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Climate change has increased water temperature and altered wind-driven water movements in aquatic systems1,2. This applies not only to the mean conditions3,4, but also to the frequency of extreme events (i.e., near the upper ends of the range of observed values5,  > 80th percentile). For example, high air temperature or powerful winds5,6,7,8,9 has affected the behaviour of surface gravity waves10. Understanding the changes in wind and wave climate provides insight into the prediction and management of climate change impacts related to coastal dynamics, such as coastal erosion and sediment budgets, water motions, and biological responses6,11,12. Several studies on the impacts of climate change on oceanic waves12,13,14,15 have been undertaken, including a recent study16 that shows a 0.41% annual increase in global wave power (WP; the transport of energy by waves, which represents the temporal variations of energy transferred from the atmosphere to the ocean surface motion over cumulative periods of time16,17 (Eq. 2) due to stronger winds caused by increases in sea surface temperature. The oceanic wave climate also responds to global atmospheric phenomena (e.g., El Niño Southern Oscillation and the Atlantic Multidecadal Oscillation), in which sea surface temperature modifies wind patterns and storm cyclogenesis12,18,19,20. A systematic long-term assessment of climate warming impacts on waves in lakes remains to be undertaken, but should include winds, which are one of the principal sources of mechanical energy for lake circulation and interbasin coupling (e.g., exchange)21,22,23,24.
The Laurentian Great Lakes, which consist of lakes Superior, Michigan, Huron, Erie, and Ontario (Fig. 1a), are the largest group of freshwater lakes on Earth; they contain 21% of the world’s volume of fresh surface water. These lakes have been affected by climate change in several ways including increased surface water temperature, longer summer stratification related hypoxia (i.e., dissolved oxygen [DO] concentrations  0.05); all the black bars are significant (i.e., p  8 m s−1) from the south and southwest that are the common wind directions over the Great Lakes23, tilt the thermocline upward in the western and northern part of the central basin due to Ekman transport of surface water southward22,38,42,43,44,45. As this hypolimnetic water upwells into shallower depths it can be transported counter clockwise by the alongshore surface currents moving to the west32. If there is a calm period following the high winds, the upwelled water in the northwestern part of the central basin will flow southward because of the pressure gradient and also in a clockwise direction (to the west) because of the Coriolis effect, and so will intrude into the western basin (i.e., a geostrophic flow) opposite to the hydraulic flow from the Detroit River (Fig. 1c)22,32,46. This causes the rapid (on the order of hours) formation of a thermocline within the northeastern portion of the western basin (Pelee Passage) due to the intrusion of low temperature bottom water22,42, which can also be hypoxic22 or anoxic (i.e., DO (approx) 0) at the sediment surface22 and contain high soluble reactive phosphorus concentrations (SRP; 0.02–0.05 mg L−1)47,48,49. Low values of sediment oxygen uptake are observed during these events in the western basin due to stratification and weak bottom shear and turbulence, which results in thicker diffusive sublayer22.
Interbasin exchange has been observed in lakes with multiple basins elsewhere (e.g., Lake Geneva50, Nechako Reservoir51) as well as in the Great Lakes region (e.g., Muskegon Bay52, Green Bay53, Kempenfelt Bay54, Pere Marquette River55). In Lake Michigan, for example, high winds can lead to coastal upwelling into Muskegon Lake causing episodic hypoxia52. In case of Lake Erie, interbasin exchange was identified as the dominant cause (63%) of hypoxia in the northeastern portion of the western basin during biweekly fishing trawls in August over the past 30 years22. However, there are no long-term continuous water quality observations to assess the occurrence and historic trends in these hypoxic events. Extreme winds prevailing from upwelling favourable directions (i.e., from the south and southwest) can generate strong surface waves and water currents through momentum flux at the air–water interface. Therefore, WP can be used as an indicator or proxy (but not the cause) of interbasin exchange. Here, we examine the historical trends in water temperature, winds and resultant waves in the context of climate change in the summer in the Great Lakes (Fig. 1a) with an emphasis on the western basin of Lake Erie (Fig. 1b). We examine data for August, which is the month when hypoxia is most likely to occur in dimictic north temperate lakes before the fall turnover, and when large HAB have