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1 2 3 4 1 Global warming affects nutrient upwelling in deep lakes 5 6 2 Robert Schwefel1*ǂ, Beat Müller2, Hélène Boisgontier1, and Alfred Wüest1,2 7 8 1 9 3 Physics of Aquatic Systems Laboratory, Margaretha Kamprad Chair, École Polytechnique 10 4 Fédérale de Lausanne (EPFL), Institute of Environmental Engineering, Lausanne, Switzerland. 11 12 5 2Eawag, Swiss Federal Institute of Aquatic Science and Technology, Surface Waters – Research 13 14 6 and Management, Kastanienbaum, Switzerland. 15 16 7 *corresponding author 17 18 8 ǂ Current address: UC Santa Barbara, 3015 Marine Science Building, Santa Barbara, CA 93106- 19 20 9 6150 United States; Phone: +1 805 8952157; E-mail: [email protected] 21 22 10 23 24 11 ORCID: 25 26 27 12 Robert Schwefel: 0000-0003-1610-4181; Beat Müller: 0000-0003-3696-9035; Hélène 28 13 Boisgontier: n/a; Alfred Wüest: 0000-0001-7984-0368 29 30 14 31 32 33 15 ABSTRACT 34 35 16 Measures to reduce lake phosphorus concentrations have been encouragingly successful in many 36 17 parts of the world. After significant eutrophication in the 20th century, nutrient concentrations 37 38 18 have declined in many natural settings. In addition to these direct anthropogenic impacts, 39 19 however, climate change is also altering various processes in lakes. Its effects on lacustrine 40 41 20 nutrient budget remain poorly understood. Here we investigate the total phosphorus (TP) 42 21 concentrations in the epilimnion of the meromictic Lake Zug under present and future climatic 43 22 conditions. Results are compared with those of other deep lakes. Data showed that TP transported 44 45 23 from the hypolimnion by convective winter mixing was the most important source of TP for the 46 24 epilimnion, reaching values more than ten times higher than the external input from the 47 48 25 catchment. We found a logarithmic relationship between winter mixing depth (WMD) and 49 26 epilimnetic TP content in spring. Warming climate affects WMD mainly due to its dependence 50 27 on autumn stratification. Model simulations predict a reduction of average WMD from 78 m 51 52 28 (current) to 65 m in 2085 assuming IPCC scenario A2. Other scenarios show similar but smaller 53 29 changes in the future. In scenario A2, climate change is predicted to reduce epilimnetic TP 54 55 30 concentrations by up to 24 % during warm winters and may consequently introduce significant 56 31 year-to-year variability in primary productivity. 57 58 32 Keywords: Limnology, Climate Change, Winter Mixing, Phosphorus, Lake Zug 59 60 61 This document is the accepted manuscript version of the following article: 62 Schwefel, R., Müller, B., Boisgontier, H., & Wüest, A. (2019). Global warming affects 63 nutrient upwelling in deep lakes. Aquatic Sciences, 81(3), 50 (11 pp.). 64 https://doi.org/10.1007/s00027-019-0637-0 65 1 2 3 4 33 1. INTRODUCTION 5 6 7 34 Excessive primary productivity fuelled by high nutrient input represents a major concern in many 8 9 35 lakes around the world (Vollenweider 1968; Dillon and Rigler 1974; Schindler 2006). Efforts to 10 11 36 limit nutrient input from agriculture and urban wastewater led to substantial input reductions in 12 37 phosphorus (P) and nitrogen concentrations and to reestablishment of oligotrophic nutrient 13 14 38 conditions in many formerly eutrophic lakes (De Pinto et al. 1986; Makarewicz 1993; Finger et 15 16 39 al. 2013; Müller et al. 2014). However, input reduction has not always resulted in a return to 17 18 40 oligotrophic conditions, oxic deepwater, or lower rates of primary production. Overall system 19 20 41 response to such drastic changes on relatively short timescales remain unclear. Biomass 21 22 42 concentrations (Anneville et al. 2005; Jeppesen et al. 2005) and oxygen depletion rates (Müller et 23 43 al. 2012a; Schwefel et al. 2018) do not always react in tandem with nutrient concentrations or 24 25 44 inputs. 26 27 28 45 Recent studies have recognized that rising air temperatures also affect lake ecosystems by 29 30 46 increasing water temperatures (Schmid et al. 2014; O’Reilly et al. 2015), which in turn affect 31 32 47 water column stability as well as nutrient and oxygen budgets of lakes (Sahoo et al. 2013; North 33 48 et al. 2014; Schwefel et al. 2016; Ficker et al. 2017; Salmaso et al. 2018; Lepori et al. 2018; 34 35 49 Rogora et al. 2018). Many studies indicate that climate warming affects eutrophication through 36 37 50 increased plankton growth rates, increased internal P loading (North et al. 2014), higher nutrient 38 39 51 inputs from the catchments (Moss et al. 2011), or shifts in phytoplankton community structure 40 41 52 (Anneville et al. 2002). Few studies, however, have directly queried the relationship between 42 water column structure and hypolimnetic nutrient upwelling into the euphotic zone of lakes 43 53 44 54 during winter mixing. While primary productivity in shallow lakes is mainly controlled by 45 46 55 inflows from the catchment, deep lakes have considerable quantities of nutrients stored in the 47 48 56 deep hypolimnion. These can increase productivity when penetrative convective events or strong 49 50 57 winds allow mixing with the euphotic zone. The deep lakes of the East African rift, Lake Kivu 51 52 58 and Lake Tanganyika, are classic examples of settings where deep mixing is the main driver of 53 59 interannual changes in productivity. Lakes Constance and Garda offer mid-latitude examples of 54 55 60 settings, where nutrient availability depends to a substantial degree on winter mixing depth 56 57 61 (WMD) (Straile et al. 2003; Salmaso et al. 2018). In oligomictic Lake Lugano, Simona (2003) 58 59 62 found that nutrient availability and phytoplankton composition depended on WMD. North et al. 60 61 63 (2014) observed no reduction in P transport from the hypolimnion to the epilimnion of Lake 62 63 64 65 1 2 3 4 64 Zürich, in spite of reduced convective mixing in winter. Yankova et al. (2017) meanwhile 5 6 65 reported decreased upwelling of nutrient-rich deep-water in Lake Zürich with potential impacts 7 8 66 on primary productivity. 