Evaluating and improving modeled turbulent heat fluxes across the North American
Great Lakes

Charusombat, Umarporn; Fujisaki‐Manome, Ayumi; Gronewold, Andrew D.; Lofgren, Brent M.; Anderson, Eric J.; Blanken, Peter D.; Spence, Christopher; Lenters, John D.; Xiao, Chuliang; Fitzpatrick, Lindsay E.; Cutrell, Gregory

doi:10.5194/hess-2017-725

Cited by 7 publications

(13 citation statements)

References 52 publications

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“…After a number of tests, the mixing factor is set to 40, which is comparable to Dai et al (). Compared to the CoLM‐Lake model, the WRF‐Lake model adopts constant surface roughness lengths for the momentum, heat, and water vapor, which may not reflect the real lake surface conditions but significantly affect the surface turbulent fluxes (Charusombat et al, ). Following Subin et al (), the constant lake surface roughness lengths are replaced by the parameterized scheme in the experiment SenExp4 for the WRF‐Lake model (Table ).…”

Section: Models Data Experiments and Methodsmentioning

confidence: 99%

Evaluating and Improving the Performance of Three 1‐D Lake Models in a Large Deep Lake of the Central Tibetan Plateau

Huang

Wang

et al. 2019

JGR Atmospheres

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The ability of FLake, WRF‐Lake, and CoLM‐Lake models in simulating the thermal features of Lake Nam Co in Central Tibetan Plateau has been evaluated in this study. All the three models with default settings exhibited distinct errors in the simulated vertical temperature profile. Then model calibration was conducted by adjusting three (four) key parameters within FLake and CoLM‐Lake (WRF‐Lake) in a series of sensitive experiments. Results showed that each model's performance is sensitive to the key parameters and becomes much better when adjusting all the key parameters relative to tuning single parameter. Overall, setting the temperature of maximum water density to 1.1 °C instead of 4 °C in the three models consistently leads to improved vertical thermal structure simulation during cold seasons; reducing the light extinction coefficient in FLake results in much deeper mixed layer and warmer thermocline during warm seasons in better agreement with the observation. The vertical thermal structure can be clearly improved by decreasing the light extinction coefficient and increasing the turbulent mixing in WRF‐Lake and CoLM‐Lake during warm seasons. Meanwhile, the modeled water temperature profile in warm seasons can be significantly improved by further replacing the constant surface roughness lengths by a parameterized scheme in WRF‐Lake. Further intercomparison indicates that among the three calibrated models, FLake (WRF‐Lake) performs the best to simulate the temporal evolution and intensity of temperature in the layers shallower (deeper) than 10 m, while WRF‐Lake is the best at simulating the amplitude and pattern of the temperature variability at all depths.

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Section: Models Data Experiments and Methodsmentioning

confidence: 99%

Evaluating and Improving the Performance of Three 1‐D Lake Models in a Large Deep Lake of the Central Tibetan Plateau

Huang

Wang

et al. 2019

JGR Atmospheres

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“…Improvements in the eddy covariance (EC) technique in recent decades has enabled direct and continuous measurements of E over lakes (Blanken et al, 2000; Spence et al, 2013). Direct measurements of E have proven to be critical for evaluating and improving the models (Charusombat et al, 2018; Fujisaki‐Manome et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Intra‐Annual and Interannual Dynamics of Evaporation Over Western Lake Erie

Shao

Chen

Chu

et al. 2020

Earth and Space Science

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Evaporation (E) is a critical component of the water and energy budget in lake systems yet is challenging to quantify directly and continuously. We examined the magnitude and changes of E and its drivers over Lake Erie-the shallowest and most southern lake of the Laurentian Great Lakes. We deployed two eddy-covariance tower sites in the western Lake Erie Basin-one located nearshore (CB) and one offshore (LI)-from September 2011 through May 2016. Monthly E varied from 5 to 120 mm, with maximum E occurring in August-October. The annual E was 635 ± 42 (±SD) mm at CB and 604 ± 32 mm at LI. Mean winter (October-March) E was 189 ± 61 mm at CB and 178 ± 25 mm at LI, accounting for 29.8% and 29.4% of annual E. Mean daily E was 1.8 mm during the coldest month (January) and 7.4 mm in the warmest month (July). Monthly E exhibited a strong positive linear relationship to the product of wind speed and vapor pressure deficit. Pronounced seasonal patterns in surface energy fluxes were observed with a 2-month lag in E from R n , due to the lake's heat storage. This lag was shorter than reports regarding other Great Lakes. Difference in E between the offshore and nearshore sites reflected within-lake spatial heterogeneity, likely attributable to climatic and bathymetric differences between them. These findings suggest that predictive models need to consider lake-specific heat storage and spatial heterogeneity in order to accurately simulate lake E and its seasonal dynamics.

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“…This effort requires that data and models be seamlessly integrated across the international border of the U.S. and Canada (Gronewold et al 2018), as demonstrated in the expansion of the National Water Model across the Great Lakes region (Mason et al 2019). Forecasting efforts could also benefit from the assimilation of state‐of‐the‐art measurements of antecedent conditions (e.g., snowpack, Arslan and Akyürek 2019, soil moisture, Entekhabi et al 2010), as well as additional runoff model intercomparisons (Gaborit et al 2017) and improvements in models of open‐water evapotranspiration (Charusombat et al 2018). Medium‐range water level forecasts would further improve with increased skill in precipitation and temperature forecasts at subseasonal to seasonal lead times (Vitart et al 2017); recent efforts in the Great Lakes region have focused on developing a suite of seasonal forecast tools for this purpose (Bolinger et al 2017).…”

Section: Discussionmentioning

confidence: 99%

A Probabilistic, Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

Semmendinger

Foley

2020

J American Water Resour Assoc

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This study contributes a bathtub‐style inundation prediction model with abstractions of coastal processes (i.e., storm surge and wave runup) for flood forecasting at medium‐range (weekly to monthly) timescales along the coastline of large lakes. Uncertainty from multiple data sources are propagated through the model to establish probabilistic bounds of inundation, providing a conservative measure of risk. The model is developed in a case study of the New York Lake Ontario shoreline, which has experienced two record‐setting floods over the course of three years (2017–2019). Predictions are developed at a parcel‐level and are validated using inundation accounts from an online survey and flyover imagery taken during the recent flood events. Model predictions are compared against a baseline, deterministic model that accounts for the same processes but does not propagate forward data uncertainties. Results suggest that a probabilistic approach helps capture observed instances of inundation that would otherwise be missed by a deterministic inundation model. However, downward biases are still present in probabilistic predictions, especially for parcels impacted by wave runup. The goal of the tool is to provide community planners and property owners with a conservative, parcel‐level assessment of flood risk to help inform short‐term emergency response and better prepare for future flood events.

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Evaluating and improving modeled turbulent heat fluxes across the North American Great Lakes

Cited by 7 publications

References 52 publications

Evaluating and Improving the Performance of Three 1‐D Lake Models in a Large Deep Lake of the Central Tibetan Plateau

Evaluating and Improving the Performance of Three 1‐D Lake Models in a Large Deep Lake of the Central Tibetan Plateau

Intra‐Annual and Interannual Dynamics of Evaporation Over Western Lake Erie

A Probabilistic, Parcel‐Level Inundation Prediction Tool for Medium‐Range Flood Forecasting in Large Lake Systems

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