Response of boreal lakes to episodic weather-induced events

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 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.

Full text

Provided for non-commercial research and education use only.
Not for reproduction, distribution or commercial use.
This article appeared in Inland Waters - Journal of the International Society of Limnology, published by The
Freshwater Biological Association. The attached offprint is provided to the author for research and education
use (including use by others within their institution for such purposes) and for sharing with interested
colleagues, provided the use of the article is for non-commercial purposes, and copies offered are not for sale
and are not distributed in a systematic way.
Please note that posting of the published version of the article to personal/academic websites or institutional
repositories is not permitted, except for authors who have paid an ‘open early’ fee. All authors are permitted
to post the submitted version of their manuscript (as prior to peer-review), provided it is clearly stated that
the manuscript is unpublished, and full bibliographic reference to the published article is given. Authors who
have paid an ‘open early’ fee are permitted to post the nal published version of the article to their personal/
academic website or institutional repository, provided that it is non-commercial and there is a link to the
published article on the journal website, https://www.fba.org.uk/journals/index.php/IW.

Inland Waters (2016) 6, pp.523–534
© International Society of Limnology 2016
DOI: 10.5268/IW-6.4.886
523
Article
Author’s 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

524
DOI: 10.5268/IW-6.4.886
Author’s personal copy
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
525
Author’s personal copy
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).

526
DOI: 10.5268/IW-6.4.886
Author’s personal copy
Kuha et al.
© International Society of Limnology 2016
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
527
Author’s personal copy
Response of boreal lakes to episodic weather-induced events
Inland Waters (2016) 6, pp.523–534
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.

528
DOI: 10.5268/IW-6.4.886
Author’s personal copy
Kuha et al.
© International Society of Limnology 2016
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).

DOI: 10.5268/IW-6.4.886
529
Author’s personal copy
Response of boreal lakes to episodic weather-induced events
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
DOI: 10.5268/IW-6.4.886
Author’s personal copy
Kuha et al.
© 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.

DOI: 10.5268/IW-6.4.886
531
Author’s personal copy
Response of boreal lakes to episodic weather-induced events
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).

532
DOI: 10.5268/IW-6.4.886
Author’s personal copy
Kuha et al.
© 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).
References
Adrian R, O’Reilly CM, Zagarese H, Baines SB, Hessen DO, Keller
W, Livingstone DM, Sommaruga R, Straile D, Van Donk E, et al.
2009. Lakes as sentinels of climate change. Limnol Oceanogr.
54:2283–2297.
Amorocho J, DeVries JJ. 1980. A new evaluation of the wind stress
coefcient over water surfaces. J Geophys Res. 85:433–442.
Anttila S, Ketola M, Kuoppamäki K, Kairesalo T. 2013. Identication
of a biomanipulation-driven regime shift in Lake Vesijärvi: implica-
tions for lake management. Freshwater Biol. 58:1494–1502.
Arvola L, George D, Livingstone DM, Järvinen M, Blenckner T,
Dokulil MT, Jennings E, Nic Aonghusa C, Nõges P, Nõges T, et al.
2010. The impact of the changing climate on the thermal character-
istics of lakes. In: George DG, editor. The impact of climate change
on European lakes. Berlin (Germany): Springer-Verlag. p. 85–101.
Aylward B, Bandyopadhyay J, Belausteguigotia J-C. 2005. In:
Ecosystems and human well-being: policy responses. Findings of the
Responses Working Group: Millennium Ecosystem Assessment.
Oxford (England): Island Press. Vol 3. p. 213–255.
Bengtsson L. 1996. Mixing in ice-covered lakes. Hydrobiologia.
322:91–97.
Benson BJ, Bond BJ, Hamilton MP, Monson RK, Hans R. 2009. Per-
spectives on next-generation technology for environmental sensor
networks. Front Ecol Environ. 8:193–200.
Bergström I, Mattsson T, Niemelä E, Vuorenmaa J, Forsius M. 2011.
Ecosystem services and livelihoods: vulnerability and adaptation to a