9 10 67 This study interprets long-term monitoring data from the deep, meromictic Lake Zug (Fig. 1) for 11 12 68 evidence of diminished deep convective mixing during winter and associated decline in 13 14 69 epilimnetic P, which is considered the limiting nutrient as in many lakes worldwide. A detailed 15 16 70 dataset spanning 35 years allows for quantitative analysis of the dependency of epilimnetic total 17 18 71 phosphorus (TP) concentrations on WMD and in turn variation in WMD with changing 19 72 meteorological conditions (Fig. 1b). A one-dimensional model provides predictions on mixing 20 21 73 behaviour under different IPCC climate scenarios and their impact on Lake Zug’s P budget. 22 23 24 74 The goals of this study were to (1) understand the epilimnetic P budget of Lake Zug and its 25 26 75 dependency on WMD, (2) estimate WMD using climate data, (3) evaluate predicted changes in 27 28 76 winter deep mixing under conditions of a warming climate, and (4) interpret the impact of WMD 29 77 on the TP input to the epilimnion. To address these goals, we analysed monthly temperature, 30 31 78 salinity, and TP profiles at the deepest location of the lake, along with estimates for external P 32 33 79 load, TP outflow, and P net sedimentation (Müller et al. 2012b). Lake Zug provides an ideal 34 35 80 dataset for evaluating the effect of global warming on the P budget in the productive surface layer 36 37 81 of deep lakes. 38 39 82 2. METHODS 40 41 42 83 Background on Lake Zug 43 44 45 84 Lake Zug is a meromictic, eutrophic lake resting at 413 m elevation in central Switzerland. The 46 47 85 lake has a surface area of 38.4 km², a volume of 3.18 km³, a mean depth of ~83 m and a 48 86 maximum depth of 198 m (Fig. 1). Due to low inflow relative to lake volume, the mean water 49 50 87 residence time of 14.2 years is long for a lake of this size. Surrounding mountains shield the 51 52 88 dominant western winds, hence, wind speeds rarely exceed 2 m s-1 (mean wind speed: 1.47 m s-1; 53 54 89 Fig. S1; Wüest and Gloor 1998). These conditions contribute to a slight salinity gradient and 55 56 90 form weak but permanent water column stratification in the hypolimnion. Complete deep mixing 57 58 91 during winter occurs only rarely. Since the beginning of continuous monitoring in 1950, Lake 59 92 Zug has mixed to maximum depth only once, in 2006 when temperatures completely 60 61 93 homogenized, and oxygen concentrations increased from anoxic conditions to ~5 mg l-1. During 62 63 64 65 1 2 3 4 94 this particular winter, other deep Swiss lakes such as Lake Geneva or meromictic Lake Lugano 5 6 95 also experienced complete mixing (Holzner et al. 2009; Schwefel et al. 2016). As a consequence 7 8 96 of the weak mixing, the water column below ~100 m is anoxic and largely separated from the 9 10 97 productive surface layer. Even rare, severe local storm events, referred to as Föhn, do not result 11 in oxygenation of the deep hypolimnion (Imboden et al. 1988). 12 98 13 14 99 Nestled in an agricultural region, Lake Zug has a long history of eutrophication caused by urban 15 16 100 waste water and agricultural runoff. The trophic history of Lake Zug has been reconstructed using 17 18 101 diatom sedimentary records dating back to 1550 (AquaPlus 2001). Historically, Lake Zug was 19 20 102 oligotrophic until ~1810 when it entered a phase of increasing TP concentrations, which 21 -3 22 103 ultimately reached values exceeding 200 mg m during the early 1980s. During this time, Lake 23 104 Zug was one of the most polluted lakes in Switzerland. Due to improved waste water treatment 24 25 105 and reduction of fertilizer use, TP concentrations declined since 1980. However, Lake Zug’s 26 27 106 volume-weighted average TP concentration remains still at high values of ~80 mg m-3 (Fig. 2). 28 29 107 Referring to current P loads, a recent study predicted TP gradually decreasing to concentrations 30 -3 st 31 108 of ~40 mg m by the end of the 21 century (Müller and Wüest 2016). 32 33 109 Monitoring data 34 35 36 110 Since 1950, the Cantonal Office for the Environment has measured temperature, oxygen, 37 38 111 conductivity, TP, and several other parameters in profiles collected at the deepest location of the 39 40 112 lake (Fig. 1d). Sampling was performed on an approximately monthly basis since 1982 at discrete 41 42 113 depths (varying over time but typically between 2.5 to 5 m resolution in the epilimnion and 10 to 43 114 20 m in the hypolimnion). Oxygen, temperature and conductivity were measured with a 44 45 115 multiparameter probe while TP was determined using the molybdenum-blue method after 46 47 116 digestion of the unfiltered samples with K2S2O8. TP consists almost entirely of dissolved 48 49 117 inorganic phosphorus (Fig. S2), TP concentrations therefore are considered to be representative 50 51 118 for the concentrations of bioavailable P. The measured profiles provided estimates of the 52 53 119 maximum depth every late winter / early spring convective mixing episode from 1982 to 2016. 54 120 Oxygen and temperature profiles collected at the end of spring (March to May) define the WMD 55 56 121 as the depth at which the temperature gradient becomes negative. This depth typically 57 58 122 corresponds to that of the highest local gradient in oxygen concentration. As a proxy for salinity, 59 60 123 conductivity profiles provided additional constraints since homogenization of the mixed surface 61 62 63 64 65 1 2 3 4 124 layer affects ionic components of the water column as well. Uncertainties in WMD depend on the 5 6 125 resolution of the profiles and typically equate to ±10 m. 7 8 9 126 Since 2005, the Cantonal Office of the Environment has measured fluvial P loads as total 10 11 127 dissolved P (TDP, which is assumed to be the bioavailable fraction) from inflows (representing 12 128 ~65 % of the catchment area) into Lake Zug. Bioavailability of particulate P was considered 13 14 129 negligible since (mostly soil-derived) particles settle in delta zones of the inflows even though a 15 16 130 small fraction of particulate P may in fact mineralize within the lake to become bioavailable. 