Lakes are key ecosystems within the global biogeosphere. However, the
environmental controls on the biological productivity of lakes – including
surface temperature, ice phenology, nutrient loads, and mixing regime – are
increasingly altered by climate warming and land-use changes. To better
characterize global trends in lake productivity, we assembled a dataset on
chlorophyll-a concentrations as well as associated water quality parameters
and surface solar radiation for temperate and cold-temperate lakes
experiencing seasonal ice cover. We developed a method to identify periods
of rapid net increase of in situ chlorophyll-a concentrations from time series data
and applied it to data collected between 1964 and 2019 across 343 lakes
located north of 40∘. The data show that the spring
chlorophyll-a increase periods have been occurring earlier in the year,
potentially extending the growing season and increasing the annual
productivity of northern lakes. The dataset on chlorophyll-a increase rates
and timing can be used to analyze trends and patterns in lake productivity
across the northern hemisphere or at smaller, regional scales. We illustrate
some trends extracted from the dataset and encourage other researchers to
use the open dataset for their own research questions. The PCI dataset and additional data files can be openly accessed at the Federated Research Data Repository at 10.20383/102.0488 (Adams et al., 2021).
Introduction
Lakes play an important role in the biogeochemical cycling of many elements
(Battin et al., 2008; Cole et al., 2007; O'Connell et al., 2020; Rousseaux and Gregg, 2013; Schindler, 1971). With over 100 million documented lakes on
earth (Verpoorter et al., 2014), evidence indicates
that the majority of global lakes are shallow, with enough light and
nutrients available to make them highly productive ecosystems
(Downing et al., 2006; Wetzel, 2001). Lakes therefore represent active sites for the
storage, transport, and transformation of carbon, nutrients (e.g., nitrogen,
phosphorus, silicon, iron), and contaminants (e.g., mercury) along the
freshwater continuum (Lauerwald
et al., 2019; Tranvik et
al., 2009). They are also sensitive to the effects of climate change
(Williamson et al.,
2009; Rouse et al., 1997).
There are multiple environmental controls on lake primary productivity,
including water temperature, ice phenology, nutrient concentrations,
circulation, mixing regime, and solar radiation (Lewis, 2011; Zohary et al.,
2009). Stressors such as climate change and nutrient pollution can
significantly impact these controls, altering the ecosystem structure and
biogeochemical functioning of lakes
(Jeppesen
et al., 2020; Markelov et al., 2019). Changes affecting northern lakes
include warmer water temperatures, enhanced stratification and hypoxia,
nutrient enrichment, light attenuation by chromophoric organic matter, and
increases in the relative abundance of toxic cyanobacteria in the
phytoplankton community (Deng et al.,
2018; Huisman and Hulot, 2005; Jeppesen et al., 2003; Creed et al., 2018). For example,
Lake Superior has seen an increase in primary production – together with increasing surface water temperatures and longer
seasonal stratification and ice-free periods – during the last
century (O'Beirne et al., 2017). Other lakes are
similarly experiencing increases in productivity. According to Lewis (2011), the current mean primary
production of lakes is 260 g C m-2 yr-1, which is 162 % higher
than earlier estimations under historical baseline conditions.
Globally, phytoplankton (i.e., algae) are the main primary producers in
lakes and generally make up the foundation of lentic food webs
(Carpenter et al., 2016). Periods of high lake productivity
coincide with a rapid increase in phytoplankton biomass. In extreme cases,
algal blooms can reach hundreds to thousands of cells per millilitre
(Henderson-Seller and Markland, 1987). These bloom events
produce large quantities of decomposing organic matter that cause the
expansion of hypoxic conditions within the lake (Watson et al., 2016). In harmful
algal blooms, certain algal species also release hepatotoxic and neurotoxic
compounds (Codd et al.,
2005). Thus, identifying trends in the timing and intensity of seasonal
algal growth and linking them to changes in environmental stressors can
help to predict the future of lake productivity and to assess the risk of
undesirable algal blooms.
Because it is challenging to measure algal abundance and growth directly,
chlorophyll-a is often used as a proxy for algae biomass and as an indicator of
the associated primary production in lakes (Huot et al., 2007).
Although other proxies have been developed
(Lyngsgaard et al., 2017),
chlorophyll-a is the most common metric to characterize trends in algal
biomass within and across lakes, especially in historical water quality
records. Tett (1987) proposes a chlorophyll-a threshold of 100 µg L-1 to define “exceptional blooms”; Jonsson et al. (2009) use a threshold of 5 µg L-1to identify a bloom; Binding et al. (2021) flag an algal bloom when
the chlorophyll-a concentrations extracted from satellite observations exceed
10 µg L-1. Such threshold values, however, do not take into
account the baseline (i.e., no-bloom) chlorophyll-a concentration specific to
a given lake or the lake's trophic status (Germán et al., 2017). Furthermore,
focusing on harmful and nuisance algal blooms alone may mask the impact that
a changing climate or other stressors may have on a lake's overall
biological productivity.
Intra-annual fluctuations in lake chlorophyll-a concentrations result from the
interactions of multiple variables and processes, including grazing by
zooplankton, competition between algal species with different growth
strategies and chlorophyll-a contents, and changes in temperature, light, and
nutrient availability (Lyngsgaard et al., 2017;
Sommer et al., 1986). In dimictic lakes, for example, there are usually two
peaks in algal biomass and hence also in chlorophyll-a concentrations in
the spring and fall, with a smaller biomass stock of slower growing species
during the summer and an even smaller stock of algae (in terms of both
biovolume and chlorophyll-a) under the ice cover in the winter (Hampton
et al., 2017).
