@article{RusakTanentzapKlugetal.2018, author = {Rusak, James A. and Tanentzap, Andrew J. and Klug, Jennifer L. and Rose, Kevin C. and Hendricks, Susan P. and Jennings, Eleanor and Laas, Alo and Pierson, Donald C. and Ryder, Elizabeth and Smyth, Robyn L. and White, D. S. and Winslow, Luke A. and Adrian, Rita and Arvola, Lauri and de Eyto, Elvira and Feuchtmayr, Heidrun and Honti, Mark and Istvanovics, Vera and Jones, Ian D. and McBride, Chris G. and Schmidt, Silke Regina and Seekell, David and Staehr, Peter A. and Guangwei, Zhu}, title = {Wind and trophic status explain within and among-lake variability of algal biomass}, series = {Limnology and oceanography letters / ASLO, Association for the Sciences of Limnology and Oceanography}, volume = {3}, journal = {Limnology and oceanography letters / ASLO, Association for the Sciences of Limnology and Oceanography}, number = {6}, publisher = {Wiley}, address = {Hoboken}, issn = {2378-2242}, doi = {10.1002/lol2.10093}, pages = {409 -- 418}, year = {2018}, abstract = {Phytoplankton biomass and production regulates key aspects of freshwater ecosystems yet its variability and subsequent predictability is poorly understood. We estimated within-lake variation in biomass using high-frequency chlorophyll fluorescence data from 18 globally distributed lakes. We tested how variation in fluorescence at monthly, daily, and hourly scales was related to high-frequency variability of wind, water temperature, and radiation within lakes as well as productivity and physical attributes among lakes. Within lakes, monthly variation dominated, but combined daily and hourly variation were equivalent to that expressed monthly. Among lakes, biomass variability increased with trophic status while, within-lake biomass variation increased with increasing variability in wind speed. Our results highlight the benefits of high-frequency chlorophyll monitoring and suggest that predicted changes associated with climate, as well as ongoing cultural eutrophication, are likely to substantially increase the temporal variability of algal biomass and thus the predictability of the services it provides.}, language = {en} } @article{SchmidtGertenHintzeetal.2018, author = {Schmidt, Silke Regina and Gerten, Dieter and Hintze, Thomas and Lischeid, Gunnar and Livingstone, David M. and Adrian, Rita}, title = {Temporal and spatial scales of water temperature variability as an indicator for mixing in a polymictic lake}, series = {Inland waters : journal of the International Society of Limnology}, volume = {8}, journal = {Inland waters : journal of the International Society of Limnology}, number = {1}, publisher = {Routledge, Taylor \& Francis Group}, address = {Abingdon}, issn = {2044-2041}, doi = {10.1080/20442041.2018.1429067}, pages = {82 -- 95}, year = {2018}, abstract = {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.}, language = {en} } @book{ZendlerSchmidtKrueger2003, author = {Zendler, Andreas and Schmidt, Silke and Kr{\"u}ger, Klaus}, title = {Komponentenorientierte Softwareentwicklungstechniken}, series = {Preprint / Universit{\"a}t Potsdam, Institut f{\"u}r Informatik}, volume = {2003, 1}, journal = {Preprint / Universit{\"a}t Potsdam, Institut f{\"u}r Informatik}, publisher = {Univ.}, address = {Potsdam}, issn = {0946-7580}, pages = {55 S.}, year = {2003}, language = {de} } @article{SchmidtLischeidHintzeetal.2018, author = {Schmidt, Silke Regina and Lischeid, Gunnar and Hintze, Thomas and Adrian, Rita}, title = {Disentangling limnological processes in the time-frequency domain}, series = {Limnology and oceanography}, volume = {64}, journal = {Limnology and oceanography}, number = {2}, publisher = {Wiley}, address = {Hoboken}, issn = {0024-3590}, doi = {10.1002/lno.11049}, pages = {423 -- 440}, year = {2018}, abstract = {State variables in lake ecosystems are subject to processes that act on different time scales. The relative importance of each of these processes changes over time, e.g., due to varying constraints of physical, biological, and biogeochemical processes. Correspondingly, continuous automatic measurements at high temporal resolution often reveal intriguing patterns that can rarely be directly ascribed to single processes. In light of the rather complex interplay of such processes, disentangling them requires more powerful methods than researchers have applied up to this point. For this reason, we tested the potential of wavelet coherence, based on the assumption that different processes result in correlations between different variables, on different time scales and during different time windows across the seasons. The approach was tested on a set of multivariate hourly data measured between the onset of an ice cover and a cyanobacterial summer bloom in the year 2009 in the Muggelsee, a polymictic eutrophic lake. We found that processes such as photosynthesis and respiration, the growth and decay of phytoplankton biomass, dynamics in the CO2-carbonate system, wind-induced resuspension of particles, and vertical mixing all occasionally served as dominant drivers of the variability in our data. We therefore conclude that high-resolution data and a method capable of analyzing time series in both the time and the frequency domain can help to enhance our understanding of the time scales and processes responsible for the high variability in driver variables and response variables, which in turn can lay the ground for mechanistic analyses.