Sea Surface Temperature (SST) is an essential variable for understanding key physical and biological processes. Blended and interpolated L4 SST products offer major advantages over alternative SST ...data sources due to their spatial and temporal completeness, yet their ability to discriminate upwelling-induced steep temperature transitions in coastal waters remains largely unassessed. Here we analysed the performance of eleven L4 GHRSST-compliant products in estimating in situ water temperatures recorded by a large network of shallow subtidal and intertidal temperature loggers deployed in shores covering regimes with a wide range of upwelling intensities. Results indicate that while most products perform satisfactorily for most of the year, performance is severely affected during the upwelling season in locations with strong upwelling. We show that upwelling negatively impacts all four metrics used to assess dataset performance (average bias, correlation, centred root-mean-square error and normalized standard deviation), leading to a considerable overestimation of coastal water temperatures (with average bias exceeding 2 °C in some cases). We also show that while the use of L3 data (i. e., prior to blending and interpolation) leads to an increase in performance compared to L4 GHRSST-compliant products, the gain is probably not substantial enough to offset issues related with their spatial and temporal inconsistency along coastlines. Our results suggest that the use of L4 GHRSST-compliant products can lead to a misrepresentation of the thermal fingerprint of upwelling, and thus should be limited (or even avoided) in locations dominated by its effects. Conversely, the use of L4 GHRSST-compliant products on locations with little to no upwelling appears to be warranted. The mismatch between in situ and remotely-sensed sea water temperatures here reported also highlights the need for implementation of long-term monitoring networks of in situ temperature loggers.
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•L4 GHRSST products give good estimates of coastal SSTs in areas without upwelling.•At the coast, the best performing L4 GHRSST products are G1SST and OSTIA.•All L4 GHRSST products overestimate coastal temperatures during upwelling.•With strong upwelling, average bias may exceed 2 °C.•L3 data perform well near the coast but have temporal and spatial gaps.
Tissue depletion of florfenicol (FF) and its metabolite florfenicol amine (FFA) was investigated in crucian carp (Carassius auratus gibelio) following administration of 10 mg/kg body weight (BW) ...daily for five consecutive days by oral gavage at water temperatures of 10 and 25 °C. Plasma and tissue samples, including muscle with skin in natural proportions, liver, and kidney, were collected from 10 fish per sampling point. The concentrations of FF and FFA in each sample were simultaneously determined using high performance liquid chromatography (HPLC) with fluorescence detection. Depletion profiles at different temperatures were estimated and withdrawal periods were calculated. FF was more rapidly eliminated at 25 °C, with apparent elimination half-life (t1/2λz) values ranging from 8.1 h in skin-on muscle to 9.74 h in plasma, than at 10 °C. In contrast, water temperature did not affect FF withdrawal. At 10 °C, the withdrawal periods were calculated as 2.81 and 2.83 days based on the maximum residue limit (MRL) of FF plus FFA and the tolerance of FFA both at 1 μg/g in skin-on muscle, respectively. At 25 °C, the corresponding withdrawal periods were calculated as 2.15 and 2.41 days, respectively. Each withdrawal period was rounded to the next day (3 days).
•An HPLC method was developed to simultaneously determine florfenicol and florfenicol amine in crucian carp tissues.•Temperature effects on the tissue depletion of florfenicol and florfenicol amine were evaluated in the crucian carp.•Withdrawal periods of florfenicol were calculated in the crucian carp reared at two different water temperatures.
Some cope better than others
Increasingly, research is revealing how organisms may, or may not, adapt to a changing climate. Understanding the limitations placed by a species's physiology can help to ...determine whether it has an immediate potential to deal with rapid change. Many studies have looked at physiological tolerance to climate change in fishes, with results indicating a range of responses. Dahlke
et al.
conducted a meta-analysis to explore how life stage may influence a species's ability to tolerate temperature change (see the Perspective by Sunday). They found that embryos and breeding adult fishes are much more susceptible to temperature change than those in other life stages and that this factor must therefore be considered in evaluations of susceptibility.
Science
, this issue p.
65
; see also p.
35
Thermal tolerance metrics for 694 marine and freshwater fish species reveal that breeding stages are most vulnerable to climate warming.
