scholarly journals Lake Surface Water Temperature Derived from 35 Years of AVHRR Sensor Data for European Lakes

2018 ◽  
Vol 10 (7) ◽  
pp. 990 ◽  
Author(s):  
Gian Lieberherr ◽  
Stefan Wunderle
Water ◽  
2019 ◽  
Vol 11 (1) ◽  
pp. 168 ◽  
Author(s):  
Matheus Tavares ◽  
Augusto Cunha ◽  
David Motta-Marques ◽  
Anderson Ruhoff ◽  
J. Cavalcanti ◽  
...  

Water temperature regulates many processes in lakes; therefore, evaluating it is essential to understand its ecological status and functioning, and to comprehend the impact of climate change. Although few studies assessed the accuracy of individual sensors in estimating lake-surface-water temperature (LSWT), comparative analysis considering different sensors is still needed. This study evaluated the performance of two thermal sensors, MODIS and Landsat 7 ETM+, and used Landsat methods to estimate the SWT of a large subtropical lake. MODIS products MOD11 LST and MOD28 SST were used for comparison. For the Landsat images, the radiative transfer equation (RTE), using NASA’s Atmospheric Correction Parameter Calculator (AtmCorr) parameters, was compared with the single-channel algorithm in different approaches. Our results showed that MOD11 obtained the highest accuracy (RMSE of 1.05 ° C), and is the recommended product for LSWT studies. For Landsat-derived SWT, AtmCorr obtained the highest accuracy (RMSE of 1.07 ° C) and is the recommended method for small lakes. Sensitivity analysis showed that Landsat-derived LSWT using the RTE is very sensitive to atmospheric parameters and emissivity. A discussion of the main error sources was conducted. We recommend that similar tests be applied for Landsat imagery on different lakes, further studies on algorithms to correct the cool-skin effect in inland waters, and tests of different emissivity values to verify if it can compensate for this effect, in an effort to improve the accuracy of these estimates.


Inland Waters ◽  
2017 ◽  
Vol 7 (1) ◽  
pp. 27-33 ◽  
Author(s):  
Martin J. Kainz ◽  
Robert Ptacnik ◽  
Serena Rasconi ◽  
Hannes H. Hager

Inland Waters ◽  
2020 ◽  
Vol 10 (3) ◽  
pp. 322-332 ◽  
Author(s):  
R. Iestyn Woolway ◽  
Eleanor Jennings ◽  
Laura Carrea

2021 ◽  
Author(s):  
Azadeh Yousefi ◽  
Marco Toffolon

<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>


2018 ◽  
Vol 77 (2) ◽  
Author(s):  
Bartosz Czernecki ◽  
Mariusz Ptak

The paper presents historical (1971-2015) and scenario-based (2006-2100) changes in surface water temperatures in 10 lakes of Poland. The analysis of historical measurement (1971-2015) showed that mean annual lake surface water temperature (LSWT) was characterised by an increasing tendency by 0.37°C∙dec-1 on average, and was higher by 0.01°C∙dec-1 than air temperature in the analogical period. The highest increase in LSWT was recorded in spring months (April, May) and in summer (July). The future changes in LSWT was based on simulations of 33 AOGCMs available in the scope of CMIP5 project for RCPs: 2.6, 4.5, 6.0, and 8.5. The developed empirical-statistical downscaling models (ESD) use the air temperature field as predictors, with consideration of autocorrelation for two preceding months. ESD models are characterised by high quality of reconstruction of water temperatures in the historical period, with correlation from 0.82 (December, February) to 0.93 (July). The future CMIP5 scenarios for the period 2006-2100 assume an increase in air temperature at the end of the 21st century from +1.8°C (RCP 2.6) to +5.1°C (RCP 8.5) in reference to the period 1971-2005. According to the downscaling models, this corresponds to an increase in water temperature in the analysed lakes ranging from +1.4°C (RCP 2.6) to +4.2°C (RCP 8.5) in the years 2081-2100, respectively, with evident variability between the adopted emission paths beginning from the period 2041-2060. At a monthly scale, water temperature will increase the slowest in February (2081-2100: RCP 2.6 = +0.5°C, RCP 8.5 = +1.8°C). The highest increase in temperature will occur from May to August (RCP 8.5 = +6°C in June).Substantial effects of transformations of the thermal regime are already observed today, e,g. in the reduction of the ice season length. According to developed scenarios, a further considerable increase in water temperature will be the primary factor determining the transformation of lake ecosystems. The obtained results provide a theoretical basis for further research conducted in the scope of many disciplines, among others hydrology, hydrobiology, ecology, water management, energy production, etc. In the case of Poland, issues related to low water resources per capita are particularly important. Contemporary studies concerning changes in water resources showed that the natural factor playing the key role in their reduction is temperature increase and therefore it should constitute for the possibly fast development of multidisciplinary concepts of mitigation policy to potential impact of climate change. 


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