scholarly journals THE COASTAL WIND FIELD OF THE SOUTHERN CAPE

1982 ◽  
Vol 1 (18) ◽  
pp. 154
Author(s):  
Ian Tyrrell Hunter
Keyword(s):  
Time Series ◽  
Coastal Zone ◽  
Wind Field ◽  
Large Scale ◽  
Coastal Region ◽  
Spatial Variations ◽  
Wind Data ◽  
Synoptic Situation ◽  
Coastal Boundary ◽  
Offshore Region

The coastal region of the South Cape is presented as a typical semideveloped coastal zone with a limited environmental dataset. The author places himself in the position of a coastal engineer requiring wind data for design purposes. The various sources of wind measurement are discussed. Time series are presented depicting responses at different sites to the same large-scale synoptic situation. Spatial variations, both across the coastal boundary and in the offshore region are emphasized. These variations are brought to the attention of those engineers who may have to extrapolate wind conditions to their site of interest.

scholarly journals Coastal Water: Wisdom, Destruction, Conflicts and Contestation. A Case of Southwest Coastal Region of Bangladesh

2021 ◽  
Author(s):  
Hamidul Huq ◽  
Tahmid Huq Easher
Keyword(s):  
Coastal Zone ◽  
Crop Production ◽  
Large Scale ◽  
Driving Forces ◽  
Saline Water ◽  
Coastal Region ◽  
Pond Water ◽  
Water Canal ◽  
Water Pond ◽  
Drainage Congestion

The coastal zone of Bangladesh is full of opportunities and vulnerabilities. Water is the central source of these opportunities and vulnerabilities. Seawater, river water, canal water, floodplain water, wetlands water, pond water is the dominant source of livelihoods of coastal people. The propel of the coastal economy is dependent on this water. But salinity alone creates vulnerability of the economy and people dependent on this water. Tidal surge, storm surge, drainage congestion, waterlogging, saline water aquaculture are the driving forces of water crises. Water crises are the unsolved issues ever despite large scale interventions. A shortage of freshwater suffers coastal people ever in regards to crop production, drinking water, health, aquaculture, and so on. Uncertainties driven by cyclones, river erosion, outsiders’ interventions-led consequences are the big challenges of the coastal zone in managing water. Coastal people are challenging, resilient, adaptive, and strong in contestations in managing water resources for their livelihoods. They exploit the opportunities using their ability of reconstruction.

scholarly journals Mapping Tidal Flats with Landsat 8 Images and Google Earth Engine: A Case Study of the China’s Eastern Coastal Zone circa 2015

2019 ◽  
Vol 11 (8) ◽  
pp. 924 ◽  
Author(s):  
Zhang ◽  
Dong ◽  
Liu ◽  
Gao ◽  
Hu ◽  
...  
Keyword(s):  
Time Series ◽  
Coastal Zone ◽  
Tidal Flat ◽  
Large Scale ◽  
Tidal Flats ◽  
Google Earth ◽  
Landsat 8 ◽  
Intertidal Flats ◽  
Multi Temporal ◽  
Google Earth Engine

Accurate and up-to-date tidal flat mapping is of much importance to learning how coastal ecosystems work in a time of anthropogenic disturbances and rising sea levels, which will provide scientific instruction for sustainable management and ecological assessments. For large-scale and high spatial-resolution mapping of tidal flats, it is difficult to obtain accurate tidal flat maps without multi-temporal observation data. In this study, we aim to investigate the potential and advantages of the freely accessible Landsat 8 Operational Land Imager (OLI) imagery archive and Google Earth Engine (GEE) for accurate tidal flats mapping. A novel approach was proposed, including multi-temporal feature extraction, machine learning classification using GEE and morphological post-processing. The 50 km buffer of the coastline from Hangzhou Bay to Yalu River in China’s eastern coastal zone was taken as the study area. From the perspective of natural attributes and unexploited status of tidal flats, we delineated a broader extent comprising intertidal flats, supratidal barren flats and vegetated flats, since intertidal flats are major component of the tidal flats. The overall accuracy of the resultant map was about 94.4% from a confusion matrix for accuracy assessment. The results showed that the use of time-series images can greatly eliminate the effects of tidal level, and improve the mapping accuracy. This study also proved the potential and advantage of combining the GEE platform with time-series Landsat images, due to its powerful cloud computing platform, especially for large scale and longtime tidal flats mapping.

scholarly journals Subsidence Monitoring of Fill Area in Yan’an New District Based on Sentinel-1A Time Series Imagery

