013 Anttila et al. 2013
Vanajavesi
(Vanajanselkä)
1.5, 2, 3–10,* 14,*
23*
25* 1.0 2013 Heini et al. 2014
Pyhäjärvi 1, 5,* 10,* 15,* 26* 25* 1.0 2009, 2013 Lepistö et al. 2010
Konnevesi
(Näreselkä)
1–40 (1 m step) 1.5, 40 1.5 2013, 2014 Kuha 2016
Yli-Kitka (Vasik-
kaselkä)
1.5, 30 30 2013 Karjalainen and
Hellsten 2015
Päijänne
(Ristinselkä)
1, 5, 10, 15, 25, 35,
50, 70, 90
90* 2013 Kuha 2016
*Discrete water temperature or dissolved oxygen concentration data obtained from Hertta-database maintained by Finnish Environment
Institute (SYKE).
Table 2. Sampling depths (m) and years (in Jun-Aug) for automated and discrete monitoring of water temperature, dissolved oxygen (DO) and
Chlorophyll a (Chl-a) in the study lakes (basins).
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nighttime uorescence (average of values measured between
24:00 and 09:00 h) to avoid effects of non-photochemical
quenching on measurements (Huot and Babin 2010,
Huotari and Ketola 2014).
As an index for water column stability, we calculated
the Schmidt Stability Index (Sc; kJ cm−2) using automatically
and manually measured water temperature proles
(Schmidt 1928, Idso 1973) as follows:
Sc (1)
where g is acceleration due to gravity (cm s−2), Ao is
surface area of the lake (m2), zm is maximum depth of
the lake (m), Az is area of the lake at depth z (m2), ρm is
average density during the isotherm (g m−2), ρz is
density (g m−3) at depth z, and zg is the depth of the
center of gravity during the isotherm (m). The sum of
all depths was calculated for the depth of the water
column (dz).
On-lake or near-shore meteorological stations at the
study lakes measured wind speed, air temperature, humidity,
and solar radiation at 1–30 min intervals (Table 3). The wind
speed data were corrected to the reference height of 10 m
(U10, m s−1; Amorocho and DeVries 1980) to remove the
effect of measurement height between the stations:
(2)
where κ is von Karman’s constant (0.4) and Uz is wind
speed (m s−1) measured at height z (m) above water surface.
Values used for bulk transfer coefcient over water,
CD, were 1.0 × 10−3 for U10 <5 m s−1 and 1.5 × 10−3 for >5 m s−1.
All meteorological data were averaged to 30 min according
to the least frequent data to remove the effect of sampling
interval between the stations. Additionally, daily (or
nightly for Chl-a) averages of individual variables were
calculated for both AWQM and meteorological data.
Lake (basin) Meteorological
station
Water temperature
sensor
Dissolved oxygen
sensor
Chl-a sensor (in
2013)
Chl-a sensor
calibration
Jyväsjärvi Vantage Pro 2,
Davis Ins. Co.,
Hayword, CA,
USA
Thermochron
1922L, Express
Thermo, San Jose,
CA, USA
3835, Aanderaa
Data Ins., Bergen,
Norway
Pallasjärvi uSonic-3, Metek
GmbH, Elmshorn,
Germany; Pt100
thermosensor
Tinytag Aquatic 2
TG-4100, Gemini
Data Loggers,
Chichester, UK
Vesijärvi
(Kajaanselkä)
Vaisala WXT520,
Vaisala Co.,
Helsinki, Finland
NTC, WTW
GmbH, Weilheim,
Germany
FDO 700 IQ,
WTW GmbH,
Weilheim,
Germany
MicroFlu, Trios,
Rastede, Germany
0.97 × sensor + 5.98
× PC −3.46,
R2 = 0.97, n = 7
Vanajavesi
(Vanajanselkä)
WMO station
02863, Finland
YSI600, YSI Inc.,
Yellow Springs,
OH, USA
YSI600, YSI Inc.,
Yellow Springs,
OH, USA
YSI600, YSI Inc.,
Yellow Springs,
OH, USA
0.88 × sensor
+8.85,
R2 = 0.39, n = 12
Pyhäjärvi WXT510, Vaisala
Co., Helsinki,
Finland
Marvet,
Helox13-25, Elke
Sensor Oy
Tallinn, Estonia
Marvet,
Helox13-25, Elke
Sensor Oy
Tallinn, Estonia
MicroFlu, Trios,
Rastede, Germany
sensor +4.00 × PC
−0.69,
R2 = 0.88, n = 10
Konnevesi
(Näreselkä)
a-Weather, a-Lab
Ltd., Keuruu,
Finland*
YSI6600V2-4,
YSI Inc., Yellow
Springs, OH, USA
YSI6600V2-4,
YSI Inc., Yellow
Springs, OH, USA
YSI6600V2-4,
YSI Inc., Yellow
Springs, OH, USA
7.85 × sensor
−3.09,
R2 = 0.75, n = 38
Yli-Kitka
(Vasikkaselkä)
DS18B20, Vaisala
Co., Helsinki,
Finland
T100, EHP
Tekniikka Ltd,
Oulu, Finland
Hach-Langen
LDO Berlin,
Germany
Päijänne
(Ristinselkä)
Jyväsjärvi data
used (distance 20
km)
TSIC50x, IST
AG, Ebnat-Kap-
pel, Switzerland
4175C, Aanderaa
Data Ins., Bergen,
Norway
*Lake Jyväsjärvi station (~55 km from Konnevesi) used for solar radiation data. PC = cyanobacterial biomass estimated with phycocyanin uorometer.
Table 3. Instrumentation used on the study lakes (basins) and chlorophyll a (Chl-a) sensor calibration with linear regression.
DOI: 10.5268/IW-6.4.886
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For subsequent analysis after observing the high-fre-
quency data, we selected mixing events followed by a
noticeable decrease (events that caused monotonic
decrease for ≥2 days) in daily average surface water (1 or
1.5 m) temperature. Events that decreased the surface
water temperature by >2 °C were studied more closely
(hereafter referred to as high disturbance events).
Maximum and minimum values from wind speed (U10),
air temperature, water temperature, and DO data were
then determined from the high-frequency (30 min or 3 h)
data during these events.
The return period (RP, number of days) for the events
was calculated as (Mays 2010):
RP = (n + 1)/m, (3)
where m is the rank of the event and n is the number of
recorded days (e.g., 92 d for full record in Jun, Jul, and
Aug 2013).