The spring increase in algal biomass generally consists of fast-growing
algal species that take advantage of the increases in temperature and light
following ice-off as well as the available inorganic nutrients that were
generated by mineralization under the ice over the winter. The shift from
spring to summer algal communities often coincides with high zooplankton
grazing rates exceeding the spring algal growth rates, hence bringing down
the total algal biomass. The high zooplankton grazing rates favour the growth
during the summer of algal species that are less edible by grazers but
which tend to grow at slower rates. Lake overturn in the fall initiates the
transition from the predominance of the slow-growing species in the summer
to the fast-growing phytoplankton species in the fall, causing a second peak
in algal biomass (Sommer et al., 1986).
A common approach for comparing chlorophyll-a trends across multiple lakes is
to consider the maximum or mean annual chlorophyll-a concentrations. For
example, Ho et al. (2019) applied the
Mann–Kendall trend test to analyze time series of annual maximum
chlorophyll-a concentrations, while Shuvo et al. (2021) used a random forest regression
approach to assess the relative importance of climatic versus non-climatic
controls on mean chlorophyll-a concentrations. Both these studies analyzed
chlorophyll concentrations derived from satellite observations rather than
measured in situ. In addition, these approaches did not specifically identify the
periods of the year when chlorophyll-a concentrations experienced rapid
changes.
Alternatively, the rate of increase in chlorophyll-a concentration can be used
to constrain the timing of rapid increases in algal biomass usually
associated with periods of high primary productivity. In this study, we
refer to these as “periods of chlorophyll-a increase” (PCIs). The weeks
leading up to a PCI are crucial to create the necessary conditions that
enable algal growth (Lewis et al., 2018). Thus, to analyze
trends in lake net primary productivity, one should consider environmental
variables, such as surface water temperature, solar radiation, and nutrient
concentrations, both during and preceding the annual PCIs.
Although the rate of chlorophyll-a concentration increase has been used to
detect algal blooms within individual water bodies, e.g., in the San
Roque reservoir (Germán et al., 2017), it has
rarely been used across large temporal (i.e., more than a few years) and
spatial (i.e., regional and up) scales. Here, we present a method for
calculating net rates of chlorophyll-a increase (RCI). The timing of PCIs and
values of the corresponding RCIs were derived from in situ chlorophyll-a
concentrations obtained for 343 lakes located at latitudes above
40∘ N. The entire dataset covers the period of 1964–2019 and
further contains data on coincident environmental control variables,
including surface solar radiation. To illustrate the potential applications
of the resulting dataset, we present some temporal trends of the
chlorophyll-a rates and their relationships with environmental variables. The
dataset is made available as an open resource that other researchers are
encouraged to use in their own work.
Data and methods
All data processing, visualizations, and analyses were carried out with
Python (ver. 3.7.6; Python Software Foundation, 2021) using the
pandas library (Reback et al., 2020),
NumPy library (Harris et al., 2020), and
Dplython library (Riederer, 2015), while QGIS/PYQGIS was used for
all spatial data analyses (ver. 3.16; QGIS.org, 2021).
Data acquisition, compilation, and quality controlLake data selection
In situ chlorophyll-a concentrations and other lake physico-chemical data were
extracted from open source international, national, and regional databases
(see Table A1 for a summary of all databases used). The data include
surface water temperature, Secchi depth, and pH as well as the
concentrations of particulate organic carbon (POC), total phosphorus (TP),
soluble reactive phosphorus (SRP), total Kjeldahl nitrogen (TKN), and
dissolved organic carbon (DOC).
To enable readers to compare the methods used by different lake monitoring
agencies and researchers to collect and process in situ samples, we provide the
links to the raw data sources and metadata files in the appendix (Tables A1–A3). When selecting data, we remained as consistent as possible by
implementing the following steps (more details can be found in the “initial
formatting” folder found in the associated GitHub repository, https://github.com/hfadams/pci/tree/main/code/initial_formatting, last access: 7 August 2022).
We only included measurements taken at ≤3 m water depth. When the
sampling depth was not provided, we assumed the sample was taken from within
the top 0.5–3 m of the lake, given that this is the usual standard sampling
protocol (Dorset Environmental Science Centre, 2010; United States Environmental Protection Agency, 2012).
We selected lakes from mid to high latitudes (≥40∘ N). Lakes
at these latitudes typically experience seasonal ice cover and thermal
stratification during the summer in contrast to low-latitude lakes that are
typically meromictic or polymictic (Woolway and Merchant, 2019).
We omitted all variable values below the corresponding analytical detection
limit. Data from different sources were individually reformatted to yield
consistent (standard) units and headings. Where needed, reported values were
averaged to yield daily mean values before being combined into a single CSV
file. When multiple chlorophyll-a data types were available (as, for example,
in the Laurentian Great Lakes data series), we selected the uncorrected data,
because most reported lake chlorophyll-a concentrations have not been
corrected for phaeophytin pigments. If no coordinates were provided, we
assigned those of the lake centroid in QGIS. Fifteen lakes had unknown
locations and were removed from the final dataset. We further restricted
ourselves to lakes that, in most years, were sampled at least six times per
year, which was considered the minimum sampling frequency to reliably detect
the yearly PCIs. Lake names were standardized by expanding on abbreviations
and removing unnecessary capitalization and special characters.