DOI: 10.5268/IW-6.4.886
533
Author’s personal copy
Response of boreal lakes to episodic weather-induced events
Inland Waters (2016) 6, pp.523–534
changing climate: VACCIA synthesis report. The Finnish
Environment 26.
Blenckner T, Adrian R, Arvola L, Järvinen M, Nõges P, Nõges P,
Pettersson K, Weyhenmeyer G. 2009. The impact of climate change
on lakes in Northern Europe. In: George DG, editor. The impact of
climate change on European lakes. Berlin (Germany) Springer-Ver-
lag. p. 339–358.
Boehrer B, Schultze M. 2008. Stratication of lakes. Rev Geophys.
46:RG2005.
Bowling LC, Salonen K. 1990. Heat uptake and resistance to mixing in
small humic forest lakes in southern Finland. Aust J Mar Fresh Res.
41:747–759.
Brönnimann S, Martius O, von Waldow H, Welker C, Luterbacher J,
Compo GP, Sardeshmukh PD, Usbeck T. 2012. Extreme winds at
northern mid-latitudes since 1871. Meteorol Z. 21:13–27.
Carpenter SR, Cole JJ, Pace ML, Batt R, Brock WA, Cline T, Coloso J,
Hodgson JR, Kitchell JF, Seekell DA, et al. 2011. Early warnings of
regime shifts: a whole-ecosystem experiment. Science. 332:1079–1082.
Charlton MN. 1980. Hypolimnion oxygen consumption in lakes:
discussion of productivity and morphometry effects. Can J Fish
Aquat Sci. 37:1531–1539.
Coloso JJ, Cole JJ, Pace ML. 2011. Short-term variation in thermal
stratication complicates estimation of lake metabolism. Aquat Sci.
73:305–315.
Crockford L, Jordan P, Melland AR, Taylor D. 2014. Storm-triggered,
increased supply of sediment-derived phosphorus to the epilimnion
in a small freshwater lake. Inland Waters. 5:15–26.
Cottingham KL, Ewing HA, Greer ML, Carey CC, Weathers KC.
2015. Cyanobacteria as biological drivers of lake nitrogen and
phosphorus cycling. Ecosphere. 6:1–19.
Drakare S, Blomqvist P, Bergström A-K, Jansson M. 2002. Primary
production and phytoplankton composition in relation to DOC input
and bacterioplankton production in humic Lake Örträsket.
Freshwater Biol. 47:41–52.
Dokulil MT. 2013. Impact of climate warming on European inland
waters. Inland Waters. 4:27–40.
Donat MG, Renggli D, Wild S, Alexander LV, Lechebusch GC, Ulbrich
U. 2011. Reanalysis suggests long-term upward trends in European
storminess since 1871. Geophys Res Lett. 38:L14703.
Flöder S, Sommer U. 1999. Diversity in plankton communities: an ex-
perimental test of the intermediate disturbance hypothesis. Limnol
Oceanogr. 44:1114–1119.
Forsius M, Anttila S, Arvola L, Bergström I, Hakola H, Heikkinen HI,
Helenius J, Hyvärinen M, Jylhä K, Karjalainen J, et al. 2013. Impacts
and adaptation options of climate change on ecosystem services in
Finland: a model based study. Curr Opin Environ Sustain. 5:26–40.
Golosov S, Terzhevik A, Zverev I, Kirillin G, Engelhardt C. 2012.
Climate change impact on thermal and oxygen regime of shallow
lakes. Tellus A. 64:17264.
Goodman KJ, Parker SM, Edmonds JW, Zeglin LH. 2015. Expanding
the scale of aquatic sciences: the role of the National Ecological
Observatory Network (NEON). Freshwater Sci. 34:377–385.
Hamilton DP, Carey CC, Arvola L, Azberger P, Brewer C, Cole JJ,
Gaiser E, Hanson PC, Ibelings BW, Jennings E, et al. 2014. A Global
