Growth modelling in accordance with daily water temperature in European grayling (Thymallus thymallusL.)

1999 ◽  
Vol 56 (6) ◽  
pp. 994-1000 ◽  
Author(s):  
J P Mallet ◽  
S Charles ◽  
H Persat ◽  
P Auger
Keyword(s):  
Growth Rate ◽  
Water Temperature ◽  
Good Description ◽  
Biological Significance ◽  
Temperature Fluctuations ◽  
Fish Growth ◽  
Von Bertalanffy ◽  
European Grayling ◽  
Daily Water Temperature ◽  
Daily Water

The model of von Bertalanffy has been and is still widely used to model fish growth, mainly because of its good description of annual growth over the whole life span. However, it does not take into account a seasonal variability in growth rate, an important phenomenon that appears quite well correlated with water temperature fluctuations in temperate climates. In the present study, we demonstrated that it was possible to model such variations by including daily water temperature in the von Bertalanffy growth formula owing to the correlation between the growth coefficient k and water temperature. The model we chose to describe such a correlation includes parameters with obvious biological significance and is mathematically well structured, which allowed an extensive use of our growth model. Hence, we use our new model to describe annual variability in the growth of European grayling (Thymallus thymallus L.) in a river section where water temperature could rise up to the thermal tolerance limit for this species, inducing reduced growth rates and severe mortality events. Finally, we were able to explain the growth rate variability from one year to the next by interannual water temperature fluctuations.

scholarly journals Characteristics of daily water temperature fluctuations in lake kierskie (West Poland)

2019 ◽  
Vol 38 (3) ◽  
pp. 41-49
Author(s):  
Mariusz Ptak ◽  
Mariusz Sojka ◽  
Bogumił Nowak
Keyword(s):  
Data Analysis ◽  
Wind Speed ◽  
Water Temperature ◽  
Temperature Fluctuations ◽  
Time Step ◽  
Air Temperatures ◽  
Daily Water Temperature ◽  
Daily Water ◽  
Positive Correlations ◽  
Stability Of Water

Abstract The paper presents the dynamics of daily water temperature fluctuations in Lake Kierskie (West Poland) and determines the effect of air temperatures and wind speed on the variability of the parameter (water temperature). The analysis was based on hourly water temperature values from the period from May 2012 to January 2018. The data analysis permitted the description of the thermal regime of waters in the lake in the hourly cycle in reference to each month, and designation of months characterised by the highest and lowest stability of water temperatures. More than half of the analysed days was characterised by a change in water temperature not exceeding 1°C. The course of water temperature showed strongly positive correlations with air temperature, and weak correlations with wind speed. Water temperatures in the lake were correlated the strongest with air temperatures with a 24 h time step.

Experimental Studies on the Thermal Performance of the Balcony-Type Flat-Plate Solar Water Heater

2014 ◽  
Vol 953-954 ◽  
pp. 111-114
Author(s):  
Sheng Xian Wei ◽  
Fen E Hu ◽  
Dong Yu Li ◽  
Hui Li
Keyword(s):  
Water Temperature ◽  
Flat Plate ◽  
Temperature Rise ◽  
Thermal Efficiency ◽  
Solar Collector ◽  
Water Heating ◽  
Solar Water Heating ◽  
Solar Water ◽  
Daily Water Temperature ◽  
Daily Water

Based on the test methods for solar water heating systems, the unsteady thermal efficiency, the daily thermal efficiency and the daily water temperature rise of the balcony-type solar water heating system with a flat-plate solar collector fixed at a large tilt angle (≥60o) have been discussed by the experimental test in Kunming. The results indicate that the average daily thermal efficiencies of the solar collector installed at 60o, 70o, 80o and 90o are around 0.35, 0.39, 0.34 and 0.40. The intercept and the slope of the unsteady thermal efficiency equations are about 0.36~0.44 and 1.62~4.01W·°C-1·m-2. The average daily water temperature rise is 25.2°C, 20.1°C, 18.2°C and 17.4°C for the solar collector fixed at 60o, 70o, 80o and 90o.

