@article{KongGhaffarDetermannetal.2022,
  author    = {Kong, Xiangzhen and Ghaffar, Salman and Determann, Maria and Friese, Kurt and Jomaa, Seifeddine and Mi, Chenxi and Shatwell, Tom and Rinke, Karsten and Rode, Michael},
  title     = {Reservoir water quality deterioration due to deforestation emphasizes the indirect effects of global change},
  series = {Water research : a journal of the International Association on Water Quality (IAWQ)},
  volume    = {221},
  journal   = {Water research : a journal of the International Association on Water Quality (IAWQ)},
  publisher = {Elsevier Science},
  address   = {Amsterdam [u.a.]},
  issn      = {0043-1354},
  doi       = {10.1016/j.watres.2022.118721},
  pages     = {12},
  year      = {2022},
  abstract  = {Deforestation is currently a widespread phenomenon and a growing environmental concern in the era of rapid climate change. In temperate regions, it is challenging to quantify the impacts of deforestation on the catchment dynamics and downstream aquatic ecosystems such as reservoirs and disentangle these from direct climate change impacts, let alone project future changes to inform management. Here, we tackled this issue by investigating a unique catchment-reservoir system with two reservoirs in distinct trophic states (meso- and eutrophic), both of which drain into the largest drinking water reservoir in Germany. Due to the prolonged droughts in 2015-2018, the catchment of the mesotrophic reservoir lost an unprecedented area of forest (exponential increase since 2015 and ca. 17.1\% loss in 2020 alone). We coupled catchment nutrient exports (HYPE) and reservoir ecosystem dynamics (GOTM-WET) models using a process-based modeling approach. The coupled model was validated with datasets spanning periods of rapid deforestation, which makes our future projections highly robust. Results show that in a short-term time scale (by 2035), increasing nutrient flux from the catchment due to vast deforestation (80\% loss) can turn the mesotrophic reservoir into a eutrophic state as its counterpart. Our results emphasize the more prominent impacts of deforestation than the direct impact of climate warming in impairment of water quality and ecological services to downstream aquatic ecosystems. Therefore, we propose to evaluate the impact of climate change on temperate reservoirs by incorporating a time scale-dependent context, highlighting the indirect impact of deforestation in the short-term scale. In the long-term scale (e.g. to 2100), a guiding hypothesis for future research may be that indirect effects (e.g., as mediated by catchment dynamics) are as important as the direct effects of climate warming on aquatic ecosystems.},
  language  = {en}
}
@article{JanssenArhonditsisBeusenetal.2015,
  author    = {Janssen, Annette B. G. and Arhonditsis, George B. and Beusen, Arthur and Bolding, Karsten and Bruce, Louise and Bruggeman, Jorn and Couture, Raoul-Marie and Downing, Andrea S. and Elliott, J. Alex and Frassl, Marieke A. and Gal, Gideon and Gerla, Daan J. and Hipsey, Matthew R. and Hu, Fenjuan and Ives, Stephen C. and Janse, Jan H. and Jeppesen, Erik and Joehnk, Klaus D. and Kneis, David and Kong, Xiangzhen and Kuiper, Jan J. and Lehmann, Moritz K. and Lemmen, Carsten and Oezkundakci, Deniz and Petzoldt, Thomas and Rinke, Karsten and Robson, Barbara J. and Sachse, Rene and Schep, Sebastiaan A. and Schmid, Martin and Scholten, Huub and Teurlincx, Sven and Trolle, Dennis and Troost, Tineke A. and Van Dam, Anne A. and Van Gerven, Luuk P. A. and Weijerman, Mariska and Wells, Scott A. and Mooij, Wolf M.},
  title     = {Exploring, exploiting and evolving diversity of aquatic ecosystem models: a community perspective},
  series = {Aquatic ecology : the international forum covering research in freshwater and marine environments},
  volume    = {49},
  journal   = {Aquatic ecology : the international forum covering research in freshwater and marine environments},
  number    = {4},
  publisher = {Springer},
  address   = {Dordrecht},
  issn      = {1386-2588},
  doi       = {10.1007/s10452-015-9544-1},
  pages     = {513 -- 548},
  year      = {2015},
  abstract  = {Here, we present a community perspective on how to explore, exploit and evolve the diversity in aquatic ecosystem models. These models play an important role in understanding the functioning of aquatic ecosystems, filling in observation gaps and developing effective strategies for water quality management. In this spirit, numerous models have been developed since the 1970s. We set off to explore model diversity by making an inventory among 42 aquatic ecosystem modellers, by categorizing the resulting set of models and by analysing them for diversity. We then focus on how to exploit model diversity by comparing and combining different aspects of existing models. Finally, we discuss how model diversity came about in the past and could evolve in the future. Throughout our study, we use analogies from biodiversity research to analyse and interpret model diversity. We recommend to make models publicly available through open-source policies, to standardize documentation and technical implementation of models, and to compare models through ensemble modelling and interdisciplinary approaches. We end with our perspective on how the field of aquatic ecosystem modelling might develop in the next 5-10 years. To strive for clarity and to improve readability for non-modellers, we include a glossary.},
