@misc{12853,
  abstract     = {{Lentic waters integrate atmosphere and catchment processes, and thus ultimately capture climate signals. However, studies of climate warming effects on lentic waters usually do not sufficiently account for a change in heat flux from the catchment through altered inflow temperature and discharge under climate change. This is particularly relevant for reservoirs, which are highly impacted by catchment hydrology and may be affected by upstream reservoirs or pre‐dams. This study explicitly quantified how the catchment and pre‐dams modify the thermal response of Rappbode Reservoir, Germany's largest drinking water reservoir system, to climate change. We established a catchment‐lake modeling chain in the main reservoir and its two pre‐dams utilizing the lake model GOTM, the catchment model mHM, and the stream temperature model Air2stream, forced by an ensemble of climate projections under RCP2.6 and 8.5 warming scenarios. Results exhibited a warming of 0.27/0.15°C decade<jats:sup>−1</jats:sup> for the surface/bottom temperatures of the main reservoir, with approximately 8%/24% of this warming attributed to the catchment warming, respectively. The catchment warming amplified the deep water warming more than at the surface, contrary to the atmospheric warming effect, and advanced stratification by about 1 week, while having a minor impact on stratification intensity. On the other hand, pre‐dams reduced the inflow temperature into the main reservoir in spring, and consequently lowered the hypolimnetic temperature and postponed stratification onset. This shielded the main reservoir from climate warming, although overall the contribution of pre‐dams was minimal. Altogether, our study highlights the importance of catchment alterations and seasonality when projecting reservoir warming, and provides insights into catchment‐reservoir coupling under climate change.}},
  author       = {{Gai, Bo and Kumar, Rohini and Hüesker, Frank and Mi, Chenxi and Kong, Xiangzhen and Boehrer, Bertram and Rinke, Karsten and Shatwell, Tom}},
  booktitle    = {{  Water resources research : an AGU journal}},
  issn         = {{1944-7973}},
  keywords     = {{climate change, coupled catchment-lake model, thermal characteristics, drinking water reservoir management, GOTMstratification}},
  number       = {{1}},
  publisher    = {{American Geophysical Union (AGU)}},
  title        = {{{Catchments Amplify Reservoir Thermal Response to Climate Warming}}},
  doi          = {{10.1029/2023wr036808}},
  volume       = {{61}},
  year         = {{2025}},
}

@misc{12225,
  abstract     = {{Lake Sevan is the largest freshwater body in the Caucasus region, situated at an altitude of 1,900 m asl. While it is a major water resource in the whole region, Lake Sevan has received little attention in international limnological literature. Although recent studies pointed to algal blooms and negative impacts of climate change and eutrophication, the physical controls on thermal dynamics have not been characterized and model-based assessments of climate change impacts are lacking. We compiled a decade of historical data for meteorological conditions and temperature dynamics in Lake Sevan and used a one-dimensional hydrodynamic model (GLM 3.1) in order to study thermal structure, the stratification phenology and their meteorological drivers in this large mountain lake. We then evaluated the representativeness of meteorological data products covering almost 4 decades (EWEMBI-dataset: 1979-2016) for driving the model and found that these data are well suited to restore long term thermal dynamics in Lake Sevan. This established model setting allowed us to identify major changes in Lake Sevan’s stratification in response to changing meteorological conditions as expected from ongoing climate change. Our results point to a changing mixing type from dimictic to monomictic as Lake Sevan will experience prolonged summer stratification periods and more stable stratification. These projected changes in stratification must be included in long-term management perspectives as they will intensify water quality deteriorations like surface algal blooms or deep water anoxia.}},
  author       = {{Shikhani, Muhammed and Mi, Chenxi and Gevorgyan, Artur and Gevorgyan, Gor and Misakyan, Amalya and Azizyan, Levon and Barfus, Klemens and Schulze, Martin and Shatwell, Tom and Rinke, Karsten}},
  booktitle    = {{Journal of Limnology}},
  issn         = {{1723-8633}},
  keywords     = {{General Lake Model (GLM), Lake Sevan, temperature stratification, EWEMBI, climate warming}},
  number       = {{s1}},
  publisher    = {{Istituto per lo Studio degli Ecosistemi (Verbania) }},
  title        = {{{Simulating thermal dynamics of the largest lake in the Caucasus region: The mountain Lake Sevan}}},
  doi          = {{10.4081/jlimnol.2021.2024}},
  volume       = {{81}},
  year         = {{2021}},
}

