Global lake responses to climate change

Nature Reviews Earth & Environment, Год журнала: 2020, Номер 1(8), С. 388 - 403

Опубликована: Июль 14, 2020

Язык: Английский

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Lake heatwaves under climate change

Nature, Год журнала: 2021, Номер 589(7842), С. 402 - 407

Опубликована: Янв. 20, 2021

Язык: Английский

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Modelling eutrophication in lake ecosystems: A review

The Science of The Total Environment, Год журнала: 2018, Номер 651, С. 2985 - 3001

Опубликована: Сен. 26, 2018

Язык: Английский

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Physics-Guided Machine Learning for Scientific Discovery: An Application in Simulating Lake Temperature Profiles

ACM/IMS Transactions on Data Science, Год журнала: 2021, Номер 2(3), С. 1 - 26

Опубликована: Май 18, 2021

Physics-based models are often used to study engineering and environmental systems. The ability model these systems is the key achieving our future sustainability improving quality of human life. This article focuses on simulating lake water temperature, which critical for understanding impact changing climate aquatic ecosystems assisting in resource management decisions. General Lake Model (GLM) a state-of-the-art physics-based addressing such problems. However, like other studying scientific systems, it has several well-known limitations due simplified representations physical processes being modeled or challenges selecting appropriate parameters. While machine learning can sometimes outperform given ample amount training data, they produce results that physically inconsistent. proposes physics-guided recurrent neural network (PGRNN) combines RNNs leverage their complementary strengths improves modeling processes. Specifically, we show PGRNN improve prediction accuracy over (by 20% even with very little data), while generating outputs consistent laws. An important aspect approach lies its incorporate knowledge encoded models. allows using few true observed data also ensuring high accuracy. Although present evaluate this methodology context dynamics temperature lakes, applicable more widely range disciplines where (also known as mechanistic) used.

Язык: Английский

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A General Lake Model (GLM 3.0) for linking with high-frequency sensor data from the Global Lake Ecological Observatory Network (GLEON)

Geoscientific model development, Год журнала: 2019, Номер 12(1), С. 473 - 523

Опубликована: Янв. 29, 2019

Abstract. The General Lake Model (GLM) is a one-dimensional open-source code designed to simulate the hydrodynamics of lakes, reservoirs, and wetlands. GLM was developed support science needs Global Ecological Observatory Network (GLEON), network researchers using sensors understand lake functioning address questions about how lakes around world respond climate land use change. scale diversity types, locations, sizes, expanding observational datasets created need for robust community model dynamics with sufficient flexibility accommodate range scientific management relevant GLEON community. This paper summarizes basis numerical implementation algorithms, including details sub-models that surface heat exchange ice cover dynamics, vertical mixing, inflow–outflow dynamics. We demonstrate suitability different types vary substantially in their morphology, hydrology, climatic conditions. supports dynamic coupling biogeochemical ecological modelling libraries integrated simulations water quality ecosystem health, options integration other environmental models are outlined. Finally, we discuss utilities analysis outputs uncertainty assessments, operation within distributed cloud-computing environment, as tool learning participants.

Язык: Английский

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A framework for ensemble modelling of climate change impacts on lakes worldwide: the ISIMIP Lake Sector

Geoscientific model development, Год журнала: 2022, Номер 15(11), С. 4597 - 4623

Опубликована: Июнь 16, 2022

Abstract. Empirical evidence demonstrates that lakes and reservoirs are warming across the globe. Consequently, there is an increased need to project future changes in lake thermal structure resulting biogeochemistry order plan for likely impacts. Previous studies of impacts climate change on have often relied a single model forced with limited scenario-driven projections relatively small number lakes. As result, our understanding effects fragmentary, based scattered using different data sources modelling protocols, mainly focused individual or regions. This has precluded identification main at global regional scales contributed lack water quality considerations policy-relevant documents, such as Assessment Reports Intergovernmental Panel Climate Change (IPCC). Here, we describe simulation protocol developed by Lake Sector Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulating ensemble models scenarios ISIMIP phases 2 3. The prescribes simulations driven forcing from gridded observations Earth system under various representative greenhouse gas concentration pathways (RCPs), all consistently bias-corrected 0.5∘ × grid. In phase 2, 11 were these 62 well-studied where available calibration historical conditions, uncalibrated 17 500 defined grid cells containing 3, this approach was expanded consider more lakes, models, processes. largest international effort temperature, structure, ice phenology local paves way

