Temperature Profiling of Waterbodies with a UAV-Integrated Sensor Subsystem

Evaluation of thermal stratification and systematic monitoring of water temperature are required for lake management. Water temperature profiling requires temperature measurements through a water column to assess the level of thermal stratification which impacts oxygen content, microbial growth, and distribution of fish. The objective of this research was to develop and assess the functions of a water temperature profiling system mounted on a multirotor unmanned aerial vehicle (UAV). The buoyancy apparatus mounted on the UAV allowed vertical takeoff and landing on the water surface for in situ measurements. The sensor node that was integrated with the UAV consisted of a microcontroller unit, a temperature sensor, and a pressure sensor. The system measured water temperature and depth from seven pre-selected locations in a lake using autonomous navigation with autopilot control. Measurements at 100 ms intervals were made while the UAV was descending at 2 m/s until it landed on water surface. Water temperature maps of three consecutive depths at each location were created from the measurements. The average surface water temperature at 0.3 m was 22.5 °C, while the average water temperature at 4 m depth was 21.5 °C. The UAV-based profiling system developed successfully performed autonomous water temperature measurements within a lake.

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Aspects of the biology of Mysis mixta (Lilljeborg 1852) (Crustacea, Mysidacea) in New Hampshire coastal waters

Canadian Journal of Zoology ◽

10.1139/z82-171 ◽

1982 ◽

Vol 60 (6) ◽

pp. 1275-1281 ◽

Cited By ~ 9

Author(s):

Stephen A. Grabe ◽

Elizabeth R. Hatch

Keyword(s):

Surface Water ◽

Water Temperature ◽

New Hampshire ◽

Coastal Waters ◽

Thermal Stratification ◽

Juvenile Growth ◽

Surface Water Temperature ◽

Spawning Period ◽

Mysis Mixta ◽

Inshore Waters

Mysis mixta spawned from December to April in coastal New Hampshire waters. Females outnumbered males throughout all but the earliest dates of the spawning period. Average clutch size was 60 eggs or larvae. Juveniles were hyperbenthic during the day and migrated into the water column at night. Juveniles remained in inshore waters until surface water temperature s exceeded 12 °C, corresponding with the onset of thermal stratification. Juvenile growth was slow through mid-April then increased sharply from late April through June. Less than 5% of the population appeared to live longer than 1 year.

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Browning‐Related Decreases in Water Transparency Lead to Long‐Term Increases in Surface Water Temperature and Thermal Stratification in Two Small Lakes

Journal of Geophysical Research Biogeosciences ◽

10.1029/2017jg004321 ◽

2018 ◽

Vol 123 (5) ◽

pp. 1651-1665 ◽

Cited By ~ 17

Author(s):

Rachel M. Pilla ◽

Craig E. Williamson ◽

Jing Zhang ◽

Robyn L. Smyth ◽

John D. Lenters ◽

...

Keyword(s):

Surface Water ◽

Water Temperature ◽

Thermal Stratification ◽

Water Transparency ◽

Surface Water Temperature ◽

Small Lakes

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Time series analysis of water surface temperature and heat flux components in the Itumbiara Reservoir (GO), Brazil

Acta Limnologica Brasiliensia ◽

10.1590/s2179-975x2012005000002 ◽

2012 ◽

Vol 23 (3) ◽

pp. 245-259 ◽

Cited By ~ 1

Author(s):

Enner Herenio de Alcântara ◽

José Luiz Stech ◽

João Antônio Lorenzzetti ◽

Evlyn Márcia Leão de Moraes Novo

Keyword(s):

