scholarly journals Evaluation of the WRF lake module (v1.0) and its improvements at a deep reservoir

2018 ◽  
Author(s):  
Fushan Wang ◽  
Guangheng Ni ◽  
William J. Riley ◽  
Jinyun Tang ◽  
Dejun Zhu ◽  
...  
Keyword(s):  
Water Temperature ◽  
Surface Property ◽  
Thermal Structure ◽  
Light Extinction ◽  
Temperature Profiles ◽  
Lake Surface ◽  
Deep Lakes ◽  
Coupled Models ◽  
Lakes And Reservoirs ◽  
Deep Reservoir

Abstract. Large lakes and reservoirs play important roles in modulating regional hydrological cycles and climate; however, their representations in coupled models remain uncertain. The existing lake module in the Weather Research and Forecasting (WRF) system (hereafter WRF-Lake), although widely used, did not accurately predict temperature profiles in deep lakes mainly due to poor lake surface property parameterizations and underestimation of heat transfer between lake layers. We therefore revised WRF-Lake by improving its (1) numerical discretization scheme; (2) surface property parameterization; (3) diffusivity parameterization for deep lakes; and (4) convection scheme, the outcome of which became WRF-rLake (i.e., revised lake model). We evaluated WRF-rLake by comparing simulated and measured water temperature at the Nuozhadu Reservoir, a deep reservoir in southwestern China. WRF-rLake performs better than its predecessor by reducing the root-mean-square-error (RMSE) against observed lake surface temperatures (LSTs) from 1.4 °C to 1.1 °C and consistently improving simulated vertical temperature profiles. We also evaluated the sensitivity of simulated water temperature and surface energy fluxes to various modelled lake processes and parameters. We found (1) large changes in surface heat fluxes (up to 60 W m−2) associated with the improved surface property parameterization and (2) that the simulated lake thermal structure depends strongly on the light extinction coefficient and vertical diffusivity. Although currently only evaluated at the Nuozhadu Reservoir, we expect that these model parameter and structural improvements could be universal and therefore recommend further testing at other deep lakes and reservoirs.

scholarly journals Evaluation of the WRF lake module (v1.0) and its improvements at a deep reservoir

2019 ◽  
Vol 12 (5) ◽  
pp. 2119-2138 ◽  
Author(s):  
Fushan Wang ◽  
Guangheng Ni ◽  
William J. Riley ◽  
Jinyun Tang ◽  
Dejun Zhu ◽  
...  
Keyword(s):  
Surface Energy ◽  
Water Temperature ◽  
Surface Property ◽  
Light Extinction ◽  
Temperature Profiles ◽  
Lake Surface ◽  
Deep Lakes ◽  
Coupled Models ◽  
Lakes And Reservoirs ◽  
Deep Reservoir

Abstract. Large lakes and reservoirs play important roles in modulating regional hydrological cycles and climate; however, their representations in coupled models remain uncertain. The existing lake module in the Weather Research and Forecasting (WRF) system (hereafter WRF-Lake), although widely used, did not accurately predict temperature profiles in deep lakes mainly due to its poor lake surface property parameterizations and underestimation of heat transfer between lake layers. We therefore revised WRF-Lake by improving its (1) numerical discretization scheme; (2) surface property parameterization; (3) diffusivity parameterization for deep lakes; and (4) convection scheme, the outcome of which became WRF-rLake (i.e., revised lake model). We evaluated the off-line WRF-rLake by comparing simulated and measured water temperature at the Nuozhadu Reservoir, a deep reservoir in southwestern China. WRF-rLake performs better than its predecessor by reducing the root-mean-square error (RMSE) against observed lake surface temperatures (LSTs) from 1.4 to 1.1 ∘C and consistently improving simulated vertical temperature profiles. We also evaluated the sensitivity of simulated water temperature and surface energy fluxes to various modeled lake processes. We found (1) large changes in surface energy balance fluxes (up to 60 W m−2) associated with the improved surface property parameterization and (2) that the simulated lake thermal structure depends strongly on the light extinction coefficient and vertical diffusivity. Although currently only evaluated at the Nuozhadu Reservoir, we expect that these model parameterization and structural improvements could be general and therefore recommend further testing at other deep lakes and reservoirs.