Relationships between the surface water (1.0 or 1.5
m) temperature decrease (°C) and the maximum wind
speed, maximum mean daily wind speed (both expressed
as change from the seasonal mean wind speed for each
site), and decrease in the air temperature during the
events were described by linear regressions. Relationships
between RP and the maximum wind speed, maximum
mean daily wind speed, and decrease in the air
temperature during the events were described with
log-linear regressions.
To evaluate the renewal or cessation of the DO
reserves in the hypolimnion during the events, change in
the DO concentration (mg L−1) was examined from the
near bottom (1 m above sediment) AWQM and discrete
data during the mixing events. The effects on hypolimnetic
DO were then divided into subgroups; complete,
substantial (+), or no renewal of the DO in the
hypolimnion; substantial renewal represented up to a
2 mg L−1 increase in the observed DO concentration.
Date Lake
Wind speed,
m s−1
Air
temperature, °C
Water temperature, °C
Maximum
Sc
kJ cm−2
Mixing
Yes/No
DO
renewal
Surface Near bottom
Event
max
Seasonal
mean
Max Min Before After Before After
9 Jun 2013 Pallasjärvi 18.0 3.6 16.3 6.1 14.3 9.4 5.0 5.0 353 No No
7 Jul 2013 20.7 15.4 10.5 15.5 13.3 6.4 6.5 No No
10 Jun 2013 Yli-Kitka 4.4 1.8 14.8 6.1 15.0 12.2 2.5 2.6 85 Yes Yes
7 Jul 2013 5.2 17.9 9.6 17.8 14.9 8.2 14.4 Yes +
7 Aug 2013 4.5 19.8 12.6 19.8 16.8 10.1 10.4 No No
30 Jun 2013 Konnevesi 11.3 3.1 19.8 16.6 22.2 18.5 5.8 5.8 519 No No
6 Jul 2013 8.0 19.9 15.5 20.4 18.3 5.9 6.0 No No
14 Jul 2013 14.7 17.9 11.7 19.4 12.2 6.0 6.1 No No
7 Aug 2013 11.4 20.4 15.1 21.3 17.5 6.2 6.4 No No
14 Jul 2013 Jyväsjärvi 7.8 2.9 18.5 12.6 21.8 17.2 6.1 6.1 258 No No
8 Aug 2013 6.7 21.2 13.9 22.3 18.9 6.1 6.1 No No
14 Jul 2013 Päijänne 7.8 2.9 18.5 12.6 22.9 16.5 4.7 4.9 1125 No No
28 Jul 2013 5.9 20.7 19.0 21.3 16.9 5.0 5.1 No No
29 Jun 2013 Vesijärvi 2.8 1.9 18.6 13.8 21.4 19.1 6.1 6.3 181 No No
15 Jul 2013 5.2 20.0 12.7 20.6 16.5 6.4 6.8 No No
11 Aug 2013 4.8 21.3 13.1 20.7 17.7 6.9 7.2 No No
15 Jul 2013 Vanajavesi 11.8 2.8 23.4 16.9 20.1 16.9 8.9* 9.4* 182 No* No*
29 Jul 2013 7.2 23.8 19.6 20.3 18.0 9.4* 9.9* No* No*
11 Aug 2013 11.3 24.0 17.5 20.0 17.8 9.9* 10.5* No* No*
8 Jun 2013 Pyhäjärvi 11.1 4.9 17.8 12.6 20.2 16.7 nd 8.4* 18 No* No*
14 Jul 2013 14.7 19.3 14.4 20.6 16.7 10.1* 18.8* Yes* Yes*
8 Aug 2013 11.4 19.5 14.6 20.8 18.2 18.6* nd Yes* Yes*
*Additional physical and/or chemical data obtained from HERTTA; database maintained by Finnish Environment Institute (SYKE) or other
discrete sampling. nd = no data.
Table 4. Meteorological and limnological variables during the strong mixing events in 2013 and the seasonal maximum stabilities in the
study lakes. The disruption of stratication was indicated with hypolimnetic dissolved oxygen (DO) response (Yes = complete renewal of
hypolimnetic DO; + = 1–2 mg L−1 introduction of DO; No = no change or decrease in DO). Sc is the Schmidt Stability Index.
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Hourly surface (1.5 m) DO data from Konnevesi were
used to calculate daily rates of net ecosystem production
(NEP), gross primary production (GPP), and respiration
(R) in the lake. Solar radiation data from Jyväsjärvi
meteorological station (55 km southwest from Konnevesi)
were used to estimate photosynthetically active radiation
(PAR). Hourly water temperature proles were obtained by
interpolating from the 3 h proler data. NEP, GPP, and R
were calculated by the open-water method (Odum 1956)
using LakeMetabolizer, an R 3.2.2 package implementa-
tion of free-water metabolism models (Venables et al. 2015
; L.A. Winslow et al. 2016).
Results
Mixing events and return period
Altogether, 50 weather-induced mixing events were
recorded in the study lakes. In 2013, 2–4 of the events per
lake were determined as high disturbance events
(continuous decrease in daily surface water temperature
>2 °C; Table 4). These high disturbance events typically
occurred within a few days in all the study lakes and were
related to low pressure weather systems passing over
Finland. During other study years, 3 high disturbance
events were determined in lakes Konnevesi, Vesijärvi, and
Pyhäjärvi (Table 5). The events were related to the highest
wind speeds and largest air temperature changes recorded
at each site (Table 4 and 5).
In summer 2013, the rst low pressure system causing
high disturbance mixing events passed over Finland in
June, the second in July, and the third in August. During the
rst event in mid-June, no full overturn was observed in
any lake (Fig. 2, Table 4), and Yli-Kitka, a large northern
lake, was not yet stratied (Fig. 2II). The second mixing
event in July caused a complete overturn in lakes Yli-Kitka
and Pyhäjärvi but not in other lakes (Fig. 2II and VIII,
Table 4). These 2 lakes had the lowest water column
stability (Table 4). The third mixing event in early August
led to warming of deeper waters, but no complete mixing
was observed in any of the study lakes (Fig. 2, Table 4).
At the time of the mixing event, Pyhäjärvi had not yet fully
recovered from the earlier mixing in July (Fig. 2VIII). In
the northernmost lake, Pallasjärvi, autumnal mixing had
already begun in early August (Fig. 2I).