2021 ◽  
Vol 13 (15) ◽  
pp. 3044
Author(s):  
Mingjie Liao ◽  
Rui Zhang ◽  
Jichao Lv ◽  
Bin Yu ◽  
Jiatai Pang ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Ground Deformation ◽  
Dynamic Change ◽  
Deformation Monitoring ◽  
Chinese Loess Plateau ◽  
Ground Subsidence ◽  
Shanxi Province ◽  
Environment Change ◽  
Loess Area

In recent years, many cities in the Chinese loess plateau (especially in Shanxi province) have encountered ground subsidence problems due to the construction of underground projects and the exploitation of underground resources. With the completion of the world’s largest geotechnical project, called “mountain excavation and city construction,” in a collapsible loess area, the Yan’an city also appeared to have uneven ground subsidence. To obtain the spatial distribution characteristics and the time-series evolution trend of the subsidence, we selected Yan’an New District (YAND) as the specific study area and presented an improved time-series InSAR (TS-InSAR) method for experimental research. Based on 89 Sentinel-1A images collected between December 2017 to December 2020, we conducted comprehensive research and analysis on the spatial and temporal evolution of surface subsidence in YAND. The monitoring results showed that the YAND is relatively stable in general, with deformation rates mainly in the range of −10 to 10 mm/yr. However, three significant subsidence funnels existed in the fill area, with a maximum subsidence rate of 100 mm/yr. From 2017 to 2020, the subsidence funnels enlarged, and their subsidence rates accelerated. Further analysis proved that the main factors induced the severe ground subsidence in the study area, including the compressibility and collapsibility of loess, rapid urban construction, geological environment change, traffic circulation load, and dynamic change of groundwater. The experimental results indicated that the improved TS-InSAR method is adaptive to monitoring uneven subsidence of deep loess area. Moreover, related data and information would provide reference to the large-scale ground deformation monitoring and in similar loess areas.

scholarly journals Meso- and microscale sea-ice motion in the East Siberian Sea as determined from ERS-1 SAR Data

1999 ◽  
Vol 45 (150) ◽  
pp. 370-383 ◽  
Author(s):  
Kim Morris ◽  
Shusun Li ◽  
Martin Jeffries
Keyword(s):  
Time Series ◽  
Sea Ice ◽  
Large Scale ◽  
Vector Fields ◽  
Lateral Displacement ◽  
Motion Vector ◽  
Sar Interferometry ◽  
East Siberian Sea ◽  
Pack Ice ◽  
Sar Data

Abstract Synthetic aperture radar- (SAR-)derived ice-motion vectors and SAR interferometry were used to study the sea-ice conditions in the region between the coast and 75° N (~ 560 km) in the East Siberian Sea in the vicinity of the Kolyma River. ERS-1 SAR data were acquired between 24 December 1993 and 30 March 1994 during the 3 day repeat Ice Phase of the satellite. The time series of the ice-motion vector fields revealed rapid (3 day) changes in the direction and displacement of the pack ice. Longer-term (≥ 1 month) trends also emerged which were related to changes in large-scale atmospheric circulation. On the basis of this time series, three sea-ice zones were identified: the near-shore, stationary-ice zone; a transitional-ice zone;and the pack-ice zone. Three 3 day interval and one 9 day interval interferometric sets (amplitude, correlation and phase diagrams) were generated for the end of December, the begining of February and mid-March. They revealed that the stationary-ice zone adjacent to the coast is in constant motion, primarily by lateral displacement, bending, tilting and rotation induced by atmospheric/oceanic forcing. The interferogram patterns change through time as the sea ice becomes thicker and a network of cracks becomes established in the ice cover. It was found that the major features in the interferograms were spatially correlated with sea-ice deformation features (cracks and ridges) and major discontinuities in ice thickness.

scholarly journals Temporal and Spatial Variations of the Biochemical Composition of Phytoplankton and Potential Food Material (FM) in Jaran Bay, South Korea

Water ◽  
2020 ◽  
Vol 12 (11) ◽  
pp. 3093
Author(s):  
Jae Hyung Lee ◽  
Won-Chan Lee ◽  
Hyung Chul Kim ◽  
Naeun Jo ◽  
Kwanwoo Kim ◽  
...  
Keyword(s):  
South Korea ◽  
Food Source ◽  
Coastal Region ◽  
Controlling Factors ◽  
Spatial Variations ◽  
Nutrient Concentrations ◽  
Food Material ◽  
Biochemical Component ◽  
Phytoplankton Communities ◽  
Biochemical Compositions