Lake Observatory Network (GLEON) for synthesizing high-fre-
quency sensor data for validation of deterministic ecological models.
Inland Waters. 5:49–56.
Heini A, Puustinen I, Tikka M, Jokiniemi A, Leppäranta M, Arvola L.
2014. Strong dependence between phytoplankton and water
chemistry in a large temperate lake: spatial and temporal perspective.
Hydrobiologia. 731:139–150.
Heino R. 1994. Climate in Finland during the period of meteorological
observations. Helsinki (Finland): Finnish Meteorological Institute
Contributions No.12.
Hov Ø, Cubasch U, Fischer E, Höppe P, Iversen T, Gunnar Kvamstø N,
Kundzewicz ZW, Rezacova D, Rios D, Santos FD, et al. 2013.
Extreme weather events in Europe: preparing for climate change
adaptation. Oslo (Norway): EASAC Report.
Huisman J, van Oostveen P, Weissing FJ. 1999. Critical depth and
critical turbulence: two different mechanisms for the development of
phytoplankton blooms. Limnol Oceanogr. 44:1781–1787.
Huot Y, Babin M. 2010. Overview of fluorescence protocols: theory, basic
concepts, and practice. In: Suggett DJ, Prášil O, Borowitzka MA, editors.
Chlorophyll a fluorescence in aquatic sciences: methods and applica-
tions. New York (NY): Springer Science+Business Media. p. 31–74.
Huotari J, Ketola M, editors. 2014. Jatkuvatoiminen levämäärien mittaus
- Hyvät mittauskäytännöt ja aineiston käsittely [Continuous algal
measurements – Best measurement practices and data processing].
Helsinki (Finland): Finnish Environment Institute. Finnish.
Huttula T, Peltonen A, Bilaletdin A, Saura M. 1992. The effects of
climate change on lake ice and water temperature. Aqua Fenn. 22.2.
Idso SB. 1973. On the concept of lake stability. Limnol Oceanogr.
18:681–683.
Imboden, DM, Wüest A. 1995. Mixing mechanisms in lakes. In:
Lerman A, Imboden DM, Gat JR, editors. Physics and chemistry of
lakes. Berlin (Germany) Springer-Verlag. p. 83–138.
[IPCC] Intergovernmental Panel on Climate Change. 2014. Climate
Change 2014: synthesis report. Contribution of Working Groups I, II,
and III to the 5th Assessment Report of the Intergovernmental Panel
on Climate Change. Geneva (Switzerland).
Jennings E, Jones S, Arvola L, Staehr PA, Gaiser E, Jones ID, Weathers
KC, Weyhenmeyer GA, Chiu C-Y, de Eyto ED. 2012. Effects of
weather-related episodic events in lakes: an analysis based on high-
frequency data. Freshwater Biol. 57:589–601.
Jentsch A, Kreyling J, Beierkuhnlein C. 2007. A new generation of climate
change experiments: events, not trends. Front Ecol Environ. 5:315–324.
Jylhä K, Tuomenvirta H, Ruosteenoja K, Niemi-Hugaerts H, Keisu K,
Karhu JA. 2010. Observed and projected future shifts of climatic
zones in Europe and their use to visualize climate change
information. Weather Climate Soc. 2:148–167.
Kallio K. 1994. Effect of summer weather on internal loading and
chlorophyll a in a shallow lake: a modeling approach. Hydrobio-
logia. 275/276:371–378.
Kallio K, Koskiaho J, Lepistö A, Kiirikki M, Tattari S. 2010. Mitä uutta
nykytekniikalla saadaan selville valuma-alue-järvi kokonaisuudesta.
In: Lepistö A, Huttula T, Granlund K, Kallio K, Kiirikki M, Kirkkala

