Abstract. Salinity determination in seawater has been car- ried out for almost 30 years using the PracticalSalinityScale 1978. However, the numerical value of so-called practi- cal salinity, computed from electrical conductivity, differs slightly from the true or absolute salinity, defined as the mass of dissolved solids per unit mass of seawater. The difference arises because more recent knowledge about the composi- tion of seawater is not reflected in the definition of practicalsalinity, which was chosen to maintain historical continuity with previous measures, and because of spatial and tempo- ral variations in the relative composition of seawater. Ac- counting for these spatial variations in density calculations requires the calculation of a correction factor δS A , which is
Abstract. In the current state of the art, salinity is a quan- tity computed from conductivity ratio measurements, with temperature and pressure known at the time of the mea- surement, and using the PracticalSalinityScale algorithm of 1978 (PSS-78). This calculation gives practical salin- ity values S. The uncertainty expected in PSS-78 values is ± 0.002, but no details have ever been given on the method used to work out this uncertainty, and the error sources to include in this calculation. Following a guide published by the Bureau International des Poids et Mesures (BIPM), using two independent methods, this paper assesses the uncertain- ties of salinity values obtained from a laboratory salinometer and Conductivity-Temperature-Depth (CTD) measurements after laboratory calibration of a conductivity cell. The re- sults show that the part due to the PSS-78 relations fits is sometimes as significant as the instrument’s. This is partic- ularly the case with CTD measurements where correlations between variables contribute mainly to decreasing the uncer- tainty of S, even when expanded uncertainties of conduc- tivity cell calibrations are for the most part in the order of 0.002 mS cm −1 . The relations given here, and obtained with the normalized GUM method, allow a real analysis of the un- certainties’ sources and they can be used in a more general way, with instruments having different specifications.
SST statistics show a clear improvement in the FOAM sys- tem at v12 compared to v11 with a reduction in global RMS error of over 25% — from 0.60 ◦ C to 0.45 ◦ C — against Figure 1. Root-mean-square (rms) errors against observations of (a) in situ surface temperature ( ◦ C), (b) AATSR satellite surface temperature ( ◦ C), (c) sub-surface temperature profiles ( ◦ C), (d) sub-surface salinity profiles (measured on the practicalsalinityscale), (e) sea level anomaly (m) and (f) sea ice concentration (fraction) for the v12 (red), v11 (blue) and free (black) trials. All statistics are compiled as averages over the full 2-year assessment period save for comparisons with AATSR data, which are only available until 8 April 2012. Where the rms errors for the free run are considerably higher than those for the assimilative runs, the x axis has been truncated in order to allow the reader to see the finer detail for the v12 and v11 runs. In these situations the rms value has been added as an annotation above the corresponding bar.
Indeed, this salinometer performs the conductivity measurement with a four electrodes cell that can also be affected by the salinity level (like the other probes). However, if the effect observed came only from the salinometer, all the graphs should present the same discrepancy between corrections at different salinities. One way to solve this question would be to perform reference practicalsalinity measurement by weighing salts or even better to know the exact composition of the solution and to deduce reference conductivity. Unfortunately, this cannot be carried out for the kind of volumes used to calibrate oceanographic probes (100 liters).
Within the sigma-layer model, the number of vertical layers over the entire horizontal computational area is constant, irrespective of the water depth. A smooth representation of the topography is obtained. Both the moving free surface and the bed follow the horizontal coordinate lines. The z- layer is not boundary fitted in the vertical plane. The number of layers is dependent on the depth of water. The layers are strictly horizontal and create a staircase representation of the bed. The mayor advantage of the z-model is that the horizontal grid lines are parallel with density interfaces (isopycnals) in regions with steep bed slopes. This is not a strict requirement but it is a welcome aspect as it helps to reduce the artificial mixing of scalar properties like salinity (Deltares, 2016). The z – layer model will provide the best fit, however it had recently been implemented and its functionality is not tested thoroughly. Applying the sigma layer model will give comparable results as the layer conditions are similar to those in the z-model. The coordinate lines are in fact horizontal for the largest part of the domain, similar to the z-model. The function ‘Anti-creep’ deployed in the sigma model (Sigma AC), suppresses artificial errors that lead to artificial mixing. The GTC’s bed topography only shows some local bed variations near the lock complex, the influences are likely very local but may have an significant effect on the simulated salinity. Essentially, this option allows the application of a third layer model which has the advantages of both the regular z-and sigma model. Sigma AC is used in this thesis, however it is estimated that anti-creep computation time for this layer model is 1.5 times longer (Deltares, 2016). The layer distribution can be set to non-equidistant, allowing more resolution in the zones of interest.
