Performance of one-dimensional hydrodynamic lake models during short-term extreme weather events

Numerical lake models are useful tools to study hydrodynamics in lakes, and are increasingly applied to extreme weather events. However, little is known about the accuracy of such models during these short-term events. We used high-frequency data from three lakes to test the performance of three one-dimensional (1D) hydrodynamic models (Simstrat, GOTM, GLM) during storms and heatwaves. Models reproduced the overall direction and magnitude of changes during the extreme events, with accurate timing and little bias. Changes in volume-averaged and surface temperatures and Schmidt stability were simulated more accurately than changes in bottom temperature, maximum buoyancy frequency, or mixed layer depth. However, in most cases the model error was higher (30-100%) during extreme events compared to reference periods. As a consequence, while 1D lake models can be used to study effects of extreme weather events, the increased uncertainty in the simulations should be taken into account when interpreting results

A multi-lake comparative analysis of the General Lake Model (GLM): Stress-testing across a global observatory network

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

Opportunities for improving recognition of coastal wetlands in global ecosystem assessment frameworks

Vegetated coastal wetlands, including seagrass, saltmarsh and mangroves, are threatened globally, yet the need to avert these losses is poorly recognized in international policy, such as in the Convention on Biological Diversity and the United Nations (UN) Sustainable Development Goals. Identifying the impact of overlooking coastal wetlands in ecosystem assessment frameworks could help prioritize research efforts to fill these gaps. Here, we examine gaps in the recognition of coastal wetlands in globally applicable ecosystem assessments. We address both shortfalls in assessment frameworks when it comes to assessing wetlands, and gaps in data that limit widespread application of assessments. We examine five assessment frameworks that track fisheries, greenhouse gas emissions, ecosystem threats, and ecosystem services. We found that these assessments inform management decisions, but that the functions provided by coastal wetlands are incompletely represented. Most frameworks had sufficient complexity to measure wetland status, but limitations in data meant they were incompletely informed about wetland functions and services. Incomplete representation of coastal wetlands may lead to them being overlooked by research and management. Improving the coverage of coastal wetlands in ecosystem assessments requires improving global scale mapping of wetland trends, developing global-scale indicators of wetland function and synthesis to quantitatively link animal population dynamics to wetland trends. Filling these gaps will help ensure coastal wetland conservation is properly informed to manage them for the outstanding benefits they bring humanity

Global CO2 Emissions From Dry Inland Waters Share Common Drivers Across Ecosystems

Many inland waters exhibit complete or partial desiccation, or have vanished due to global change, exposing sediments to the atmosphere. Yet, data on carbon dioxide (CO2) emissions from these sediments are too scarce to upscale emissions for global estimates or to understand their fundamental drivers. Here, we present the results of a global survey covering 196 dry inland waters across diverse ecosystem types and climate zones. We show that their CO2 emissions share fundamental drivers and constitute a substantial fraction of the carbon cycled by inland waters. CO2 emissions were consistent across ecosystem types and climate zones, with local characteristics explaining much of the variability. Accounting for such emissions increases global estimates of carbon emissions from inland waters by 6% (~0.12 Pg C y−1). Our results indicate that emissions from dry inland waters represent a significant and likely increasing component of the inland waters carbon cycle

Global CO2 emissions from dry inland waters share common drivers across ecosystems

Many inland waters exhibit complete or partial desiccation, or have vanished due to global change, exposing sediments to the atmosphere. Yet, data on carbon dioxide (CO2) emissions from these sediments are too scarce to upscale emissions for global estimates or to understand their fundamental drivers. Here, we present the results of a global survey covering 196 dry inland waters across diverse ecosystem types and climate zones. We show that their CO2 emissions share fundamental drivers and constitute a substantial fraction of the carbon cycled by inland waters. CO2 emissions were consistent across ecosystem types and climate zones, with local characteristics explaining much of the variability. Accounting for such emissions increases global estimates of carbon emissions from inland waters by 6% (~0.12 Pg C y−1). Our results indicate that emissions from dry inland waters represent a significant and likely increasing component of the inland waters carbon cycle

Exploring, exploiting and evolving diversity of aquatic ecosystem models: A community perspective

