scholarly journals INVESTIGATING THE LITTORAL TRANSPORT OF SEDIMENT ALONG THE NASESE COASTLINE, SUVA, FIJI ISLANDS

2020 ◽  
pp. 48
Author(s):  
Cyril Rachman ◽  
Yuyun Qomariyah
Keyword(s):  
Meteorological Data ◽  
Breaking Waves ◽  
Theoretical Concept ◽  
Longshore Current ◽  
Wind Rose ◽  
Littoral Drift ◽  
Sediment Properties ◽  
Longshore Transport ◽  
Fiji Islands ◽  
The Impact

The research investigated the current Nasese Coastline area from the impact of Littoral transport, a term used for the transport of non-cohesive sediments, i.e. mainly sand, along the foreshore and the shoreface of Nasese due to the action of the breaking waves and the longshore current. The littoral transport is also called the longshore transport or the littoral drift. The theoretical concept coastal sediment properties can be used to evaluate properties of sediment on site to avoid serve impacts towards the Suva Port in the future. The method used was on-site measurement by surveying to generate coastal profiles. Along with this, 1464 hours of wind data for the months of November and December 2019 were used to generate the frequency of the magnitude of Wind and the Direction using Wind Rose Plots for Meteorological Data. The movement of the sediments through the Beach Profiles agrees with the output generated Wind Directions. Therefore, Suva Port, may consider this situation in terms of the routine maintenance of dredging in order to sustain the acceptable depth of the Sea Port.Recorded Presentation from the vICCE (YouTube Link): https://youtu.be/vW05xoiNa_w

scholarly journals A GROSS LONGSHORE TRANSPORT RATE FORMULA

1972 ◽  
Vol 1 (13) ◽  
pp. 47 ◽  
Author(s):  
Cyril J. Galvin
Keyword(s):  
Current Velocity ◽  
Empirical Relation ◽  
Field Measurements ◽  
Transport Rate ◽  
Longshore Current ◽  
Physical Explanation ◽  
Littoral Drift ◽  
Longshore Transport ◽  
Term Field

Gross longshore transport rates for 11 long-term field measurements are predicted reasonably well by the empirical relation, Q=2H2, where Q is longshore transport rate in 100,000 yd3/yr, and H is a mean breaker height in feet. A physical explanation of this empirical relation assumes: (1) most littoral drift is transported in suspension; (2) longshore current velocity is predicted by V-gmTsin28j,; (3) the empirical relation is an equation for conservation of suspended sediment in the longshore current.

scholarly journals LONGSHORE CURRENTS IN ONE AND MULTI-BAR PROFILES RELATION TO LITTORAL DRIFT

2011 ◽  
Vol 1 (8) ◽  
pp. 15
Author(s):  
Per Bruun
Keyword(s):  
Wave Breaking ◽  
Breaking Waves ◽  
Current Theory ◽  
Current Approach ◽  
Rip Currents ◽  
Longshore Current ◽  
Littoral Drift ◽  
Longshore Currents ◽  
Average Water ◽  
Sloping Beach

This paper deals with longshore current theories. Introductorily it gives a brief review of wave theories for breaking waves including theoretical, laboratory as well as field results. Next the longshore current theory based on the momentum inflow over a uniformly sloping beach and bottom (Putnam, Munk and Traylor, 1949) is discussed with special reference to its friction factor. The following chapters deal with two new longshore current theories - both based on the continuity principle. One of them called the rip current approach assumes that all water thrown in by wave breaking runs out in rip currents and will probably be valid for profiles with well developed bars and waves approaching the shore almost perpendicularly. The other theory considers the fact that water from a wave breaking under an angle with the bar flows in with a certain phase difference in time longshore and this will create a longshore slope of the average water table, therefore also a longshore current. The water may return to sea uniformly as undertow or in rip currents or by a combination of both. This theory is particularly valid for waves breaking under a certain, not too small, angle with the bar. In both cases the momentum in the breaking waves is ignored because field observations show that in a well developed bar profile most of the momentum has disappeared inside the bar after wave breaking. Examples of computation of current velocities for one bar as well as multi-bar profiles are given. Next the possible relation between longshore currents and littoral drift is discussed.

scholarly journals The modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
Rui Zhang ◽  
Yujie Meng ◽  
Hejia Song ◽  
Ran Niu ◽  
Yu Wang ◽  
...  
Keyword(s):  
Air Pollution ◽  
High Temperature ◽  
Air Pollutants ◽  
Meteorological Data ◽  
Effect Of Temperature ◽  
Distributed Lag ◽  
Modification Effect ◽  
Incidence Risk ◽  
The Impact ◽  
The Relationship

