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Provisioning a sufficient stable source of food requires sound knowledge about current and upcoming threats to agricultural production. To that end machine learning approaches were used to identify the prevailing climatic and soil hydrological drivers of spatial and temporal yield variability of four crops, comprising 40 years yield data each from 351 counties in Germany. Effects of progress in agricultural management and breeding were subtracted from the data prior the machine learning modelling by fitting smooth non-linear trends to the 95th percentiles of observed yield data. An extensive feature selection approach was followed then to identify the most relevant predictors out of a large set of candidate predictors, comprising various soil and meteorological data. Particular emphasis was placed on studying the uniqueness of identified key predictors. Random Forest and Support Vector Machine models yielded similar although not identical results, capturing between 50% and 70% of the spatial and temporal variance of silage maize, winter barley, winter rapeseed and winter wheat yield. Equally good performance could be achieved with different sets of predictors. Thus identification of the most reliable models could not be based on the outcome of the model study only but required expert's judgement. Relationships between drivers and response often exhibited optimum curves, especially for summer air temperature and precipitation. In contrast, soil moisture clearly proved less relevant compared to meteorological drivers. In view of the expected climate change both excess precipitation and the excess heat effect deserve more attention in breeding as well as in crop modelling.
Local biodiversity patterns are expected to strongly reflect variation in topography, land use, dispersal boundaries, nutrient supplies, contaminant spread, management practices, and other anthropogenic influences. Contrary to this expectation, studies focusing on specific taxa revealed a biodiversity homogenization effect in areas subjected to long-term intensive industrial agriculture. We investigated whether land use affects biodiversity levels and community composition (alpha- and beta-diversity) in 67 kettle holes (KH) representing small aquatic islands embedded in the patchwork matrix of a largely agricultural landscape comprising grassland, forest, and arable fields. These KH, similar to millions of standing water bodies of glacial origin, spread across northern Europe, Asia, and North America, are physico-chemically diverse and differ in the degree of coupling with their surroundings. We assessed aquatic and sediment biodiversity patterns of eukaryotes, Bacteria, and Archaea in relation to environmental features of the KH, using deep-amplicon-sequencing of environmental DNA (eDNA). First, we asked whether deep sequencing of eDNA provides a representative picture of KH aquatic biodiversity across the Bacteria, Archaea, and eukaryotes. Second, we investigated if and to what extent KH biodiversity is influenced by the surrounding land use. We hypothesized that richness and community composition will greatly differ in KH from agricultural land use compared with KH in grasslands and forests. Our data show that deep eDNA amplicon sequencing is useful for in-depth assessments of cross-domain biodiversity comprising both micro- and macro-organisms, but has limitations with respect to single-taxa conservation studies. Using this broad method, we show that sediment eDNA, integrating several years to decades, depicts the history of agricultural land-use intensification. Aquatic biodiversity was best explained by seasonality, whereas land-use type explained little of the variation. We concluded that, counter to our hypothesis, land use intensification coupled with landscape wide nutrient enrichment (including atmospheric deposition), groundwater connectivity between KH and organismal (active and passive) dispersal in the tight network of ponds, resulted in a biodiversity homogenization in the KH water, leveling off today's detectable differences in KH biodiversity between land-use types. These findings have profound implications for measures and management strategies to combat current biodiversity loss in agricultural landscapes worldwide.
Land-use type temporarily affects active pond community structure but not gene expression patterns
(2022)
Changes in land use and agricultural intensification threaten biodiversity and ecosystem functioning of small water bodies. We studied 67 kettle holes (KH) in an agricultural landscape in northeastern Germany using landscape-scale metatranscriptomics to understand the responses of active bacterial, archaeal and eukaryotic communities to land-use type. These KH are proxies of the millions of small standing water bodies of glacial origin spread across the northern hemisphere. Like other landscapes in Europe, the study area has been used for intensive agriculture since the 1950s. In contrast to a parallel environmental DNA study that suggests the homogenization of biodiversity across KH, conceivably resulting from long-lasting intensive agriculture, land-use type affected the structure of the active KH communities during spring crop fertilization, but not a month later. This effect was more pronounced for eukaryotes than for bacteria. In contrast, gene expression patterns did not differ between months or across land-use types, suggesting a high degree of functional redundancy across the KH communities. Variability in gene expression was best explained by active bacterial and eukaryotic community structures, suggesting that these changes in functioning are primarily driven by interactions between organisms. Our results indicate that influences of the surrounding landscape result in temporary changes in the activity of different community members. Thus, even in KH where biodiversity has been homogenized, communities continue to respond to land management. This potential needs to be considered when developing sustainable management options for restoration purposes and for successful mitigation of further biodiversity loss in agricultural landscapes.