Preliminary observations of migrating adult freshwater eels (Anguilla australis australis Richardson) in Tasmania

1984 ◽  
Vol 35 (4) ◽  
pp. 471 ◽  
Author(s):  
RD Sloane
Keyword(s):  
Water Temperature ◽  
Significant Positive Correlation ◽  
River Flow ◽  
Downstream Migration ◽  
Single Male ◽  
Male Specimen ◽  
Anguilla Australis ◽  
Daily Water Temperature ◽  
Daily Water ◽  
Freshwater Eels

The downstream migration of maturing adult short-finned eels, A. a. australis, was recorded at an eel trap on the Clyde River during the 1981-1982 season. 5.2 t of eels entered the trap from November to April with 53% of the catch taken during January. Of the 190 eels examined, only a single male specimen was found. Female A. a. australis migrated downstream at a mean length of 94.5 cm, a mean weight of 1700 g and a mean age of 22.1 years (range 18-30 years). There was a significant positive correlation between catch and water temperature, temperatures above 12�C being associated with eel migration. The peak catch occurred during January and coincided with the highest record of mean daily water temperature (20.5�C). The migration did not appear to be influenced by moon phase, and river flow was controlled.

Performance Analysis of Integrated Collector System with Immersed Coil Heat Exchanger

2014 ◽  
Vol 660 ◽  
pp. 740-744 ◽  
Author(s):  
Lukmon Owolabi Afolabi ◽  
Hussain Hamoud Al-Kayiem ◽  
Tesfamichael Baheta Aklilu
Keyword(s):  
Heat Exchanger ◽  
Water Temperature ◽  
System Efficiency ◽  
Daily Water Temperature ◽  
Solar Intensity ◽  
Collector Efficiency ◽  
Daily Water ◽  
The Mean ◽  
Coil Heat Exchanger ◽  
Coil Heat

The performance of integrated solar collector / thermal energy storage with immersed heat exchanger was investigated experimentally at the Solar Research Site, University Technology PETRONAS, (4.4224oN, 100.9904oE), Malaysia. The experimental set up consisted of 150 liters storage tank capacity with immersed coil heat exchanger, single glazing 1.5m2 flat plate collector with 15o tilt to the horizontal. The circulation of the working fluid was by forced in closed loop with a mini solar pump. Aluminum cell foam was attached to the absorber as extended surface. The surface of the collector was coated with black ornament to improve its absorption. The system was tested under clear skys, for two cases; with and without water drawn-off for seven days per case studied. The performance evaluation data obtained for case1 at the mean maximum solar intensity was 503.98 W/m2 were: maximum daily water temperature 63°C, average daily water temperature 46°C, collector efficiency 63% and system efficiency 43%. Whilst for case 2, the mean maximum solar intensity was 473.11 W/m2, the maximum daily water temperature 54°C, average daily water temperature 39.36°C, collector efficiency 54% and system efficiency 39%. The system efficiency for case 2 showed that the heat exchanger performed slighlty better and the water drawn-off effect is minimal.

Estimating bioenergetics model parameters for fish with incomplete recapture histories

2010 ◽  
Vol 67 (7) ◽  
pp. 1075-1085
Author(s):  
Bradley E. Thompson ◽  
Daniel B. Hayes
Keyword(s):  
Water Temperature ◽  
Fish Growth ◽  
Individual Growth ◽  
Model Parameters ◽  
Time Interval ◽  
Minimum Residual ◽  
Daily Water ◽  
Free Ranging ◽  
Model Fish ◽  
Modeling Growth

Traditional methods for modeling growth of free-ranging fish are often limited by missing recapture observations that prevent individual growth estimates for a given time interval. Our purpose is to present a method for modeling growth rates of juvenile steelhead ( Oncorhynchus mykiss ) that addresses this limitation. Age-1 juvenile steelhead were individually marked with passive integrated transponder (PIT) tags, released in a Michigan, USA, watershed, and sampled monthly (May–November) with barge electrofishing. Individual growth was modeled using daily water temperature and observed fish sizes as inputs and by determining the proportion of maximum consumption parameter (P) for the bioenergetics equation that provided a minimum residual squared error. Results demonstrate that individual steelhead growth can be accurately modeled using water temperature and a temporally specific P shared by all individuals. Advantages of using this method to model fish growth include the ability to bridge data gaps where observations are lacking in individual length histories, rigorously test for differences in P across time periods, and estimate variability of P among fish within a given stream reach.