Abstract Background Although exposure to air pollution has been linked to many health issues, few studies have quantified the modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo, China. Methods The data of daily incidence of influenza and the relevant meteorological data and air pollution data in Ningbo from 2014 to 2017 were retrieved. Low, medium and high temperature layers were stratified by the daily mean temperature with 25th and 75th percentiles. The potential modification effect of temperature on the relationship between air pollutants and daily incidence of influenza in Ningbo was investigated through analyzing the effects of air pollutants stratified by temperature stratum using distributed lag non-linear model (DLNM). Stratified analysis by sex and age were also conducted. Results Overall, a 10 μg/m3 increment of O3, PM2.5, PM10 and NO2 could increase the incidence risk of influenza with the cumulative relative risk of 1.028 (95% CI 1.007, 1.050), 1.061 (95% CI 1.004, 1.122), 1.043 (95% CI 1.003, 1.085), and 1.118 (95% CI 1.028, 1.216), respectively. Male and aged 7–17 years were more sensitive to air pollutants. Through the temperature stratification analysis, we found that temperature could modify the impacts of air pollution on daily incidence of influenza with high temperature exacerbating the impact of air pollutants. At high temperature layer, male and the groups aged 0–6 years and 18–64 years were more sensitive to air pollution. Conclusion Temperature modified the relationship between air pollution and daily incidence of influenza and high temperature would exacerbate the effects of air pollutants in Ningbo.

scholarly journals Using Satellite Data for CBRN (Chemical, Biological, Radiological, and Nuclear) Threat Detection, Monitoring, and Modelling

2021 ◽  
Author(s):  
Gary Sutlieff ◽  
Lucy Berthoud ◽  
Mark Stinchcombe
Keyword(s):  
Satellite Data ◽  
Meteorological Data ◽  
Atmospheric Composition ◽  
Chemical Detection ◽  
List Type ◽  
Data Types ◽  
Wide Range ◽  
Future Technologies ◽  
The Impact ◽  
Near Future

Abstract CBRN (Chemical, Biological, Radiological, and Nuclear) threats are becoming more prevalent, as more entities gain access to modern weapons and industrial technologies and chemicals. This has produced a need for improvements to modelling, detection, and monitoring of these events. While there are currently no dedicated satellites for CBRN purposes, there are a wide range of possibilities for satellite data to contribute to this field, from atmospheric composition and chemical detection to cloud cover, land mapping, and surface property measurements. This study looks at currently available satellite data, including meteorological data such as wind and cloud profiles, surface properties like temperature and humidity, chemical detection, and sounding. Results of this survey revealed several gaps in the available data, particularly concerning biological and radiological detection. The results also suggest that publicly available satellite data largely does not meet the requirements of spatial resolution, coverage, and latency that CBRN detection requires, outside of providing terrain use and building height data for constructing models. Lastly, the study evaluates upcoming instruments, platforms, and satellite technologies to gauge the impact these developments will have in the near future. Improvements in spatial and temporal resolution as well as latency are already becoming possible, and new instruments will fill in the gaps in detection by imaging a wider range of chemicals and other agents and by collecting new data types. This study shows that with developments coming within the next decade, satellites should begin to provide valuable augmentations to CBRN event detection and monitoring. Article Highlights There is a wide range of existing satellite data in fields that are of interest to CBRN detection and monitoring. The data is mostly of insufficient quality (resolution or latency) for the demanding requirements of CBRN modelling for incident control. Future technologies and platforms will improve resolution and latency, making satellite data more viable in the CBRN management field

scholarly journals Influence of the Spatial Pressure Distribution of Breaking Wave Loading on the Dynamic Response of Wolf Rock Lighthouse

2021 ◽  
Vol 9 (1) ◽  
pp. 55
Author(s):  
Darshana T. Dassanayake ◽  
Alessandro Antonini ◽  
Athanasios Pappas ◽  
Alison Raby ◽  
James Mark William Brownjohn ◽  
...  
Keyword(s):  
Dynamic Response ◽  
Pressure Distribution ◽  
Distinct Element ◽  
Breaking Waves ◽  
Breaking Wave ◽  
Wave Loading ◽  
Wave Impact ◽  
Pressure Distributions ◽  
Impact Area ◽  
The Impact