Data from the other study years indicated 3 high
disturbance events in Konnevesi, Vesijärvi, and Pyhäjärvi
(Table 5). In Konnevesi, mixing was caused by increased
daily wind speeds ranging from 5.2 to 8.5 m s−1 and a
simultaneous drop in air temperatures (Table 5). In
Vesijärvi, relatively low wind speeds (<5 m s−1) resulted in
the transport of heat into the hypolimnion during the events
(Table 5). In 2009, 3 high disturbance events were observed
in Pyhäjärvi, all related to high wind speed and a simultane-
ous drop in air temperature (Table 5). Additionally,
complete mixing of the water column in Pyhäjärvi occurred
on 13 June and 18 July, although these were not classied
as major events in the data due to weak stratication of the
lake. Maximum wind speeds during these events were 8.9
and 7.8 m s–1, and air temperatures decreased to 10.4 and
13.7 °C from 19.8 and 21.9°C, respectively.
When all the events, dened as a 2-day continuous
decrease in the surface water temperature, were compared
to meteorological drivers, no signicant relationship
between surface water temperature decrease and maximum
(30 min) wind speed was found (Fig. 3a) during the events.
A positive relationship was observed, however, between
surface water temperature decrease and maximum daily
wind speed during the events (Fig. 3b; R2 = 0.352,
p = 0.012, n = 50). The event-related decrease in air
Date Lake Wind speed, m s−1 Air temperature,
°C
Water temperature, °C Maximum
seasonal
Sc
kJ cm−2
Mixing
Yes/No
DO
renewal
Surface Near bottom
Event
max
Seasonal
mean
Max Min Before After Before After
8 Jun 2014 Konnevesi 5.4 2.8 21.0 5.7 17.9 14.5 6.8 6.8 661 No No
12 Jun 2014 8.5 14.8 9.7 16.8 11.9 6.8 6.8 No No
8 Aug 2014 5.2 20.5 14.6 23.6 20.0 7.3 7.4 No No
2 Jul 2011 Vesijärvi 4.1 2.0 26.1 15.1 23.1 20.8 9.6 9.8 176 No No
10 Jul 2011 4.8 23.3 15.8 23.3 20.9 9.8 10.1 No No
27 Jul 2011 4.9 23.6 14.1 23.1 21.1 10.1 10.3 No No
1 Jun 2009 Pyhäjärvi 10.5 5.0 21.2 9.1 17.2 13.6 13.6 13.1 57 Ye s +
2 Jul 2009 13.8 25.4 14.2 21.1 17.6 13.4 17.0 Yes Yes
9 Aug 2009 11.9 25.0 14.0 21.3 17.6 18.7 17.5 Yes Yes
Table 5. Meteorological and limnological variables during the high disturbance events in Konnevesi in 2014, in Vesijärvi in 2011 and in
Pyhäjärvi in 2009 and seasonal maximum stabilities in the study lakes. The disruption of stratication was indicated with hypolimnetic dissolved
oxygen (DO) response (Yes = complete renewal of hypolimnetic DO; + = 1–2 mg L−1 introduced DO, No = no change or decrease in DO).
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Inland Waters (2016) 6, pp.523–534
temperature also correlated positively with the decrease in
the surface water temperature (Fig. 3c; R2 = 0.466,
p = 0.001, n = 50). The RP of the disturbance in the water
column did not correlate with the maximum wind speed
(Fig. 3d) but had a signicant relationship with the
maximum daily wind speed (Fig. 3e; R2 = 0.121, p = 0.014,
n = 50) and with the air temperature decrease during the
events (Fig. 3f; R2 = 0.310, p < 0.001, n = 50). RP for the
high disturbance events varied between 20 and 92 d.
During the studied summers, complete mixing occurred
only in lakes Yli-Kitka and Pyhäjärvi, and their Sc remained
<100 kJ cm−2 (Fig. 4a). Jyväsjärvi, the smallest and most
humic of the study lakes, had the most stable water column,
indicated by the highest post-event difference between
surface and near-bottom water temperatures (Fig. 4b–c).
Hypolimnetic dissolved oxygen response
In each lake, DO concentration of the hypolimnion
responded differently to the high disturbance events. Distinct
increases in DO concentrations were observed when the
lakes were extensively mixed. Only lakes Yli-Kitka and
Pyhäjärvi had a complete renewal or substantial increase in
their hypolimnetic DO reserves during the events (Table 4
and 5). Before the rst mixing event in June 2013, Yli-Kitka
had not formed stable stratication and therefore had no
hypolimnetic DO decit. During the second mixing event in
July, the hypolimnetic DO concentration increased from
9.1 to 9.5 mg L−1 in the lake (Table 4). Conversely, in
mesotrophic Pyhäjärvi, the hypolimnetic conditions were
entirely related to DO reserves mixed downward from the
Fig. 2. Episodic events in lakes (a, I) Pallasjärvi, (b, II) Yli-Kitka, (c, III) Jyväsjärvi, (d, IV) Päijänne, (e, V, i) Konnevesi, (f, VI, j) Vesijärvi, (g, VII, k)
Vanajavesi, and (h, VIII, l) Pyhäjärvi during summer (Jun–Aug) 2013: (a–h) daily averages of meteorological variables, (I–VIII) daily averages of water
temperature at selected depths, (black points) daily gross primary productivity (GPP), and (grey points) respiration (R: plotted on negative scale to
illustrate consumption of oxygen O2; i: from Konnevesi only), and (i–l) nighttime averages of chlorophyll a estimates (Chl-a: from Konnevesi, Vesijärvi,
Vanajavesi, and Pyhäjärvi only). Note the squared wind speed scale. See Table 2 for details: for lakes with proler, the selected depths are in the
following order from top line: 1, 2, 3, 5, 7, 10, 15, 20, 25, 30, 35, and 40 m according to lake depth. Black arrows and vertical dashed lines indicate
timing of high disturbance in the surface (1.0 or 1.5 m) water temperature (decrease in temperature >2 °C). Black circles = data from discrete sampling.
530
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© International Society of Limnology 2016
Fig. 3. Surface water (depth of 1.0 or 1.5 m) temperature decrease (°C), and return period (days) of events plotted against (a, d) the maximum
(30 min) wind speed and (b, e) the maximum mean daily wind speed, expressed as change from the seasonal mean wind speed for each site,
and (c, f) decrease in the air temperature during the events.
Fig. 4. Maximum seasonal stability (black bars) and number of occasions of complete mixing (grey bars) on the study lakes in 2013; (a) lake
characteristics plotted against average difference in water temperature between epilimnion and hypolimnion after the events, (b) lake mean depth
to surface area ratio, and (c) water colour in the study lakes.