Distribution of the 343 lake sampling locations in the PCI
dataset. Lake sampling points are clustered by proximity, where marker size
and value indicate the number of unique locations represented by each point
(light blue markers with white text). Enlarged sections show each lake
sampling location (blue markers) along with the location of the 320
paired SSR stations (orange markers). Base map credit: ESRI, 2011.
With the above selection criteria, the final dataset contained 52116
potential PCIs for 343 lakes at ≥40∘ N and covering the
period 1964–2019. The location of the lake sampling locations in the PCI
dataset are shown in Fig. 1.
Surface solar radiation data
Open source in situ surface solar radiation (SSR) data for the period 1950–2020
were collected from stations paired with the selected lakes (see Table A2
for data sources). Each lake was paired with the closest SSR station using
the nearest neighbour function in QGIS, allowing for a maximum radius of
3 degrees (Schwarz et
al., 2018; Fig. 1). In the dataset provided here, the geodesic distance
between each lake and its paired SSR station as well as the
differences in elevation are given.
The SSR data temporal resolutions varied from minutes to months. Hence, where
needed, the SSR data were resampled to yield monthly mean values. For the
Experimental Lakes Area (ELA) in Ontario, Canada, the data were converted
from photosynthetically active radiation (PAR) to SSR, where the PAR
wavelength range (400–700 nm) was averaged to 550 nm.
Lake characteristics
For each lake, we calculated the trophic status index (TSI) based on the
mean chlorophyll-a concentration over the sampling period. This TSI value was
used to assign the lake to the corresponding trophic state category
according to Carlson and Simpson (1996). The HydroLAKES
shapefile yielded the lake's surface area, mean depth, and volume
(ver. 1.0; Messager et al., 2016). Lake
elevation was extracted from a digital elevation model (DEM)
(Danielson and Gesch, 2010), and each lake was assigned its
corresponding climate zone using HydroATLAS data
(ver. 1.0; Linke et al., 2019). The metadata
for these variables are published as part of the data publication (Adams et
al., 2021), and a summary table of associated lake data is provided in the
appendix (Table A4).
Workflow for detecting PCIs and processing data. For each lake
sampling point, chlorophyll-a (Chl-a) data are smoothed with a
Savitzky–Golay filter, and then PCIs are detected based on peaks in the
chlorophyll-a concentration. PCIs are flagged as spring, fall, or single
PCIs. The data density is shown at key points along the workflow.
Detecting seasonal periods of chlorophyll-a increase
Periods of chlorophyll-a increase (PCIs) were identified based on the
normalized net rate of change in chlorophyll-a concentration (NRCC) at each
lake sampling point throughout the year. To locate the start and end of a
PCI, we smoothed the annual chlorophyll-a time series using a Savitzky–Golay
filter (SciPy.signal savgol_filter) and flagged optima in the
smoothed data (SciPy.signal find_peaks) using functions from
the open source SciPy ecosystem (Virtanen et al., 2020).
The procedure is illustrated in Fig. 2.
The NRCC at any given time during the year was calculated by computing the
first derivative of the smoothed chlorophyll-a concentration versus time and then
dividing the derivative value by the corresponding chlorophyll-a concentration.
For each lake and each year, the start of the first PCI was defined as the
day the NRCC surpassed 0.4 d-1. This threshold rate was selected
following a series of sensitivity tests (details provided in the
supplementary information). A threshold NRCC value was considered more preferable
than a threshold RCI value, because it accounts for variations among lakes
and among years in the baseline chlorophyll-a concentrations during the
non-growing season.
The PCI ended on the day the peak in chlorophyll-a concentration was reached –
that is, just before the NRCC turned negative. If a threshold NRCC of 0.4 d-1 was not reached during a given year, the PCI began when the NRCC
first became positive. The second (fall) PCI was identified in the same way,
following the end of the first (spring) PCI. If the annual chlorophyll-a
concentration only yielded one peak value in the smoothed data series, only
one PCI was identified for that year, which was then labelled as a “single
PCI” year. Years with more than two chlorophyll-a peaks or with no peaks
were not included in the PCI dataset.
Example of spring and fall PCIs in Lake Windermere's north basin
in 1988. The solid grey line is the chlorophyll-a concentration (µg L -1), and the solid black line is the chlorophyll-a concentration
smoothed with a Savitzky–Golay filter. The dashed line is the normalized
rate of change in chlorophyll-a (NRCC) (d-1), where the first
derivative is divided by the smoothed chlorophyll-a concentration and is
plotted using the right axis. The PCI begins when the NRCC surpasses a
threshold of 0.4 d-1, as shown in the first (spring) PCI, and ends when
the NRCC turns negative, which is when the peak chlorophyll-a concentration
is reached. When a peak is detected but the NRCC does not surpass a
threshold of 0.4 d-1, the PCI begins when the NRCC surpasses 0 d-1, as shown in the second (fall) PCI. The PCI and pre-PCI (two weeks
leading up to the PCI) are shown in dark and light grey shading,
respectively.
Depending on data availability, the pre-PCI period was defined as the one-
or two-week period immediately preceding the PCI start day. For each
pre-PCI, the mean surface water temperature, SSR, and TP concentration were
compiled. These served as simple indicators of how favourable in-lake
conditions were to initiate algal growth
(Lyngsgaard et al., 2017). An
example of a year with a spring and fall PCI is shown in Fig. 3. Note that
we use the label “fall” to indicate the second yearly PCI, although in
some cases, the fall PCI was initiated before the fall equinox.
Summary of variables in the PCI dataset. Associated lake data
(e.g., lake depth, surface area, volume, climate zone) are available in the
Appendix (Table A4).