The survivability analysis of offshore rock lighthouses requires several assumptions of the pressure distribution due to the breaking wave loading (Raby et al. (2019), Antonini et al. (2019). Due to the peculiar bathymetries and topographies of rock pinnacles, there is no dedicated formula to properly quantify the loads induced by the breaking waves on offshore rock lighthouses. Wienke’s formula (Wienke and Oumeraci (2005) was used in this study to estimate the loads, even though it was not derived for breaking waves on offshore rock lighthouses, but rather for the breaking wave loading on offshore monopiles. However, a thorough sensitivity analysis of the effects of the assumed pressure distribution has never been performed. In this paper, by means of the Wolf Rock lighthouse distinct element model, we quantified the influence of the pressure distributions on the dynamic response of the lighthouse structure. Different pressure distributions were tested, while keeping the initial wave impact area and pressure integrated force unchanged, in order to quantify the effect of different pressure distribution patterns. The pressure distributions considered in this paper showed subtle differences in the overall dynamic structure responses; however, pressure distribution #3, based on published experimental data such as Tanimoto et al. (1986) and Zhou et al. (1991) gave the largest displacements. This scenario has a triangular pressure distribution with a peak at the centroid of the impact area, which then linearly decreases to zero at the top and bottom boundaries of the impact area. The azimuthal horizontal distribution was adopted from Wienke and Oumeraci’s work (2005). The main findings of this study will be of interest not only for the assessment of rock lighthouses but also for all the cylindrical structures built on rock pinnacles or rocky coastlines (with steep foreshore slopes) and exposed to harsh breaking wave loading.

scholarly journals Understanding the seasonality of performance resilience to climate volatility in Mediterranean dairy sheep

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Valentina Tsartsianidou ◽  
Vanessa Varvara Kapsona ◽  
Enrique Sánchez-Molano ◽  
Zoitsa Basdagianni ◽  
Maria Jesús Carabaño ◽  
...  
Keyword(s):  
Genetic Control ◽  
Milk Production ◽  
Eastern Mediterranean ◽  
Meteorological Data ◽  
Genetic Correlations ◽  
Farm Animals ◽  
Seasonal Effects ◽  
Cold Weather ◽  
Adverse Weather ◽  
The Impact

AbstractAs future climate challenges become increasingly evident, enhancing performance resilience of farm animals may contribute to mitigation against adverse weather and seasonal variation, and underpin livestock farming sustainability. In the present study, we develop novel seasonal resilience phenotypes reflecting milk production changes to fluctuating weather. We evaluate the impact of calendar season (autumn, winter and spring) on animal performance resilience by analysing 420,534 milk records of 36,908 milking ewes of the Chios breed together with relevant meteorological data from eastern Mediterranean. We reveal substantial seasonal effects on resilience and significant heritable trait variation (h2 = 0.03–0.17). Resilience to cold weather (10 °C) of animals that start producing milk in spring was under different genetic control compared to autumn and winter as exemplified by negative genetic correlations (− 0.09 to − 0.27). Animal resilience to hot weather (25 °C) was partially under the same genetic control with genetic correlations between seasons ranging from 0.43 to 0.86. We report both favourable and antagonistic associations between animal resilience and lifetime milk production, depending on calendar season and the desirable direction of genetic selection. Concluding, we emphasise on seasonal adaptation of animals to climate and the need to incorporate the novel seasonal traits in future selective breeding programmes.

Prognosis of water levels in a moor groundwater system influenced by hydrology and water extraction using an artificial neural network 

2021 ◽  
Author(s):  
Sascha Flaig ◽  
Timothy Praditia ◽  
Alexander Kissinger ◽  
Ulrich Lang ◽  
Sergey Oladyshkin ◽  
...  
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Mean Squared Error ◽  
Meteorological Data ◽  
Water Levels ◽  
Typical Structure ◽  
Water Abstraction ◽  
Physical Knowledge ◽  
Artificial Neural ◽  
The Impact