Food material (FM) derived from biochemical components (e.g., proteins, lipids, and carbohydrates) of phytoplankton can provide important quantitative and qualitative information of the food available to filter-feeding animals. The main objective of this study was to observe the seasonal and spatial variations of the biochemical compositions of phytoplankton and to identify the major controlling factors of FM as a primary food source in Jaran Bay, a large shellfish aquaculture site in South Korea. Based on monthly sampling conducted during 2016, significant monthly variations in the depth-integrated concentrations of major inorganic nutrients and chlorophyll a within the euphotic water column and a predominance (49.9 ± 18.7%) of micro-sized phytoplankton (>20 μm) were observed in Jaran Bay. Carbohydrates were the dominant biochemical component (51.8 ± 8.7%), followed by lipids (27.3 ± 3.8%) and proteins (20.9 ± 7.4%), during the study period. The biochemical compositions and average monthly FM levels (411.7 ± 93.0 mg m−3) in Jaran Bay were not consistent among different bays in the southern coastal region of South Korea, possibly due to differences in controlling factors, such as environmental and biological factors. According to the results from multiple linear regression, the variations in FM could be explained by the relatively large phytoplankton and the P* (PO43− − 1/16 × NO3−) and NH4+ concentrations in Jaran Bay. The macromolecular compositions and FM, as alternatives food source materials, should be monitored in Jaran Bay due to recent changes in nutrient concentrations and phytoplankton communities.

Movements and survival of sonically tagged farmed Atlantic salmon released in Cobscook Bay, Maine, USA

2006 ◽  
Vol 63 (7) ◽  
pp. 1218-1223 ◽  
Author(s):  
Frederick G. Whoriskey ◽  
Paul Brooking ◽  
Gino Doucette ◽  
Stephen Tinker ◽  
Jonathan W. Carr
Keyword(s):  
Atlantic Salmon ◽  
Salmo Salar ◽  
Coastal Zone ◽  
Coastal Region ◽  
Movement Patterns ◽  
Tidal Currents ◽  
Bay Of Fundy ◽  
Wild Salmon ◽  
Receiver Array ◽  
Farmed Atlantic Salmon

Abstract We sonically tagged and released farmed Atlantic salmon (Salmo salar) from a cage site in Cobscook Bay, Maine, USA. The fish were released in January (n = 75) and in April and May (n = 198) 2004 to study their movement patterns and survival and to assess the possibility of recapturing them. Inshore and offshore waters in this region are subject to intense tidal currents. Tagged salmon dispersed >1 km from the cage site within a few hours of their release. Mortality was high within Cobscook Bay and the surrounding coastal region (56% of the winter (January) releases; 84% of the spring (March) releases), probably the result of seal predation. Most surviving fish exited the coastal zone and entered the Bay of Fundy along the routes of the dominant tidal currents, passing through Canadian waters. No tagged fish were detected during the wild salmon spawning season in autumn 2004 in any of the 43 monitored salmon rivers draining into the Bay of Fundy, or during 2005 either in the Magaguadavic River, the site of the hatchery in which the fish were reared to the smolt stage, or by a limited coastal receiver array.

scholarly journals Effects of a 5-year trawling ban on the local benthic community in a wind farm in the Dutch coastal zone

2014 ◽  
Vol 72 (3) ◽  
pp. 962-972 ◽  
Author(s):  
Magda J. N. Bergman ◽  
Selma M. Ubels ◽  
Gerard C. A. Duineveld ◽  
Erik W. G. Meesters
Keyword(s):  
Coastal Zone ◽  
Benthic Community ◽  
Large Scale ◽  
Wind Farm ◽  
Diversity Index ◽  
Benthic Assemblages ◽  
The North ◽  
Wiener Diversity Index ◽  
Short Period ◽  
Reference Areas

Abstract As part of a large impact study in a wind farm (OWEZ) in the Dutch coastal zone, the effects of exclusion of bottom trawling on the benthic community were studied by comparison with nearby reference areas which were regularly fished. In addition to a standard boxcorer for common macrofauna, a Triple-D dredge was used to collect longer-lived, more sparsely distributed infauna and epifauna. Multivariate analysis did not reveal any difference between the assemblages in and outside OWEZ with respect to abundance, biomass, and production after a 5-year closure. The Shannon–Wiener diversity index pointed to a significantly higher diversity in OWEZ compared with some of the reference areas. A minority of the bivalve species assumed to be sensitive to trawling showed higher abundances (Spisula solida) or larger sizes (Tellina fabula, Ensis directus) in OWEZ than in some of the reference areas. In general, samples collected with the Triple-D showed more differences between areas than boxcore samples. No evidence was also found that the species composition in OWEZ relative to the reference areas had changed in the period between 1 (2007) and 5 (2011) years after closure. The change observed in all areas between 2007 and 2011 was mainly due to relatively small variations in species abundances. In conclusion, 5 years after the closure of OWEZ to fisheries, only subtle changes were measured in the local benthic community, i.e. a higher species diversity and an increased abundance and lengths of some bivalves. Depleted adult stocks, faunal patchiness, and a limited time for recovery (5 years) might explain that a significant recovery could not be found. The current study shows that designation of large-scale marine protected areas as planned for the North Sea will not automatically imply that restoration of benthic assemblages can be expected within a relatively short period of years.