534
DOI: 10.5268/IW-6.4.886
Author’s personal copy
Kuha et al.
© International Society of Limnology 2016
T, Koponen S, Koskiaho J, Liukko N, Malve O, et al., editors. Uudet
menetelmät ympäristöntutkimuksessa ja seurannassa - pilottina
Säkylän Pyhäjärvi [New environmental research and monitoring
methods – a pilot study in the Säkylän Pyhäjärvi area]. Helsinki
(Finland): Finnish Environment Institute. p. 10–18. Finnish.
Karjalainen SM, Hellsten S, editors. 2015. Posionjärven ja Kitkajärven
tila ennen, nyt ja tulevaisuudessa. Kitka-MuHa -hankkeen loppu-ra-
portti [The state of lake Posionjärvi and lake Kitkajärvi in the past,
present, and future. The results of Kitka-MuHa project]. Helsinki
(Finland): Finnish Environment Institute. Finnish.
Klug JL, Richardson DC, Ewing HA, Hargreaves BR, Samal NR,
Vachon D, Pierson DC, Lindsey AM, O’Donnell DM, Efer SW,
Weathers KC. 2012. Ecosystem effects of a tropical cyclone on a
network of lakes in northeastern North America. Environ Sci
Technol. 46:11693–11701.
Kratz T, Arzberger P, Benson B, Chiu C-Y, Chiu K, Ding L, Fountain T,
Hamilton D, Hanson B, Hu Y-H, et al. 2006. Towards a Global Lake
Ecological Observatory Network. In: Simola H. editor. Suurjärvi-
seminaari 2006. Joensuu (Finland): Publ Karelian Inst. 145:51–63.
Kuha J. 2016. Automated water quality monitoring of humic lakes by
using the optical properties of water [PhD dissertation]. [Jyväskylä
(Finland)]: University of Jyväskylä..
Kuha J, Palomäki A, Keskinen T, Karjalainen J. 2016. Negligible effect
of hypolimnetic oxygenation on the trophic state of Lake Jyväsjärvi,
Finland. Limnologica. 58:1–6.
Kuusisto E. 1981. On water temperatures of lakes in Finland.
Geophysica. 17:167–176.
Lepistö A, Huttula T, Koponen S, Kallio K, Lindfors A, Tarvainen M,
Sarvala J. 2010. Monitoring of spatial water quality on lakes by
remote sensing and transect measurements. Aquat Ecosyst Health
Manag. 13:176–184.
Levin SA. 1992. The problem of pattern and scale in ecology. Ecology.
73:1943–1967.
Lewis WM Jr. 1983. A revised classication of lakes based on mixing.
Can J Fish Aquat Sci. 40:1779–1787.
Lohila A, Tuovinen J-P, Hatakka J, Aurela M, Vuorenmaa J, Haakana
M, Laurila T. 2015. Carbon dioxide and energy uxes over a northern
boreal lake. Boreal Env Res. 20:474–488.
MacIntyre S, Fram JP, Kushner PJ, Bettez ND, O’Brien WJ, Hobbie
JE, Kling GW. 2009. Climate-related variations in mixing dynamics
in an Alaskan arctic lake. Limnol Oceanogr. 54:2401–2417.
Mays LW. 2010. Water resources engineering. 2nd ed. Hoboken (NJ):
John Wiley & Sons.
Nordbo A, Launiainen S, Mammarella I, Leppäranta M, Huotari J,
Ojala A, Vesala T. 2011. Long-term energy ux measurements and
energy balance over a small boreal lake using eddy covariance
technique. J Geophys Res. 116:D02119.
Nõges P, Adrian R, Anneville O, Arvola L, Blenckner T, George G,
Jankowski T, Järvinen M, Maberly S, Padisák J, et al. 2010. The
impact of variations in the climate on seasonal dynamics of phy-
toplankton. In: George DG, editor. The impact of climate change
on European lakes. Berlin (Germany): Springer-Verlag. p.
253–274.
Nõges P, Nõges T, Ghianti M, Sena F, Fresner R, Friedl M, Mildner J.
2011. Increased nutrient loading and rapid changes in phytoplankton
expected with climate change in stratied South European lakes:
sensitivity of lakes with different trophic state and catchment
properties. Hydrobiologia. 667:255–270.
Obrador B, Staehr PA, Christensen JPC. 2014. Vertical patterns of
metabolism in three contrasting stratied lakes. Limnol Oceanogr.
59:1228–1240.
Odum HT. 1956. Primary production in owing waters. Limnol
Oceanogr. 1:102–117.
Padisák J. 1993. The inuence of different disturbance frequencies on
the species richness, diversity and equitability of phytoplankton in
shallow lakes. Hydrobiologia. 249:135–156.
Paerl HW, Huisman J. 2008. Blooms like it hot. Science. 320:57–58.
Pulkkanen M. 2013. Under-ice temperature and oxygen conditions in
boreal lakes [dissertation]. [Jyväskylä (Finland)]: University of
Jyväskylä.
Reynolds CS. 2006. Ecology of phytoplankton: ecology, biodiversity
and conservation. Cambridge (UK): Cambridge University Press.
Schmidt W. 1928. Über Temperature- und Stabilitätsverhältnisse von
Seen. Geogr Ann. 10:531–540.
Solomon CT, Bruesewitz DA, Richardson DC, Rose KC, Van de
Bogert MC, Hanson PC, Kratz TK, Larget B, Adrian R, Leroux B, et
al. 2013. Ecosystem respiration: drivers of daily variability and
background respiration in lakes around the globe. Limnol Oceanogr.
58:849–866.
Soranno PA, Carpenter SR, Lathrop RC. 1997. Internal phosphorus
loading in Lake Mentoda: response to external loads and weather.
Can J Fish Aquat Sci. 54:1883–1893.
Spigel RH, Imberger J. 1987. Mixing processes relevant to phytoplank-
ton dynamics in lakes. N Zeal J Mar Freshw Res. 21:361–377.
van de Bogert MC, Bade DL, Carpenter SR, Cole JJ, Pace ML, Hanson
PC, Langman OC. 2012. Spatial heterogeneity strongly affects
estimates of ecosystem metabolism in two north temperate lakes.
Limnol Oceanogr. 57:1689–1700.
Venables WN, Smith DM, R Core Team. 2015. An introduction to R –
Notes on R: programming environment for data analysis and
graphics. Version 3.2.2.
Wetzel RG. 2001. Limnology: lake and river ecosystems. 3rd ed. San
Diego (CA): Academic Press.
Wilhelm S, Adrian R. 2008. Impact of summer warming on the thermal
characteristics of a polymictic lake and consequences for oxygen,
nutrients and phytoplankton. Freshwater Biol. 53:226–237.
Williamson CE, Saros JE, Vincent WF, Smol JP. 2009. Lakes and
reservoirs as sentinels, integrators, and regulators of climate change.
Limnol Oceanogr. 54:2273–2282.
Winslow LA, Zwart JA, Batt RD, Dugan HA, Woolway RI, Corman
JR, Hanson PC, Read JS. 2016. LakeMetabolizer: an R package for
estimating lake metabolism from free-water oxygen using diverse
statistical models. Inland Waters. 6:622–636.
Woolway RI, Jones ID, Feuchtmayr H, Maberly SC. 2015. A
comparison of the diel variability in epilimnetic temperature for ve
lakes in the English Lake District. Inland Waters. 5:139–154.