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Inland Waters (2016) 6, pp.523–534
upper water column. Longer stratication periods led to a
decrease of the hypolimnetic DO reserves. In 2009 the
3 episodic mixing events resulted in substantial increase or
complete renewal of hypolimnetic DO concentration in the
lake (Table 5); on 1 June hypolimnetic DO concentration
increased from 9 to 10.0 mg L−1, on 2 July from 3.9 to
8.7 mg L−1, and on 9 August from 0.6 to 8.2 mg L−1. The 2
other mixing events (on 13 June and 18 July) caused no
substantial change in the surface water temperature due to
weak stratication but increased the hypolimnetic DO
concentrations. On 13 June and 18 July the DO increased
from 8.1 to 10.7 mg L−1 and from 3.5 to 7.0 mg L−1, respec-
tively. In the other study lakes, no substantial increases in
the hypolimnetic DO concentrations were observed.
Chlorophyll and lake metabolism response
In summer 2013, the highest Chl-a values were measured
in Vanajavesi and the lowest in Konnevesi, varying
between 11.2 and 20.0 µg L−1 and 1.4 and 6.0 µg L−1,
respectively (Fig. 2). In Vesijärvi, Chl-a varied between
1.6 and 6.4 µg L−1, with the lowest values recorded toward
the end of stratied season (Fig. 2j). In Vanajavesi, Chl-a
remained at a relatively constant level (11–15 µg L−1) in
the beginning of summer, but after the mixing event in
mid-July, Chl-a increased from the pre-event average of
12.1 to 16.4 µg L−1 (Fig. 2k). Unfortunately, in July there
was a gap in the Vanajavesi data, and we could not evaluate
the effects in the lake in detail. In Pyhäjärvi, the rst
mixing event preceded the substantial increase in Chl-a
(from 5.7 to 11.2 µg L−1) starting on 11 June. On 25 June,
however, the Chl-a values had already returned to the
previous level (Fig. 2l). In all study lakes, all maximum
Chl-a values were recorded within 10 days, starting on
18 July in Pyhäjärvi. In most lakes, the high disturbance
events diluted the surface water Chl-a by mixing with
metalimnetic and hypolimnetic water, resulting in lower
Chl-a concentrations. Mean daily NEP (GPP − R) in
Konnevesi varied between −0.3 and 0.2 mg L−1 d−1 O2, with
a seasonal average close to zero. Estimates of GPP and R
remained low throughout the season, both having their
highest values in the early summer at the beginning and
toward the end of thermal stratication (Fig. 2i).
Discussion
Changes in wind speed and air temperature are known to
modify thermal stability and heat distribution of lakes
(Imboden and Wüest 1995, Wilhelm and Adrian 2008). In
agreement with that, our study lakes also responded to the
episodic weather-induced events, but their response varied
depending on the strength of thermal stratication, which in
turn depended on, among other factors, the morphometric
characteristics of the lake. Low pressure systems with high
wind speeds, but with no substantial change in air
temperature, caused complete mixing only in Pyhäjärvi, a
large, weakly stratied clear-water lake in southern Finland.
Yli-Kitka, a large clear-water northern lake, has been
considered dimictic, but a cold and windy period in July
2013 resulted in a complete mixing of the lake. By contrast,
the smallest and most humic lake, Jyväsjärvi, had the most
stable water column, typical of small brown-coloured lakes
(Bowling and Salonen 1990). Shallow lakes are usually
more vulnerable to mixing than deeper lakes (Boehrer and
Schultze 2008, Arvola et al. 2010, Woolway et al. 2015), but
strong external forcing for complete mixing may also be
needed if the lake is small enough and sheltered against
wind (Bowling and Salonen 1990, Nordbo et al. 2011).
The short return periods found in this study were
similar to those found in other strongly stratied lakes in
Europe. For example, Blelham (UK) and Slotssø
(Denmark), studied by Jennings et al. (2012), also had
short return periods (0.1–0.2 yr). In both lakes, the recovery
period of stratication was <15 d. In our study lakes wind
speeds were only moderate compared to those recorded by
Jennings et al. (2012), but even after strong wind, the lakes
may have short-lived response in their stability. For
instance, the effects of hurricane Irene on the thermal strat-
ication of lakes in the United States and Canada were
observable for only 1 week (Klug et al. 2012).
Both Jennings et al. (2012) and Klug et al. (2012) showed
that despite short-term effects on thermal conditions, the
weather-forcing related effects on chemistry and biology of
the lakes can be long lasting. Responses to nutrient and
dissolved organic carbon concentration changes as well as
changes in metabolic processes may affect the productivity
of lakes (e.g., Drakare et al. 2002, Coloso et al. 2011,
Solomon et al. 2013). In our lakes, the effects on the
hypolimnetic DO conditions were relevant only in the lakes
with weak stability and after substantial mixing. DO content
of the water column is known to be strongly linked to mixing
(Golosov et al. 2012), and when mixing occurs after a long
stratied period with hypolimnetic anoxia, upwelling of
accumulated hypolimnetic nutrients may fertilize phyto-
plankton production (e.g., Huisman et al. 1999, Wilhelm and
Adrian 2008, Crockford et al. 2014). In our study, Vanajavesi
showed the most prolonged increase in Chl-a after the
episodic mixing, but a similar increase was also observed in
Konnevesi. Despite the change in Chl-a in Konnevesi,
however, no changes in GPP and/or R could be found, which
contradicts observations by Obrador et al. (2014) that mixing
may inuence the metabolic activity of a lake. The estimate
of lake metabolism in Konnevesi was based on the results of
one DO sensor at the surface, therefore providing a rough
estimate of the metabolism (van de Bogert et al. 2012,
Obrador et al. 2014).
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© International Society of Limnology 2016
In lakes Vesijärvi and Pyhäjärvi, water column mixing
clearly decreased Chl-a. Mixing is known to dilute Chl-a in
deeper water layers (MacIntyre et al. 2009), but changes in
the thermal stability may also affect the community
composition of phytoplankton (Wilhelm and Adrian 2008,
Nõges et al. 2010, Cottingham et al. 2015) because mixing
conditions generally favor diatoms (Reynolds 2006),
whereas stagnant stable conditions are benecial to cyano-
bacteria (Paerl and Huisman 2008). In Pyhäjärvi, the early
summer bloom originates from a rapid succession of
diatoms in the water column (Kallio et al. 2010), typical for
many temperate and boreal lakes (e.g., Wetzel 2001), but
also partly from the resuspension of settled phytoplankton
that mixing in the lake promotes (Finnish Environment
Institute, unpubl. data).
This study demonstrated that, in summer, lakes in
Finland may face similar episodic weather-induced mixing
events regardless of their geographical location because the
low pressure areas entering from Atlantic Ocean can cross
the whole country (Heino 1994). High wind speeds together
with a decrease in air temperature could lead to a rapid
change in thermal stratication of any of the study lakes,
which cannot be detected by less frequent traditional
monitoring. The climate-related changes in air temperature
are predicted to take place mostly in autumn and winter in
northern Europe (Jylhä et al. 2010), without any direct
effect on wind speeds or cold periods in summer (Hov et al.