Machine-Learning for the Prediction of Lost Circulation Events - Time Series Analysis and Model Evaluation

2021 ◽  
Author(s):  
Arturo Magana-Mora ◽  
Mohammad AlJubran ◽  
Jothibasu Ramasamy ◽  
Mohammed AlBassam ◽  
Chinthaka Gooneratne ◽  
...  
Keyword(s):  
Machine Learning ◽  
Time Series ◽  
Real Time ◽  
Large Scale ◽  
Model Comparison ◽  
Drilling Fluid ◽  
Sensor Data ◽  
False Alarms ◽  
Suitable Model ◽  
Lost Circulation

Abstract Objective/Scope. Lost circulation events (LCEs) are among the top causes for drilling nonproductive time (NPT). The presence of natural fractures and vugular formations causes loss of drilling fluid circulation. Drilling depleted zones with incorrect mud weights can also lead to drilling induced losses. LCEs can also develop into additional drilling hazards, such as stuck pipe incidents, kicks, and blowouts. An LCE is traditionally diagnosed only when there is a reduction in mud volume in mud pits in the case of moderate losses or reduction of mud column in the annulus in total losses. Using machine learning (ML) for predicting the presence of a loss zone and the estimation of fracture parameters ahead is very beneficial as it can immediately alert the drilling crew in order for them to take the required actions to mitigate or cure LCEs. Methods, Procedures, Process. Although different computational methods have been proposed for the prediction of LCEs, there is a need to further improve the models and reduce the number of false alarms. Robust and generalizable ML models require a sufficiently large amount of data that captures the different parameters and scenarios representing an LCE. For this, we derived a framework that automatically searches through historical data, locates LCEs, and extracts the surface drilling and rheology parameters surrounding such events. Results, Observations, and Conclusions. We derived different ML models utilizing various algorithms and evaluated them using the data-split technique at the level of wells to find the most suitable model for the prediction of an LCE. From the model comparison, random forest classifier achieved the best results and successfully predicted LCEs before they occurred. The developed LCE model is designed to be implemented in the real-time drilling portal as an aid to the drilling engineers and the rig crew to minimize or avoid NPT. Novel/Additive Information. The main contribution of this study is the analysis of real-time surface drilling parameters and sensor data to predict an LCE from a statistically representative number of wells. The large-scale analysis of several wells that appropriately describe the different conditions before an LCE is critical for avoiding model undertraining or lack of model generalization. Finally, we formulated the prediction of LCEs as a time-series problem and considered parameter trends to accurately determine the early signs of LCEs.

MetaTP

2021 ◽  
Vol 5 (3) ◽  
pp. 1-28
Author(s):  
Weida Zhong ◽  
Qiuling Suo ◽  
Abhishek Gupta ◽  
Xiaowei Jia ◽  
Chunming Qiao ◽  
...  
Keyword(s):  
Time Series ◽  
Large Scale ◽  
Multivariate Time Series ◽  
Modern Society ◽  
Training Data ◽  
Traffic Prediction ◽  
Temporal Prediction ◽  
Reference Space ◽  
Meta Learning ◽  
Real World Datasets

With the popularity of smartphones, large-scale road sensing data is being collected to perform traffic prediction, which is an important task in modern society. Due to the nature of the roving sensors on smartphones, the collected traffic data which is in the form of multivariate time series, is often temporally sparse and unevenly distributed across regions. Moreover, different regions can have different traffic patterns, which makes it challenging to adapt models learned from regions with sufficient training data to target regions. Given that many regions may have very sparse data, it is also impossible to build individual models for each region separately. In this paper, we propose a meta-learning based framework named MetaTP to overcome these challenges. MetaTP has two key parts, i.e., basic traffic prediction network (base model) and meta-knowledge transfer. In base model, a two-layer interpolation network is employed to map original time series onto uniformly-spaced reference time points, so that temporal prediction can be effectively performed in the reference space. The meta-learning framework is employed to transfer knowledge from source regions with a large amount of data to target regions with a few data examples via fast adaptation, in order to improve model generalizability on target regions. Moreover, we use two memory networks to capture the global patterns of spatial and temporal information across regions. We evaluate the proposed framework on two real-world datasets, and experimental results show the effectiveness of the proposed framework.

Sign in / Sign up

Export Citation Format

Share Document