been observed in the western basin of Lake Erie. August is also the time when the spatial extent of hypoxia in the central basin is the largest and when the aforementioned upwelling into the western basin is likely to occur22,40,56. The data examined are from buoys with the longest historical records (Fig. 1a and Table S1). We examine winds from the south and southwest directions, which are the common wind directions over the Great Lakes during August, and which are favourable for upwelling into the western basin of Lake Erie. The results show that the WP in Great Lakes has increased in the past 40 years. A pattern in WP (a proxy for hypoxic upwelling events into the western basin of Lake Erie) has also increased in frequency over this time, which has implications for the water quality (e.g., dissolved oxygen and total phosphorus) of the lake. The increased frequency of interbasin upwelling was confirmed using historical records of lake bottom water temperature (LBT), as well as dissolved oxygen and total phosphorus concentrations. This is the first time that WP has been identified as an indicator of climate change-driven biogeochemical responses in lakes.
Long-term trends in WP and LST in the Great Lakes
First, we investigate the historical trends in average lake surface temperature (LST), wind, and waves in the Great Lakes during August. Results show that LST and LSTw (hereinafter subscript ”w” is used to denote the variables measured during upwelling favourable winds from 180° to 270°, clockwise from north) have both increased significantly (p  0.2% year−1) since 1980, although lower trends were observed in lake Erie and Michigan (Figs. S1–S5 ((a) and (b)) and Table S1). These changes in the LST correspond to a warming trend in air temperature (Tair); the average Tair over the Great Lakes increased significantly by ~ 0.4 (pm) 0.2 ((pm) standard error) oC decade−1 since 1980 (Fig. S6a,b). There was an associated significant increase in wind speed (W) over the Great Lakes during August (Ww) of ~ 0.4 (pm) 0.1 m s−1 decade−1 for winds from the south and southwest (Figs. S1–S5 ((c) and (d)) and Table S1). Consequently, the wind stress associated with wind from the south and southwest over the water surface of the Great Lakes (({tau }_{w}=0.0012{rho }_{air}{W}_{w}^{2}), where ({rho }_{air})=1.22 kg m−3 is the density of air57, and the wind speed is measured 10 m above the water) increased significantly by 0.006 (pm) 0.002 Pa decade−1 during August (3.0 (pm) 0.9% year−1; Figs. S1–S5 ((e) and (f)) and Table S1).
The effects of increased wind stress can also be seen in wave power, which is a function of the square of significant wave height (the mean value of the largest third of the wave heights during typically 1 h, SWH) and the wave period (({T}_{p}); i.e., (WP propto {{T}_{p} times SWH}^{2})); and changes in wind are reflected in wave power ((WP propto {W}^{2.4}) and (propto {W}^{5}) for developing and fully developed waves, respectively; see “Materials and methods”). The average SWH and SWHw in the Great Lakes during August have increased significantly by 0.03 (pm) 0.02 and 0.04 (pm) 0.03 m decade−1, respectively (i.e., ~ 1.0 (pm) 0.8% and ~ 1.7 (pm) 1.5% year−1, respectively), and this is largely driven by the increase in the frequency of extreme surface winds58 (Figs. S1–S5g and h; WP responds to changes in mean values, but it is more sensitive to extreme events because WP (propto { SWH}^{2})16). Consequently, the average WP and WPw in the Great Lakes during August have increased by ~ 0.04 (pm) 0.02 and ~ 0.06 (pm) 0.03 kW m−1 decade−1, respectively (i.e., ~ 1.0 (pm) 0.6% and ~ 2.0 (pm) 0.9% year−1, respectively; Fig. 2). In Lake Erie, WPw during August increased significantly by 0.02 (pm) 0.01 kW m−1 decade−1 (1.4 (pm) 0.2% year−1; Fig. 2 and Table S1; the increasing trend in WP = 0.02 (pm) 0.02 or 0.5 (pm) 0.1% was not statistically significant). It is relevant to note that these results are based on observations from a single buoy per lake; the one with the longest available data records (Fig. 1a and Table S2). However, the wind records and historical wave trends between buoys Sta. NDBC 45005 and Port Stanley in Lake Erie (Fig. 1a), which are ~ 130 km apart, are consistent based on the available records. Specifically, wind speed and direction in 2018 have Pearson correlation coefficients, r  > 0.6 (Fig. S7a,b, respectively); Ww and WPw are also correlated with r = 0.51 and 0.67, respectively, during August of 1990–2018 and the buoys show similar temporal increases in WPw (~ 0.025 (pm) 0.02 and 0.02 ± 0.01 kW m−1 decade−1 in Port Stanley and Sta. NDBC 45005, respectively). The trends in historical LSTw and WPw are related statistically (i.e., higher mutual information; Fig. S8) similar to the relationship described for global sea surface temperature and oceanic WP used as an indicator of climate change16.
Figure 2