Species’ vulnerability to climate change depends on the most temperature-sensitive life stages, but for major animal groups such as fish, life cycle bottlenecks are often not clearly defined. We used observational, experimental, and phylogenetic data to assess stage-specific thermal tolerance metrics for 694 marine and freshwater fish species from all climate zones. Our analysis shows that spawning adults and embryos consistently have narrower tolerance ranges than larvae and nonreproductive adults and are most vulnerable to climate warming. The sequence of stage-specific thermal tolerance corresponds with the oxygen-limitation hypothesis, suggesting a mechanistic link between ontogenetic changes in cardiorespiratory (aerobic) capacity and tolerance to temperature extremes. A logarithmic inverse correlation between the temperature dependence of physiological rates (development and oxygen consumption) and thermal tolerance range is proposed to reflect a fundamental, energetic trade-off in thermal adaptation. Scenario-based climate projections considering the most critical life stages (spawners and embryos) clearly identify the temperature requirements for reproduction as a critical bottleneck in the life cycle of fish. By 2100, depending on the Shared Socioeconomic Pathway (SSP) scenario followed, the percentages of species potentially affected by water temperatures exceeding their tolerance limit for reproduction range from ~10% (SSP 1–1.9) to ~60% (SSP 5–8.5). Efforts to meet ambitious climate targets (SSP 1–1.9) could therefore benefit many fish species and people who depend on healthy fish stocks.
The effect of fiber type (cotton, polyester, and rayon), temperature, and use of detergent on the number of microfibers released during laundering of knitted fabrics were studied during accelerated ...laboratory washing (Launder-Ometer) and home laundering experiments. Polyester and cellulose-based fabrics all shed significant amounts of microfibers and shedding levels were increased with higher water temperature and detergent use. Cellulose-based fabrics released more microfibers (0.2–4 mg/g fabric) during accelerated laundering than polyester (0.1–1 mg/g fabric). Using well-controlled aquatic biodegradation experiments it was shown that cotton and rayon microfibers are expected to degrade in natural aquatic aerobic environments whereas polyester microfibers are expected to persist in the environment for long periods of time.
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•All fabrics types studied released significant microfibers during accelerated and home laundering.•Cotton and rayon knitted fabrics released more microfibers than polyester during laundering.•The use of detergent increases the generation of microfibers during laundering.•Fabrics with higher abrasion resistance, lower hairiness, and higher yarn strength released less microfibers.•Cellulose-based fibers degrade in aquatic conditions whereas polyester fibers do not.
Categorizing and Naming MARINE HEATWAVES Hobday, Alistair J.; Oliver, Eric C. J.; Gupta, Alex Sen ...
Oceanography (Washington, D.C.),
06/2018, Letnik:
31, Številka:
2
Journal Article
Recenzirano
Odprti dostop
Considerable attention has been directed at understanding the consequences and impacts of long-term anthropogenic climate change. Discrete, climatically extreme events such as cyclones, floods, and ...heatwaves can also significantly affect regional environments and species, including humans. Climate change is expected to intensify these events and thus exacerbate their effects. Climatic extremes also occur in the ocean, and recent decades have seen many high-impact marine heatwaves (MHWs)—anomalously warm water events that may last many months and extend over thousands of square kilometers. A range of biological, economic, and political impacts have been associated with the more intense MHWs, and measuring the severity of these phenomena is becoming more important. Progress in understanding and public awareness will be facilitated by consistent description of these events. Here, we propose a detailed categorization scheme for MHWs that builds on a recently published classification, combining elements from schemes that describe atmospheric heatwaves and hurricanes. Category I, II, III, and IV MHWs are defined based on the degree to which temperatures exceed the local climatology and illustrated for 10 MHWs. While there is a long-term increase in the occurrence frequency of all MHW categories, the largest trend is a 24% increase in the area of the ocean where strong (Category II) MHWs occur. Use of this scheme can help explain why biological impacts associated with different MHWs can vary widely and provides a consistent way to compare events. We also propose a simple naming convention based on geography and year that would further enhance scientific and public awareness of these marine events.