The influence of water depth and forcing factors on the performances of Machine Learning approaches for the simulation of lake surface water temperature

2021 ◽  
Author(s):  
Azadeh Yousefi ◽  
Marco Toffolon
Keyword(s):  
Machine Learning ◽  
Surface Water ◽  
Water Temperature ◽  
Temporal Evolution ◽  
Time Window ◽  
Proper Time ◽  
Learning Approaches ◽  
Surface Water Temperature ◽  
Lake Surface ◽  
Deep Lakes

<p>Some attempts to predict water temperature in lakes by means of machine learning (ML) approaches have been pursued in recent years, relying on the performances that ML showed in many different contexts. The existing literature is focused on specific applications, and does not provide a general framework. Therefore, we systematically tested the role of different forcing factors on the accuracy of the simulation of lake surface water temperature (LSWT), comparing ML results with those obtained for a synthetic case study by means of a physically-based one-dimensional model, GLM. Among the available supervised ML tools, we considered artificial neural network (ANN) with back propagation, one of the most common and successful methods.</p><p>In our modelling exercise, we found that the two most important factors influencing the ability of ML to predict LSWT in temperate climates are air temperature (AT) and the day of the year (DOY). All the other meteorological inputs provide only minor improvements if considered additionally to AT and DOY, while they cannot be used as single predictors. The analysis showed that an important role is played by lake depth because a larger volume per unit of surface area implies a larger heat capacity of the lake, which smooths the temporal evolution of LSWT.  Such a filtering behaviour of deep lakes is not reproduced by standard ML methods, and requires an ad hoc pre-processing of AT input, which needs to be averaged with a proper time window. Moreover, while shallow lakes tend to be relatively well-mixed also in summer, deeper lakes can develop a strong stratification that tends to isolate the surface layer, modifying the thermally reactive volume and thus affecting the temporal evolution of LSWT. These considerations suggest that the physical dynamics of lakes, and especially of deep lakes, needs to be carefully considered also when adopting “black-box” approaches such as ML.</p><p> </p>

scholarly journals Satellite-derived light extinction coefficient and its impact on thermal structure simulations in a 1-D lake model

2017 ◽  
Vol 21 (1) ◽  
pp. 377-391 ◽  
Author(s):  
Kiana Zolfaghari ◽  
Claude R. Duguay ◽  
Homa Kheyrollah Pour
Keyword(s):  
Water Temperature ◽  
Extinction Coefficient ◽  
Lake Erie ◽  
Thermal Structure ◽  
Meteorological Station ◽  
Light Extinction ◽  
Bottom Water Temperature ◽  
Column Temperature ◽  
Kd Values ◽  
The Impact

Abstract. A global constant value of the extinction coefficient (Kd) is usually specified in lake models to parameterize water clarity. This study aimed to improve the performance of the 1-D freshwater lake (FLake) model using satellite-derived Kd for Lake Erie. The CoastColour algorithm was applied to MERIS satellite imagery to estimate Kd. The constant (0.2 m−1) and satellite-derived Kd values as well as radiation fluxes and meteorological station observations were then used to run FLake for a meteorological station on Lake Erie. Results improved compared to using the constant Kd value (0.2 m−1). No significant improvement was found in FLake-simulated lake surface water temperature (LSWT) when Kd variations in time were considered using a monthly average. Therefore, results suggest that a time-independent, lake-specific, and constant satellite-derived Kd value can reproduce LSWT with sufficient accuracy for the Lake Erie station. A sensitivity analysis was also performed to assess the impact of various Kd values on the simulation outputs. Results show that FLake is sensitive to variations in Kd to estimate the thermal structure of Lake Erie. Dark waters result in warmer spring and colder fall temperatures compared to clear waters. Dark waters always produce colder mean water column temperature (MWCT) and lake bottom water temperature (LBWT), shallower mixed layer depth (MLD), longer ice cover duration, and thicker ice. The sensitivity of FLake to Kd variations was more pronounced in the simulation of MWCT, LBWT, and MLD. The model was particularly sensitive to Kd values below 0.5 m−1. This is the first study to assess the value of integrating Kd from the satellite-based CoastColour algorithm into the FLake model. Satellite-derived Kd is found to be a useful input parameter for simulations with FLake and possibly other lake models, and it has potential for applicability to other lakes where Kd is not commonly measured.