On the basis of the current status (and planning to 2020) of farmland area, crops (rice, sugarcane, wheat, sweet potatoes, corn, vegetables, etc.), and the corresponding V index, it could be recognized some communes of concern as PhuocAn, VinhThanh, PhuocKhanh (Nhon Trach district) and PhuocBinh, TanHiep, BauCan, LongPhuoc, PhuocThai, CamDuong, SuoiTrau (Long Thanh district). For the aquaculture sector, areas of concern were PhuocAn (NhonTrach district) and a part of PhuocThai (LongThanh district); however, tiger prawn, white shrimp, and some fish types adapting to high salinity (grouper, seabass, cobia, mullet, catfish, etc.) have been currently fed, therefore vulnerability level could be negligible. Besides, local agricultural sector will be gradually reduced according to planning, related objects in the vulnerable areas due to SI thereby would be also declined.
The paired catchments, Ernies and Lemon, are located in the south west of Western Australia, some 250 km south of Perth (Fig. 1). The areas of Lemon and Ernies catchments are 344 ha and 270 ha, respectively. The catchments have Mediterranean climate, with cool, wet winters and warm to hot, dry, summers. Ernies catchment was established as forested control. The annual pan evaporation and annual rainfall for both the catchments are approximately 650 mm and 1600 mm respectively. Typically more than 80% of aver- age annual rainfall falls in the six months from May to Octo- ber. Rainfall generally exceeds pan evaporation for only four months of the year (June to September). The native forest was dominated by jarrah (Eucalyptus marginata). Approx- imately 53% of the native vegetation of the Lemon catch- ment was cleared in 1977 to develop a comprehensive un- derstanding of the streamflow and salinity generation pro- cesses following land use change (Fig. 3). The cleared area of Lemon catchment was used for sheep grazing. The Lemon and Ernies catchments have broad and flat valley, with typ- ical surface slope of about 12% and 5%, respectively. Both catchments are characterised by the presence of duricrust or sandy and gravelly superficial deposits on the surface over- lying kaolinite-rich weathered material. The lateritic profile consists of two hydrologically distinct layers, the surface soil layer is typically 50–650 cm thick of high hydraulic conduc- tivity, overlying a deep kaolinitic sandy clay 10–30 m sub- soil of much lower hydraulic conductivity. The permanent groundwater system lies about 20 m below the soil surface in the forested areas.
A previously calibrated and tested SWAT model for the study region is used to simulate salt fate and transport using the developed salinity module. The SWAT model is detailed in Wei et al. (2018). The region was divided into 72 subbasins (see Fig. 3b). The digital elevation model (DEM), stream network, soil map, land-use map, climate data, streamflow, and canal diversion data were obtained from the USGS, NRCS, and several state agencies, as summarized in Wei et al. (2018). A method was developed to apply SWAT to highly managed irrigated watersheds, and included designat- ing each cultivated field as an individual HRU (see Fig. 3b for the map of fields), crop rotations to simulate the effects of changing crop types for each field during the 11-year simula- tion, seepage to the aquifer from the earthen irrigation canals, and SWAT’s auto-irrigation algorithms to trigger irrigation events based on plant water demand for both surface water irrigation and groundwater irrigation. The method resulted in 5270 HRUs. Implementing canal seepage required a slight change to the SWAT modelling code to add pre-processed, estimated canal seepage to the HRU aquifer. Canal seepage rates were obtained from field measurements (Susfalk et al., 2008; Martin et al., 2014).