@misc{12230,
  abstract     = {{Model ensembles have several benefits compared to single-model applications but are not frequently used within the lake modelling community. Setting up and running multiple lake models can be challenging and time consuming, despite the many similarities between the existing models (forcing data, hypsograph, etc.). Here we present an R package, LakeEnsemblR, that facilitates running ensembles of five different vertical one-dimensional hydrodynamic lake models (FLake, GLM, GOTM, Simstrat, MyLake). The package requires input in a standardised format and a single configuration file. LakeEnsemblR formats these files to the input required by each model, and provides functions to run and calibrate the models. The outputs of the different models are compiled into a single file, and several post-processing operations are supported. LakeEnsemblR's workflow standardisation can simplify model benchmarking and uncertainty quantification, and improve collaborations between scientists. We showcase the successful application of LakeEnsemblR for two different lakes.}},
  author       = {{Moore, Tadhg N. and Mesman, Jorrit P. and Ladwig, Robert and Feldbauer, Johannes and Olsson, Freya and Pilla, Rachel M. and Shatwell, Tom and Venkiteswaran, Jason J. and Delany, Austin D. and Dugan, Hilary and Rose, Kevin C. and Read, Jordan S.}},
  booktitle    = {{Environmental modelling & software with environment data news}},
  issn         = {{1873-6726}},
  keywords     = {{Ensemble modeling, Vertical one-dimensional lake model, R package, Calibration, Thermal structure, Hydrodynamics}},
  publisher    = {{Elsevier BV}},
  title        = {{{LakeEnsemblR: An R package that facilitates ensemble modelling of lakes}}},
  doi          = {{10.1016/j.envsoft.2021.105101}},
  volume       = {{143}},
  year         = {{2021}},
}

@misc{12239,
  abstract     = {{The modelling community has identified challenges for the integration and assessment of lake models due to the diversity of modelling approaches and lakes. In this study, we develop and assess a one-dimensional lake model and apply it to 32 lakes from a global observatory network. The data set included lakes over broad ranges in latitude, climatic zones, size, residence time, mixing regime and trophic level. Model performance was evaluated using several error assessment metrics, and a sensitivity analysis was conducted for nine parameters that governed the surface heat exchange and mixing efficiency. There was low correlation between input data uncertainty and model performance and predictions of temperature were less sensitive to model parameters than prediction of thermocline depth and Schmidt stability. The study provides guidance to where the general model approach and associated assumptions work, and cases where adjustments to model parameterisations and/or structure are required.}},
  author       = {{Bruce, Louise C. and Frassl, Marieke A. and Arhonditsis, George B. and Gal, Gideon and Hamilton, David P. and Hanson, Paul C. and Hetherington, Amy L. and Melack, John M. and Read, Jordan S. and Rinke, Karsten and Rigosi, Anna and Trolle, Dennis and Winslow, Luke and Adrian, Rita and Ayala, Ana I. and Bocaniov, Serghei A. and Boehrer, Bertram and Boon, Casper and Brookes, Justin D. and Bueche, Thomas and Busch, Brendan D. and Copetti, Diego and Cortés, Alicia and de Eyto, Elvira and Elliott, J. Alex and Gallina, Nicole and Gilboa, Yael and Guyennon, Nicolas and Huang, Lei and Kerimoglu, Onur and Lenters, John D. and MacIntyre, Sally and Makler-Pick, Vardit and McBride, Chris G. and Moreira, Santiago and Özkundakci, Deniz and Pilotti, Marco and Rueda, Francisco J. and Rusak, James A. and Samal, Nihar R. and Schmid, Martin and Shatwell, Tom and Snorthheim, Craig and Soulignac, Frédéric and Valerio, Giulia and van der Linden, Leon and Vetter, Mark and Vinçon-Leite, Brigitte and Wang, Junbo and Weber, Michael and Wickramaratne, Chaturangi and Woolway, R. Iestyn and Yao, Huaxia and Hipsey, Matthew R.}},
  booktitle    = {{Environmental modelling & software with environment data news }},
  issn         = {{1873-6726}},
  keywords     = {{Lake model, Stratification, GLM, Model assessment, Global observatory data, Network science}},
  number       = {{4}},
  pages        = {{274--291}},
  publisher    = {{Elsevier Science}},
  title        = {{{A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network}}},
  doi          = {{10.1016/j.envsoft.2017.11.016}},
  volume       = {{102}},
  year         = {{2018}},
}