Heat Flux ◽

Time Series ◽

Surface Water ◽

Surface Temperature ◽

Water Temperature ◽

Water Surface ◽

Surface Water Temperature ◽

Water Surface Temperature ◽

Cyclical Pattern ◽

Good Agreement

AIM: Water temperature plays an important role in ecological functioning and in controlling the biogeochemical processes of the aquatic system. Conventional water quality monitoring is expensive and time consuming. It is particularly challenging for large water bodies. Conversely, remote sensing can be considered a powerful tool to assess important properties of aquatic systems because it provides synoptic and frequent data acquisition over large areas. The objective of this study was to analyze time series of surface water temperature and heat flux to advance the understanding of temporal variations in a hydroelectric reservoir. METHOD: MODIS water-surface temperature (WST) level 2, 1 km nominal resolution data (MOD11L2, version 5) were used. All available clear-sky MODIS/Terra images from 2003 to 2008 were used, resulting in a total of 786 daytime and 473 nighttime images. Time series of surface water temperature was obtained computing the monthly mean in a 3×3 window of three reservoir selected sites: 1) near the dam, 2) at the centre of the reservoir and 3) in the confluence of the rivers. In-situ meteorological data from 2003 to 2008 were used to calculate surface energy budget time series. Cross-wavelet, coherence and phase analysis were carried out to compute the correlation between daytime and nighttime surface water temperatures and the computed heat fluxes. RESULTS: The monthly mean of the day-time WST shows lager variability than the night-time WST. All time series (daytime and nighttime) have a cyclical pattern, passing for a minimum (June - July) and a maximum (December and January). Fourier and the Wavelet Analysis were applied to analyze this cyclical pattern. The daytime time series, presents peaks in 4.5, 6 12 and 36 months and the nighttime WST shows the highest spectral density at 12, 6, 3 and 2 months. The multiple regression analysis shows that for daytime WST, the heat flux terms explain 89% of the annual variation (RMS = 0.89 °C, p < 0.0013). For nighttime, the heat flux terms explain 94% (RMS = 0.53 °C, p < 0.0002). CONCLUSION: The daytime WST and shortwave radiation presents a good agreement for periods of 6 (with shortwave retarded) and 12 months (with shortwave advanced); For nighttime WST and longwave the good agreement is present for 1, 3, 6 and 12 months, all with longwave advanced in relation to WST.

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Effects of reservoir operation and climate change on thermal stratification of a canyon-shaped reservoir, in northwest China

Water Science & Technology Water Supply ◽

10.2166/ws.2017.068 ◽

2017 ◽

Vol 18 (2) ◽

pp. 418-429 ◽

Cited By ~ 5

Author(s):

Yang Li ◽

Ting-lin Huang ◽

Zi-zhen Zhou ◽

Sheng-hai Long ◽

Hai-han Zhang

Keyword(s):

Climate Change ◽

Surface Water ◽

Water Temperature ◽

Water Column ◽

Air Temperature ◽

Thermal Regime ◽

Reservoir Operation ◽

Thermal Stratification ◽

Surface Water Temperature ◽

Water Column Stability

Abstract Thermal stratification has a significant impact on water quality and ecological characteristics. Reservoir operation and climate change have an effect on the thermal regime. The Jinpen Reservoir is a large canyon-shaped reservoir located in Shaanxi Province with a strong thermal stratification, which resulted in an anaerobic condition in the hypolimnion. We used a hydrodynamic module based on MIKE 3 to simulate the thermal structure of the Jinpen Reservoir and study the relationship between the thermal regime, reservoir operation and climate change. Based on the daily hydrological and climatic data from 2004 to 2013, we made 13 hypothetical simulated conditions that included extreme change of inflow volume, water level, air temperature, radiation, inflow water temperature and selective withdrawal to explore the effect of different factors on the thermal regime. The results showed that the period of thermal stratification, water column stability and surface water temperature were influenced by these factors. With the increase of air temperature, the simulation results indicated a stronger thermal stratification and a higher surface water temperature, which could cause water safety problems. Deep withdrawal could decrease water column stability and prompt water column mixing early, which could be used by reservoir managers to optimize the reservoir operation.

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Baseline Water Temperature: Estimation of the Annual Cycle of Surface Water Temperature in Lakes in North-Central Poland over the 1951–1968 Period

Water ◽

10.3390/w12123574 ◽

2020 ◽

Vol 12 (12) ◽

pp. 3574

Author(s):

Andrzej Hutorowicz

Keyword(s):

