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

Mesman, Jorrit P.; Ayala, Ana I.; Adrian, Rita; Eyto, Elvira de; Frassl, Marieke A.; Goyette, Stéphane; Kasparian, Jérôme; Perroud, Marjorie; Stelzer, Julio Alberto Alegre; Pierson, Don; Ibelings, Bastiaan Willem

doi:10.1016/j.envsoft.2020.104852

Cited by 36 publications

(25 citation statements)

References 50 publications

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“…Such reasoning is supported by the greater decrease in PSD 5‐95 (which is related to temperature changes) in shallower vs. deeper lakes. Similar storm‐induced changes in Schmidt stability was found in previous studies conducted on three of the lakes in our dataset (Feeagh: Andersen et al 2020; Calderó‐Pascual et al 2020; Feeagh, Erken, and Müggelsee: Mesman et al 2020). Internal seiches, upwelling, and internal waves breaking on the shores can also mix colder hypolimnetic water with the warmer epilimnetic water from storms in deeper lakes (i.e., Kirillin and Shatwell 2016; Kasprzak et al 2017).…”

Section: Discussionsupporting

confidence: 90%

The extent and variability of storm‐induced temperature changes in lakes measured with long‐term and high‐frequency data

Doubek

Anneville

Dur

et al. 2021

Limnology & Oceanography

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The intensity and frequency of storms are projected to increase in many regions of the world because of climate change. Storms can alter environmental conditions in many ecosystems. In lakes and reservoirs, storms can reduce epilimnetic temperatures from wind-induced mixing with colder hypolimnetic waters, direct precipitation to the lake's surface, and watershed runoff. We analyzed 18 long-term and high-frequency lake datasets from 11 countries to assess the magnitude of wind-vs. rainstorm-induced changes in epilimnetic temperature. We found small day-to-day epilimnetic temperature decreases in response to strong wind and heavy rain during stratified conditions. Day-to-day epilimnetic temperature decreased, on average, by 0.28 C during the strongest windstorms (storm mean daily wind speed among lakes: 6.7 AE 2.7 m s −1 , 1 SD) and by 0.15 C after the heaviest rainstorms (storm mean daily rainfall: 21.3 AE 9.0 mm). The largest decreases in epilimnetic temperature were observed ≥2 d after sustained strong wind or heavy rain (top 5 th percentile of wind and rain events for each lake) in shallow and medium-depth lakes. The smallest decreases occurred in deep lakes. Epilimnetic temperature change from windstorms, but not rainstorms, was negatively correlated with maximum lake depth. However, even the largest storm-induced mean epilimnetic temperature decreases were typically <2 C. Day-to-day temperature change, in the absence of storms, often exceeded storm-induced temperature changes. Because storminduced temperature changes to lake surface waters were minimal, changes in other limnological variables (e.g., nutrient concentrations or light) from storms may have larger impacts on biological communities than temperature changes.

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Section: Discussionsupporting

confidence: 90%

The extent and variability of storm‐induced temperature changes in lakes measured with long‐term and high‐frequency data

Doubek

Anneville

Dur

et al. 2021

Limnology & Oceanography

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“…Within the framework of the Lake Model Intercomparison Project (LakeMIP, Stepanenko et al, 2010), the performance of different 1D models was compared for a number of reference sites, targeting also at the improvement of model parameterizations (Stepanenko et al, 2013;Stepanenko et al, 2014;Thiery et al, 2014;Guseva et al, 2020). Perroud et al (2009) compared four different 1D models, that were previously applied to small water bodies, to the large Lake Geneva, and Mesman et al (2020) investigated the performance of three 1D models under 55 extreme weather events like storms and heat waves. Most of the comparison among 3D models focused on systems where circulation patterns and internal waves had a major influence.…”

Section: Introductionmentioning

confidence: 99%

Effects of dimensionality on the performance of hydrodynamic models

Ishikawa

González

Golyjeswski

et al. 2021

Preprint

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Abstract. Numerical models are an important tool for simulating temperature, hydrodynamics and water quality in lakes and reservoirs. Existing models differ in dimensionality by considering spatial variations of simulated parameters (e.g., flow velocity and water temperature) in one (1D), two (2D) or three (3D) spatial dimensions. The different approaches are based on different levels of simplification in the description of hydrodynamic processes and result in different demands in computational power. The aim of this study is to compare three models with different dimensionality and to analyze differences between model results in relation to model simplifications. We analyze simulations of thermal stratification, flow velocity, and substance transport by density currents in a medium-sized drinking water reservoir in the subtropical zone, using three widely used open-source models: GLM (1D), CE-QUAL-W2 (2D) and Delft3D (3D). The models were operated with identical initial and boundary conditions over a one-year period. Their performance was assessed by comparing model results with measurements of temperature, flow velocity and turbulence. Results show that all models were capable of simulating the seasonal changes in water temperature and stratification. Flow velocities, only available for the 2D and 3D approaches, were more challenging to reproduce, but 3D simulations showed closer agreement with observations. With increasing dimensionality, the quality of the simulations also increased in terms of error, correlation and variance. None of the models provided good agreement with observations in terms of mixed layer depth, which also affects the spreading of inflowing water as density currents, and the results of water quality models that build on outputs of the hydrodynamic models.

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“…Ensembles offer several possibilities for weekly or seasonal forecasting efforts (e.g., Krishnamurti et al, 2000), and LakeEnsemblR can be run not only with multiple models, but also forced with several different weather forecasts. Studies of processes in lake physics that are difficult to model, such as consequences of extreme weather events (Mesman et al, 2020) or lake ice phenology (Yao et al, 2014), can especially benefit from an ensemble approach. While LakeEnsemblR currently only covers hydrodynamic models, its predictions can also serve as input for water quality models.…”

Section: Discussionmentioning

confidence: 99%

LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

Moore¹,

Mesman²,

Ladwig³

et al. 2022

Preprint

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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 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 files 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, sharing of output files, and improve collaborations between aquatic scientists. We showcase the successful application of LakeEnsemblR for two different lakes.

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

Cited by 36 publications

References 50 publications

The extent and variability of storm‐induced temperature changes in lakes measured with long‐term and high‐frequency data

The extent and variability of storm‐induced temperature changes in lakes measured with long‐term and high‐frequency data

Effects of dimensionality on the performance of hydrodynamic models

LakeEnsemblR: An R package that facilitates ensemble modelling of lakes

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