The curse of the past
(2021)
One challenge for modern agricultural management schemes is the reduction of harmful effects on the envi-ronment, e.g. in terms of the emission of nutrients. Sampling the effluent of tile drains is a very efficient way to sample seepage water from larger areas directly underneath the main rooting zone. Time series of solute con-centration in tile drains can be linked to agricultural management data and thus indicate the efficacy of individual management measures. To that end, the weekly runoff and solute concentration were determined in long-term measurement campaigns at 25 outlets of artificial tile drains at 19 various arable fields in the German federal state of Mecklenburg-Vorpommern. The study sites were distributed within a 23,000 km(2) region and were deemed representative of intense arable land use. In addition, comprehensive meteorological and man-agement data were provided. To disentangle the different effects, monitoring data were subjected to a principal component analysis. Loadings on the prevailing principal components and spatial and temporal patterns of the component scores were considered indicative of different processes. Principal component scores were then related to meteorological and management data via random forest modelling. Hydrological conditions and weather were identified as primary driving forces for the nutrient discharge behaviour of the drain plots, as well as the nitrogen balance. In contrast, direct effects of recent agricultural management could hardly be identified. Instead, we found clear evidence of the long-term and indirect effects of agriculture on nearly all solutes. We conclude that tile drain effluent quality primarily reflected the soil-internal mobilisation or de-mobilisation of nutrients and related solutes rather than allowing inferences to be drawn about recent individual agricultural management measures. On the other hand, principal component analysis revealed a variety of indirect and long-term effects of fertilisation on solutes other than nitrogen or phosphorus that are still widely overlooked in nutrient turnover studies.
This study investigates possible impacts of four global warming levels (GWLs: GWL1.5, GWL2.0, GWL2.5, and GWL3.0) on drought characteristics over Niger River basin (NRB) and Volta River basin (VRB). Two drought indices-Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI)-were employed in characterizing droughts in 20 multi-model simulation outputs from the Coordinated Regional Climate Downscaling Experiment (CORDEX). The performance of the simulation in reproducing basic hydro-climatological features and severe drought characteristics (i.e., magnitude and frequency) in the basins were evaluated. The projected changes in the future drought frequency were quantified and compared under the four GWLs for two climate forcing scenarios (RCP8.5 and RCP4.5). The regional climate model (RCM) ensemble gives a realistic simulation of historical hydro-climatological variables needed to calculate the drought indices. With SPEI, the simulation ensemble projects an increase in the magnitude and frequency of severe droughts over both basins (NRB and VRB) at all GWLs, but the increase, which grows with the GWLs, is higher over NRB than over VRB. More than 75% of the simulations agree on the projected increase at GWL1.5 and all simulations agree on the increase at higher GWLs. With SPI, the projected changes in severe drought is weaker and the magnitude remains the same at all GWLs, suggesting that SPI projection may underestimate impacts of the GWLs on the intensity and severity of future drought. The results of this study have application in mitigating impact of global warming on future drought risk over the regional water systems.