17 18 131 Data were then scaled up to the entire lake catchment, assuming proportional TDP concentrations 19 20 132 for the remaining ~35 % of the area. The P export was estimated from the Lorze outflow at Cham 21 22 133 (Fig. 1d). Details concerning the P load calculations, export, and net sedimentation can be found 23 134 in Müller and Wüest (2016) and below. 24 25 26 135 The Cham weather station (47.188°N, 8.465°E) situated along Lake Zug’s northern shore 27 -1 28 136 provided temperature (°C), wind speed (m s ), wind direction (°), air pressure (hPa), and relative 29 30 137 humidity (%) data (Fig. 1d). The Luzern weather station (47.058°N, 8.300°E) provided cloud 31 -2 32 138 cover (octa) and solar radiation (W m ) data, which were not available from the Cham station. 33 139 Cloud cover was not measured in Luzern after 2015 and a data gap exists between 1.1.1998 and 34 35 140 31.3.1998. The Zürich weather station (47.378°N, 8.566°E) provided data for this period instead. 36 37 141 The correlation coefficient between cloud cover in Zürich and that in Luzern was 0.76. The 38 39 142 meteorological data were used to determine its correlation with WMD. The one-dimensional lake 40 41 143 model SIMSTRAT described below was forced with the meteorological data and provided 42 estimates of lake temperature and water column dynamics under predicted future climatic 43 144 44 145 conditions. 45 46 47 146 Data analysis 48 49 st st 50 147 To quantify winter temperatures, measured hourly temperatures from December 1 to March 1 51 52 148 were integrated as temperature-hours (°C h). (For example, a constant temperature of 10°C over 53 149 10 hours corresponds to 100 °C h). A wind shear estimate of 휏 = 휌퐶 푢2 (N m-2) with wind- 54 10 10 55 150 dependent drag coefficient, 퐶 , air density, ρ, and wind speed at 10 m high, u10, set according to 56 10 57 151 Wüest and Lorke (2003), was averaged over time (December to March) to quantify wind 58 59 152 exposure during winter. Estimates for water column stability at the end of summer were 60 61 62 63 64 65 1 2 3 4 153 calculated as Schmidt stability (J m-2). Datasets containing WMDs, temperature-hours, wind 5 6 154 exposure, and Schmidt stability were available for a period from 1994 to 2016. 7 8 9 155 Monitoring profiles allowed for TP content estimates (tons; t) spanning from 1982 to present. The 10 푎푏푠 11 156 annual P renewal ∆푃푤푖푛푡푒푟 in the upper 15 m of the water column during convective mixing in 12 13 157 winter can be estimated as the difference between the maximum depth-integrated epilimnion TP 14 158 content after winter (푃15 ) and the TP content remaining at the end of the preceding stratified 15 푚푎푥 16 159 season (푃15 ): 17 푟푒푚 18 푎푏푠 15 15 19 160 ∆푃푤푖푛푡푒푟[t] = 푃푚푎푥 − 푃푟푒푚. (1) 20 21 22 161 As shown in Müller et al. (2012a), the upper 15 m of the water column provide a good 23 24 162 approximation for the average summer epilimnion and euphotic zone depths (see Fig. S3 for Lake 25 26 163 Zug). Ongoing reoligotrophication leads to decreasing TP throughout the entire water column and 27 164 to strong intra-annual fluctuations in the epilimnion (Fig. 2). Moreover, the decrease of TP in the 28 29 165 hypolimnion leads to a decline in ∆푃푎푏푠 as well. To correct for these systematics, we express 30 푤푖푛푡푒푟 31 166 relative TP renewal as 32 33 15 15 34 푃푚푎푥−푃푟푒푚 167 ∆푃푤푖푛푡푒푟[%] = 100 × , (2) 35 푃푚푎푥 36 37 15 38 168 where 푃푚푎푥 is the TP content of the entire lake at the time when 푃푚푎푥 was estimated. To interpret 39 169 the physical meaning of the quantity ∆푃 , two hypothetical extremes are instructive to 40 푤푖푛푡푒푟 41 170 discuss. First, if convective mixing is not deeper than the defined euphotic depth of 15 m, no P is 42 15 15 43 171 supplied from the hypolimnion and ∆푃푤푖푛푡푒푟 ≈ 0% (푃푚푎푥 might still differ slightly from 푃푟푒푚 due 44 45 172 to differences in P inflow and outflow, or due to P sedimentation during winter). Second, in the 46 푃15 47 173 case of full convective mixing, TP concentrations in the lake are homogenized and 푚푎푥 is close 48 푃푚푎푥 49 15 50 174 to the ratio of the euphotic depth divided by the mean lake depth. If 푃푟푒푚 is negligible, this ratio 51 175 (15 m / 83 m = 18 % in Lake Zug) indicates the maximum possible value for ∆푃 (again 52 푤푖푛푡푒푟 53 176 neglecting external load, export, and P settling). 54 55 56 177 For comparison, the expected P input at a certain WMD was also calculated based on averaged 57 58 178 fall TP concentrations (using TP profiles from a base period of 1982 to present). This calculation 59 60 179 assumed that TP was completely homogenized in spring down to the WMD. Then, neglecting all 61 62 63 64 65 1 2 3 4 180 changes due to inflows, outflows, or net sedimentation during winter, the TP content in the 5 15 6 181 uppermost 15 m (푃푚푎푥′) after mixing could be calculated as 7 8 15 푉15 9 182 푃푚푎푥′ = 푃푊푀퐷 × (3) 10 푉푊푀퐷 11 12 183 where 푉15 is the volume of the uppermost 15 m, 푉푊푀퐷 is the volume of the homogenized layer 13 14 184 after winter mixing and 푃푊푀퐷 is the TP content in this homogenized layer before winter mixing 15 16 185 (end of fall) based on the 1982 to 2015 average. The expected ∆푃푤푖푛푡푒푟[%] can then be 17 15 ′ 15 18 186 calculated using eq. 2 with 푃푚푎푥 instead of 푃푚푎푥 and using the averaged remaining TP from the 19 15 20 187 upper 15 m in fall as 푃푟푒푚. The maximum value of 17.2 % at 197 m mixing depth is slightly less 21 15 22 188 than the ratio of the epilimnion depth to the mean lake depth (~18 %), since the term 푃푟푒푚 in eq. 23 24 189 2 is not exactly zero. Using TP profiles from other lakes, the same relationship can be established 25 190 for other systems with different morphology. We decided to compare Lake Zug with two 26 27 191 additional deep Swiss lakes with highly variable WMD and irregularly occurring complete 28 29 192 homogenization in winter (Lake Zurich and Lake Geneva) and with Lake Tanganyika, a typical 30 31 193 example of a deep African lake with vertical mixing (here driven by wind instead of convection) 32 33 194 as the main driver for productivity. TP profiles from 1982-2015 (Lake Zug), 1982-2013 (Lake 34 35 195 Zürich), and 1982-2012 (Lake Geneva) were used for the comparison. For Lake Tanganyika, the 36 196 calculation is based on one single PO4 profile taken from Edmond et al. (2003). Calculations 37 38 197 based on another published profile from Degens et al. (1971) gave very similar results. 