Historical patterns in wave power in Great Lakes. 10 year moving average of wave power (WP) during the August (a) and during August with the wind from south and southwest and (WPw; b). The dashed lines show the linear regression (statistical results provided in Table S1).

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The long-term variations in WP and LST may be related to the global atmospheric phenomena. The LSTw anomaly in all the lakes show an increasing trend beginning in 1995 (Fig. S9a), which corresponds to the switch from the negative mode of the Atlantic Multidecadal Oscillation (AMO) to the positive mode (associated with increased tropical cyclone activity and stronger westerly winds) between the 1980s and the early 2000s (Fig. S9b)16. Both the WPw and LSTw anomaly are positively correlated with the AMO (r ~ 0.50 and ~ 0.55, respectively, since 1990). Similar to global oceanic wave power16, peaks in WPw in the Great Lakes are associated with strong El Niño years (i.e., Multivariate El Niño/Southern Oscillation (MEI) greater than 1.5; Fig. S9c,d), which can contribute to the enhanced wind energy due to increased cyclonic events16. MEI and WPw in Great Lakes are generally correlated by r  > 0.45 since 1990, however, the impacts of global atmospheric events on temperature and water dynamics of Great Lakes requires further study.
Episodic hypoxic upwelling events in the western basin of Lake Erie
We used historical records (Table S2) of long-term near-bottom water temperature (1998–2018) and dissolved oxygen (2007–2018) in the northeastern portion of the western basin of Lake Erie as well as wave observations in the western portion of the central basin (1980–2018 in Sta. NDBC 45005, Fig. 1) in August to determine the frequency of hypoxic upwelling events and the impacts of these events on the total phosphorus concentration in the northeast portion of the western basin. These analyses do not include the local hypoxia due to periods of calm and warm atmospheric conditions that may occur annually31 and, which are different than episodic upwelling events. Intrusion of cold hypoxic hypolimnetic water from the central basin into the western basin, following high winds from upwelling favourable directions, can cause a sudden drop (on the order of hours) in LBT and dissolved oxygen (DO) when the hypolimnetic water in the central basin is hypoxic22. The LBT time series in the western basin from 2017 to 2018 show that LBT decreased more than 3 °C in less than 12 h during upwelling events; e.g., 9–16, 18–22 and 26–31 August 2018 at Sta E (Fig. 3b) and 24–29 August 2017 at Leamington and Sta E (Fig. S10b). The records of LBT measured by the Ontario Ministry of Natural Resources and Forestry (MNRF) in August in Leamington Ontario between 1998 and 2018 detected 23 events of intrusion of cold water, which are consistent with upwelling (the blue symbols in Fig. 4a).
Figure 3

Wave power and bottom water temperature during August 2018 in the western basin of Lake Erie. (a) Time series of wave power (WP; black line), wave period (Tp; magenta), and significant wave height (SWH; blue) recorded at Sta. NDBC 45005. (b) Time series of dissolved oxygen (DO; red) and water temperature (LBT; blue dashed-line) in Sta. E at 1 m above the bed and bottom water temperature in Leamington (blue solid-line) in August 2018. The red triangles represent the observed hypoxic events in the western basin of Lake Erie. The wave power of the waves from south and southwest (i.e., favourable for upwelling) are positive preceding upwelling.