scholarly journals Comparison of Historical Water Temperature Measurements with Landsat Analysis Ready Data Provisional Surface Temperature Estimates for the Yukon River in Alaska

2021 ◽  
Vol 13 (12) ◽  
pp. 2394
Author(s):  
Carson Baughman ◽  
Jeffrey Conaway
Keyword(s):  
Standard Deviation ◽  
Surface Temperature ◽  
Water Temperature ◽  
In Situ Measurements ◽  
Thematic Mapper ◽  
Landsat 8 ◽  
Yukon River ◽  
Daily Water Temperature ◽  
Daily Water

Water temperature is a key element of freshwater ecological systems and a critical element within natural resource monitoring programs. In the absence of in situ measurements, remote sensing platforms can indirectly measure water temperature over time and space. The Earth Resources Observation and Science (EROS) Center has processed archived Landsat imagery into analysis ready data (ARD), including Level-2 Provisional Surface Temperature (pST) estimates derived from the Landsat 4–5 Thematic Mapper (TM), Landsat 7 Enhanced Thematic Mapper Plus (ETM+), and Landsat 8 Thermal Infrared Sensor (TIRS). We compared in situ measurements of water temperature within the Yukon River in Alaska with 52 instances of pST estimates between June 2014 and September 2020. Agreement was good with an RMSE of 2.25 °C and only a slight negative bias in the estimated mean daily water temperature of −0.47 °C. For the 52 dates compared, the average daily water temperature measured by the USGS streamgage was 11.3 °C with a standard deviation of 5.7 °C. The average daily pST estimate was 10.8 °C with a standard deviation of 6.1 °C. At least in the case of large unstratified rivers in Alaska, ARD pST can be used to infer water temperature in the absence of or in tandem with ground-based water temperature monitoring campaigns.

Are Growth Parameters Estimated from Tagging and Age–Length Data Comparable?

1988 ◽  
Vol 45 (6) ◽  
pp. 936-942 ◽  
Author(s):  
R. I. C. C. Francis
Keyword(s):  
Growth Rate ◽  
Growth Rates ◽  
Growth Parameters ◽  
Growth Models ◽  
Maximum Length ◽  
Fish Growth ◽  
Data Sets ◽  
Von Bertalanffy ◽  
Length Data ◽  
The Mean

The two most common ways of estimating fish growth use age–length data and tagging data. It is shown that growth parameters estimated from these two types of data have different meanings and thus are not directly comparable. In particular, the von Bertalanffy parameter l∞ means asymptotic mean length at age for age–length data, and maximum length for tagging data, when estimated by conventional methods. New parameterizations are given for the von Bertalanffy equation which avoid this ambiguity and better represent the growth information in the two types of data. The comparison between growth estimates from these data sets is shown to be equivalent to comparing the mean growth rate of fish of a given age with that of fish of length equal to the mean length at that age. How much these growth rates may differ in real populations remains unresolved: estimates for two species of fish produced markedly different results, neither of which could be reproduced using growth models. Existing growth models are shown to be inadequate to answer this question.

scholarly journals Opportunities and Limits of Using Meteorological Reanalysis Data for Simulating Seasonal to Sub-Daily Water Temperature Dynamics in a Large Shallow Lake

Water ◽  
2018 ◽  
Vol 10 (5) ◽  
pp. 594 ◽  
Author(s):  
Marieke Frassl ◽  
Bertram Boehrer ◽  
Peter Holtermann ◽  
Weiping Hu ◽  
Knut Klingbeil ◽  
...  
Keyword(s):  
Water Temperature ◽  
Shallow Lake ◽  
Reanalysis Data ◽  
Large Shallow Lake ◽  
Daily Water Temperature ◽  
Temperature Dynamics ◽  
Daily Water
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