2013). In the future, however, a changing climate may
support stronger storms in summer and consequently more
efcient mixing periods during the stratication. Increasing
wind speeds have already been observed in northern
Europe, but their relationship to climate change is not
known (Blenckner et al. 2009, Donat et al. 2011,
Brönnimann et al. 2012, Hov et al. 2013). In the future, the
stratied period in Europe is predicted to lengthen
(Blenckner et al. 2009) in both dimictic (Bergström et al.
2011) and polymictic lakes (Adrian et al. 2009), and hy-
polimnetic oxygen depletion may become more prevalent.
However, if the stratied period in European lakes
lengthens in the future as has been predicted (Adrian et al.
2009, Blenckner et al. 2009, Bergström et al. 2011), hy-
polimnetic oxygen depletion may become more prevalent
in both dimictic and polymictic lakes.
Considering projected future changes in weather-in-
duced events, more detailed studies with short- and
long-term perspectives of the response of lake ecosystems
are needed (Jentsch et al. 2007, Williamson et al. 2009).
Networking AWQM has untapped potential (Goodman et
al. 2015, Hamilton et al. 2014), but our study clearly
shows that uniform measuring schema with hypothesis-
oriented data collecting and mining is demanded for
extensive and profound analysis of data from the AWQM
networks.
Conclusions
Our results demonstrated that several weather-induced
incomplete or complete mixing events per summer may
take place in each of the study lakes independent of their
geographic location. The lakes responded to the episodic
weather-induced events individually, however, depending
on their morphometric characteristics, trophic status, and
other specic properties. AWQM data provide a unique
opportunity to analyze in detail the responses of the study
lakes. With caution, the results could be applied to other
boreal lakes with less frequent sampling to predict their
sensitivity to weather-induced mixing.
Acknowledgements
We are grateful to everyone who participated in data
collection at the monitoring sites. This research was
supported by the VALUE Doctoral Program and Projects
Vetcombo and MMEA funded by Tekes. The study was
also funded by the integrated EU project MARS
(Managing Aquatic ecosystems and water Resources under
multiple Stress) within Framework Programme 7, Theme
ENV.2013.6.2-1: Water resources management under
complex, multi-stressor conditions (Contract No. 603378).
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... At the same time, surface temperature tends to slightly decrease during storms (Mesman et al. 2020;Doubek et al. 2021), potentially reducing growth rates of light-saturated phytoplankton (Trombetta et al. 2019). In addition, a deeper mixed layer can increase light limitation for growth (Diehl et al. 2002) and deepening dilutes concentrations by mixing phytoplankton over a larger volume of water (Kuha et al. 2016). Sediment resuspension due to shear stress in shallower parts of the lake may simultaneously release nutrients and limit light availability (Ji et al. 2018). ...
... Such moderate wind speeds may cause nutrient upwelling without strongly deepening the mixed layer, and as such promoted growth. Stronger wind speeds, however, caused stronger nutrient upwelling, but also more mixed layer deepening and consequently light limitation (Kuha et al. 2016;Jalil et al. 2020). This explanation is supported by the volume-averaged nutrient concentrations over the depth of the euphotic zone after the wind perturbations (Supplement S6 Supporting Information); strong wind speeds increased nutrient concentrations in the mixed layer, but this was not accompanied by increased phytoplankton concentrations. ...
... Phytoplankton concentration after wind events also increased in the first numerical experiment when incoming shortwave radiation was high. Observed decreases in phytoplankton concentration after a storm have been explained by a combination of dilution due to mixed layer deepening and exposure to more stringent and dynamic light conditions (Ibelings et al. 1994;De Eyto et al. 2016;Kuha et al. 2016). Lastly, a higher pre-event surface temperature resulted in more negative effects of wind on phytoplankton concentration in the first experiment. ...
Article
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Extreme wind events affect lake phytoplankton by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases in phytoplankton concentration after strong wind events have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. We coupled a one‐dimensional physical model to a biogeochemical model to investigate the factors regulating short‐term phytoplankton responses to summer wind events, now and under expected warmer future conditions. We simulated physical, chemical, and biological dynamics in Lake Erken, Sweden, and found that strong wind could increase or decrease the phytoplankton concentration in the euphotic zone 1 week after the event, depending on antecedent lake physical and chemical conditions. Wind had little effect on phytoplankton concentration if the mixed layer was deep prior to wind exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted phytoplankton concentration, whereas higher surface water temperatures decreased concentrations after wind events. Medium‐intensity wind events resulted in more phytoplankton than high‐intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind events affect phytoplankton concentration. These findings help to better understand how wind impacts vary as a function of local environmental conditions and how climate warming and changing extreme weather dynamics will affect lake ecosystems.
... At the same time, surface temperature tends to decrease during storms (Kuha et al. 2016;Mesman et al. 2020), potentially reducing light-saturated phytoplankton growth rates (Trombetta et al. 2019). Additionally, a deeper mixed layer can increase light limitation for growth (Diehl et al. 2002) and deepening dilutes concentrations by mixing phytoplankton over a larger volume of water (Kuha et al. 2016). ...
... At the same time, surface temperature tends to decrease during storms (Kuha et al. 2016;Mesman et al. 2020), potentially reducing light-saturated phytoplankton growth rates (Trombetta et al. 2019). Additionally, a deeper mixed layer can increase light limitation for growth (Diehl et al. 2002) and deepening dilutes concentrations by mixing phytoplankton over a larger volume of water (Kuha et al. 2016). Sediment resuspension due to shear stress in shallower parts of the lake may simultaneously release nutrients and limit light availability (Ji et al. 2018). ...
... Negative influence of wind on phytoplankton concentrations has been shown in various systems (e.g. Fitch and Moore 2007; Kuha et al. 2016;Jalil et al. 2020). However, a positive effect (relative to low wind speed) of moderate wind speed but a negative effect of high wind speeds has, to our knowledge, not often been shown in the literature. ...