39 40 41 198 One-dimensional modelling 42 43 44 199 The one-dimensional lake model SIMSTRAT (Goudsmit et al. 2002) was used to predict thermal 45 46 200 structure and winter mixing behaviour of Lake Zug. SIMSTRAT is a buoyancy-extended k–ε 47 201 lake model with additional parameterization for internal seiches. Previous studies have used 48 49 202 SIMSTRAT to interpret climate change, heat budgets, and WMD in lakes (Peeters et al. 2002; 50 51 203 Fink et al. 2014; Schwefel et al. 2016; Gaudard et al. 2017). Simulations used a model period 52 53 204 ranging from 10 June 1993 to 1 January 2017 with an initial 18 months period treated as a spin- 54 55 205 up phase. The model results based on meteorological inputs between 1995 and 2017 (described 56 206 above) were compared with monitoring profiles. 57 58 59 207 Secchi depth measurements were used to estimate absorption of short wavelength radiation 60 61 208 following methods used in previous modelling studies for Lake Geneva (Schwefel et al. 2016). 62 63 64 65 1 2 3 4 209 The wind drag coefficient C10 was treated as a constant. Using a wind-dependent C10 as input 5 6 210 (Wüest and Lorke 2003) resulted in higher root-mean square errors after calibration. Higher 7 8 211 errors probably resulted from low wind speeds causing large uncertainties in estimates for C10. 9 10 212 Slight differences between wind speeds measured at the meteorological station and the wind 11 directly affecting the lake surface also contribute to these errors. Because of Lake Zug’s long 12 213 13 214 (~14.2 yr) residence time and because most inflow remains in the epilimnion (not affecting the 14 15 215 deep water), the simulation ignored inflows and outflow. This procedure was validated for Lake 16 17 216 Geneva, a lake with an even shorter residence time than Lake Zug (Schwefel et al. 2016). 18 19 20 217 Lake Zug’s salinity gradient poses a challenge in nutrient and hydrodynamic modelling of the 21 22 218 water column because it causes the lake to remain stratified throughout the winter. Since sources 23 219 and sinks for salinity are not well quantified, salinity estimates provided by one-dimensional 24 25 220 numerical models cannot be easily validated by monitoring data. For the model, we assumed a 26 27 221 constant salinity profile for the entire modelling period in order to assure chemical stratification 28 29 222 in deeper layers of Lake Zug during the critical late winter and spring period. It was set to the 30 31 223 average salinity gradient after winter mixing during the 1995 to 2016 modelling period. 32 33 224 Climate change scenarios 34 35 36 225 To determine water column response to changing climatic conditions, the model used input 37 38 226 temperatures predicted according to regional variants of IPCC climate scenarios for central 39 40 227 Switzerland (CH2011 2011). Other forcing parameters such as wind or cloud cover were held 41 42 228 constant since climate scenarios offer no consistent predictions for those parameters. A total of 43 229 ten different model runs were performed. These included a reference run for the years 1995 to 44 45 230 2016, and nine model runs with temperatures predicted around the years 2035, 2060, and 2085 46 47 231 according to IPCC emission scenarios A2, A1B, and RCP3CP. Table 1 lists the mean temperature 48 49 232 increases for each scenario. To set up realistic boundary conditions, the model started at year 50 51 233 1993 and run up until 1995 as a spin-up period. The year 1993 was selected based on the 52 53 234 availability of meteorological data without major data gaps. Predicted seasonal changes in air 54 235 temperatures (Table S1) were added to the measured temperatures of the reference scenario 1995- 55 56 236 2016 to create air temperatures for the additional model runs under warming climate. 57 58 59 60 61 62 237 3. RESULTS 63 64 65 1 2 3 4 238 Phosphorus budget 5 6 7 239 As shown in Fig. 2a, the TP content of Lake Zug decreased over the last 35 years by ~60 %, from 8 9 240 more than 600 t in 1980 to ~250 t in 2015. Measured TP profiles taken from the deepest location 10 -1 -1 11 241 of the lake indicate an average decrease of -16 t yr between 1982 and 2006 and of -5.5 t yr 12 242 since 2006 (Fig. 2). The change in the lake’s TP content is a function of input sources (Pin; from 13 14 243 rivers, atmospheric deposition, and direct sewage treatment plant releases), net sedimentation 15 16 244 (NS), and TP outflow via the River Lorze (Pout): 17 18 ∆푃 19 245 = 푃푖푛 − 푃표푢푡 − 푁푆 . (4) 20 ∆푡 21 22 246 Each of these terms were determined to provide separate empirical TP estimates independent of 23 24 247 those from water column profiles. Between 2005 and 2015, the annual TDP load from tributaries 25 -1 26 248 including precipitation, storm water overflow and direct inputs reached 5.9 ± 1.1 t yr while 27 -1 28 249 annual outflow was 6.8 ± 1.4 t yr . Sediment cores indicated a slightly decreasing net 29 250 sedimentation rate for the same period with a mean value of 8.4 ± 1.0 t yr-1. Estimation of net 30 31 251 sedimentation rates based on the P budget (eq. 4) yielded a similar value but with larger error (7.5 32 33 252 ± 12.0 t yr-1). The theoretical budget calculations thus match observed changes in the lake 34 -1 -1 35 253 (observed: -9.8 t yr since 2016; calculated according to eq. 4: -9.3 t yr ). Both modelled and 36 37 254 observed results indicate that ongoing decline in TP is slow compared to intra-annual fluctuations 38 39 255 within the epilimnion (see below). The use of TDP instead of TP from tributaries can partially 40 256 account for differences in the observed and calculated values. However, although the exact 41 42 257 quantification of the fraction of mineralizing particulate P in the tributaries is not known, several 43 44 258 former studies confirmed that its contribution is of minor importance (Gächter and Wehrli 1998; 45 46 259 Moosmann et al. 2006). 