In order to assess the robustness of the model results, seven Mediterranean salinity simulations were run in total (Ta- ble 1); two halite saturation scenarios (halite-normal and halite-quarter), two gypsum saturation scenarios (gypsum- normal and gypsum-half ) and three brackish lagoon scenar- ios (fresh-half, fresh-normal and fresh-double). A detailed analysis was carried out on all seven of these simulations and the full data can be accessed at http://www.bridge.bris.ac.uk/ resources/simulations. However, for each high/low Mediter- ranean salinity scenario (380 psu, 130 psu, 5 psu), the results were remarkably similar. Generally, the climate anomalies had the same direction of change and were brought about through the same mechanisms, although the magnitude of change was different depending on the exchange strength (varied µ, see Table 1); reducing the exchange damped the anomalies, enhancing the exchange exaggerated the anoma- lies. Therefore for clarity, the following discussion is focused on the three most pertinent simulations (one per set of scenar- ios). For the hypersaline Mediterranean scenarios we chose those simulations with a direction of change in the coefficient of exchange (µ) that best represents the physical constriction of the gateways that is most likely to have occurred (see the discussion in Sect. 2.3.3); this is halite-quarter and gypsum- half. These reduced-exchange simulations also produce far less extreme (though still very large) salinity fluxes through the gateways than their unrestricted (i.e. unchanged µ) coun- terparts, halite-normal and gypsum-normal (Table 1). For the hyposaline Mediterranean scenarios the most appropri- ate simulation to discuss is fresh-normal. This is because we do not know whether Mediterranean–Atlantic exchange in- creased or decreased during these events. All anomalies are given with respect to Messinian control.
Chunking, which was introduced in , relies on the ob- servation that only the SVs are relevant for the final form of the hypothesis. Therefore, the large QP problem can be bro- ken down into a series of smaller QP problems, whose ulti- mate goal is to identify all of the nonzero Lagrange multipli- ers and discard all of the zero Lagrange multipliers. Chunk- ing seriously reduces the size of the matrix from the number of training examples squared to approximately the number of nonzero Lagrange multipliers squared. However, chunk- ing still may not handle large-scale training problems, since even this reduced matrix may not fit into memory.
Nowadays, the use of satellite images and digital soil mapping techniques become increasingly important in soil studies. In the world practice, the use of space monitoring of soil salinity is currently one of the most topical areas of the soil science. In this regard, the main objective of this work is to develop an operational method for large-scale salt survey and to make the corresponding map of soil salinity based on the research of connection patterns between soil salinity levels and spectral properties of a satellite image, using the methods of space monitoring and digital soil mapping. The object of the study are soils of the southern part of the Akdalinsky irrigation array. The goal of the work is to develop an operational method of large-scale salt survey of soil based on the research of connection patterns between soil salinity levels and spectral properties of satellite images. This work is conducted with the use of both traditional ground-based and space methods of soil research. Based on the study on the connection between spectral properties of satellite images – vegetation indices and the ratio of the different bands QuickBird images and electrical conductivity of soils, we revealed the possibility of using these indicators as interpretive signs of soil salinity levels. At the same time, it was found that the tone of the image in separate bands of a QuickBird image is sufficiently informative to assess soil salinity; the most informative ratios appeared to be the ratios of the tones of individual shooting bands and vegetation indices. Using the ratio values of the image tone in different bands and the values of vegetation indices, we compiled regression equations between the value of electrical conductivity, measured in the field, and the spectral properties of different bands of a QuickBird image. Statistically reliable regression equations were obtained for barley and wheat crops. At the next stage, with the use of the obtained regression equations in the GIS environment, we charted a map of soil salinity under crops of barley and wheat. It should be noted that for alfalfa and rice crops, we failed to obtain statistically reliable regression equations that describe the dependence of the spectral properties of a QuickBird image with soil salinity and this is mainly due to the timing of satellite imagery. More careful selection of the shooting time, which may be the subject of research at the next stages of the works, can correct the situation. The main conclusion is that the success of the developed approach is largely predetermined by the timing of shooting and, accordingly, the timing of the field sample survey.