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Figure 4

Number of hypoxic upwelling events in the western basin. (a) The number of hypoxic upwelling events based on patterns in wave power at Sta. NDBC 45005 (dark grey: average WPw  > 0.44 kW m−1, light grey: 0.37  8 m s−1 from similar directions, which corresponds to the ~ 80th percentile of wind speeds and is greater than the sum of the average and standard deviation of the wind speed (~ 6 and 2 m s−1, respectively). This wind threshold is consistent with Rao et al.’s44 wind speed that led to upwelling, which resulted in a fish kill along the north shore of the central basin in 2012.
We used a least-square method to find a wave pattern (i.e., wave direction, duration, and power) that could be applied to predict the number of upwelling events that could be hypoxic between 1998 and 2018 based on LBT observations. A rapid decrease in the LBT at both Sta E and Leamington (12 km vs. 20 km from the Pelee Passage, respectively) occurred during events in which the average WP was  > 0.44 kW m−1 (i.e., 22–24 August 2017; Fig. S10a,b). The model predicted 25 upwelling events at Leamington (dark bars in Fig. 4a) of which 23 were observed (as stated above; no data were available for 2012; blue circles in Fig. 4a) for waves from south and southwest that lasted for at least 15 h with an average wave power greater than 0.37 kW m−1. Of the 23 observed events, the model predicted 21 events providing a root mean square error [RMSE] of 0.20 events. We validated the model predictions using the biweekly DO measurements from MNRF cruises between 2007 and 2018, which happened to sample 17 of the 23 observed events of low LBT. We note, however, that two hypoxic upwelling events were also recorded outside the study period, i.e., early September; this supports the study’s focus on August. Hypoxic conditions (DO  1.6 events year−1 in 2018 based on a 10-year moving average. Specifically, 21 of 49 (~ 43%) upwelling events in the last four decades have occurred in the past 10 years. Thirty-two of these were strong events with WP  > 0.44 kW m−1, 15 of which (~ 47%) occurred after 2009. Interestingly, this pattern in wave power (i.e., waves from south and southwest that last for  > 15 h with an average WP  > 0.37 kW m−1 from the historical data) was also observed in August 1980 (Fig. 4a), when the LBT dropped following rapid formation of a thermocline, which at the time was attributed to the upwelling of hypolimnetic water from the central basin40,42. These results indicate that an increase in extreme winds from south and southwest during August, over the last four decades, has resulted in more frequent upwelling from the central basin into the western basin and consequently a greater number of episodic hypoxic events in that part of Lake Erie.
The effect of upwelling on phosphorus concentrations was examined through an analysis of the water column-average total phosphorus (TP) observations from biweekly cruises conducted by the MNRF at station W5 (Fig. 1b). We examined the available data recorded between 15 July and 15 September from 2000 to 2018 (3–5 records year−1; 66 observations in total), which is a period in which linear patterns in TP vs. sampling date were not evident (p  >   > 0.05). The z-score (standard deviate) was determined for the data within a given year (({mathrm{Z}}_{mathrm{TP}}=left(mathrm{TP}-{mathrm{TP}}_{mathrm{mean}}right)/mathrm{SD}), where ({mathrm{TP}}_{mathrm{mean}}) is the annual average of TP and SD is the standard deviation). Positive ({mathrm{Z}}_{mathrm{TP}}) values (i.e., (mathrm{TP} >{mathrm{TP}}_{mathrm{mean}})) were observed in 11 cases in which the sampling occurred  1) observed during 5 August–8 September sampling (black solid circles in Fig. 4b). Statistical comparison revealed that the average ({mathrm{Z}}_{mathrm{TP}}) was significantly higher during upwelling vs. non-upwelling samples (i.e., 0.95 ± 0.18, n = 11 vs. − 0.26 ± 0.12, n = 25; ANOVA F1,34 = 29.64, p  More