scholarly journals Satellite-Derived Light Extinction Coefficient and its Impact on Thermal Structure Simulations in a 1-D Lake Model

2016 ◽  
Author(s):  
Kiana Zolfaghari ◽  
Claude R. Duguay ◽  
Homa Kheyrollah Pour
Keyword(s):  
Water Temperature ◽  
Extinction Coefficient ◽  
Lake Erie ◽  
Thermal Structure ◽  
Weather Prediction ◽  
Light Extinction ◽  
Light Extinction Coefficient ◽  
Kd Values ◽  
Yearly Average ◽  
The Impact

Abstract. One essential optical parameter to specify in lake models is water clarity, which is parameterized based on the light extinction coefficient (Kd). A global constant value of Kd is usually specified in lake models. One-dimensional (1-D) lake models are most often used as lake parameterization schemes in numerical weather prediction and regional climate models. This study aimed to improve the performance of the 1-D Freshwater Lake (FLake) model using satellite-derived Kd for Lake Erie. The CoastColour algorithm is applied to MERIS satellite imagery to estimate Kd and evaluated against Kd derived from Secchi disk depth (SDD) field-based measurements collected during Lake Erie cruises. A good agreement is found between field and satellite-derived Kd (RMSE = 0.63 m-1, MBE = −0.09 m-1, I_a = 0.65) (in situ data was collected in 2004, 2005, 2008, 2011, 2012). The constant (0.2 m-1) and satellite-derived Kd values as well as radiation fluxes and meteorological station observations are then used to run FLake at the location of a buoy where lake surface water temperature (LSWT) was measured in 2008. Results improved compared to using a constant Kd value (0.2 m-1) (lake-specific yearly average Kd value: RMSE = 1.54 ºC, MBE = −0.08 ºC; constant Kd value: RMSE = 1.76 ºC, MBE = −1.26 ºC). No significant improvement is found in FLake simulated LSWT when Kd variations in time are considered using a monthly average. Therefore, results suggest that a time-independent, lake-specific, and constant satellite-derived Kd value can reproduce LSWT with sufficient accuracy. A sensitivity analysis is also performed to assess the impact of various Kd values on the simulation of mean water column temperature (MWCT), mixed layer depth (MLD), water temperature isotherms as well as ice dates and thickness. Results show that FLake is sensitive to variations in Kd to estimate the thermal structure of Lake Erie. Dark waters result in warmer spring and colder fall temperatures compare to clear waters. Dark waters always produce warmer MWCT, shallower MLD, longer ice cover duration, and thicker ice. The sensitivity of FLake to Kd variations is more pronounced in the simulation of MWCT and MLD. The model is particularly sensitive to Kd values below 0.5 m-1. This is the first study to assess the value of integrating Kd from the satellite-based CoastColour algorithm into the FLake model. Satellite-derived Kd is found to be a useful input parameter for simulations with FLake and possibly other lake models, and with potential for applicability to other lakes where Kd is not commonly measured.

scholarly journals Forecasting water temperature in lakes and reservoirs using seasonal climate prediction

2021 ◽  
pp. 117286
Author(s):  
Daniel Mercado-Bettín ◽  
Francois Clayer ◽  
Muhammed Shikhani ◽  
Tadhg N. Moore ◽  
María Dolores Frías ◽  
...  
Keyword(s):  
Water Temperature ◽  
Climate Prediction ◽  
Seasonal Climate ◽  
Seasonal Climate Prediction ◽  
Lakes And Reservoirs

Lake stratification and temperature profiles simulated using downscaled GCM output