Язык: Английский

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Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

Reviews of Geophysics, Год журнала: 2024, Номер 62(1)

Опубликована: Фев. 11, 2024

Abstract Lake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the impacts of future change lake and related processes, use mathematical models is essential. In this study, we provide a comprehensive review water temperature modeling. We begin discussing physical concepts that regulate in lakes, which serve as primer for description process‐based models. then an overview different sources observational data, including situ monitoring satellite Earth observations, used field classify various available, discuss model performance, commonly performance metrics optimization methods. Finally, analyze emerging modeling approaches, forecasting, digital twins, combining deep learning, evaluating structural differences through ensemble modeling, adapted management, coupling This aimed at diverse group professionals working fields limnology hydrology, ecologists, biologists, physicists, engineers, remote sensing researchers from private public sectors who are interested understanding its applications.

Язык: Английский

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A system of metrics for the assessment and improvement of aquatic ecosystem models

Environmental Modelling & Software, Год журнала: 2020, Номер 128, С. 104697 - 104697

Опубликована: Март 13, 2020

In this paper, we introduce the CSPS framework for hierarchical assessment of aquatic ecosystem models built on a range metrics and characteristic signatures relevant to condition. The is comprised four levels: 0) conceptual validation; 1) comparison simulated state variables with observations ('state validation'); 2) fluxes measured process rates ('process 3) system-level emergent properties, patterns relationships ('system validation'). Of these, only levels 0 1 are routinely undertaken at present. To highlight diverse contexts modelling community, present several case studies improved validation approaches using level 0–3 hierarchy. We envision that community–driven adoption these will lead more rigorously assessed models, ultimately accelerating advances in model structure function, confidence predictions.

Язык: Английский

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How to model algal blooms in any lake on earth

Current Opinion in Environmental Sustainability, Год журнала: 2018, Номер 36, С. 1 - 10

Опубликована: Сен. 25, 2018

Algal blooms increasingly threaten lake and reservoir water quality at the global scale, caused by ongoing climate change nutrient loading. To anticipate these algal blooms, models to project future worldwide are required. Here we present state-of-the-art in projection modelling explore requirements of an ideal model. Based on this, identify current challenges opportunities for such model development. Since most building blocks present, foresee that any earth can be developed near future. Finally, think bloom a scale will provide valuable contribution policymaking, particular with respect SDG 6 (clean sanitation).

Язык: Английский

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Lake thermal structure drives interannual variability in summer anoxia dynamics in a eutrophic lake over 37 years

Hydrology and earth system sciences, Год журнала: 2021, Номер 25(2), С. 1009 - 1032

Опубликована: Фев. 25, 2021

Abstract. The concentration of oxygen is fundamental to lake water quality and ecosystem functioning through its control over habitat availability for organisms, redox reactions, recycling organic material. In many eutrophic lakes, depletion in the bottom layer (hypolimnion) occurs annually during summer stratification. temporal spatial extent hypolimnetic anoxia determined by interactions between external drivers (e.g., catchment characteristics, nutrient loads, meteorology) as well internal feedback mechanisms matter recycling, phytoplankton blooms). How these interact evolution decadal timescales will determine, part, future quality. this study, we used a vertical one-dimensional hydrodynamic–ecological model (GLM-AED2) coupled with calibrated hydrological (PIHM-Lake) simulate thermal dynamics Lake Mendota (USA) 37 year period. calibration validation consisted global sensitivity evaluation application an optimization algorithm improve fit observed simulated data. We calculated stability indices (Schmidt stability, Birgean work, stored heat), identified spring mixing stratification periods, quantified energy required mixing. To qualify which factors were most important driving interannual variation anoxia, applied random-forest classifier multiple linear regressions modeled variables onset offset, ice duration, gross primary production). exhibited prolonged each summer, lasting 50–60 d. heat budget, timing stratification, production epilimnion prior predictors periods Mendota. Interannual variability was largely driven physical factors: earlier combination higher strongly affected duration anoxia. A measured step change upward 2010 unexplained GLM-AED2 model. Although cause remains unknown, possible include invasion predacious zooplankton Bythotrephes longimanus. As budget depended primarily on meteorological conditions, likely increase near result projected climate region.

Язык: Английский

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