47 48 49 260 In the epilimnion, TP decrease is overlaid by strong seasonal dynamics. As indicated in Fig. 1c, 50 261 dissolved P is taken up by phytoplankton during stratification, transported as particulate P in 51 52 262 biomass to the hypolimnion (gross sedimentation), and is then replenished by TP-rich 53 푎푏푠 푎푏푠 54 263 hypolimnetic water (∆푃푤푖푛푡푒푟) during winter mixing. The average value for ∆푃푤푖푛푡푒푟 between 55 56 264 2000 and 2016 was 70 t. This upwelling exceeds the annual external load (푃푖푛) from the 57 58 265 catchment by an order of magnitude. 59 60 61 266 Deep convective winter mixing 62 63 64 65 1 2 3 4 267 Between 1982 and 2016, WMD fluctuated around a mean depth of 78 ± 25 m (Fig. 3a). Notable 5 6 268 excursions from the mean value included (1) a deep mixing event in February 2006, when 7 8 269 cooling-induced convection extended almost to maximum depth and (2) a significant decrease in 9 10 270 deep mixing in the late winters of 2014 and 2015. These two mild winters were preceded by 11 stable lake stratification at the end of autumn. WMD did not correlate with wind exposure (r2 = 12 271 13 272 0.015) indicating that convection represents the primary mechanism for winter mixing. This 14 15 273 finding is supported by the shallow Monin-Obukhov depth (Fig. S4). However, WMD showed 16 17 274 some correlation with higher powers of wind speed integrated over winter periods (r2 = 0.38 for 18 19 275 the sixth power). Hence, occasional strong winter wind events contribute to deep mixing while 20 21 276 average winds do not play a role at all. As expected, WMD correlated negatively with end of 22 277 summer stratification (derived from the measured profile in August; r2 = -0.39; p = 0.02; Fig. 3c) 23 24 278 and with winter temperature (r2 = -0.35; p = 0.04; Fig. 3b). These results are consistent with those 25 26 279 from similar analyses of Lake Geneva (Schwefel et al. 2016) which exhibits a strong correlation 27 28 280 between winter temperature and WMD (r2 = 0.73), but only a weak correlation between winter 29 30 281 wind speed and WMD. One reason for the weaker relationship between Lake Zug’s winter 31 32 282 temperatures and WMD is its stable chemical stratification, which leaves the water column less 33 283 susceptible to external forcing. The strong dependency of WMD on autumn stratification may 34 35 284 also play an important role. 36 37 38 285 Winter mixing and phosphorus upwelling 39 40 41 286 By the end of winter 2015, WMD reached only 35 m, the shallowest value ever observed during 42 regular monitoring (see above). An extraordinarily warm 2014 followed by a mild 2014/2015 43 287 44 288 winter contributed to this record. The observed dependency of WMD on stability and winter 45 46 289 temperatures also increases the likelihood of shallow WMDs under future climatic conditions. As 47 48 290 shown in Fig. 4, a decrease in WMD will affect the amount of TP upwelled to the epilimnion 49 50 291 (∆푃푤푖푛푡푒푟). Regression of WMD data from 1988 to 2016 (a period for which sufficient WMD 51 52 292 and P data exist), gave the following logarithmic relationship: 53 54 293 ∆푃 [%] = 3.5 ln(푊푀퐷 − 17.4). (5) 55 푤푖푛푡푒푟 56 57 294 This relationship describes the estimated TDP upwelling fairly well (r2 = 0.62). 58 59 60 61 62 63 64 65 1 2 3 4 295 Model results 5 6 7 296 The model was calibrated using the PEST (Model-Independent Parameter Estimation and 8 9 297 Uncertainty Analysis) software package (Doherty 2013). Table 2 lists the calibration results for 10 11 298 the model parameters. SIMSTRAT reproduced the temperature structure of Lake Zug with a root 12 299 mean square error (RMSE) of 0.80 °C. The simulation gave RMSE values of 1.76 °C for the 13 14 300 epilimnion, which were considerably higher than those calculated for the hypolimnion (0.42 °C). 15 16 301 Similar to previous 1D-simulations (e.g. Perroud et al. 2009), the highest differences were 17 18 302 observed for the thermocline. The volume-averaged mean temperature estimate of 5.96 °C was in 19 20 303 excellent agreement with the measured value of 6.00 °C for the reference period 1995-2016. 21 22 304 Modelled WMD values were reasonably consistent with observed values for warmer winters 23 305 (which gave shallow WMDs), but the model overestimated the number of winters with very deep 24 25 306 WMDs. To avoid systematic overestimation of WMD, winters in which modelled WMD 26 27 307 exceeded 120 m (five winters in total) were omitted from further analysis. 28 29 30 308 For the IPCC climate scenario A2, which predicts the greatest increases in air temperature, the 31 32 309 simulated WMD decreases from 78 m in the reference scenario to 76 m in 2035, 72 m in 2060, 33 310 and 65 m in 2085. Results based on scenario A1B resembled those based on scenario A2 but 34 35 311 showed less pronounced trends. For example, A2 assumptions result in a 65 m maximum WMD 36 37 312 by 2085, whereas A1B assumptions estimate a 68 m maximum WMD for the same time interval 38 39 313 (Fig. 3a). The WMD results modelled using RCP3CP assumptions do not change for any of the 40 41 314 modelled time periods. Volume-averaged lake temperatures increase from 5.96°C to up to 7.19°C 42 in 2085 for scenario A2. For other climate scenarios, the results were similar but changes at the 43 315 44 316 end of the century were less pronounced than those predicted for scenario A2. Scenarios A1B and 45 46 317 RCP3CP predict respective mean temperatures of 7.00°C and 6.37°C by 2085 (Table 1). Neither 47 48 318 of these showed significant divergence from respective 2060 estimates. 49 50 51 52 53 319 4. DISCUSSION 54 55 56 320 Importance of WMD for P input into the epilimnion 57 58 59 321 Since the 1980s, TP content declined in Lake Zug due to a ban of P in detergents and reduced 60 61 322 application of fertilizers in agriculture. This trend appears throughout the water column but is 62 63 64 65 1 2 3 4 323 overlaid by strong seasonal variation in the epilimnion, where winter mixing causes a pronounced 5 6 324 maximum in TP followed by a continuous decline during the subsequent stratification period 7 8 325 (Fig. 2). The degree of TP depletion observed at the end of the stratified season illustrates the 9 10 326 critical role of internal upwelling by winter mixing in supplying nutrients for biomass production. 