  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}
}
@misc{MooijTrolleJeppesenetal.2010,
  author    = {Mooij, Wolf M. and Trolle, Dennis and Jeppesen, Erik and Arhonditsis, George B. and Belolipetsky, Pavel V. and Chitamwebwa, Deonatus B. R. and Degermendzhy, Andrey G. and DeAngelis, Donald L. and Domis, Lisette Nicole de Senerpont and Downing, Andrea S. and Elliott, J. Alex and Fragoso Jr., Carlos Ruberto and Gaedke, Ursula and Genova, Svetlana N. and Gulati, Ramesh D. and H{\aa}kanson, Lars and Hamilton, David P. and Hipsey, Matthew R. and 't Hoen, Jochem and H{\"u}lsmann, Stephan and Los, F. Hans and Makler-Pick, Vardit and Petzoldt, Thomas and Prokopkin, Igor G. and Rinke, Karsten and Schep, Sebastiaan A. and Tominaga, Koji and Van Dam, Anne A. and Van Nes, Egbert H. and Wells, Scott A. and Janse, Jan H.},
  title     = {Challenges and opportunities for integrating lake ecosystem modelling approaches},
  series = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  journal   = {Zweitver{\"o}ffentlichungen der Universit{\"a}t Potsdam : Mathematisch-Naturwissenschaftliche Reihe},
  number    = {1326},
  issn      = {1866-8372},
  doi       = {10.25932/publishup-42983},
  url       = {http://nbn-resolving.de/urn:nbn:de:kobv:517-opus4-429839},
  pages     = {35},
  year      = {2010},
  abstract  = {A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.},
  language  = {en}
}
@article{MooijTrolleJeppesenetal.2010,
  author    = {Mooij, Wolf M. and Trolle, Dennis and Jeppesen, Erik and Arhonditsis, George B. and Belolipetsky, Pavel V. and Chitamwebwa, Deonatus B. R. and Degermendzhy, Andrey G. and DeAngelis, Donald L. and Domis, Lisette Nicole de Senerpont and Downing, Andrea S. and Elliott, J. Alex and Fragoso Jr, Carlos Ruberto and Gaedke, Ursula and Genova, Svetlana N. and Gulati, Ramesh D. and H{\aa}kanson, Lars and Hamilton, David P. and Hipsey, Matthew R. and 't Hoen, Jochem and H{\"u}lsmann, Stephan and Los, F. Hans and Makler-Pick, Vardit and Petzoldt, Thomas and Prokopkin, Igor G. and Rinke, Karsten and Schep, Sebastiaan A. and Tominaga, Koji and Van Dam, Anne A. and Van Nes, Egbert H. and Wells, Scott A. and Janse, Jan H.},
  title     = {Challenges and opportunities for integrating lake ecosystem modelling approaches},
  series = {Aquatic ecology},
  volume    = {44},
  journal   = {Aquatic ecology},
  publisher = {Springer Science + Business Media B.V.},
  address   = {Dordrecht},
  issn      = {1573-5125},
  doi       = {10.1007/s10452-010-9339-3},
  pages     = {633 -- 667},
  year      = {2010},
  abstract  = {A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.},
  language  = {en}
}
@misc{MarceGeorgeBuscarinuetal.2016,
  author    = {Marce, Rafael and George, Glen and Buscarinu, Paola and Deidda, Melania and Dunalska, Julita and de Eyto, Elvira and Flaim, Giovanna and Grossart, Hans-Peter and Istvanovics, Vera and Lenhardt, Mirjana and Moreno-Ostos, Enrique and Obrador, Biel and Ostrovsky, Ilia and Pierson, Donald C. and Potuzak, Jan and Poikane, Sandra and Rinke, Karsten and Rodriguez-Mozaz, Sara and Staehr, Peter A. and Sumberova, Katerina and Waajen, Guido and Weyhenmeyer, Gesa A. and Weathers, Kathleen C. and Zion, Mark and Ibelings, Bas W. and Jennings, Eleanor},
  title     = {Automatic High Frequency Monitoring for Improved Lake and Reservoir Management},
  series = {Frontiers in plant science},
  volume    = {50},
  journal   = {Frontiers in plant science},
  publisher = {American Chemical Society},
  address   = {Washington},
  issn      = {0013-936X},
  doi       = {10.1021/acs.est.6b01604},
  pages     = {10780 -- 10794},
  year      = {2016},
  abstract  = {Recent technological developments have increased the number of variables being monitored in lakes and reservoirs using automatic high frequency monitoring (AHFM). However, design of AHFM systems and posterior data handling and interpretation are currently being developed on a site-by-site and issue-by-issue basis with minimal standardization of protocols or knowledge sharing. As a result, many deployments become short-lived or underutilized, and many new scientific developments that are potentially useful for water management and environmental legislation remain underexplored. This Critical Review bridges scientific uses of AHFM with their applications by providing an overview of the current AHFM capabilities, together with examples of successful applications. We review the use of AHFM for maximizing the provision of ecosystem services supplied, by lakes and reservoirs (consumptive and non consumptive uses, food production, and recreation), and for reporting lake status in the EU Water Framework Directive. We also highlight critical issues to enhance the application of AHFM, and suggest the establishment of appropriate networks to facilitate knowledge sharing and technological transfer between potential users. Finally, we give advice on how modern sensor technology can successfully be applied on a larger scale to the management of lakes and reservoirs and maximize the ecosystem services they provide.},
  language  = {en}
}