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Physicochemical and chemical properties of soils and BioC
The physicochemical and chemical characteristics of the soils and BioC, as well as selected chemical properties of the HAs isolated from the soil and BioC are shown in Table 1.
Table 1 Physicochemical and chemical characteristics of soils, BioC and isolated HAs.
Full size table

The properties of soils and BioC, such as the d, Corg, A, pH, and Q, were presented in detail previously4. Briefly, soils were characterised by a typical d value for mineral soils ≈ 2.60 g cm−3, and by a relatively low content of Corg and a high content of A. The pH of the soils was weakly acidic. The examined soils were characterised by low Q values, indicating a low content of organic structures dissociating to the negative surface charge (mainly carboxylic and phenolic groups). The HAs obtained from fallow and grassland were characterised by high QHA values (about 50 times higher in comparison with the Q values of fallow and grassland). The d value of BioC was typical for organic materials (1.46 g cm−3), moreover, the BioC contained a high content of OM, which was expressed as Corg. The pH of BioC was alkaline. This material was also characterised by a high Q value, which indicated its favourable sorption properties.
The results of our studies showed that the E2/6 values were similar for the HAs originated from the two studied soils, suggesting a similar ratio of lignin-type compounds resistant to humification to the structures with a high humification degree. The ΔlgK reached values of 0.83 and 0.86 for HAs isolated from grassland and fallow, respectively, indicating a low degree of HA humification (Kumada’s classification for low humification degree of HAs: ΔlgK = 0.8–1.1)33. Slightly higher ΔlgK values obtained for the grassland HAs compared with the fallow suggested a higher content of less humified compounds, such as cellulose, hemicellulose, and lignin34.
The ΔlgK of HAs isolated from BioC reached a value of 0.54, suggesting the presence of highly humified compounds, in comparison with soil HAs (Kumada’s classification for high humification degree of HAs: ΔlgK  8.0, above which the OH groups are deprotonated26, therefore we only report results in this pH range. Changes in the QHA values as a function of pH (Fig. 4A–D) were monotonic; these values increased towards an alkaline pH, which resulted from the fact that other fractions of functional groups dissociated successively at increasing pH values. Generally, in the first month of the experiment, the highest QHA values were observed for HAs obtained from fallow and grassland with the lowest BioC dose (Fig. 4A,C). This fact indicated that these HAs had the best sorption properties. In the last month of the experiment, the QHA values changed in an ambiguous way. The QHA at pH 9.0 values of HAs isolated from pure BioC were lower than those obtained from the soil, and moreover, BioC did not have an obvious effect on the QHA values of the soil HAs. Previous studies4 on impact of BioC on the physicochemical properties of Haplic Luvisol under different land uses, showed that BioC added to soil caused a significant increase in Q values in the last year of the experiment. Thus, we can conclude that BioC introduced OM with a variable surface charge but did not affect the soil’s QHA. It is possible that the BioC doses used in our experiment were insufficient to raise the QHA values.
Figure 4

Dependence of surface negative charge (QHA) on pH of the HAs solution. HAs obtained from fallow (A,B) and grassland (C,D) amended with BioC in 1st and 28th month of field experiment, as well as HAs obtained from BioC.

Full size image

Influence of BioC amendment on structure and chemical properties of HAs in fallow and grassland: spectroscopic approach
The analyses of the HAs isolated from fallow and grassland amended with BioC showed changes in the structural properties of these compounds. The E2/6 parameter estimated from UV–Vis data was changing both under the influence of different BioC doses and during the 3 years of the experiment. However, it should be assumed that the observed changes were of a different nature for fallow (Fig. 5A) and for grassland (Fig. 5B), due to varied trends in the activity of BioC on the analysed soils.
Figure 5

Changes in E2/6 values obtained for HAs of fallow (A) and grassland (B) amended with BioC (0, 1, 2, 3 kg m−2) as a function of time. Average values from 3 replicates in each term, ± standard deviation. Other letter designations indicate significant differences between values at α  More

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