11 Winter mixing specifically supplies an estimated 70 t yr-1 of TP to the epilimnion, an amount that 12 327 13 328 greatly exceeds the annual external input of ~6 t yr-1. 14 15 16 329 Winter mixing depth and meteorological forcing 17 18 19 330 Although the dependency of WMD on winter temperatures was found to be weaker in Lake Zug 20 21 331 than in Lake Geneva (Schwefel et al. 2016), WMD still correlated weakly with winter 22 332 temperatures. Autumn stratification has a much stronger impact. Since summer and autumn 23 24 333 temperatures strongly affect autumn stratification, winter temperature variation is not sufficient 25 26 334 for explaining variation of winter mixing of Lake Zug. Wind drives winter mixing in shallow 27 28 335 lakes and plays an important role in generating hypolimnetic currents in deep lakes (MacIntyre et 29 30 336 al. 1999; Rueda et al. 2003), which in turn cause vertical diffusivity (Wüest and Lorke 2003). 31 32 337 While strong wind events may have some influence on WMD, the influence of average wind 33 338 speed however is negligible compared to that of autumn stratification. The weaker than expected 34 35 339 influence of meteorological conditions on WMD may partly result from the weak but permanent 36 37 340 salinity gradient. This gradient dampens WMD fluctuations and prevents winter mixing from 38 39 341 extending below depths of ~100 m, except in the case of extreme cold events such as winter 40 41 342 2005/06. Given ongoing climate warming, the increase in both autumn stratification and winter 42 temperatures will continue to reduce WMD. According to the model, the IPCC climate scenario 43 343 44 344 A2 predicts the largest increase in air temperatures, which will cause a WMD decrease from the 45 46 345 present average of 78 m to 65 m by 2085. 47 48 49 346 Effects of changing WMD on the P budget 50 51 52 347 According to eq. 5, this decrease in WMD results in lower epilimnetic TP concentrations after 53 348 winter mixing (Fig. 4a). The relationship between TP upwelling and mixing depth can 54 55 349 alternatively be calculated from the average TP concentration profile at the end of autumn (red 56 57 350 line in Fig. 4a). The values derived from this method were generally lower than the observed 58 59 351 values. This systematic difference is due to P inflow from the catchment in winter and due to 60 61 352 turbulent upward diffusion. Such additional terms are included in the observed ∆푃푤푖푛푡푒푟 (data 62 63 64 65 1 2 3 4 353 points in Fig. 4; eq. 5), but not in calculations using the averaged end-autumn TP profiles and 5 6 354 lake bathymetry (red line in Fig. 4a). 7 8 9 355 As a result of ongoing oligotrophication, the annual mean total TP content of Lake Zug declined 10 -3 11 356 to ~250 t with a current average epilimnion TP concentration of 64 mg m in spring. Assuming 12 357 no changes in external TP inputs, and using the results from Fig. 4a, the model runs predict a ~5 13 14 358 mg m-3 climate-induced decrease in epilimnetic TP concentration by 2085. While the average 15 16 359 decreases in TP seem to be low, declining epilimnetic P values are especially pronounced in the 17 18 360 case of shallow WMDs (Fig. 4a). While WMD values have fallen below 40 m only twice since 19 20 361 1982 (in 2014 and 2015), extremely shallow WMDs are predicted to become more common with 21 22 362 higher temperatures (e.g. in seven out of 22 winters by 2085 according to IPCC scenario A2B; 23 363 Fig. 3a). For extreme WMD shallowing, the predicted decrease in TP is substantial: a 40 m 24 25 364 mixing depth, for example, causes a 24 % decline from average epilimnetic TP values. 26 27 28 365 Comparison with other lakes 29 30 31 366 With the same method, similar relationships can be established for other lakes given the 32 367 bathymetry and P profiles (Fig. 4b). Assuming a homogeneous mixing and an epilimnion 33 34 368 thickness of 15 m, Lake Geneva shows a behaviour strikingly similar to that of Lake Zug. 35 36 369 Specifically, if WMD of Lake Geneva reaches 40 % of the maximum lake depth, convective 37 38 370 mixing entrains 80 % of the lake’s TP content and distributes it into the epilimnetic P reservoir. 39 40 371 Therefore, a further increase in WMD exerts only a minor influence on the epilimnetic P budgets 41 42 372 of Lake Geneva. The mean WMD of Lake Geneva currently reaches ~180 m, or 58 % of the 43 373 maximum depth (Schwefel et al. 2016) and is therefore below the depth at which WMD can 44 45 374 significantly affect P supply into the epilimnion. For comparison, in Lake Zug, WMD advances 46 47 375 to an average depth of 78 m (39 % of the maximum) where small variations still exert a strong 48 49 376 influence on TP in the epilimnion (Fig. 4a). A predicted decline or shallowing of the WMD by 13 50 51 377 m or 6 % leads to a predicted decrease in epilimnetic TP upwelling of 7 %. In Lake Geneva, a 6 52 53 378 % decrease in WMD would result in only a 4 % change of TP upwelling. 54 55 379 The relationship between nutrient input into the upper layer of Lake Tanganyika and mixing 56 57 380 resembles that of temperate lakes investigated in this study. Our assumptions (e.g. an epilimnion 58 59 381 depth of 15 m and horizontally homogenous mixing throughout the lake area) however do not 60 61 382 apply to such a lake. Values for Lake Tanganyika should only be interpreted as a sensitivity 62 63 64 65 1 2 3 4 383 measure of the lake experiencing changes in the deep mixing during the dry season. Compared to 5 6 384 Lake Zug, Lake Tanganyika (1470 m deep) is permanently stratified and mixes seasonally down 7 8 385 to 200 to 300 m depth (Coulter 1968; O’Reilly et al. 2003). In lakes where wind is the dominant 9 10 386 driver of water column mixing, increased surface temperatures cause shallower mixing depths 11 due to stronger surface stratification. Therefore, observational evidence indicates that mixing 12 387 13 388 depths may have decreased for Lake Tanganyika as well even if convection is not the dominant 14 15 389 driver of vertical mixing in this system (O’Reilly et al. 2003). A reduction in WMD by ~100 m, 16 17 390 as proposed by O’Reilly et al. (2003) would cause a decline in Pwinter from 53 % to 34 % of the 18 19 391 maximal possible input. This decline exceeds that predicted for Lake Zug by a factor of almost 20 21 392 three assuming the same changes in relative mixing depth. 