VariableUnitsDescriptionCommentsTimingSeason of occurrenceThree possible PCIs: spring, fall, or single PCIA single PCI occurs when there is only one maximum in the smoothed yearly chlorophyll-a concentration time series for the yearPeriod of chlorophyll-a increase(PCI) start dayDay of yearDay of year when the PCIbeginsPeriod of chlorophyll-a increase(PCI) end dayDay of yearDay of year when the PCI endsRate of chlorophyll-a increase(RCI)µg L-1 d-1Difference in chlorophyll-aconcentration between start and end of the PCI divided by the duration of the PCIOne RCI value is associated with each PCINormalized rate of change inchlorophyll-a (NRCC)d-1RCI divided by the initialchlorophyll-a concentrationAccounts for variable standing stock of chlorophyll-aRate of particulate organic carbon(POC) increasemg L-1 d-1Same calculation as RCI butusing start and end POCconcentrationsProxy for the rate of change in total algal biomassRCI: rate of POC increasemg chlorophyll-a mg-1 POCAccounts for variable chlorophyll-a content of algal biomassMean PCI surface watertemperature∘CMean value during the PCI and the 14 d pre-PCIMean PCI surface solar radiationW m-2Mean value during the PCI and the 14 d pre-PCIMean PCI total phosphorus (TP)mg L-1Mean values during the PCI(Co-)limiting macronutrientsMean PCI soluble reactivephosphorus (SRP)mg L-1Mean PCI total Kjeldahl nitrogen(TKN)mg L-1Mean PCI Secchi depthmProxy for turbidityMean PCI pHpH unitsTrophic status index (TSI)Range: 0–100Calculated from chlorophyll-a concentrations across all years the lake was sampledBasis for assigning trophic statusTrophic statusTrophic classTrophic status class assignedbased on TSI: oligotrophic,mesotrophic, eutrophic, orhypereutrophicTSI thresholds are those of the North American Lake Management Society
Once the PCI and pre-PCI durations were determined, the mean values of the
variables listed in Table 1 were calculated. This was done for each lake and
for each year data were available. In the dataset, each row represents a
single PCI and includes the timing and duration, RCI value, and the mean
values for all other relevant lake variables, including SSR, averaged for
the PCI and pre-PCI. Note that, along with the variables in Table 1, we
included the total number of samples collected each year and the mean time
between samples. Thus, if desired, the user can filter the dataset for a
higher sampling frequency than done here. The supplementary information of
the dataset also identifies the organization responsible for monitoring each
lake.
Distributions of (a) year of occurrence, (b) mean time between
samples, (c) lake trophic status index, and (d) lake latitude for each PCI
in the dataset. Data are grouped by “double PCI” or “single PCI” year.
The data is skewed toward more recent years and higher latitudes. Lakes in
the oligotrophic category (TSI <40) have a higher proportion of
single PCIs. These “rain-cloud plots” show the same data visualized in three
different ways for each group: frequency distribution, boxplot with
quartiles (outliers as represented as points), and a jitter plot of data
points as different ways to visualize the data (Allen et
al., 2021). Note that the amplitude of the frequency distribution is not
proportional between categories.
Dataset: data distributionsDataset characteristics
Most lakes in the dataset are located between 50 and 60∘ N. The
majority of available open data are from organizations within the United
Kingdom, Sweden, Canada, and the United States. The years with available
data in the dataset are unevenly distributed. The majority of PCIs fall in
the period 2005–2019 (Fig. 4a), likely due to a combination of increased
lake monitoring efforts and a push in recent years towards greater
accessibility of publicly funded data (Hallegraeff et al., 2021; Roche
et al., 2020). Most sampling frequencies are in the range of 25 to 30 d,
with additional peaks at 7 and 14 d (Fig. 4b). Thus, with a few
exceptions, the PCIs included in the dataset occurred in lakes sampled at a
monthly frequency or better.
Frequency distributions of (a) duration, (b) start day
(day of year), and (c) end day (day of year) of the PCIs, grouped by PCI
type. Single PCIs have the longest range in length, while fall PCIs tend to
be the shortest. Single PCIs have the largest range of start and end days,
while the spring and fall PCIs tend to start and end within a smaller
window. These rain-cloud plots show the same data visualized in three different
ways for each group: frequency distribution, boxplot with quartiles
(outliers represented as points), and a jitter plot of data points.
The distribution of trophic states of the PCIs recorded in the dataset are
1.6 % oligotrophic, 18.6 % mesotrophic, 75.2 % eutrophic, and 4.6 %
hypereutrophic. Single PCIs dominate oligotrophic lakes, where they make up
96.1 % of all PCIs (Fig. 4c). This may reflect the severe nutrient
limitation in oligotrophic lakes, which prevents the occurrence of a second
annual algal PCI (Rigosi et al., 2014). Oligotrophic lakes also tend to dominate at latitudes ≥55∘ N (Fig. 4d), where lower water temperatures and lower
cumulative solar radiation may further limit algal growth (Lewis, 2011). The PCI durations range
from 3 to 275 d, with a median of 68 d (Fig. 5a). Fall PCIs tend to
be shorter than spring and single PCIs, with the latter exhibiting the most
variable start and end days (Fig. 5b).
Distributions of selected water quality variables during
PCIs: (a) log rate of chlorophyll-a increase, (b) mean surface water
temperature, (c) log mean total phosphorus (TP), and (d) mean Secchi depth.