1998 ◽  
Vol 38 (11) ◽  
pp. 217-226 ◽  
Author(s):  
Hany Hassan ◽  
Toshiya Aramaki ◽  
Keisuke Hanaki ◽  
Tomonori Matsuo ◽  
Robert Wilby
Keyword(s):  
Water Temperature ◽  
Flow Direction ◽  
Future Climate Change ◽  
Temperature Profiles ◽  
Downscaling Method ◽  
Surface Climate ◽  
Statistical Relationships ◽  
June July ◽  
The Uk ◽  
Lake Stratification

A mathematical in-lake water temperature model (WATEMP-Lake) was developed to investigate future responses of lake stratification and temperature profiles to future climate change due to rising concentrations of atmospheric greenhouse gases (GHGs). The model was used to simulate daily water temperature profiles and stratification characteristics in summer (June, July, and August-JJA) for Suwa Lake in Japan as a case study. For future assessments, the model uses surface climate variables obtained from a downscaling method that was applied to the UK Hadley Centre's coupled ocean/atmosphere model forced by combined CO2 and sulphate aerosol changes (HadCM2SUL). The downscaling method employed mean sea level surface pressure to derive three airflow indices identified as: the total shear vorticity (Z) -a measure of cyclonicity -, the strength of the resultant flow (F), and the overall flow direction (D). Statistical relationships between these indices and seven daily meteorological time series were formulated to represent climate variable series at sites around Suwa Lake. These relationships were used to downscale the observed climatology of 1979-1995 and that of 2080-2099 using HadCM2SUL outputs.

scholarly journals Effects of Long-Term Increases in Water Temperature and Stratification on Large Artificial Water-Source Lakes in South Korea

Water ◽  
2021 ◽  
Vol 13 (17) ◽  
pp. 2341
Author(s):  
Soon-Ju Yu ◽  
Ju-Yeon Son ◽  
Ho-Yeong Kang ◽  
Yong-Chul Cho ◽  
Jong-Kwon Im
Keyword(s):  
South Korea ◽  
Water Temperature ◽  
Aquatic Ecosystems ◽  
Water Source ◽  
Lake Surface ◽  
Lake Water Quality ◽  
Slope Estimator ◽  
Artificial Water ◽  
Lake Stratification

Long-term changes in air and water temperatures and the resulted stratification phenomena were observed for Soyang Lake (SY), Paldang Lake (PD), Chungju Lake (CJ), and Daecheong Lake (DC) in South Korea. Non-parametric seasonal Kendall and Mann-Kendall tests, Sen slope estimator, and potential energy anomaly (PEA) were applied. The lake surface water temperatures (LSWTs) of SY and DC increased at the same rate (0.125 °C/y), followed by those of CJ (0.071 °C/y) and PD (0.06 °C/y). Seasonally, the LSWT increase rates for all lakes, except PD, were 2–3 times higher than the air temperature increase rates. The lake stratification intensity order was similar to those of the LSWT increases and correlations. SY and DC displayed significant correlations between LSWT (0.99) and PEA (0.91). Thus, the LSWT significantly affected stratification when the water temperature increased. PD demonstrated the lowest correlation between LSWT and PEA. Inflow, outflow, rainfall, wind speed, and retention time were significantly correlated, which varied within and between lakes depending on lake topographical, hydraulic, and hydrological factors. Thus, hydraulic problems and nutrients should be managed to minimize their effects on lake water quality and aquatic ecosystems because lake cyanobacteria can increase as localized water temperatures increase.