Recalling that sample A was called high saline solution; the viscosity of this sample was measured at different shear rates. Figure 5 shows the rheological behavior of high saline sample. As the results depict, the DCN dramatically increases the viscosity of solution at both low and high shear rates compared to the effect of DCN on the viscosity of low saline sample. For instance, the difference between the viscosities of low saline with DCN and without them at low shear rates is relatively equal to 22 cP. In addition, the difference between the viscosities of low saline with DCN and without them at high shear rates is relatively equal to 12 cP. This trend indicates that the effect of DCN on the solution viscosity of high saline sample is more predominant at both high and low shear rates compared with the low saline sample. When compare two graphs for the sample B, one can see little difference between them; therefore regardless of having less clay content this sample has more positive effect than A. Scanning Electron Microscope was used to determine the morphology (size and shape) of nanoparticles. Figure 4 shows the SEM images of HPAM nanoclay particles. In Figure 4a and 4b, it is obvious that 200000 and 20000 ppm salinity were coated with 9 %wt nanoclay, respectively. For conducting a more accurate study, polymer solutions and nanoclay particles were examined by SEM.
Additionally, differential item functioning studies have to be conducted in more detail to allow clarification of identifiable gender bias linked to social skills. Gender differences in social skills measures are commonly ob- served in the literature (Del Prette & Del Prette, 2005; Bartholomeu, Silva, & Montiel, 2011). Despite, item bias by gender suggests that other latent constructs or measurement errors are being assessed in the test and that gender differences in the measure can be arising and inflated by item bias. Hence the exclusion of these items from the final scale can be a solution to avoid such measurement problem.
A three-year (2014-2016) trial was conducted at the experimental station of Dubai based International Center for Biosaline Agriculture. Two grasses (Sporobolusarabicus and Paspalumvaginatum) were grown in typical sandy soil of UAE “Entisols” and irrigated with three water salinity levels (EC 10, 20, 30 dS/m). Sprinkler irrigation system was used to irrigate grasses. Soil salinity (ECe) was assessed at two depths (0-25 & 25-50 cm) over a period of three years (2014-2016). The salinity monitoring results revealed it increases at both depths, lowest being in 2014 and the highest in 2016. The surface (0-25) as well as subsurface (25-50 cm) salinity is almost similar within the plots where same irrigation water was applied. Where difference occurs between soil salinity at two depths, it is insignificant and within standard deviation range. The root zone salinity at both depths in the years 2014, 2015 & 2016 is higher than the irrigation water salinity (EC = 10 dS/m). However, within same year the soil salinity is less than the water salinity of the respective irrigation waters (20 & 30 dS/m) during 2014, revealing salinity is well managed at higher irrigation water salinity. The root-zone salinity of both grasses in general increases with the increase of irrigation water salinity i.e., 10, 20 and 30 dS/m at both depths 0-25 & 25-50 cm. Relatively the higher soil salinity is recorded at the subsurface (25-50 cm). The highest root zone salinity being in Paspalumvaginatum grass with the application of fertilizers and irrigation with 30 dS/m water. Both grasses survived three water salinity levels and hence have the potential for further exploitation in the UAE and other GCC countries where similar soil and environmental conditions prevail. Other forages such as alfalfa (Medicago sativa) and Rhodes grass (Chloris guyana) require large quantities of water – from 15,700 to 48,000 m 3 ha -1 yr -1 depending on soil and climate – often drawn from non-renewable groundwater sources.