<p>In order to prevent possible negative impacts of water abstraction in an ecologically sensitive moor south of Munich (Germany), a “predictive control” scheme is in place. We design an artificial neural network (ANN) to provide predictions of moor water levels and to separate hydrological from anthropogenic effects. As the moor is a dynamic system, we adopt the „Long short-term memory“ architecture.</p><p>To find the best LSTM setup, we train, test and compare LSTMs with two different structures: (1) the non-recurrent one-to-one structure, where the series of inputs are accumulated and fed into the LSTM; and (2) the recurrent many-to-many structure, where inputs gradually enter the LSTM (including LSTM forecasts from previous forecast time steps). The outputs of our LSTMs then feed into a readout layer that converts the hidden states into water level predictions. We hypothesize that the recurrent structure is the better structure because it better resembles the typical structure of differential equations for dynamic systems, as they would usually be used for hydro(geo)logical systems. We evaluate the comparison with the mean squared error as test metric, and conclude that the recurrent many-to-many LSTM performs better for the analyzed complex situations. It also produces plausible predictions with reasonable accuracy for seven days prediction horizon.</p><p>Furthermore, we analyze the impact of preprocessing meteorological data to evapotranspiration data using typical ETA models. Inserting knowledge into the LSTM in the form of ETA models (rather than implicitly having the LSTM learn the ETA relations) leads to superior prediction results. This finding aligns well with current ideas on physically-inspired machine learning.</p><p>As an additional validation step, we investigate whether our ANN is able to correctly identify both anthropogenic and natural influences and their interaction. To this end, we investigate two comparable pumping events under different meteorological conditions. Results indicate that all individual and combined influences of input parameters on water levels can be represented well. The neural networks recognize correctly that the predominant precipitation and lower evapotranspiration during one pumping event leads to a lower decrease of the hydrograph.</p><p>To further demonstrate the capability of the trained neural network, scenarios of pumping events are created and simulated.</p><p>In conclusion, we show that more robust and accurate predictions of moor water levels can be obtained if available physical knowledge of the modeled system is used to design and train the neural network. The artificial neural network can be a useful instrument to assess the impact of water abstraction by quantifying the anthropogenic influence.</p>

Quantifying recent lake level changes in Patagonia (Argentina and Chile): Back to the future?

2021 ◽  
Author(s):  
Daniel Ariztegui ◽  
Clément Pollier ◽  
Andrés Bilmes
Keyword(s):  
Water Level ◽  
Lake Level ◽  
Satellite Images ◽  
Meteorological Data ◽  
Tierra Del Fuego ◽  
Water Level Fluctuations ◽  
Lake Levels ◽  
The Past ◽  
Rainfall Variations ◽  
The Impact

<p>Lake levels in hydrologically closed-basins are very sensitive to climatically and/or anthropogenically triggered environmental changes. Their record through time can provide valuable information to forecast changes that can have substantial economical and societal impact.</p><p>Increasing precipitation in eastern Patagonia (Argentina) have been documented following years with strong El Niño (cold) events using historical and meteorological data. Quantifying changes in modern lake levels allow determining the impact of rainfall variations while contributing to anticipate the evolution of lacustrine systems over the next decades with expected fluctuations in ENSO frequencies. Laguna Carrilaufquen Grande is located in the intermontane Maquinchao Basin, Argentina. Its dimension fluctuates greatly, from 20 to 55 km<sup>2</sup> water surface area and an average water depth of 3 m. Several well-preserved gravelly beach ridges witness rainfall variations that can be compared to meteorological data and satellite images covering the last ~50 years. Our results show that in 2016 lake level was the lowest of the past 44 years whereas the maximum lake level was recorded in 1985 (+11.8 m above the current lake level) in a position 1.6 km to the east of the present shoreline. A five-years moving average rainfall record of the area was calculated smoothing the extreme annual events and correlated to the determined lake level fluctuations. The annual variation of lake levels was up to 1.2 m (e.g. 2014) whereas decadal variations related to humid-arid periods for the interval 2002 to 2016 were up to 9.4 m. These data are consistent with those from other monitored lakes and, thus, our approach opens up new perspectives to understand the historical water level fluctuations of lakes with non-available monitoring data.</p><p> </p><p>Laguna de los Cisnes in the Chilean section of the island of Tierra del Fuego, is a closed-lake presently divided into two sections of 2.2 and 11.9 km<sup>2</sup>, respectively. These two water bodies were united in the past forming a single larger lake. The lake level was  ca. 4 m higher than today as shown by clear shorelines and the outcropping of large Ca-rich microbialites. Historical data, aerial photographs and satellite images indicate that the most recent changes in lake level are the result of a massive decrease of water input during the last half of the 20<sup>th</sup> century triggered by an indiscriminate use of the incoming water for agricultural purposes. The spectacular outcropping of living and fossil microbialites is not only interesting from a scientific point of view but has also initiated the development of the site as a local touristic attraction. However, if the use of the incoming water for agriculture in the catchment remains unregulated the lake water level might drop dangerously and eventually the lake might fully desiccate.</p><p>These two examples illustrate how recent changes in lake level can be used to anticipate the near future of lakes. They show that ongoing climate changes along with the growing demand of natural resources have already started to impact lacustrine systems and this is likely to increase in the decades to come.</p>

Contrasting facies patterns between river-dominated and symmetrical wave-dominated delta deposits