}, language = {en} } @article{GilingStaehrGrossartetal.2017, author = {Giling, Darren P. and Staehr, Peter A. and Grossart, Hans-Peter and Andersen, Mikkel Rene and Boehrer, Bertram and Escot, Carmelo and Evrendilek, Fatih and Gomez-Gener, Lluis and Honti, Mark and Jones, Ian D. and Karakaya, Nusret and Laas, Alo and Moreno-Ostos, Enrique and Rinke, Karsten and Scharfenberger, Ulrike and Schmidt, Silke R. and Weber, Michael and Woolway, R. Iestyn and Zwart, Jacob A. and Obrador, Biel}, title = {Delving deeper: Metabolic processes in the metalimnion of stratified lakes}, series = {Limnology and oceanography}, volume = {62}, journal = {Limnology and oceanography}, publisher = {Wiley}, address = {Hoboken}, issn = {0024-3590}, doi = {10.1002/lno.10504}, pages = {1288 -- 1306}, year = {2017}, abstract = {Many lakes exhibit seasonal stratification, during which they develop strong thermal and chemical gradients. An expansion of depth-integrated monitoring programs has provided insight into the importance of organic carbon processing that occurs below the upper mixed layer. However, the chemical and physical drivers of metabolism and metabolic coupling remain unresolved, especially in the metalimnion. In this depth zone, sharp gradients in key resources such as light and temperature co-occur with dynamic physical conditions that influence metabolic processes directly and simultaneously hamper the accurate tracing of biological activity. We evaluated the drivers of metalimnetic metabolism and its associated uncertainty across 10 stratified lakes in Europe and North America. We hypothesized that the metalimnion would contribute highly to whole-lake functioning in clear oligotrophic lakes, and that metabolic rates would be highly variable in unstable polymictic lakes. Depth-integrated rates of gross primary production (GPP) and ecosystem respiration (ER) were modelled from diel dissolved oxygen curves using a Bayesian approach. Metabolic estimates were more uncertain below the epilimnion, but uncertainty was not consistently related to lake morphology or mixing regime. Metalimnetic rates exhibited high day-to-day variability in all trophic states, with the metalimnetic contribution to daily whole-lake GPP and ER ranging from 0\% to 87\% and < 1\% to 92\%, respectively. Nonetheless, the metalimnion of low-nutrient lakes contributed strongly to whole-lake metabolism on average, driven by a collinear combination of highlight, low surface-water phosphorous concentration and high metalimnetic volume. Consequently, a single-sensor approach does not necessarily reflect whole-ecosystem carbon dynamics in stratified lakes.}, language = {en} } @phdthesis{Schmidt2017, author = {Schmidt, Silke Regina}, title = {Analyzing lakes in the time frequency domain}, url = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-406955}, school = {Universit{\"a}t Potsdam}, pages = {VIII, 126}, year = {2017}, abstract = {The central aim of this thesis is to demonstrate the benefits of innovative frequency-based methods to better explain the variability observed in lake ecosystems. Freshwater ecosystems may be the most threatened part of the hydrosphere. Lake ecosystems are particularly sensitive to changes in climate and land use because they integrate disturbances across their entire catchment. This makes understanding the dynamics of lake ecosystems an intriguing and important research priority. This thesis adds new findings to the baseline knowledge regarding variability in lake ecosystems. It provides a literature-based, data-driven and methodological framework for the investigation of variability and patterns in environmental parameters in the time frequency domain. Observational data often show considerable variability in the environmental parameters of lake ecosystems. This variability is mostly driven by a plethora of periodic and stochastic processes inside and outside the ecosystems. These run in parallel and may operate at vastly different time scales, ranging from seconds to decades. In measured data, all of these signals are superimposed, and dominant processes may obscure the signals of other processes, particularly when analyzing mean values over long time scales. Dominant signals are often caused by phenomena at long time scales like seasonal cycles, and most of these are well understood in the limnological literature. The variability injected by biological, chemical and physical processes operating at smaller time scales is less well understood. However, variability affects the state and health of lake ecosystems at all time scales. Besides measuring time series at sufficiently high temporal resolution, the investigation of the full spectrum of variability requires innovative methods of analysis. Analyzing observational data in the time frequency domain allows to identify variability at different time scales and facilitates their attribution to specific processes. The merit of this approach is subsequently demonstrated in three case studies. The first study uses a conceptual analysis to demonstrate the importance of time scales for the detection of ecosystem responses to climate change. These responses often occur during critical time windows in the year, may exhibit a time lag and can be driven by the exceedance of thresholds in their drivers. This can only be detected if the temporal resolution of the data is high enough. The second study applies Fast Fourier Transform spectral analysis to two decades of daily water temperature measurements to show how temporal and spatial scales of water temperature variability can serve as an indicator for mixing in a shallow, polymictic lake. The final study uses wavelet coherence as a diagnostic tool for limnology on a multivariate high-frequency data set recorded between the onset of ice cover and a cyanobacteria summer bloom in the year 2009 in a polymictic lake. Synchronicities among limnological and meteorological time series in narrow frequency bands were used to identify and disentangle prevailing limnological processes. Beyond the novel empirical findings reported in the three case studies, this thesis aims to more generally be of interest to researchers dealing with now increasingly available time series data at high temporal resolution. A set of innovative methods to attribute patterns to processes, their drivers and constraints is provided to help make more efficient use of this kind of data.}, language = {en} }