The global luxury brand market has been growing steadily during the last two decades, along with the gradual expansion of the scope of its market, the ever-expanding offer of luxury categories, a rapid growth in emerging markets, and the recent increasing young consumers’ luxury consumption worldwide (Amatulli & Guido, 2011; Kang & Park, 2016; Shukla, Banerjee, & Singh, 2016). Luxury fashion goods comprise apparel, accessories, handbags, shoes, watches, jewellery, and perfume, for which just the mere use or display of a particular brand brings prestige to the owner and functional utility becomes a side issue (Amatulli & Guido, 2011). Luxury fashion sector counts for a major proportion of global luxury goods sales and is one of the product categories with the strongest growth during recent years (Fionda & Moore, 2009). Within the luxury fashion sector, there are many unique characteristics including the speed of change as well as the scale and number of fashion items that are marketed using a single luxury brand name. As such, the branding and marketing of luxury fashion brands are more complex and costly than other sectors (Fionda & Moore, 2009). Luxury fashion brand is distinctive, it invariably operates as an experiential brand, and it functions as a means of creating and communicating an identity for the brand user (Fionda & Moore, 2009; Kang & Park, 2016; Shukla et al., 2016).
However, the amount of obtainable personal life logging data has enormously increased and stands in need of effective processing, analysis, and visualisation to provide hidden insights owing to the lack of semantic information (particularly in spatiotemporal data), complexity, large volume of trivial records, and absence of effective information visualisation on a large scale. Meanwhile, new technologies such as visual analytics have emerged with great potential in data mining and visualisation to overcome the challenges in handling such data and to support individuals in many aspects of their life. Thus, this thesis contemplates the importance of scalability and conducts a compre- hensive investigation into visual analytics and its impact on the process of knowledge discovery from the European Commission project MyHealthAvatar at the Centre for Visualisation and Data Analytics by actively involving individuals in order to establish a credible reasoning and effectual interactive visualisation of such multivariate data with particular focus on lifestyle and personal events.
For each model an ANN has been designed and trained. Each single model has been tested before use in the cascading simulation. The results of the individual ANNs are presented in Table 2. In general the ANNs are able to simulate the model results. For the rainfall-runoff simulation an improved model has been developed which also uses previous flow val- ues as input. These two rainfall-runoff models correspond with the first two columns of Table 3. This table and Fig. 7 present the results of the coupled models in cascade. Not all predictions are accurate, due to several problems. In the river model, the flow can normally be well simulated if it has a uniform cross-section and no flooding area’s. In this model however it was difficult to obtain accurate results because of the lower accuracy of the output of the rainfall-runoff model. In addition, the ANN of the estuary could hardly distinguish the two processes with different time scales. The Secchi- depth, finally, is proportional to the salinity without any time delay and gave perfect results. The final, coupled model per- forms not very good, because errors are accumulated in the cascading modeling scenario. These errors mainly stem from the rainfall-runoff and salinity models. One way to test if the accumulation of errors is a dominant factor, is to design sin- gle ANNs covering two or more sub-systems at once. It is for example interesting to see if a single neural network can predict the outcome of the river model, if it only uses rainfall and evaporation data as input. The results of three additional neural networks are presented in the third column of Table 3. The results are comparable for the river model, however they get worse if the estuary model is implemented. This analysis
NaCl salinity reduced the plant height of Catharanthus rose us G. Don (Table 1). However, the shoot: root ratio was increased with increasing salinity level. Leaf area and number of buds, flowers and pods produced by the plant were also decreased with increase in salinity level. Salt grown plants exhibited low biomass (dry wt) production. This adverse effect of salinity, however, was significant only at higher salinity levels (100 and 200 mM NaCl). A decrease in overall growth of plants due to salinity has been reported by a number of workers (Hoffman et al. 1989, ImamulHuq and Larher 1983, Goniaetal. 1994). An increase in shoot: root ratio in Catharanthus is indicati ve of an adverse effect of NaCl salinity on the root growth suggesting salt sensitive nature of the plant.