This study was carried out using Moderate Resolution Imaging Spectroradiometer (MODIS) 1 km × 1 km resolution records on board Terra and Aqua satellites and in-situ measurements during the period ...(2003–2019). In spite of the presence of increasing atmospheric warming, in summer when evaporation is maximal, in fresh-water Lake Kinneret, satellite data revealed the absence of surface water temperature (SWT) trends. The absence of SWT trends in the presence of increasing atmospheric warming is an indication of the influence of increasing evaporation on SWT trends. The increasing water cooling, due to the above-mentioned increasing evaporation, compensated for increasing heating of surface water by regional atmospheric warming, resulting in the absence of SWT trends. In contrast to fresh-water Lake Kinneret, in the hypersaline Dead Sea, located ~100 km apart, MODIS records showed an increasing trend of 0.8 °C decade−1 in summer SWT during the same study period. The presence of increasing SWT trends in the presence of increasing atmospheric warming is an indication of the absence of steadily increasing evaporation in the Dead Sea. This is supported by a constant drop in Dead Sea water level at the rate of ~1 m/year from year to year during the last 25-year period (1995–2020). In summer, in contrast to satellite measurements, in-situ measurements of near-surface water temperature in Lake Kinneret showed an increasing trend of 0.7 °C decade−1.
•ERT, MARS, M5Tree, RF and MLPNN for modeling daily lake surface temperature.•Prediction accuracy of the three machines models are compared with air2stream model.•Air2stream had better accuracy ...compared to ERT, MARS, M5Tree, RF and MLPNN.•ERT model worked the best compared to MARS, M5Tree, RF and MLPNN.
Prediction of rivers and lakes water temperature plays an important role in hydrology, ecology, and water resources planning and management. Recently, machines learning approaches have been widely used for modelling water temperature, and the obtained results vary depending on the kind of models and the selections of the appropriates predictors. In the present paper, a new family of machines learning are proposed and compared to the famous air2stream model, using a large data set collected at 25 lakes in the northern part of Poland. The proposed models were: (i) the extremely randomized trees (ERT), (ii) the multivariate adaptive regression splines (MARS), (iii) the M5 Model tree (M5Tree), (iv) the random forest (RF), and (v) the multilayer perceptron neural network (MLPNN). The models were developed using the air temperature as input variables and the component of the Gregorian calendar (year, month and day) number. Results obtained were evaluated using several statistical indices: the root mean square error (RMSE), the mean absolute error (MAE), correlation coefficient (R) and Nash-Sutcliffe efficiency coefficient (NSE). Obtained results reveals that the air2stream model outperformed all other machines learning models and worked best with high accuracy at all the 25 lakes, and none of the ERT, MARS, M5Tree, RF and MLPNN models was able to provides an improvement of the water temperature prediction compared to the air2stream.
A better understanding of risk factors and the predictive capability of water management program (WMP) data in detecting Legionella are needed to inform the efforts aimed at reducing Legionella ...growth and preventing outbreaks of Legionnaires’ disease. Using WMPs and Legionella testing data from a national lodging organization in the United States, we aimed to (1) identify factors associated with Legionella detection and (2) assess the ability of WMP disinfectant and temperature metrics to predict Legionella detection. We conducted a logistic regression analysis to identify WMP metrics associated with Legionella serogroup 1 (SG1) detection. We also estimated the predictive values for each of the WMP metrics and SG1 detection. Of 5435 testing observations from 2018 to 2020, 411 (7.6%) had SG1 detection, and 1606 (29.5%) had either SG1 or non-SG1 detection. We found failures in commonly collected WMP metrics, particularly at the primary test point for total disinfectant levels in hot water, to be associated with SG1 detection. These findings highlight that establishing and regularly monitoring water quality parameters for WMPs may be important for preventing Legionella growth and subsequent disease. However, while unsuitable water quality parameter results are associated with Legionella detection, this study found that they had poor predictive value, due in part to the low prevalence of SG1 detection in this dataset. These findings suggest that Legionella testing provides critical information to validate if a WMP is working, which cannot be obtained through water quality parameter measurements alone.
This study examines the impact of the world's largest dam, the Three Gorges Dam (TGD), on the thermal dynamics of the Yangtze River (China). The analysis uses long-term observations of river water ...temperature (RWT) in four stations and reconstructs the RWT that would have occurred in absence of the TGD. Relative to pre-TGD conditions, RWT consistently warmed in the region due to air temperature (AT) increase. In addition, the analysis demonstrates that the TGD significantly affected RWT in the downstream reach. At the closest downstream station (Yichang) to the TGD, the annual cycle of RWT experienced a damped response to AT and a marked seasonal alteration: warming during all seasons except for spring and early summer which were characterized by cooling. Both effects were a direct consequence of the larger thermal inertia of the massive water volume stored in the TGD reservoir, causing the downstream reach to be more thermally resilient. The approach used here to quantify the separate contributions of climate and human interventions on RWT can be used to set scientific guidelines for river management and conservation planning strategies.
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