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

Water ◽  
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.

scholarly journals Analysis of Momentary Variations in the Quality of Water on Specific Criteria in Cole Mere

2021 ◽  
Vol 1 (1) ◽  
pp. 8-12
Keyword(s):  
Surface Water ◽  
Field Test ◽  
The Other ◽  
Surface Water Temperature ◽  
Laboratory Examination ◽  
Average Surface ◽  
Average Temperature ◽  
Quality Of Water ◽  
June July

Momentary changes in some criteria regarding the quality of water were examined by conducting field test and laboratory examination in Cole Mere during the summer months (June, July and August) of 2013. 10 locations were chosen (inside the lake) and each was sampled nearly one month away from the other. The average surface water temperature was documented in June, July and August samplings were 14.1 °C, 21.9 °C and 18.2 °C respectively. The variations in the average temperature were numerically notable (p=<0.001). The average absorption levels of Cholorophyll a were 9.3 μgl⁻ ¹, 15.2 μgl⁻ ¹ and 39.8 μgl⁻ ¹for June, July and August respectively and there was a noticeable difference observed between the months at p=0.001. The detected momentary change and the rising levels of summer chlorophyll a absorption are exact evidence of eutrophic estate. However, no notable variations were observed regarding pH and declined oxygen between the months.

scholarly journals Water temperature in the lakes of Northern Poland during the bathing season

2018 ◽  
Vol 15 (1) ◽  
pp. 75-90 ◽  
Author(s):  
Rajmund Skowron
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Catchment Area ◽  
Littoral Zone ◽  
Surface Water Temperature ◽  
The Baltic Sea ◽  
The Mean ◽  
The Baltic ◽  
June July ◽  
Favorable Conditions

Abstract The study presents characteristics of the bathing season on the basis of stationary daily measurements of surface water temperature in the lakes in the period 1971-2015 conducted by the Institute of Meteorology and Water Management. These measurements were taken in the littoral zone (from bridges) of 28 lakes at 7:00 (6:00 GMT). In order to determine representativeness of these measurements, the author also documents the comparison of water temperature with its values at various points of the lake and its daily course. Stationary surface water temperature measurements provided the basis for the characteristics of the average, the earliest and the latest dates of the beginning and end of the bathing seasons, their duration and mean water temperatures in the summer months. Hence, a new parameter (tsum) is introduced to define the mean surface water temperature for the summer months (June, July and August), and compare water temperature in lakes over a larger area (the Baltic Sea catchment area). The most favorable conditions for bathing in Polish lakes are found in the western part of the Wielkopolskie Lakeland (lakelands: Łagowskie, Poznańskie, Sławskie) from the beginning of July to the end of August, when the surface water temperature in lakes generally exceeds 18°C. Furthermore, the best conditions for bathing in the water are from 10:00 to 18:00. When choosing a place to relax, holidaymakers should also consider bathing locations, infrastructure and safety conditions.

scholarly journals Temperature Profiling of Waterbodies with a UAV-Integrated Sensor Subsystem

Drones ◽  
2020 ◽  
Vol 4 (3) ◽  
pp. 35
Author(s):  
Cengiz Koparan ◽  
Ali Bulent Koc ◽  
Calvin Sawyer ◽  
Charles Privette
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Autonomous Navigation ◽  
Water Surface ◽  
Thermal Stratification ◽  
Lake Management ◽  
Temperature Measurements ◽  
Surface Water Temperature ◽  
Average Water Temperature ◽  
Integrated Sensor

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.

scholarly journals 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 ◽  
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.

Impact of climate warming on the surface water temperature of plateau lake

2021 ◽  
Author(s):  
Zongqi Peng ◽  
Jiaying Yang ◽  
Yi Luo ◽  
Kun Yang ◽  
Chunxue Shang
Keyword(s):  
Surface Water ◽  
Water Temperature ◽  
Climate Warming ◽  
Surface Water Temperature ◽  
Plateau Lake
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