The mean rate of chlorophyll-a increase is lowest in the single PCI category
and highest in the fall PCIs. For the single PCIs, temperature is evenly
distributed across the annual range, as they occur throughout the ice-free
season. Total phosphorus concentrations are lowest during the spring PCIs,
which likely reflects a greater control of P limitation on algal growth
during spring compared to summer and fall. Each PCI category has a similar
range in Secchi depth, between 0 and 5 m. Rain-cloud plots show the frequency
distribution, boxplot with quartiles (outliers as represented as points),
and a jitter plot of data points for each group.
Environmental conditions during PCIs
Rates of chlorophyll-a increase during the PCIs exhibit log-normal
distributions (Fig. 6a). The mean chlorophyll-a rate is lowest in the
single PCI category and highest in the fall PCIs. Mean surface water
temperature has a distinct bimodal spring–fall distribution (Fig. 6b). For
the single PCIs, the corresponding mean temperatures are evenly distributed
across the annual range, which reflects the large spread in the timing of
the single PCIs (Fig. 5b). Total P concentrations are lowest during the
spring PCIs (Fig. 6c), consistent with a greater control of P limitation
on algal growth during spring compared to summer and fall (Kirillin et al., 2012). Secchi depth during the
PCIs ranges from 0.01 to 15.4 m, with fall PCIs experiencing the lowest mean
Secchi depth (Fig. 6d), as turbidity generally increases after the spring
bloom.
Rate of chlorophyll-a increase (RCI) trends in the dataset, grouped by (a) trophic status, (b) latitude, and (c) climate zone. Lakes of a higher trophic status have a higher mean RCI, while lakes at higher latitudes have lower RCI (with considerable overlap between all categories). Grouping by climate zone shows minimal effect on RCI. The number of lakes represented by each violin is shown in grey text on the panels. Climate zones are as follows: 7 = cold and mesic; 8 = cool, temperate, and dry; 10 = warm, temperate, and mesic. White circles indicate the mean value for each violin.
Dataset: examples of trends
The PCI delineation and the estimation of RCI can, in principle, be applied to
any lake for which time series chlorophyll-a concentration data are
available. By creating a dataset comprising many lakes and covering
multi-year time periods, it becomes possible to extract global trends in
lake chlorophyll-a. Here, we provide a few illustrative examples of how the
dataset can be interrogated, setting the stage for its use and extension by
other researchers.
Chlorophyll-a rates: trophic status,
latitude and climate zone
When grouped by trophic status, mean and median chlorophyll-a growth rates
(RCIs) show the expected increase from oligotrophic to hypereutrophic lakes
(Fig. 7a). The rates in the different trophic categories, however, cover
large and overlapping ranges. When grouped according to latitude, lakes
between 40 and 50∘ N exhibit the widest range in RCIs (Fig. 7b),
in part due to the high proportion of lakes in this latitude range. The
highest latitude lakes (60–70∘ N) tend to have the lowest RCIs,
which may reflect the cooler temperatures experienced (Lewis, 2011).
The lakes are spread across three climate zones: cold and mesic; cool,
temperate, and dry; and warm, temperate, and mesic (Fig. 7c). There is
considerable overlap in RCI across the climate zones, with no systematic
differences in the mean and median RCI values between the zones.
While variations in chlorophyll-a rates of increase (RCIs) are often assumed
to reflect comparable differences in algal biomass growth rates, it is
important to note that the chlorophyll-a to biomass ratio varies within and
among lakes. In particular, chlorophyll-a to biomass ratios are known to be
sensitive to variations in solar radiation, temperature, algal species, and
cell size (Baumert and Petzodt, 2008; Inomura et al., 2019; Geider, 1987; Álvarez et al.,
2017). The summer ratio of chlorophyll-a to biomass (the latter typically
expressed as particulate organic carbon concentration) generally increases
with increasing latitude, because algae are adapted to harvest the more
variable daylight conditions, including longer summer photoperiods, at
higher latitudes (Behrenfeld et al.,
2016; Taylor et al., 1997). By contrast, cooler temperatures at higher
latitudes may result in higher chlorophyll-a to biomass ratios because of
lower growth rates, at least when the algae are nutrient replete
(Behrenfeld et al., 2016). Thus, the use of a
relative rate (NRCC) as the threshold value for defining a PCI and as a
metric reported in the dataset facilitates comparisons between lakes of
different trophic status or standing stock of chlorophyll-a.
(a) Start and end days for the spring, fall, and single PCIs for
all the lakes in the dataset; spring and single PCI categories show trends toward
earlier start and end days, while fall PCI start days occur earlier
in the year. (b) Start and end days of the PCIs as a function of temperature
(top two rows in panel B, linear regression trend line in black) suggest a
positive relationship between PCI timing and surface water temperature in
the spring and a negative relationship in the fall. Longer PCIs occur at
moderate surface water temperatures, which are observed less often during the
fall PCIs (trend line fitting data in the bottom row are locally weighted
scatterplot smoothing).
Chlorophyll-a rates: temperature and
climate warming
The start and end days of the spring and single PCIs show temporal trends
towards occurrence earlier in the year (Fig. 8a). Earlier springtime algal
activity could be linked to global warming. The latter is expected to result
in earlier ice break up and earlier surface water temperature conditions
favourable for algal growth (Markelov et al., 2019). The start and end days
of the spring PCIs show a positive correlation with increasing temperature
(Fig. 8b). By contrast, little or even negative correlations are seen for
the fall PCIs. Thus, all other conditions unchanged, a warmer climate would
see earlier spring blooms but few temporal shifts for the fall PCIs and,
possibly, even a slight delay. For the spring and single PCIs, the duration
shows a maximum around 10 ∘C. Therefore, moderate temperatures
near or slightly above 10 ∘C should, on average, produce the
longest lasting algal growth events. The same trend is not seen for the fall
PCIs, possibly because they occur when water temperatures are already above
10 ∘C.