Controlling metalimnetic bloom of Planktothrix rubescens by a novel water withdrawal strategy: a modelling study

2021 ◽  
Author(s):  
Chenxi Mi ◽  
Marieke Frassl ◽  
David Hamilton ◽  
Tom Shatwell ◽  
Xiangzhen Kong ◽  
...  
Keyword(s):  
Early Stage ◽  
Management Strategies ◽  
Water Withdrawal ◽  
Light Extinction ◽  
Quality Model ◽  
Operational Strategies ◽  
Planktothrix Rubescens ◽  
Flushing Rate ◽  
Toxic Species ◽  
Lakes And Reservoirs

<p>Aggregations of cyanobacteria in lakes and reservoirs are commonly associated with surface blooms, but may also occur in the metalimnion as subsurface or deep chlorophyll maxima. Metalimnetic cyanobacteria blooms are of great concern when potentially toxic species, such as Planktothrix rubescens (P. rubescens), are involved. Apparently, metalimnetic blooms of P. rubescens have increased in frequency and severity in recent years so there is a strong need to establish the external factors controlling its growth. We hypothesized that P. rubescens blooms in reservoirs can be managed by modifying the water withdrawal strategy and altering the light climate experienced by the algae. We tested our hypothesis in Rappbode Reservoir by establishing a series of withdrawal and light scenarios based on a calibrated water quality model (CE-QUAL-W2). Our scenarios demonstrated that metalimnetic water withdrawal reduced P. rubescens biomass in the reservoir. According to the simulation results we defined an optimal withdrawal volume to control P. rubescens blooms in the reservoir as approximately 10 million m<sup>3</sup> during its blooming period. The numerical results also indicated that P. rubescens growth can be most effectively suppressed if the metalimnetic withdrawal is applied in the early stage of its rapid growth (i.e. before the occurrence of blooms). Additionally, the results showed that P. rubescens biomass gradually decreased with increasing light extinction and nearly disappeared when the extinction coefficient exceeded 0.55 m<sup>-1</sup>.  Our results indicated that close linkages among in situ measurements, model simulations, empirical growth rate and flushing rate calculations could inform management strategies to minimise the harmful impacts of P. rubescens in water supplies. Such a strategy could be used in reservoir operational strategies as an adaptation way to offset the rise in P. rubescens populations that has been linked to climate change.</p>

scholarly journals Pure rotational-Raman channels of the Esrange lidar for temperature and particle extinction measurements in the troposphere and lower stratosphere

2013 ◽  
Vol 6 (1) ◽  
pp. 91-98 ◽  
Author(s):  
P. Achtert ◽  
M. Khaplanov ◽  
F. Khosrawi ◽  
J. Gumbel
Keyword(s):  
Lower Stratosphere ◽  
Thermal Structure ◽  
Atmospheric Temperature ◽  
Temperature Measurements ◽  
Integration Time ◽  
Aerosol Extinction ◽  
Temperature Profiles ◽  
Backscatter Coefficient ◽  
Upper Troposphere ◽  
532 Nm

Abstract. The Department of Meteorology at Stockholm University operates the Esrange Rayleigh/Raman lidar at Esrange (68° N, 21° E) near the Swedish city of Kiruna. This paper describes the design and first measurements of the new pure rotational-Raman channel of the Esrange lidar. The Esrange lidar uses a pulsed Nd:YAG solid-state laser operating at 532 nm as light source with a repetition rate of 20 Hz and a pulse energy of 350 mJ. The minimum vertical resolution is 150 m and the integration time for one profile is 5000 shots. The newly implemented channel allows for measurements of atmospheric temperature at altitudes below 35 km and is currently optimized for temperature measurements between 180 and 200 K. This corresponds to conditions in the lower Arctic stratosphere during winter. In addition to the temperature measurements, the aerosol extinction coefficient and the aerosol backscatter coefficient at 532 nm can be measured independently. Our filter-based design minimizes the systematic error in the obtained temperature profile to less than 0.51 K. By combining rotational-Raman measurements (5–35 km height) and the integration technique (30–80 km height), the Esrange lidar is now capable of measuring atmospheric temperature profiles from the upper troposphere up to the mesosphere. With the improved setup, the system can be used to validate current lidar-based polar stratospheric cloud classification schemes. The new capability of the instrument measuring temperature and aerosol extinction furthermore enables studies of the thermal structure and variability of the upper troposphere/lower stratosphere. Although several lidars are operated at polar latitudes, there are few instruments that are capable of measuring temperature profiles in the troposphere, stratosphere, and mesosphere, as well as aerosols extinction in the troposphere and lower stratosphere with daylight capability.

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