Large-scale crop yield failures are increasingly associated with food price spikes and food insecurity and are a large source of income risk for farmers. While the evidence linking extreme weather to yield failures is clear, consensus on the broader set of weather drivers and conditions responsible for recent yield failures is lacking. We investigate this for the case of four major crops in Germany over the past 20 years using a combination of machine learning and process-based modelling. Our results confirm that years associated with widespread yield failures across crops were generally associated with severe drought, such as in 2018 and to a lesser extent 2003. However, for years with more localized yield failures and large differences in spatial patterns of yield failures between crops, no single driver or combination of drivers was identified. Relatively large residuals of unexplained variation likely indicate the importance of non-weather related factors, such as management (pest, weed and nutrient management and possible interactions with weather) explaining yield failures. Models to inform adaptation planning at farm, market or policy levels are here suggested to require consideration of cumulative resource capture and use, as well as effects of extreme events, the latter largely missing in process-based models. However, increasingly novel combinations of weather events under climate change may limit the extent to which data driven methods can replace process-based models in risk assessments.
Natural ponds are perceived as spatially and temporally highly variable ecosystems. This perception is in contrast to the often-applied sampling design with high spatial but low temporal replication. Based on a data set covering a period of six years and 20 permanently to periodically inundated ponds, we investigated whether this widely applied sampling design is sufficient to identify differences between single ponds or single years with regard to water quality and macrophyte community composition as measures of ecosystem integrity.
In our study, the factor "pond", which describes differences between individual ponds, explained 56 % and 63 %, respectively, of the variance in water quality and macrophyte composition. In contrast, the factor "year" that refers to changes between individual years, contributed less to understand the observed variability in water quality and macrophyte composition (10 % and 7 % respectively, of the variance explained). The low explanation of variance for "year" and the low year-to-year correlation for the single water quality parameter or macrophyte coverage values, respectively, indicated high but non-consistent temporal variability affecting individual pond patterns.
In general, the results largely supported the ability of the widely applied sampling strategy with about one sampling date per year to capture differences in water quality and macrophyte community composition between ponds. Hence, future research can be rest upon sampling designs that give more weight to the number of ponds than the number of years in dependence on the research question and the available resources. Nonetheless, pond research would miss a substantial amount of information (7 to 10 % of the variance explained), when the sampling would generally be restricted to one year. Moreover, we expect that the importance of multiple-year sampling will likely increase in periods and regions of higher hydrological variability compared to the average hydrological conditions encountered in the studied period.
This study investigates possible impacts of four global warming levels (GWLs: GWL1.5, GWL2.0, GWL2.5, and GWL3.0) on drought characteristics over Niger River basin (NRB) and Volta River basin (VRB). Two drought indices-Standardized Precipitation Index (SPI) and Standardized Precipitation-Evapotranspiration Index (SPEI)-were employed in characterizing droughts in 20 multi-model simulation outputs from the Coordinated Regional Climate Downscaling Experiment (CORDEX). The performance of the simulation in reproducing basic hydro-climatological features and severe drought characteristics (i.e., magnitude and frequency) in the basins were evaluated. The projected changes in the future drought frequency were quantified and compared under the four GWLs for two climate forcing scenarios (RCP8.5 and RCP4.5). The regional climate model (RCM) ensemble gives a realistic simulation of historical hydro-climatological variables needed to calculate the drought indices. With SPEI, the simulation ensemble projects an increase in the magnitude and frequency of severe droughts over both basins (NRB and VRB) at all GWLs, but the increase, which grows with the GWLs, is higher over NRB than over VRB. More than 75% of the simulations agree on the projected increase at GWL1.5 and all simulations agree on the increase at higher GWLs. With SPI, the projected changes in severe drought is weaker and the magnitude remains the same at all GWLs, suggesting that SPI projection may underestimate impacts of the GWLs on the intensity and severity of future drought. The results of this study have application in mitigating impact of global warming on future drought risk over the regional water systems.
Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected "normal" behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the "stable" principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
Understanding the hydrologic connectivity between kettle holes and shallow groundwater, particularly in reaction to the highly variable local meteorological conditions, is of paramount importance for tracing water in a hydro(geo)logically complex landscape and thus for integrated water resource management. This article is aimed at identifying the dominant hydrological processes affecting the kettle holes' water balance and their interactions with the shallow groundwater domain in the Uckermark region, located in the north-east of Germany. For this reason, based on the stable isotopes of oxygen (delta O-18) and hydrogen (delta H-2), an isotopic mass balance model was employed to compute the evaporative loss of water from the kettle holes from February to August 2017. Results demonstrated that shallow groundwater inflow may play the pivotal role in the processes taking part in the hydrology of the kettle holes in the Uckermark region. Based on the calculated evaporation/inflow (E/I) ratios, most of the kettle holes (86.7%) were ascertained to have a partially open, flow-through-dominated system. Moreover, we identified an inverse correlation between E/I ratios and the altitudes of the kettle holes. The same holds for electrical conductivity (EC) and the altitudes of the kettle holes. In accordance with the findings obtained from this study, a conceptual model explaining the interaction between the shallow groundwater and the kettle holes of Uckermark was developed. The model exhibited that across the highest altitudes, the recharge kettle holes are dominant, where a lower ratio of E/I and a lower EC was detected. By contrast, the lowest topographical depressions represent the discharge kettle holes, where a higher ratio of E/I and EC could be identified. The kettle holes existing in between were categorized as flow-through kettle holes through which the recharge takes place from one side and discharge from the other side.
Understanding the hydrologic connectivity between kettle holes and shallow groundwater, particularly in reaction to the highly variable local meteorological conditions, is of paramount importance for tracing water in a hydro(geo)logically complex landscape and thus for integrated water resource management. This article is aimed at identifying the dominant hydrological processes affecting the kettle holes' water balance and their interactions with the shallow groundwater domain in the Uckermark region, located in the north-east of Germany. For this reason, based on the stable isotopes of oxygen (delta O-18) and hydrogen (delta H-2), an isotopic mass balance model was employed to compute the evaporative loss of water from the kettle holes from February to August 2017. Results demonstrated that shallow groundwater inflow may play the pivotal role in the processes taking part in the hydrology of the kettle holes in the Uckermark region. Based on the calculated evaporation/inflow (E/I) ratios, most of the kettle holes (86.7%) were ascertained to have a partially open, flow-through-dominated system. Moreover, we identified an inverse correlation between E/I ratios and the altitudes of the kettle holes. The same holds for electrical conductivity (EC) and the altitudes of the kettle holes. In accordance with the findings obtained from this study, a conceptual model explaining the interaction between the shallow groundwater and the kettle holes of Uckermark was developed. The model exhibited that across the highest altitudes, the recharge kettle holes are dominant, where a lower ratio of E/I and a lower EC was detected. By contrast, the lowest topographical depressions represent the discharge kettle holes, where a higher ratio of E/I and EC could be identified. The kettle holes existing in between were categorized as flow-through kettle holes through which the recharge takes place from one side and discharge from the other side.
Groundwater levels are monitored by environmental agencies to support the sustainable use of groundwater resources. For this purpose continuous and spatially comprehensive monitoring in high spatial and temporal resolution is desired. This leads to large datasets that have to be checked for quality and analysed to distinguish local anthropogenic influences from natural variability of the groundwater level dynamics at each well. Both technical problems with the measurements as well as local anthropogenic influences can lead to local anomalies in the hydrographs. We suggest a fast and efficient screening method for the identification of well-specific peculiarities in hydrographs of groundwater head monitoring networks. The only information required is a set of time series of groundwater heads all measured at the same instants of time. For each well of the monitoring network a reference hydrograph is calculated, describing expected “normal” behaviour at the respective well as is typical for the monitored region. The reference hydrograph is calculated by multiple linear regression of the observed hydrograph with the “stable” principal components (PCs) of a principal component analysis of all groundwater head series of the network as predictor variables. The stable PCs are those PCs which were found in a random subsampling procedure to be rather insensitive to the specific selection of the analysed observation wells, i.e. complete series, and to the specific selection of measurement dates. Hence they can be considered to be representative for the monitored region in the respective period. The residuals of the reference hydrograph describe local deviations from the normal behaviour. Peculiarities in the residuals allow the data to be checked for measurement errors and the wells with a possible anthropogenic influence to be identified. The approach was tested with 141 groundwater head time series from the state authority groundwater monitoring network in northeastern Germany covering the period from 1993 to 2013 at an approximately weekly frequency of measurement.