Mean PCI surface solar radiation (SSR) grouped by PCI type
(single, spring, or fall). White circles show the mean value for each
violin. The mean SSR during spring PCIs is lower than that of single and
fall PCIs, which have similar distributions.
Surface solar radiation during PCIs: seasonal
distributions and distances to lakes
The mean SSR during spring PCIs in the dataset is approximately 100 W m-2 (Fig. 9), which is lower than the mean SSR values of single and
fall PCIs, which are both close to 175 W m-2. This difference in mean SSR
between spring and fall PCIs is expected, given the longer daylight hours
and more intense sunlight experienced in summer and fall compared to early
spring. The similarity in mean SSR between single and fall PCIs may be
related to the observation that, at higher latitudes (>55∘ N), single PCIs occur more commonly than double PCIs (Fig. 4d). Higher latitude lakes tend to bloom only once during the summer months,
taking advantage of the period of the year with the highest SSR
(Behrenfeld et al., 2016; Lewis, 2011). In
support of this, Fig. 5b and c show that single PCIs tend to occur
between late spring and early fall. On the other hand, at lower latitudes
(40–45∘ N), double PCIs are more common than single PCIs, likely
due to the higher temperatures and longer periods of sufficient daylight
experienced during the spring and fall “shoulder seasons” at these
latitudes.
Frequency distribution of distances between the lake sampling
points and the nearest surface solar radiation (SSR) sampling stations in
decimal degrees. Most lake–SSR distances are within 200 km of each
other. Cloud cover, atmospheric aerosols, and their interactions are a major
control on incident SSR at a given surface location; therefore, the SSR
values may become less representative of the paired lake with increasing
distances. The middle line in the boxplot shows the median value.
Despite the defining importance of sunlight for photosynthesis, in situ SSR time
series data are rarely measured systematically as part of lake monitoring
programs (Sterner et al., 1997). Although
gridded reanalysis datasets that include solar radiation parameters exist,
their comparability with in situ SSR measurements remains questionable
(Wohland et al., 2020). In gathering
open source data, we compiled in situ SSR measurements from locations as close as
possible to the lakes with chlorophyll-a data. Nonetheless, many of the SSR
values in our dataset were collected at considerable distances from the
corresponding lakes (up to ∼ 300 km, Fig. 10). For our
dataset, only ∼ 10 % of the locations where SSR was measured
are less than 20 km away from the corresponding lakes, while ∼ 40 % are 20–50 km away, ∼ 43 % are 50–100 km away, and
∼ 7 % are more than 100 km away. Hence, in a significant
number of cases, the actual mean SSR during a PCI may differ from the in situ mean
SSR reported here due to differences in cloud cover and levels of
atmospheric aerosols (among other factors) (Alpert and Kishcha, 2008). Users
are therefore advised to consider this limitation when making use of the SSR
values in our dataset. Overall, we recognize a need for SSR data to be more
systematically measured and reported as part of lake-monitoring programs, in
particular for oligotrophic lakes.
Code and data availability
All code is available in the project GitHub repository
(https://github.com/hfadams/pci, last access: 7 August 2022) and in Zenodo
(10.5281/zenodo.6972355, Adams, 2022). The PCI dataset and additional
data files can be openly accessed at the Federated Research Data Repository
at 10.20383/102.0488 (Adams et al., 2021).
Conclusions
We present a novel way to delineate annual periods of chlorophyll-a increase
(PCIs) in lakes that, presumably, overlap with periods of algal growth. We
apply this approach to derive the chlorophyll-a rates of increase (RCIs)
during the PCIs of 343 lakes from cold and cold-temperate regions in the
Northern Hemisphere and covering the period 1964–2019. The derived RCIs are
assembled in an open source dataset, together with additional information on
the lakes, including water quality, trophic state, and surface solar
radiation. Note that the dataset can be paired with other databases, such as
HydroLAKES (https://www.hydrosheds.org/products/hydrolakes, last access: July 2022, Messager et al., 2016),
HydroATLAS (https://www.hydrosheds.org/hydroatlas, last access: July 2022, Linke et al., 2019), and GLCP (Meyer et al., 2020), to access additional
lake and/or watershed attributes. Our dataset is designed to support
comparative analyses of the controls on lake chlorophyll-a dynamics and also, by
extension, algal dynamics within and between lakes. We present several
examples of such analyses. We hope these will encourage others to use the
dataset in their own research and to further expand the dataset's
geographical reach and information content.
Summary of sources and licensing for the chlorophyll-a data.
Direct links to the datasets are provided where possible, and lake names can
be searched within the database. Note that not all lakes in these databases
met the requirements to be retained in the PCI dataset. * Last access: August 2021.