Surface Water ◽

Water Temperature ◽

Surface Water Temperature ◽

Average Water Temperature ◽

Temperature Estimation ◽

Average Temperature ◽

Water Temperature Data ◽

Peak Function ◽

Average Water ◽

June July

Water temperature is an important ecological variable that affects the functioning of lakes. Unfortunately, for many lakes there are no long-term observations enabling the assessment of changes in water temperatures. This makes it difficult to include this aspect in research into the biology, ecology and chemistry of such lakes. This paper presents a literature review related to changes of surface water temperatures in lakes and in particular describing the response of water temperatures and stratification to changing climate in Polish lakes. On this basis, a model based on the available data on water temperature in 931 Polish lakes in the years 1951–1968 was proposed, which allows to estimate the baseline water temperature on any day of the year. This model is calculated using the complementary error peak function on the 0–3 m water temperature dataset, which provides the best reduction of diurnal temperature fluctuations. It can be an alternative to the average temperature of surface waters, which are calculated on the basis of systematically collected data. Based on the average water temperature data obtained from 56 thermal profiles in 10 lakes in 2010–2019, the equation was analogically calculated. The average monthly water temperatures in June, July, August and September and the change in water temperature (0.24–0.30 °C decade−1) in the period 1951–1968/2010–2019 were estimated then. Similar regional or single lake trends have been found in studies by other authors covering a similar or longer period of time. The proposed method, which is suitable for simulating temperatures, especially in summer, enables the determination of the value of changes in surface water temperature in Polish lakes when only thermal profiles data from different dates are available, which can be especially helpful when analyzing hydrobiological results.

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Spring warming period of Polish lake waters in a yearly thermal cycle

Limnological Review ◽

10.2478/v10194-012-0055-3 ◽

2012 ◽

Vol 12 (3) ◽

pp. 147-157 ◽

Cited By ~ 2

Author(s):

Rajmund Skowron

Keyword(s):

Surface Water ◽

Water Temperature ◽

Thermal Stratification ◽

Thermal Structure ◽

Thermal Gradients ◽

Surface Water Temperature ◽

Spring Warming ◽

Warming Period ◽

Lake Waters ◽

The Vertical Distribution

AbstractThe study describes thermal regimes of thirty selected Polish lakes in the spring season. The author used 35-year series of daily measurements of surface water temperature in the years 1961-2005 and the measurements of the vertical distribution of water temperature taken in tens of selected water bodies. The diversified pace of the increase in surface water temperature (SWT) during the spring warming period makes it possible to distinguish two thermal phases: the early and late phases of spring warming. The limits of those phases are marked by the dates of the disappearance of ice cover and the dates when the SWT stays well over the threshold values, which amount to 4°C and 15°C respectively. The SWT increase in the lakes (April and May) causes changes in the water’s vertical thermal structure (the formation of epi- and metalimnion) and considerable dynamics of its descriptive parameters, such as water temperature, thermal stratification coefficient, thermal gradients, heat resources, etc.

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The end of the Paleogene – A new surface-water temperature record for the Oligocene-Miocene Transition from the western Atlantic (IODP Site U1405)

Rendiconti online della Società Geologica Italiana ◽

10.3301/rol.2014.32 ◽

2014 ◽

Vol 31 ◽

pp. 35-35

Author(s):

André Bornemann

Keyword(s):

Surface Water ◽

Water Temperature ◽

Temperature Record ◽

Surface Water Temperature ◽

Western Atlantic

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Surface Water Temperature Predictions at a Mid-Latitude Reservoir under Long-Term Climate Change Impacts Using a Deep Neural Network Coupled with a Transfer Learning Approach

Water ◽

10.3390/w13081109 ◽

2021 ◽

Vol 13 (8) ◽

pp. 1109

Author(s):

Nobuaki Kimura ◽

Kei Ishida ◽

Daichi Baba

Keyword(s):

Climate Change ◽

Surface Water ◽

Water Temperature ◽

Transfer Learning ◽

Air Temperature ◽

Climate Models ◽

Temporal Variations ◽

Training Data ◽

Surface Water Temperature

Long-term climate change may strongly affect the aquatic environment in mid-latitude water resources. In particular, it can be demonstrated that temporal variations in surface water temperature in a reservoir have strong responses to air temperature. We adopted deep neural networks (DNNs) to understand the long-term relationships between air temperature and surface water temperature, because DNNs can easily deal with nonlinear data, including uncertainties, that are obtained in complicated climate and aquatic systems. In general, DNNs cannot appropriately predict unexperienced data (i.e., out-of-range training data), such as future water temperature. To improve this limitation, our idea is to introduce a transfer learning (TL) approach. The observed data were used to train a DNN-based model. Continuous data (i.e., air temperature) ranging over 150 years to pre-training to climate change, which were obtained from climate models and include a downscaling model, were used to predict past and future surface water temperatures in the reservoir. The results showed that the DNN-based model with the TL approach was able to approximately predict based on the difference between past and future air temperatures. The model suggested that the occurrences in the highest water temperature increased, and the occurrences in the lowest water temperature decreased in the future predictions.

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