During the last decades, increasing exports of both dissolved organic carbon (DOC) and iron were observed from peat catchments in North America and Europe with potential consequences for water quality of streamwater and carbon storages of soils. As mobilisation and transport processes of DOC and iron in peat catchments are only partly understood, the purpose of this study was to elucidate these processes in an intensively monitored and studied system. Specifically, it was hypothesised that dissimilatory iron reduction in riparian peatland soils mobilises DOC initially adsorbed to iron minerals. During stormflow conditions, both DOC and iron will be transported into the stream network. Ferrous iron may be reoxidised at redox interfaces on its way to the stream, and subsequently, ferric iron could be transported together with DOC as complexes. To test these hypotheses, generalised additive models (GAMs) were applied to 14 years of weekly time series of discharge and concentrations of selected solutes measured in a German headwater stream called Lehstenbach. This stream drains a 4.19-km(2) forested mountain catchment; one third of which is covered by riparian peatland soils. We interpreted results of different types of GAM in the way that (a) iron reduction drove the mobilisation of DOC from peatland soils and that (b) both iron and DOC were transported as complexes after their joint mobilisation to and within the steam. It was speculated that low nitrate availability in the uppermost wetland soil layer, particularly during the growing season, promoted iron reduction and thus the mobilisation of DOC. However, the influence of nitrate on the DOC mobilisation remains relatively uncertain. This influence could be further investigated using methods similar to the GAM analysis conducted here for other catchments with long-term data as well as detailed measurements of the relevant species in riparian wetland soils and the adjacent stream network.
Due to increasing demands for irrigation using groundwater as a source there is an urgent need for efficient methods that shed light on the resulting anthropogenic impacts on the connected aquifers. Thus an innovative approach is introduced, that aims to identify predominant geochemical changes in the groundwater system. The approach involves a principal component analysis as a promising tool to disentangle the effects of different impacts and even to give a quantitative assessment of the respective effect strength at each site. The study was applied in an irrigation region of the Nuthe River Basin, State Brandenburg, Northeast Germany. The results identify the negative impacts on the groundwater quality in the aquifer used for irrigation. A decrease of shallow groundwater quality under irrigation due to contamination with fertilizers (NO3, Cl, K, Na) and a slight shift in the redox system is indicated. Beside this direct impact on the shallow groundwater a long-term impact on a deeper groundwater resource could be identified. There is clear evidence, that the contamination is not restricted to the shallow groundwater but that extraction from deeper wells increasingly includes deeper, uncontaminated groundwater resources into the local irrigation cycle. The approach can be used as a basic tool for the adaptation of sustainable agricultural irrigation management strategies.
Human-driven fragmentation of landscapes leads to the formation of transition zones between ecosystems that are characterised by fluxes of matter, energy and information. These transition zones may offer rather inhospitable habitats that could jeopardise biodiversity. On the other hand, transition zones are also reported to be hotspots for biodiversity and even evolutionary processes. The general mechanisms and influence of processes in transition zones are poorly understood. Although heterogeneity and diversity of land use of fragments and the transition zones between them play an important role, most studies only refer to forested transition zones. Often, only an extrapolation of measurements in the different fragments themselves is reported to determine gradients in transition zones. This paper contributes to a quantitative understanding of agricultural landscapes beyond individual ecotopes, and towards connected ecosystem mosaics that may be beneficial for the provision of ecosystem services.
In crop modeling and yield predictions, the heterogeneity of agricultural landscapes is usually not accounted for. This heterogeneity often arises from landscape elements like forests, hedges, or single trees and shrubs that cast shadows. Shading from forested areas or shrubs has effects on transpiration, temperature, and soil moisture, all of which affect the crop yield in the adjacent arable land. Transitional gradients of solar irradiance can be described as a function of the distance to the zero line (edge), the cardinal direction, and the height of trees. The magnitude of yield reduction in transition zones is highly influenced by solar irradiance-a factor that is not yet implemented in crop growth models on a landscape level. We present a spatially explicit model for shading caused by forested areas, in agricultural landscapes. With increasing distance to forest, solar irradiance and yield increase. Our model predicts that the shading effect from the forested areas occurs up to 15 m from the forest edge, for the simulated wheat yields, and up to 30 m, for simulated maize. Moreover, we estimated the spatial extent of transition zones, to calculate the regional yield reduction caused by shading of the forest edges, which amounted to 5% to 8% in an exemplary region.