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Extreme wind events affect lake phytoplankton amongst others by deepening the mixed layer and increasing internal nutrient loading. Both increases and decreases of phytoplankton biomass after storms have been observed, but the precise mechanisms driving these responses remain poorly understood or quantified. In this study, we coupled a one-dimensional physical model to a biogeochemical model to investigate the factors regulating short-term phytoplankton responses to summer storms, now and under expected high-temperature conditions. We simulated physical, chemical and biological dynamics in Lake Erken, Sweden, to take advantage of a long-term time series that we used to calibrate our model. We found that wind storms could increase or decrease the phytoplankton concentration one week after the storm, depending on antecedent lake physical and chemical conditions. Storms had little effect on phytoplankton biomass if the mixed layer was deep prior to storm exposure. Higher incoming shortwave radiation and hypolimnetic nutrient concentration boosted growth, whereas higher surface water temperatures decreased phytoplankton concentration after storms. Medium-intensity wind speeds resulted in more phytoplankton biomass after storms than high-intensity wind. Simulations under a future climate scenario did not show marked differences in the way wind affects phytoplankton growth following storms. Our study shows that storm impacts on lake phytoplankton are complex and likely to vary as a function of local environmental conditions.
... Prior studies provide several hypotheses for what environmental drivers likely trigger cyanobacterial growth or accumulation of cyanobacterial surface scums apart from nutrient concentrations, including higher temperatures, which result in increased growth (Hamilton et al., 2009;Paerl & Huisman, 2008); light-induced triggering of cell germination and growth (Karlsson-Elfgren et al., 2004;Roelofs & Oglesby, 1970); water column mixing resulting in more recruitment of dormant cells from the sediment and/or dilution of surface water cyanobacterial density, which can occur due to temperature changes, precipitation events, or wind (Carey et al., 2014;de Eyto et al., 2016;Jennings et al., 2012;Kuha et al., 2016); strong thermal stratification resulting in greater incidence of surface scums (Carey, Ibelings, et al., 2012); and wind-driven aggregation of cells or colonies in nearshore zones (Cyr, 2017;Roelofs & Oglesby, 1970). The development of forecast models with uncertainty partitioning is needed to compare and evaluate these hypotheses in a predictive framework. ...
... In keeping with a Bayesian approach, where previous knowledge of a system is incorporated into the current analysis, we leveraged the expertise of our working group and published research to develop a list of 82 candidate physical variables hypothesized to influence G. echinulata densities (Carey et al., 2014;Carey, Ibelings, et al., 2012;Cyr, 2017;de Eyto et al., 2016;Hamilton et al., 2009;Jennings et al., 2012;Karlsson-Elfgren et al., 2004;Kuha et al., 2016;Paerl & Huisman, 2008;Roelofs & Oglesby, 1970). ...
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Near‐term ecological forecasts provide resource managers advance notice of changes in ecosystem services, such as fisheries stocks, timber yields, or water quality. Importantly, ecological forecasts can identify where there is uncertainty in the forecasting system, which is necessary to improve forecast skill and guide interpretation of forecast results. Uncertainty partitioning identifies the relative contributions to total forecast variance introduced by different sources, including specification of the model structure, errors in driver data, and estimation of current states (initial conditions). Uncertainty partitioning could be particularly useful in improving forecasts of highly variable cyanobacterial densities, which are difficult to predict and present a persistent challenge for lake managers. As cyanobacteria can produce toxic and unsightly surface scums, advance warning when cyanobacterial densities are increasing could help managers mitigate water quality issues. Here, we fit thirteen Bayesian state‐space models to evaluate different hypotheses about cyanobacterial densities in a low nutrient lake that experiences sporadic surface scums of the toxin‐producing cyanobacterium, Gloeotrichia echinulata. We used data from several summers of weekly cyanobacteria samples to identify dominant sources of uncertainty for near‐term (one‐week to four‐week) forecasts of G. echinulata densities. Water temperature was an important predictor of cyanobacterial densities during model fitting and at the four‐week forecast horizon. However, no physical covariates improved model performance over a simple model including the previous week’s densities in one‐week ahead forecasts. Even the best fit models exhibited large variance in forecasted cyanobacterial densities and did not capture rare peak occurrences, indicating that significant explanatory variables when fitting models to historical data are not always effective for forecasting. Uncertainty partitioning revealed that model process specification and initial conditions dominated forecast uncertainty. These findings indicate that long‐term studies of different cyanobacterial life stages and movement in the water column as well as measurements of drivers relevant to different life stages could improve model process representation of cyanobacteria abundance. In addition, improved observation protocols could better define initial conditions and reduce spatial misalignment of environmental data and cyanobacteria observations. Our results emphasize the importance of ecological forecasting principles and uncertainty partitioning to refine and understand predictive capacity across ecosystems.
... Our microcosm experiment comprised three distinct temperature regimes: constant temperatures and two variable temperature regimes with diurnal fluctuations of ±3°C and 3-day heatwaves of 6°C above ambient, all replicated over the natural temperature range of the model system (i.e., 10-28°C, Figure 1). These variable temperature regimes were selected to mimic naturally occurring temperature events in habitats our study organisms encounter naturally (i.e., small ponds and rock pools) (Jacobs et al., 2008;Kuha et al., 2018). ...
... The diurnal fluctuation regime comprised five temperature levels, which experienced the same mean temperature as the constant regimes but with a fluctuation of ±3°C every 12 hr (i.e., 10-16°C, 13-19°C, 16-22°C, 19-25°C, and 22-28°C), mimicking diurnal fluctuations in small rock pools (Jacobs et al., 2008). The heatwave was performed at four different temperature levels (13, 16, 19, and 22°C), with conditions identical to the constant regime except for an increase of 6°C for 72 hr, 20 days after animals were exposed to the parasite, mimicking a short heatwave (Kuha et al., 2018). We chose these temperature levels because of their relevance for our host and pathogen system, as no infection occurs below 12°C and hosts have high mortality above 30°C (Kirk et al., 2018;Kirk et al., 2019). ...
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The dynamics of host-parasite interactions are highly temperature-dependent and may be modified by increasing frequency and intensity of climate-driven heat events. Here, we show that altered patterns of temperature variance lead to an almost order-of-magnitude shift in thermal performance of host and pathogen life-history traits over and above the effects of mean temperature and, moreover, that different temperature regimes affect these traits differently. We found that diurnal fluctuations of ±3°C lowered infection rates and reduced spore burden compared to constant temperatures in our focal host Daphnia magna exposed to the microsporidium para- site Ordospora colligata. In contrast, a 3-day heatwave (+6°C) did not affect infection rates, but increased spore burden (relative to constant temperatures with the same mean) at 16°C, while reducing burden at higher temperatures. We conclude that changing patterns of climate variation, superimposed on shifts in mean temperatures due to global warming, may have profound and unan- ticipated effects on disease dynamics.