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

Storm impacts on phytoplankton community dynamics in lakes

In many regions across the globe, extreme weather events, such as storms, have increased in frequency, intensity and duration. Ecological theory predicts that such extreme events should have large impacts on ecosystem structure and function. For lake ecosystems, high winds and rainfall associated with storms are linked by short term runoff events from catchments and physical mixing of the water column. Although we have a well-developed understanding of how such wind and precipitation events alter lake physical processes, our mechanistic understanding of how these short-term disturbances 48 translate from physical forcing to changes in phytoplankton communities is poor. Here, we provide a conceptual model that identifies how key storm features (i.e., the frequency, intensity, and duration of wind and precipitation) interact with attributes of lakes and their watersheds to generate changes in a lake’s physical and chemical environment and subsequently phytoplankton community structure and dynamics. We summarize the current understanding of storm-phytoplankton dynamics, identify knowledge gaps with a systematic review of the literature, and suggest future research directions by generating testable hypotheses across a global gradient of lake types and environmental conditions.Fil: Stockwell, Jason D.. University of Vermont; Estados UnidosFil: Adrian, Rita. Leibniz Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Andersen, Mikkel. Dundalk Institute of Technology; IrlandaFil: Anneville, Orlane. Institut National de la Recherche Agronomique; FranciaFil: Bhattacharya, Ruchi. University of Missouri; Estados UnidosFil: Burns, Wilton G.. University of Vermont; Estados UnidosFil: Carey, Cayelan C.. Virginia Tech University; Estados UnidosFil: Carvalho, Laurence. Freshwater Restoration & Sustainability Group; Reino UnidoFil: Chang, ChunWei. National Taiwan University; República de ChinaFil: De Senerpont Domis, Lisette N.. Netherlands Institute of Ecology; Países BajosFil: Doubek, Jonathan P.. University of Vermont; Estados UnidosFil: Dur, Gaël. Shizuoka University; JapónFil: Frassl, Marieke A.. Griffith University; AustraliaFil: Gessner, Mark O.. Leibniz Institute of Freshwater Ecology and Inland Fisheries; AlemaniaFil: Hejzlar, Josef. Biology Centre of the Czech Academy of Sciences; República ChecaFil: Ibelings, Bas W.. University of Geneva; SuizaFil: Janatian, Nasim. Estonian University of Life Sciences; EstoniaFil: Kpodonu, Alfred T. N. K.. City University of New York; Estados UnidosFil: Lajeunesse, Marc J.. University of South Florida; Estados UnidosFil: Lewandowska, Aleksandra M.. Tvarminne Zoological Station; FinlandiaFil: Llames, Maria Eugenia del Rosario. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata. Instituto de Investigaciones Biotecnológicas. Universidad Nacional de San Martín. Instituto de Investigaciones Biotecnológicas; ArgentinaFil: Matsuzaki, Shin-ichiro S.. National Institute for Environmental Studies; JapónFil: Nodine, Emily R.. Rollins College; Estados UnidosFil: Nõges, Peeter. Estonian University of Life Sciences; EstoniaFil: Park, Ho-Dong. Shinshu University; JapónFil: Patil, Vijay P.. US Geological Survey; Estados UnidosFil: Pomati, Francesco. Swiss Federal Institute of Water Science and Technology; SuizaFil: Rimmer, Alon. Kinneret Limnological Laboratory; IsraelFil: Rinke, Karsten. Helmholtz-Centre for Environmental Research; AlemaniaFil: Rudstam, Lars G.. Cornell University; Estados UnidosFil: Rusak, James A.. Ontario Ministry of the Environment and Climate Change; CanadáFil: Salmaso, Nico. Research and Innovation Centre - Fondazione Mach; ItaliaFil: Schmitt, François. Laboratoire d’Océanologie et de Géosciences; FranciaFil: Seltmann, Christian T.. Dundalk Institute of Technology; IrlandaFil: Souissi, Sami. Universite Lille; FranciaFil: Straile, Dietmar. University of Konstanz; AlemaniaFil: Thackeray, Stephen J.. Lancaster Environment Centre; Reino UnidoFil: Thiery, Wim. Vrije Unviversiteit Brussel; Bélgica. Institute for Atmospheric and Climate Science; SuizaFil: Urrutia Cordero, Pablo. Uppsala University; SueciaFil: Venail, Patrick. Universidad de Ginebra; SuizaFil: Verburg, Piet. 8National Institute of Water and Atmospheric Research; Nueva ZelandaFil: Williamson, Tanner J.. Miami University; Estados UnidosFil: Wilson, Harriet L.. Dundalk Institute of Technology; IrlandaFil: Zohary, Tamar. Israel Oceanographic & Limnological Research; IsraelGLEON 20: All Hands' MeetingRottnest IslandAustraliaUniversity of Western AustraliaUniversity of AdelaideGlobal Lake Ecological Observatory Networ