22 23 393 The deepest parts of Lake Zürich have TP concentrations up to 15 times higher than the overall 24 25 394 volume average (Fig. 4b). This strong TP gradient in the water column explains the difference 26 27 395 between Lake Zürich and the other lakes shown in Fig. 4b. Due to warmer temperatures in the 28 29 396 last few years, the WMD of Lake Zürich ranged between 60 and 100 m (i.e. 44 to 73% of 30 31 397 maximum lake depth). In contrast, the lake mixed almost completely on a regular basis before the 32 33 398 1990s (Yankova et al. 2017). As shown in Fig. 4b, this corresponds to a decrease in TP upwelling 34 399 to the epilimnion from the hypolimnion of up to ~40 %. Yankova et al. (2017) investigated 35 36 400 dissolved inorganic P patterns in Lake Zürich since 1977 and found an even greater decrease. The 37 38 401 results however are not directly comparable to those reported here since dissolved inorganic P in 39 40 402 Lake Zürich is strongly influenced by the prevalent and predominant cyanobacterium 41 42 403 Planktothrix rubescens. Regardless of whether TP or dissolved inorganic P is considered, the 43 44 404 decrease in WMD over the last few years has caused a significant reduction in P upwelling to the 45 405 productive surface layer and has thus enhanced the ongoing oligotrophication. 46 47 48 406 Conclusion and Outlook 49 50 51 407 The four examples described above illustrate that shifts in winter mixing depth can lead to very 52 53 408 different outcomes depending on the lake system considered. These differences however can be 54 409 explained by basic factors such as TP profiles throughout the water column, lake bathymetry, and 55 56 410 relative mixing depth. In general, climate warming will result in reduced nutrient availability in 57 58 411 spring which is the limiting factor for primary production. Hence, reduced WMD will – in 59 60 412 parallel with the ongoing reoligotrophication – lead to a reduction in algal growth in lakes. 61 62 63 64 65 1 2 3 4 413 However, changes in nutrient availability may also cause shifts in phytoplankton communities, 5 6 414 which in turn affect nutrient budgets. These can cause still even more complex nutrient level 7 8 415 responses to climate warming. This study also focused solely on TP response in the epilimnion, 9 10 416 contrasting trends may occur in the hypolimnion. Reduced WMD leads to accumulation of TP in 11 deeper layers (Yankova et al. 2017) and may cause hypoxic or even anoxic conditions that can 12 417 13 418 enhance P release from lake sediments (O’Reilly et al. 2003; Sahoo et al. 2013; Schwefel et al. 14 15 419 2016). While this mechanism would not affect primary production in Lake Zug during periods 16 17 420 when mixing reaches depths of only ~70 m, extremely cold winters could cause occasional deep 18 19 421 mixing that would transport significant amounts of TP into the epilimnion. By contrast, mild 20 21 422 winters, which are predicted to occur more frequently in the future, may cause the opposite 22 423 cascade of shallow WMD leading to limited epilimnetic P input and associated reduction of 23 24 424 primary productivity in spring. With reduced nutrient input during winter mixing, inputs from the 25 26 425 catchment might become more influential than they are in the current system. Either way, climate 27 28 426 change will introduce greater variation in mixing depths, epilimnetic TP concentrations, and 29 30 427 primary productivity. 31 32 428 33 34 429 ACKNOWLEDGMENTS 35 36 37 430 We sincerely thank then anonymous reviewers and the editor for their valuable comments. In 38 431 addition, we are grateful to René Gächter for valuable comments to an earlier version of the 39 40 432 manuscript. We also acknowledge the support of the Amt für Umweltschutz Zug, especially 41 433 Bruno Mathis and Peter Keller, who provided observational data from Lake Zug. Long-term 42 43 434 monitoring data for Lake Geneva were provided by the Commission International pour la 44 435 Protection des Eaux du Léman (CIPEL) and by the SOERE OLA Information System (http://si- 45 436 ola.inra.fr), INRA Thonon-les-Bains. Data from Lake Zürich were provided by AWEL Zürich. 46 47 437 The CH2011 climate predictions were obtained from the Center for Climate Systems Modeling 48 438 (C2SM; www.ch2011.ch). Meteorological data of this study are archived and distributed by the 49 50 439 Swiss Federal Office of Meteorology and Climatology, MeteoSwiss (obtained via their data 51 440 portal, IDAWEB). Additional information on the data can be provided on request by 52 441 [email protected]. 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Sci Rep 7:13770 19 20 563 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 564 44 45 565 Fig. 1. a) Photo of Lake Zug. b) Illustration of the nutrient exchange during winter mixing. During winters with 46 566 shallow mixing, fewer nutrients are transported into the epilimnion. c) Schematic diagram illustrating the relevant 47 567 processes affecting the epilimnetic P budget of lakes. d) Map of Lake Zug (with arrow indicating outflow) along with 48 568 the location of the meteorological station and the sampling site for the deepest location in the lake (map data from 49 Swisstopo DV 5704 000 000, reproduced with permission from Swisstopo / JA100119). The bottom right corner 50 569 51 570 shows a map of Switzerland with the position of Lake Zug marked as black dot. 52 53 571 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 572 25 26 573 Fig. 2. TP content (a) and volume-weighted average TP concentration (b) for Lake Zug over the entire water column 27 574 (blue) and the epilimnion (green) from 1982 to present. 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 575 34 35 576 Fig. 3. (a) Time series for observed winter mixing depth (WMD) 1995-2016 (black squares). Red circles represent 36 577 the model results for the same period under increased air temperatures expected by 2035, 2060 and 2085 according 37 578 to the IPCC climate scenario A2. (b) WMD as a function of temperature hours (as time integral of the temperature 38 o 39 579 relative to 0 C over the winter period), and (c) WMD as a function of Schmidt stability at the end of summer 40 580 (August). As in panel a, black squares refer to the observed values, red circles represent model results. 