... It has been suggested that the lack of a coherent pattern of TC events across the region during the recovery from the LIA and into the modern warm era may be a reflection of the stochastic nature of hurricane landfalls (Donnelly et al., 2015;. Further, retrospective attribution of specific TCs may sometimes be difficult as storms may impact different lakes in different ways (Kuha et al., 2016) depending on the relative distance of lakes from the eye of a storm, and associated wind field (Donnelly et al., 2015;D. J. Wallace et al., 2014). ...
Article
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Major Tropical Cyclone (TC) events cause extensive damage in coastal regions of the western North Atlantic Basin. The short instrumental record leaves significant gaps in understanding long‐term trends in TC recurrence and intensity, creating uncertainty about future storm trends. Analysis of an ∼520‐year core record from Harvey Lake, located >80 km from the Atlantic coast in southwestern New Brunswick, Canada was carried out using: (a) end‐member mixing analysis (EMMA) of lake sediment grain size data to identify storm‐linked sedimentological processes; and (2) ITRAX X‐ray fluorescence (XRF) derived element/ratios (Fe, Ti, Ca/Sr, Zr/Rb, K/Rb, and Br + Cl/Al) associated with precipitation, weathering, catchment runoff, and air masses. Three derived end members were correlated to heavy rainfall events (EM01), spring freshet (EM02), and TCs (EM03). CONISS analysis of the EMMA and XRF core data resulted in recognition of four unique climatic zones distinguished by distinct distributions of TC and rainfall/weathering/runoff/and air masses. Numerous, major (EM01) rainfall events and (EM03) TC events characterized the basal core record during the early Little Ice Age (LIAa; Zone 1) phase, terminating at ∼1645. A near cessation of heavy rainfall and TC events differentiated the subsequent colder LIAb (∼1645–1825; Zone 2) and subsequent Little Ice Age Transition (∼1825–1895; Zone 3). A resurgence of major rainfall and TC events occurred during recovery from the LIA starting in ∼1895 (Zone 4). EMMA provides a robust tool for recognition of TC and major rainfall events, and greatly expands the potential for paleo‐storm activity research well inland from coastal regions.
... Mean summer water color at the surface of Lake Konnevesi is typically low (about 6 mg/L Pt). Given mean summer chlorophyll-a and total phosphorus about 4.2 µg/L and 6 µg/L at uppermost 1 m layer, the trophic state in this lake varies from mainly oligotrophic to sometimes mesotrophic (Kuha et al. 2016). ...
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Depth-resolved water temperature data on the thermal environment of lakes are often hindered by sparse temporal frequency, limited depth resolution, or short duration that create many challenges for long-term analysis. Where high frequency and depth-resolved data exist, they can provide a wealth of knowledge about how lakes are responding to a changing climate. In this study, we analyzed 302 profiles of summer mean water temperature (July to September), which includes 6756 unique observations, from Lake Konnevesi (Finland) and investigated changes in lake surface and deep-water temperature from 1984 to 2021. Statistical analysis of this dataset suggests a substantial warming of lake surface water temperature (0.41 °C decade ⁻¹ ) but no significant change in the deepest layer (0.00 °C decade ⁻¹ ). Our analysis also suggested the inter-annual variability in lake surface temperature correlated significantly with the upward trends of summer mean air temperature and solar radiation, but suggested no significant effect of observed changes in ice break-up dates or changes in near-surface wind speed. None of the investigated predictors correlated with the change in deep-water temperature. Due to the variable response of lake surface and bottom water temperature to climate change in this high-latitude lake, our data suggest a substantial increase in lake thermal stability. Our study supports the growing literature on lake thermal responses to climate change, and illustrates the unique contrast of climate change impacts at the surface and at depth in lake ecosystems, with deep waters acting as a potential thermal refuge to aquatic organisms within a warming world.
... Strong mixing events occurred primarily during late winter to early spring and in late fall. These events, however, were not consistent with the spring and fall turnover dynamics commonly found in temperate and boreal dimictic lakes where low temperature surface waters become mixed with warmer bottom water [23,42]. In Jackson lakes, near-surface warm water consistently mixed with bottom cold water. ...
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Cyanobacterial communities of three co-located eutrophic sandpit lakes were surveyed during 2016 and 2017 over season and depth using high-throughput DNA sequencing of the 16S rRNA gene. All three lakes were stratified except during April 2017 when the lakes were recovering from a strong mixing event. 16S rRNA gene V4 sequences were parsed into operational taxonomic units (OTUs) at 99% sequence identity. After rarefaction of 139 samples to 25,000 sequences per sample, a combined total of 921,529 partial 16S rRNA gene sequences were identified as cyanobacteria. They were binned into 19,588 unique cyanobacterial OTUs. Of these OTUs, 11,303 were Cyanobium. Filamentous Planktothrix contributed 1537 and colonial Microcystis contributed 265. The remaining 6482 OTUs were considered unclassified. For Planktothrix and Microcystis one OTU accounted for greater than 95% of the total sequences for each genus. However, in both cases the non-dominant OTUs clustered with the dominant OTUs by date, lake, and depth. All Planktothrix OTUs and a single Cyanobium OTU were detected below the oxycline. All other Cyanobium and Microcystis OTUs were detected above the oxycline. The distribution of Cyanobium OTUs between lakes and seasons can be explained by an epidemic-like response where individual OTUs clonally rise from a diverse hypolimnion population when conditions are appropriate. The importance of using 99% identity over the more commonly used 97% is discussed with respect to cyanobacterial community structure. The approach described here can provide another valuable tool for assessing cyanobacterial populations and provide greater insight into the controls of cyanobacterial blooms.
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Global warming is likely to lengthen the seasonal duration of larval release by parasites. We exposed freshwater mussel hosts, Anodonta anatina, from two high-latitude populations to high, intermediate, and low temperatures throughout the annual cercarial shedding period of the sympatric trematodes Rhipidocotyle fennica and R. campanula, sharing the same transmission pathway. At the individual host level, under warmer conditions, the timing of the cercarial release in both parasite species shifted towards seasonally earlier period while its duration did not change. At the host population level, evidence for the lengthening of larvae shedding period with warming was found for R. fennica. R.campanula started the cercarial release seasonally clearly earlier, and at a lower temperature, than R. fennica. Furthermore, the proportion of mussels shedding cercariae increased, while day-degrees required to start the cercariae shedding decreased in high-temperature treatment in R. fennica. In R. campanula these effects were not found, suggesting that warming can benefit more R. fennica. These results do not completely support the view that climate warming would invariably increase the seasonal duration of larval shedding by parasites, but emphasizes species-specific differences in temperature-dependence and in seasonality of cercarial release.