DatabaseRegionLake(s)Data licenceOpen CanadaOntario, ManitobaLaurentian great lakes (https://open.canada.ca/data/en/dataset/cfdafa0c-a644-47cc-ad54-460304facf2e*), Hamilton Harbour, Riding Mountain lakes (https://open.canada.ca/data/en/dataset/2a55313f-26fc-4872-9a57-2a7bf2a4cc38*)Open Government Licence (https://open.canada.ca/en/open-government-licence-canada*)Lake WinnipegDataStreamManitobaLake Winnipeg (https://lakewinnipegdatastream.ca/explore/#/dataset/d9f476e5-a80b-4499-9c94-e61d8b83dba3/?ref=search&characteristic_media=undefined&characteristic_characteristic_name=Chlorophyll a&characteristic_method_speciation=undefined&characteristic_sample_fraction=undefined&characteristic_field=undefined&characteristic_unit=undefined*)Open Government Licence (https://open.canada.ca/en/open-government-licence-canada*)CanWIN Data HUBManitobaLake Winnipeg (https://canwin-datahub.ad.umanitoba.ca/*)Open Data (https://opendefinition.org/od/2.1/en/*)IISD-ELA privatedatabaseOntarioExperimental lakes 114, 224, 239, and 442 (https://www.iisd.org/ela/science-data/our-data/data-requests/*)Data Sharing Agreement (https://cf.iisd.net/ela/wp-content/uploads/2022/01/iisd-ela-data-sharing-agreement.pdf*)Alberta Environmentand Parks datarepositoryAlbertaMany lakes sampled by Alberta Environment and Parks (http://environment.alberta.ca/apps/EdwReportViewer/LakeWaterQuality.aspx*)Open Government Licence (https://open.canada.ca/en/open-government-licence-canada*)LUBW data and mapserviceGermanyConstance Untersee (https://udo.lubw.baden-wuerttemberg.de/public/index.xhtml*)User agreement (https://www.lubw.baden-wuerttemberg.de/umweltinformationssystem/nutzungsvereinbarung*)Water Information System Sweden (VISS)SwedenHundreds of lakes monitored across Sweden (https://viss.lansstyrelsen.se/*)CC0 license – free use (https://viss.lansstyrelsen.se/About.aspx?aboutPageID=5*)UK EnvironmentAgencyUKMany lakes monitored across the UK (https://environment.data.gov.uk/water-quality/view/download*)Terms of use (https://support.environment.data.gov.uk/hc/en-gb/articles/360015443132-Terms-and-Conditions*)UK Centre for Ecologyand HydrologyUKBassenthwaite (https://catalogue.ceh.ac.uk/documents/91d763f2-978d-4891-b3c6-f41d29b45d55*), Belhalm tarn (https://catalogue.ceh.ac.uk/documents/393a5946-8a22-4350-80f3-a60d753beb00*), Derwent water (https://catalogue.ceh.ac.uk/documents/106844ff-7b4c-45c3-8b4c-7cfb4a4b953b*), Esthwaite water (https://catalogue.ceh.ac.uk/documents/87360d1a-85d9-4a4e-b9ac-e315977a52d3*), Grasmere (https://catalogue.ceh.ac.uk/documents/b891c50a-1f77-48b2-9c41-7cc0e8993c50*), Loch leven (https://catalogue.ceh.ac.uk/documents/2969776d-0b59-4435-a746-da50b8fd62a3*), Lake Windermere (north basin) (https://catalogue.ceh.ac.uk/documents/f385b60a-2a6b-432e-aadd-a9690415a0ca*), Lake Windermere (south basin) (https://catalogue.ceh.ac.uk/documents/e3c4d368-215d-49b2-8e12-74c99c4c3a9d*)Open Government Licence v3 (https://eidc.ceh.ac.uk/licences/OGL/plain*), Terms of use (https://eidc.ceh.ac.uk/licences/lakesEcology/plain*)Environmental DataInitiative portalGlobalCentral long lake, East long lake, Giles lake, Lacawac, May lake (https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-ntl.354.4*), Paul lake, Peter lake, Tuesday lake, Waynwood lake, West long lake (https://portal.edirepository.org/nis/mapbrowse?packageid=knb-lter-ntl.354.4*)Creative Commons license CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/*)Knowledge Networkfor Biocomplexity(KNB)ColoradoOneida lake (https://knb.ecoinformatics.org/view/kgordon.35.96*)Open Data Commons Attribution License (https://opendatacommons.org/licenses/by/1-0/*)University of WisconsinNLTERWisconsinAllequash lake, Big Muskellunge lake, Crystal lake, Crystal bog (https://lter.limnology.wisc.edu/node/55078*), Sparkling lake, Trout lake, Trout bog (https://lter.limnology.wisc.edu/node/55078*), Fish lake, Lake Mendota, lake Monona, Lake Wingra (https://lter.limnology.wisc.edu/dataset/north-temperate-lakes-lter-chlorophyll-madison-lakes-area-1995-current*)Data use agreement (https://lter.limnology.wisc.edu/about/ntl-lter-data-access-policy*)USGS and USEPAwater qualityUSAUSGS-491528094470601, USGS-492142094421501 (https://www.waterqualitydata.us/*)User guide (https://www.waterqualitydata.us/portal_userguide/*)
Summary of sources and licensing for the surface solar radiation
data. Direct links to the databases are provided where possible, but the
Environment and Climate Change Canada (ECCC) and IISD-ELA data were acquired
through communication with the curators. * Last access: July 2022.