Many hydrological models have been calibrated and validated using hydrographs alone. Because streamflow integrates water fluxes in space, many distributed hydrological models tend to have multiple feasible descriptions of hydrological processes. This equifinality usually leads to substantial prediction uncertainty. In this study, additional constraintsnamely, the spatial patterns of long-term average evapotranspiration (ET), shallow groundwater level, and land cover changewere used to investigate the reduction of equifinality and prediction uncertainty in the Soil and Water Assessment Tool (SWAT) in the Wami River basin in Tanzania. The additional constraints were used in the set-up, parameter emulation and calibration of the SWAT model termed an improved hydrological model (IHM). The IHM was then compared with a classical hydrological model (CHM) that was also developed using the SWAT model but without additional constraints. In the calibration, the CHM used only the hydrograph, but the IHM used the hydrograph and the spatial pattern of long-term average ET as an additional constraint. The IHM produced a single, unique behavioural simulation, whereas the CHM produced many behavioural simulations that resulted in prediction uncertainty. The performance of the IHM with respect to the hydrograph was more consistent than that of the CHM, and the former clearly captured the mean behaviour of ET in the river basin. Therefore, we conclude that additional constraints substantially reduce equifinality and prediction uncertainty in a distributed hydrological model.
West Africa has been afflicted by droughts since the declining rains of the 1970s. Therefore, this study examines the characteristics of drought over the Niger River Basin (NRB), investigates the influence of the drought on the river flow, and projects the impacts of future climate change on drought. A combination of observation data and regional climate simulations of past (1986-2005) and future climates (2046-2065 and 2081-2100) were analyzed. The standardized precipitation index (SPI) and standardized precipitation and evapotranspiration index (SPEI) were used to characterize drought while the standardized runoff index (SRI) was used to quantify river flow. Results of the study show that the historical pattern of drought is consistent with previous studies over the Basin and most part of West Africa. RCA4 ensemble gives realistic simulations of the climatology of the Basin in the past climate. Generally, an increase in drought intensity and frequency are projected over NRB. The coupling between SRI and drought indices was very strong (P < 0.05). The dominant peaks can be classified into three distinct drought cycles with periods 1-2, 2-4, 4-8 years. These cycles may be associated with Quasi-Biennial Oscillation (QBO) and El-Nino Southern Oscillation (ENSO). River flow was highly sensitive to precipitation in the NRB and a 1-3 month lead time was found between drought indices and SRI. Under RCP4.5, changes in the SPEI drought frequency range from 1.8 (2046-2065) to 2.4 (2081-2100) month year(-1) while under RCP8.5, the change ranges from 2.2 (2046-2065) to 3.0 month year(-1) (2081-2100). Niger Middle sub-basin is likely to be mostly impacted in the future while the Upper Niger was projected to be least impacted. Results of this study may guide policymakers to evolve strategies to facilitate vulnerability assessment and adaptive capacity of the basin in order to minimize the negative impacts of climate change.
We applied coarse spectral analysis to more than 2 decades of daily near-surface water temperature (WT) measurements from Muggelsee, a shallow polymictic lake in Germany, to systematically characterize patterns in WT variability from daily to yearly temporal scales. Comparison of WT with local air temperature indicates that the WT variability patterns are likely attributable to both meteorological forcing and internal lake dynamics. We identified seasonal patterns of WT variability and showed that WT variability increases with increasing Schmidt stability, decreasing Lake number and decreasing ice cover duration, and is higher near the shore than in open water. We introduced the slope of WT spectra as an indicator for the degree of lake mixing to help explain the identified temporal and spatial scales of WT variability. The explanatory power of this indicator in other lakes with different mixing regimes remains to be established.