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Temperature and intraspecific competition are important factors influencing the growth of all organisms, including parasites. The temperature increase is suggested to stimulate the development of parasites within poikilothermic hosts. However, at high parasite densities, this effect could be diminished, due to stronger intraspecific competition. Our study, for the first time, addressed the joint effects of warming and parasite abundances on parasite growth in poikilothermic hosts. The growth of the common fish parasite larvae (trematode Diplostomum pseudospathaceum ) within the rainbow trout at different infection intensities and temperatures (15°C and 18°C) was experimentally investigated. The results showed that temperature was positively correlated with both parasite infection success and growth rates. The growth rates increased much more compared to those in many free-living poikilothermic animals. Atypically for a majority of parasites, D. pseudospathaceum larvae grow faster when abundant (Allee effect). The possible causes for this phenomenon (manipulation cost sharing, etc.) are discussed in this study. Importantly, limited evidence of the interaction between temperature and population density was found. It is likely that temperature did not change the magnitude of the Allee effect but affected its timing. The impact of these effects is supposed to become more pronounced in freshwater ecosystems under current climate changes.
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Extreme wind storms can strongly influence short‐term variation in lake ecosystem functioning. Climate change is affecting storms by altering their frequency, duration, and intensity, which may have consequences for lake ecosystem resistance and resilience. However, catchment and lake processes are simultaneously affecting antecedent lake conditions which may shape the resistance and resilience landscape prior to storm exposure. To determine whether storm characteristics or antecedent lake conditions are more important for explaining variation in lake ecosystem resistance and resilience, we analyzed the effects of 25 extreme wind storms on various biological and physiochemical variables in a shallow lake. Using boosted regression trees to model observed variation in resistance and resilience, we found that antecedent lake conditions were more important (relative importance = 67%) than storm characteristics (relative importance = 33%) in explaining variation in lake ecosystem resistance and resilience. The most important antecedent lake conditions were turbidity, Schmidt stability, %O2 saturation, light conditions, and soluble reactive silica concentrations. We found that storm characteristics were all similar in their relative importance and results suggest that resistance and resilience decrease with increasing duration, mean precipitation, shear stress intensity, and time between storms. In addition, we found that antagonistic or opposing effects between the biological and physiochemical variables influence the overall resistance and resilience of the lake ecosystem under specific lake and storm conditions. The extent to which these results apply to the resistance and resilience of different lake ecosystems remains an important area for inquiry.
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Metabolism is a fundamental process in ecosystems that crosses multiple scales of organization from individual organisms to whole ecosystems. To improve sharing and reuse of published metabolism models, we developed LakeMetabolizer, an R package for estimating lake metabolism from in situ time series of dissolved oxygen, water temperature, and, optionally, additional environmental variables. LakeMetabolizer implements 5 different metabolism models with diverse statistical underpinnings: bookkeeping, ordinary least squares, maximum likelihood, Kalman filter, and Bayesian. Each of these 5 metabolism models can be combined with 1 of 7 models for computing the coefficient of gas exchange across the air–water interface (k). LakeMetabolizer also features a variety of supporting functions that compute conversions and implement calculations commonly applied to raw data prior to estimating metabolism (e.g., oxygen saturation and optical conversion models). These tools have been organized into an R package that contains example data, example use-cases, and function documentation. The release package version is available on the Comprehensive R Archive Network (CRAN), and the full open-source GPL-licensed code is freely available for examination and extension online. With this unified, open-source, and freely available package, we hope to improve access and facilitate the application of metabolism in studies and management of lentic ecosystems.
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We present a data set covering three months of carbon dioxide (CO2) and energy fluxes measured by the eddy covariance method over a northern boreal lake that collects waters from a surrounding catchment dominated by upland forest and wetlands. The data period comprises more than half of the open-water period of 2013. The 30-min averages of CO2 fluxes ranged from –0.02 to 0.05 mg m–2 s–1. The monthly CO2 balances varied from 20 to 30 g m–2 (emission) between July and September, and decreased in October. A small daytime uptake of CO2, probably caused by the aquatic plants growing near the measurement mast, was observed from July to September. In September, we observed a temporary enhancement of CO2 efflux, which was attributed to both high wind speed and rapid cooling of the water and subsequent water column overturn. This peak was accompanied by a period of high sensible heat flux (SHF) from the water to the atmosphere, which is known to enhance the mixing of the water. The seasonal CO2 flux during the open-water period from the shallow part of the lake was estimated to be 120 g m–2 yr–1, which corresponds to a loss of approximately 25 g m–2 yr–1 from the terrestrial part of the catchment, assuming that the observed lake CO2 emissions result from the decomposition of the imported carbon. At midday, the net energy received by the lake was used mostly to heat the water, and only a minor part of it was converted to SHF and latent heat flux (LHF), with more energy used for the latter. While the SHF showed a clear diurnal cycle with a peak early in the morning and no flux in the afternoon, the diurnal pattern of LHF was more even, with evaporation occurring throughout the day until the freezing of the lake. Our data from this northern lake highlight the importance of thermal water mixing in the air–lake CO2 flux dynamics and imply that this flux constitutes a significant part of the annual catchment-scale carbon budget.
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Transport phenomena are among the most important processes in natural systems. Chemical compounds, the constituents of biogeochemical systems, are in continual motion in all parts of the earth. The thermal motion of atoms and molecules is perceived on the macroscopic level as molecular diffusion i.e., as the slow but persistent movement “down along the concentration gradient.” Although the average speed of the atoms is on the order of tens to hundreds of meters per second, the net transport is small, because the molecules do not maintain the same direction long enough. Thus, typical molecular diffusion coefficients of solutes in water are approximately 10-9 m2s - 1 corresponding to characteristic annual transport distances of approximately 20 cm. In solids the diffusion coefficients even drop to values as low as 10-14m2s-1 or less.
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In winter in Finland the land is covered by snow, fresh water bodies as well as the neighbouring areas of the Baltic Sea are covered by ice, and the ground is frozen. Snow and ice research is thus a natural field of geophysics in this country. The main topics have been the seasonal snow cover and sea ice. The research began well in the 1800s for practical monitoring purposes and for the sake of general interest in natural sciences. Until 1940 the activity was focused on the establishment and development of snow and ice monitoring systems. In 1950-1970 basic research questions began to arise as well as contacts to polar research formed. A major increase in the volume of research took place in the 1970s due to the expansion of the winter shipping in Finland and due to the general increase in human activity at high latitudes. During the last decade climate and environmental questions have become increasingly important which has created a wider activity in snow and ice research. Also a new generation with many new doctoral theses has come into the field which gives good promises for the future of snow and ice research in Finland.