Use Of Water Temperature Research Articles

Straw mulching improved yield of field buckwheat (Fagopyrum) by increasing water-temperature use and soil carbon in rain-fed farmland

Understanding the effect of straw mulching on buckwheat was useful to improve buckwheat yield in arid and semi-arid areas, and this study aimed to clarify the significance of straw mulching practices in buckwheat yield, water-temperature use, and soil traits. Agronomic traits, yield, water– temperature use, and soil parameters were measured and analyzed among straw mulching treatments (NM: 0%, HM: 50%, AM: 100%) in a buckwheat field in 2011–2012. Positive correlation of significant difference were found between straw mulching and yield (P <0.05), soil temperature (P <0.05), plant height (P <0.01), chlorophyll (P <0.01), accumulated temperature (AT) (P <0.01), temperature use efficiency (TUE) (P <0.05), and water use efficiency (WUE) (P <0.01). Soil urease activity, soil invertase activity, and soil acid phosphatase activity increased 0.96, 0.97, and 0.88 units, respectively when straw mulching increased 1 unit. Soil organic carbon (SOC) increased 1.81 units when soil urease increased 1 unit and decreased 0.92 units when soil invertase activity increased 1 unit. Buckwheat yield increased 0.82, 0.72, 0.99, and 0.81 units respectively when accumulated temperature (AT), temperature use efficiency (TUE), water use efficiency (WUE), and soil organic carbon (SOC) increased 1 unit. Straw mulching increased buckwheat yield due to the improvement of soil organic carbon and water- temperature use efficiency, about 80% surface of straw mulching could be used for buckwheat production in arid and semi-arid areas.

Can water level stations be used for thermal assessment in aquatic ecosystem?

AbstractMany studies focus on stream water temperature (WT) because it is considered a key ecological factor. However, few of them have investigated the use of WT data from water level monitoring networks, which often measure WT as ancillary data. Our study was conducted in southern Belgium at a high temporal resolution with continuous data recorded at intervals of 10 min between 2012 and 2016 and large spatial scale greater than 16,000 km2. This study aimed to assess whether a regional water level network (140 stations) is reliable for continuous WT monitoring based on a Bland–Altman analysis with WT collected through a European monitoring network (Water Framework Directive). This study also investigates whether WT data acquired by water level stations can be used to perform both state‐of‐the‐art visualization of thermal regimes and spatio‐temporal queries for specific ecological monitoring.We found that the water level stations were reliable tools in recording continuous WT in the streams of the study area. The temperature difference between the two WT monitoring networks was −0.57°C on average. Our positive results promote the use of WT from water level stations in order to globally characterize the thermal regime of streams as well as to provide spatial or temporal information on this regime at high frequencies. As an example, our data showed the effectiveness for brown trout (Salmo trutta fario L.) in spatializing thermal risk areas related to the thermal requirement of this fish species; in 2015, 19% of stations located in brown trout fish zone recorded temperatures above 25°C.

Effect of the combination of a cold water temperature and exogenous estrogens on feminization, growth, gonadosomatic index and fat muscle content of Nile tilapia Oreochromis niloticus (Linnaeus, 1758)

96 Normal 0 21 false false false EN-US JA X-NONE /* Style Definitions */ table.MsoNormalTable {mso-style-name:Tabla normal; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-parent:; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:.0001pt; mso-pagination:widow-orphan; font-size:12.0pt; font-family:Times New Roman; mso-ansi-language:EN-US; mso-fareast-language:PL;} The use of water temperature to modify sex proportion in Nile tilapia has been suggested as an alternative to eliminating the use of steroids since it was discovered that it has an important role in the sex differentiation process. However, the use of cold-water temperatures to achieve feminization has rarely been explored and never has it been combined with exogenous estrogens. This work aimed to determine if the proportion of females could be increased by combining low concentrations of estrogens with a cold-water temperature, and describing its effect on growth parameters, gonadosomatic index, and fat muscle content. Two experiments were carried out, each one set at a specific water temperature (27.5 and 21.5oC). In each experiment four treatments (per triplicate) were evaluated (control -no estrogens-, estradiol-17β E 2 , 17 α -ethinylestradiol EE 2 and diethylstilbestrol DES). Higher feminization rates were observed in all groups, including the control group, reared at 21.5oC. Growth parameters showed significantly higher values ( P < 0.05) for the groups reared at 27.5oC. The control group and the group fed E 2 reared at 21.5oC showed significantly higher ( P < 0.05) values of gonadosomatic index than that observed in the groups reared at 27.5oC. Fat muscle content was significantly higher ( P < 0.05) in both experiments for the groups fed E 2 and EE 2 . The fry used showed a high tendency towards feminization by exposure to a cold-water temperature. Our results allow us to consider E 2 (21.5oC) the best option for the development of YY technology. 96 Normal 0 21 false false false EN-US JA X-NONE