2021 ◽  
Vol 91 (3) ◽  
pp. 262-295
Author(s):  
BRIAN J. WILLIS ◽  
TAO SUN ◽  
R. BRUCE AINSWORTH
Keyword(s):  
Sea Level Rise ◽  
Sea Level ◽  
Preferential Flow ◽  
Return Flow ◽  
Lateral Migration ◽  
Littoral Drift ◽  
Delta Front ◽  
Reference Case ◽  
Depositional Processes ◽  
The Impact

Abstract Process-physics-based, coupled hydrodynamic–morphodynamic delta models are constructed to understand preserved facies heterogeneities that can influence subsurface fluid flow. Two deltaic systems are compared that differ only in the presence of waves: one river dominated and the other strongly influenced by longshore currents. To understand an entire preserved deltaic succession, the growth of multiple laterally adjacent delta lobes is modeled to define delta axial to marginal facies trends through an entire regressive–transgressive depositional succession. The goal is to refine a facies model for symmetrical wave-dominated deltas (where littoral drift diverges from the delta lobe apex). Because many factors change depositional processes on deltas, the description of the river-dominated example is included to provide a direct reference case from which to define the impact of waves on preserved facies patterns. Both systems display strong facies trends from delta axis to margin that continued into inter-deltaic areas. River-dominated delta regression preserved a dendritic branching of compensationally stacked bodies. Transgression, initiated by sea-level rise, backfilled the main channel and deposited levees and splays on the submerging delta top. Wave-dominated deltas developed dual clinoforms: a shoreface clinoform built as littoral drift carried sediment away from the river month and onshore, and a subaqueous delta-front clinoform composed of sediment accumulated below wave base. Although littoral drift in both directions away from the delta axis stabilized the position of the river at the shoreline, distributary-channel avulsions and lateral migration of river flows across the subaqueous delta top produced heterogeneities in both sets of clinoform deposits. Separation of shoreface and subaqueous delta-front clinoforms across a subaqueous delta top eroded to wave base produced a discontinuity in progradational vertical successions that appeared gradual in some locations but abrupt in others. Littoral drift flows away from adjacent deltas converged in inter-deltaic areas, producing shallow water longshore bars cut by wave-return-flow channels with associated terminal mouth bars. Transgression initiated by sea-level rise initially led to vertical aggradation of wave-reworked sheet sands on the subaqueous delta top and then retreating shoreface barrier sands as the subaerial delta top flooded. Pseudo inter-well flow tests responded to local heterogeneities in the preserved deposits. As expected, abandoned channels in the river-dominated case defined shoreline-perpendicular preferential flow paths and wave-dominated delta deposits are more locally homogeneous, but scenarios for development of more pronounced shore-parallel heterogeneity patterns for wave-influenced deltas are discussed. The results highlight the need to consider the dual clinoform nature of wave-dominated delta deposition for facies prediction and subsurface interpretation.

scholarly journals Toward a Weather-Based Forecasting System for Fire Blight and Downy Mildew

Atmosphere ◽  
2018 ◽  
Vol 9 (12) ◽  
pp. 484 ◽  
Author(s):  
Ana Firanj Sremac ◽  
Branislava Lalić ◽  
Milena Marčić ◽  
Ljiljana Dekić
Keyword(s):  
Downy Mildew ◽  
Fire Blight ◽  
Meteorological Data ◽  
Weather Prediction ◽  
Weather Forecast ◽  
Atmospheric Model ◽  
Plasmopara Viticola ◽  
Forecasting System ◽  
Nwp Model ◽  
The Impact

The aim of this research is to present a weather-based forecasting system for apple fire blight (Erwinia amylovora) and downy mildew of grapevine (Plasmopara viticola) under Serbian agroecological conditions and test its efficacy. The weather-based forecasting system contains Numerical Weather Prediction (NWP) model outputs and a disease occurrence model. The weather forecast used is a product of the high-resolution forecast (HRES) atmospheric model by the European Centre for Medium-Range Weather Forecasts (ECMWF). For disease modelling, we selected a biometeorological system for messages on the occurrence of diseases in fruits and vines (BAHUS) because it contains both diseases with well-known and tested algorithms. Several comparisons were made: (1) forecasted variables for the fifth day are compared against measurements from the agrometeorological network at seven locations for three months (March, April, and May) in the period 2012–2018 to determine forecast efficacy; (2) BAHUS runs driven with observed and forecast meteorology were compared to test the impact of forecasted meteorological data; and (3) BAHUS runs were compared with field disease observations to estimate system efficacy in plant disease forecasts. The BAHUS runs with forecasted and observed meteorology were in good agreement. The results obtained encourage further development, with the goal of fully utilizing this weather-based forecasting system.

Sign in / Sign up

Export Citation Format

Share Document