DatabaseRegionData licenceCommentsETH Zurich GEBA (https://geba.ethz.ch/*)GlobalData availability (https://geba.ethz.ch/data-retrieval/disclaimer-copyright.html*)Agriculture AB Station Data (https://agriculture.alberta.ca/acis/weather-data-viewer.jsp*)AlbertaTerms of use (https://agriculture.alberta.ca/acis/data-disclaimer.jsp*)Data provided by Alberta Agriculture andForestry, and Alberta Climate Information Service (ACIS) (August 2020) (https://acis.alberta.ca/*)Baseline Solar RadiationNetwork (https://bsrn.awi.de/*)GlobalCreative Commons licence CC-BY 4.0 (https://creativecommons.org/licenses/by/4.0/*)10.1594/PANGAEA.880000 (Driemel et al., 2018)Environment and ClimateChange Canada (ECCC)CanadaOpen Government Licence (https://open.canada.ca/en/open-government-licence-canada*)Source: direct communication with ECCCIISD-ELA private database (https://www.iisd.org/ela/science-data/our-data/data-requests/*)OntarioData Sharing Agreement (https://cf.iisd.net/ela/wp-content/uploads/2022/01/iisd-ela-data-sharing-agreement.pdf*)Source: direct communication with IISD-ELA
Summary of miscellaneous databases used to acquire lake
attributes. Follow embedded links to access the database and metadata. * Last access: July 2022.
DatabaseDescriptionGlobal Multi-resolution Terrain ElevationData (GMTED2010) (https://www.usgs.gov/coastal-changes-and-impacts/gmted2010?qt-science_support_page_related_con=0#qt-science_support_page_related_con*)Global digital elevation model used to extract lake and SSR station elevation in QGIS (Danielson and Gesch, 2010)HydroLAKES V1.0 (https://www.hydrosheds.org/products/hydrolakes*)Global lake shapefile used to assign lake area, mean depth, and volume (ver. 1.0; Messager et al., 2016)HydroATLAS V1.0 (https://www.hydrosheds.org/hydroatlas*)Global shapefile for regional attributes, used to assign climate zone to each lake (ver. 1.0; Linke et al., 2019)
Lake attributes published alongside the PCI dataset
(https://doi.org/10.20383/102.0488, Adams et al., 2021). * Last access: July 2022.
AttributeUnitsDescriptionlakeNameLake name, reformatted from original filelake_latDecimal degreesLake latitude, collected from original data files and HydroLAKES data (Messager et al., 2016)lake_longDecimal degreesLake longitude, collected from original data files and HydroLAKES data (Messager et al., 2016)tsiRange from 0–100Calculated from mean chlorophyll-a concentration across all years the lake was sampled, based on guidelines from the North American Lake Management Society (https://www.nalms.org/secchidipin/monitoring-methods/trophic-state-equations/*)trophic_statusOligotrophic, mesotrophic, eutrophic, hypereutrophicAssigned using lake trophic status indexclimate_zoneIntegerClimate zone of each lake, assigned using the HydroATLAS database (Linke et al., 2019)lake_elevm above sea levelElevation of the lake, extracted from the Global Multi-resolution Terrain Elevation Data (GMTED2010) model (Danielson and Gesch, 2010) (https://www.usgs.gov/core-science-systems/eros/coastal-changes-and-impacts/gmted2010?qt-science_support_page_related_con=0#qt-science_support_page_related_con*)lake_areakm2Total lake surface area, extracted from the HydroLAKES database (Messager et al., 2016)lake_volumekm3Total lake volume, extracted from the HydroLAKES database(Messager et al., 2016)mean_lake_depthmMean lake depth, extracted from the HydroLAKES database(Messager et al., 2016)start_samplingYearYear when lake sampling startedend_samplingYearYear when lake sampling endeddays_sampledDaysTotal number of days where lake data were recorded in the original datasetyears_sampledYearsTotal number of years where lake data were recorded in the original datasetsampling_frequencySamples per dayNumber of samples collected that year, divided by the number of days in the sampling seasonmean_time_between_samplesDaysAverage number of days between sample collection(sampling resolution)lake_data_sourceNAName of database where the original lake data were sourcedcountryNAName of the country where the lake is locatedvariablesNAList of the variables found in the PCI dataset for each lakessr_stationNAStation name as assigned in original databasessr_idNAID number in original database (where available)ssr_sourceNAName of database where the original SSR data were sourcedssr_latDecimal degreesSSR station latitudessr_longDecimal degreesSSR station longitude
Continued.
AttributeUnitsDescriptiongeo_dist_kmkmGeodesic distance between the paired lake and SSR stationssr_elevm above sea levelElevation of the SSR station, extracted from the Global Multi-resolution Terrain Elevation Data (GMTED2010) model (Danielson and Gesch, 2010) (https://www.usgs.gov/coastal-changes-and-impacts/gmted2010?qt-science_support_page_related_con=0#qt-science_support_page_related_con*)ssr_lake_elev_diffmDifference in elevation between the paired lake and SSR station(positive when the SSR station is at a higher elevation)ssr_startYearYear when SSR sampling startedssr_endYearYear when SSR sampling endedssr_years_sampledYearsTotal number of years where SSR data were recorded in the original datasetssr_original_resolutionMonth or dayResolution of the original SSR data before being resampled to achieve a daily resolution
The supplement related to this article is available online at: https://doi.org/10.5194/essd-14-5139-2022-supplement.
Author contributions
All authors took part in development of the study. SS, BDP, and PVC
conceptualized the study, while HA and JY developed the methods and carried
out the data collection and data post-processing. HA wrote the original
manuscript with contributions from JY, BDP, SS, HKP, and PVC. All authors
reviewed and edited the final paper.
Competing interests
The contact author has declared that none of the authors has any competing interests.
Disclaimer
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Acknowledgements
We would
like to thank all of the institutions and authors listed in the Supplement for making their data open source and free to support our work.
Financial support
This work was funded by the Lake Futures project within the Global Water Futures (GWF) project, supported by the Canada First Research Excellence Fund (CFREF).
Review statement
This paper was edited by Birgit Heim and reviewed by two anonymous referees.
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