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 581 24 25 582 Fig. 4. a) Measured ∆푃푤푖푛푡푒푟 for Lake Zug as a function of winter mixing depth (WMD; black dots). The blue line 26 583 shows the logarithmic fit ∆푃 [%] = 3.5 ln (푊푀퐷 − 17.4). The red line shows expected phosphorus upwelling 27 푤푖푛푡푒푟 28 584 (see methods section for calculation). b) Relative P flux into the upper 15 m of Lakes Zug, Zürich, Geneva, and 29 585 Tanganyika as a function of the relative depth (maximum depth = 100 %). The maximum TP input corresponds to 30 586 complete homogenization of the lake. 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 587 Table 1. Mean air temperature increase, Tair (°C), volume-averaged annual mean lake temperatures 퐓̅ (°C), and 5 588 winter mixing depth, WMD (m), for different IPCC scenarios. Table S1 lists the seasonal variation in air 6 589 temperatures. 7 8 IPCC Reference 2035 2060 2085 9 Scenario (1995 to 2016) 10 11 퐓̅ 퐖퐌퐃 Tair 퐓̅ 퐖퐌퐃 Tair 퐓̅ 퐖퐌퐃 Tair 퐓̅ W퐌퐃 12 A2 6.00 (meas) 76 (meas) +1.06 6.29 76 +2.22 6.67 72 +3.74 7.19 65 13 A1B +1.18 6.33 80 +2.27 6.99 74 +3.20 7.00 68 14 5.96 (mod) 78 (mod) 15 RCP3CP +1.14 6.32 81 +1.33 6.38 79 +1.36 6.37 80 16 590 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 591 Table 2. Non-dimensional calibration parameters used in the SIMSTRAT numerical lake model (Goudsmit et al. 5 592 2002) 6 7 Parameter Description Value [non-dim.] 8 α Fraction of wind energy transferred to seiche energy 0.0020 9 10 p1 Correction factor for absorption of infrared radiation 1.136 11 p2 Correction factor for sensible heat flux 1.286 12 qnn Distribution coefficient for seiche energy 1.238 13 C Wind drag coefficient 10 m above water 0.0029 14 10 15 593 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Figure 1 Click here to access/download;Figure;Figure_1_new..eps Figure 2 Click here to access/download;Figure;Figure_2_new.eps Figure 3 Click here to access/download;Figure;Figure_3_new.eps Figure 4 Click here to access/download;Figure;Figure_4_new.eps Supporting Information Click here to access/download;Attachment to manuscript;Schwefel_Lake_Zug_SI_V2.docx

1 2 3 4 Global warming affects nutrient upwelling in deep lakes 5 6 Supporting Information 7 8 9 10 11 Robert Schwefel1*, Beat Müller2, Hélène Boisgontier1, and Alfred Wüest1,2 12 13 1Physics of Aquatic Systems Laboratory, Margaretha Kamprad Chair, École Polytechnique 14 15 Fédérale de Lausanne, Institute of Environmental Engineering, Lausanne, Switzerland. 16 17 2Eawag, Swiss Federal Institute of Aquatic Science and Technology, Surface Waters – Research 18 and Management, Kastanienbaum, Switzerland. 19 20 21 22 23 Content 24 25 Table S1: Seasonal changes in future air temperatures according to IPCC emission scenarios. 26 27 28 Figure S1: Histogram of wind speed at Lake Zug. 29 30 Figure S2: Concentrations of total phosphorus, total dissolved phosphorus and dissolved 31 inorganic phosphorus end of winter and in summer. 32 33 34 Figure S3: Euphotic zone depth of Lake Zug. 35 36 Figure S4: Monin-Obukhov length for Lake Zug. 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 Table S1: Seasonal changes in air temperatures according to different IPCC emission scenarios for the years 2035, 2060 and 7 2085. The predictions were taken from the climate report CH2011. 8 9 Scenario ΔT ΔT ΔT ΔT ΔT 10 March – May June – August Sept. – Nov. Dec. – Feb. average 11 A2 2035 +0.83 +1.15 +1.12 +1.13 +1.06 12 A2 2060 +1.95 +2.54 +2.14 +2.26 +2.22 13 A2 2085 +3.27 +4.36 +3.69 +3.64 +3.74 14 A1B 2035 +0.94 +1.28 +1.25 +1.26 +1.18 15 A1B 2060 +1.99 +2.59 +2.18 +2.31 +2.27 16 A1B 2085 +2.77 +3.74 +3.16 +3.12 +3.20 17 RCP3PD 2035 +0.89 +1.21 +1.19 +1.26 +1.14 18 RCP3PD 2060 +1.19 +1.55 +1.30 +1.38 +1.33 19 RCP3PD 2085 +1.18 +1.59 +1.35 +1.33 +1.36 20 21 22

23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Figure S1: Histogram of hourly measured wind speed at the meteorological station Cham (Fig. 1d) between June 1993 and 50 July 2017 (in total 43,340 measurement points). In 78% of the measurements, wind speed is lower than 2 m s-1. 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 Figure S2: Concentrations of dissolved inorganic phosphorus, total dissolved phosphorus and total phosphorus after 30 convective winter mixing (left) and in summer (right). 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 Figure S1: Euphotic zone depth of Lake Zug defined as depth where 99% of the light is absorbed. Absorption coefficients were 59 calculated based on Secchi depth according to Fink et al. (2014b) and were also used for the 1D model SIMSTRAT. 60 61

62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 Figure S4: Monin-Obukhov length calculated for the years 2000 to 2003. Monin-Obukhov length was calculated as 푳푴푶 = ퟑ 26 흆풂풊풓푪ퟏퟎ ퟑ/ퟐ 풖ퟏퟎ −( ) . The air (water) density is denoted as 흆풂풊풓 (흆풘풂풕풆풓), the drag coefficient as 푪ퟏퟎ, the van Kármán constant k 27 흆풘풂풕풆풓 풌 푱푩 휶품 28 (=0.41) and 푱푩 = 푯풏풆풕 with the gravity acceleration constant g, the thermal expansivity α and the specific heat 흆풘풂풕풆풓풄풑 29 흆 풄 . The heat flux was calculated according to Fink et al. (2014a). Values were calculated based on observational data 30 풘풂풕풆풓 풑 31 as daily averages. Surface temperature was not available on a daily basis and had to be interpolated from monthly lake 32 measurements. Hence, some of the larger outlier might have been caused by too sparse surface temperature data. 33 34 35 36 37 38 39 40 41 42 43 References 44 45 Fink G, Schmid M, Wahl B, Wolf T, Wüest A (2014a) Heat flux modifications related to climate- 46 47 induced warming of large European lakes. Water Resour Res 50:2072–2085. doi: 48 10.1002/2013WR014448 49 50 Fink G, Schmid M, Wüest A (2014b) Large lakes as sources and sinks of anthropogenic heat: 51 Capacities and limits. Water Resour Res 50:7285–7301. doi: 10.1002/2014WR015509 52 53 54 55 56 57 58 59 60 61 62 63 64 65