Heat tracer test in a riparian zone: Laboratory experiments and numerical modelling

The use of water temperature as a tracer has been widely utilized for characterizing the dynamics of water flow and heat transport in riparian zones. By using experimental approaches, we investigated the effects of water temperature, hydraulic head, and heat radiation on water flow and thermal dynamics in riparian zones through sand tank experiments, which simulate the dynamics of water flow and heat transport in riparian zones. Water of low temperature was pumped into the sand tank, and changes of temperature at different locations of the sand tank were measured. Temperature data was examined for three different water temperatures (4.0 °C, 6.0 °C, and 9.5 °C), two different hydraulic heads (25 cm and 45 cm), and two different radiation temperatures (no radiation and 22 °C). The thermal dynamic variation pattern in different types of water temperatures, hydraulic heads, and radiation combinations, was also analyzed using a HYDRUS-2D model. The temperature sensors, located near the inlet infiltration boundary, required a shorter time to reach the steady state, because the temperature declined more rapidly near the inlet. The effect of lateral inflow on the temperature gradient was obvious. The temperature gradient in the horizontal direction gradually decreased, and the vertical temperature gradient gradually increased. In the initial stage of infiltration, the temperature gradient in the horizontal direction was larger than the temperature gradient in the vertical direction, however, as time goes on, the temperature gradient in the vertical direction was larger than the temperature gradient in the horizontal direction. In addition, the horizontal temperature gradient of the top sand layer was less affected by the water temperature. The closer the temperature observation point of the same horizontal section was to the infiltration boundary, the higher the rate of temperature difference changes. Comparison of the predicted and observed thermal dynamics variation of the 2-D sand tank shows good agreement, indicating that the major mechanism for water flow and thermal dynamics variation was hydraulic head. The sensitivity analysis results illustrate that the model was most sensitive to hydraulic head (H), followed by Van Genuchten parameter (α), permeability coefficient (Ks), water temperature (T), Van Genuchten parameter (n), residual moisture content (θr), and saturated moisture content (θs). The variation of each parameter was linear with the change of temperature field. Parameters which were positively related to the temperature field were T, α, θr and θs, which means the parameter value becomes larger as the temperature becomes larger, and vice versa. The parameters which were negatively related to the temperature field were H, Ks and n.

A Study on the Correlation Between Water Temperature in Rivers and Atmospheric Temperature

As in the past, water temperature in rivers that showed difference between water and atmospheric temperatures is used in the snow-melting ditches for snow management.Recently, for the purpose of providing air and hot-conditioning system and hot water supply system, the use of water temperature in rivers has been given much attention. To utilize water temperature, understanding its fluctuation is necessary. As such, water temperatures at six points in the Uono River are measured by recorders, their fluctuations are investigated, and water and atmospheric temperature relationships are studied.The water temperature fluctuation repeats every one year period and every one day period. However, both periodic phases in water temperature fluctuation start a little later than that of the atmospheric temperature. Likewise, both amplitudes are less than that of the latter. Regression analyses between the daily mean water and atmospheric temperature fluctuations are examined. Principally, atmospheric temperature influences water temperature. Thus, for the daily mean water and atmospheric temperatures, a high correlation is obtained. However, residuals due to snowfall, melting snow and yearly phase differences between water and atmospheric temperatures are observed. As such, multiple regression analyses are conducted where three predictor variables are used, namely, the daily mean atmospheric temperature, the moving mean atmospheric temperature and the depth of snowfall during the preceding day. Results showed a higher correlation than that obtained from simple regression analyses. Hence, it is possible to predict the daily mean water temperature by using these multiple regression equations.