scholarly journals The Impact of Biophysical Processes on Sediment Transport in the Wax Lake Delta (Louisiana, USA)

Water ◽  
2020 ◽  
Vol 12 (7) ◽  
pp. 2072
Author(s):  
Courtney Elliton ◽  
Kehui Xu ◽  
Victor H. Rivera-Monroy
Keyword(s):  
Sediment Transport ◽  
Optical Sensors ◽  
Coastal Wetland ◽  
Inorganic Nitrogen ◽  
Wind Waves ◽  
Water Levels ◽  
Vegetation Growth ◽  
Daily Data ◽  
Wax Lake Delta ◽  
The Impact

Sediment transport in coastal regions is regulated by the interaction of river discharge, wind, waves, and tides, yet the role of vegetation in this interaction is not well understood. Here, we evaluated these variables using multiple acoustic and optical sensors deployed for 30–60 days in spring and summer/fall 2015 at upstream and downstream stations in Mike Island, a deltaic island within the Wax Lake Delta, LA, USA. During a flooding stage, semidiurnal and diurnal tidal impact was minimal on an adjacent river channel, but significant in Mike Island where vegetation biomass was low and wave influence was greater downstream. During summer/fall, a “vegetated channel” constricted the water flow, decreasing current speeds from ~13 cm/s upstream to nearly zero downstream. Synchrony between the upstream and downstream water levels in spring (R2 = 0.91) decreased in summer/fall (R2 = 0.84) due to dense vegetation, which also reduced the wave heights from 3–20 cm (spring) to nearly 0 cm (summer/fall). Spatial and temporal differences in total inorganic nitrogen and orthophosphate concentrations in the overlying and sediment porewater were evident as result of vegetation growth and expansion during summer/fall. This study provides key hourly/daily data and information needed to improve the parameterization of biophysical models in coastal wetland restoration projects.

scholarly journals THE IMPACT OF BARRIER ISLAND DEGRADATION ON HURRICANE-INDUCED SEDIMENT TRANSPORT

2018 ◽  
pp. 49
Author(s):  
Ke Liu ◽  
Qin Chen ◽  
Kelin Hu
Keyword(s):  
Sediment Transport ◽  
Storm Surge ◽  
Coastal Wetland ◽  
Transport Model ◽  
Three Dimensional ◽  
Barrier Islands ◽  
Coastal Morphology ◽  
Major Barrier ◽  
Barataria Bay ◽  
The Impact

Hurricanes are recognized as a strong forcing in changing coastal morphology by redistributing sediments. Barrier islands protect estuaries from storm surge and severe waves and confine water and sediment discharge into estuaries during a hurricane event. In this study, we developed a three-dimensional, fully coupled storm surge, waves, and sediment transport model. The impacts of barrier islands degradation on hurricane hydrodynamics and sediment dynamics were evaluated by comparing a hypothetical model configuration for four major barrier islands in Terrebonne Bay and Barataria Bay against a baseline configuration. With the hypothetical deterioration of barrier islands, model results showed that the sediment transport from the shelf to the estuary increased in Terrebonne Bay but decreased in Barataria Bay. In the simulations, most of the deposition on coastal wetland still originated from the bay even when the barrier islands were degraded.

scholarly journals Evaluation of Low-Cost Sensors for Ambient PM2.5 Monitoring

2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Marek Badura ◽  
Piotr Batog ◽  
Anetta Drzeniecka-Osiadacz ◽  
Piotr Modzel
Keyword(s):  
Linear Relationship ◽  
Optical Sensors ◽  
Low Cost ◽  
Sensor Performance ◽  
Sensor Model ◽  
Daily Data ◽  
Large Dispersion ◽  
Time Averages ◽  
Relative Errors ◽  
The Impact

Low-cost sensors are an opportunity to improve the spatial and temporal resolution of particulate matter data. However, such sensors should be calibrated under conditions close to the final ones before any monitoring actions. The paper presents the results of a collocated comparison of four models of low-cost optical sensors with a TEOM 1400a analyser. SDS011 (Nova Fitness), ZH03A (Winsen), PMS7003 (Plantower), and OPC-N2 (Alphasense) sensors were used in this research. Three copies of each sensor model were placed in a common box to compare the sensor performance under the same measurement conditions. Monitoring of the PM2.5 fraction was conducted for almost half a year from 21 August 2017 to 19 February 2018 in Wrocław (Poland). Reproducibility between sensor units was assessed on the basis of coefficient of variation (CV). CV values were lower than 7% in the case of SDS011 and PMS7003 sensors and equal to 20% for OPC-N2 units. CV was higher than 50% for ZH03A, mainly due to malfunctions. During the measurements, the trends of outputs from sensors were generally similar to TEOM data, but significant overestimation of PM2.5 concentrations was observed for the sensor raw data. A high linear relationship between TEOM and sensors was noticed for 1 min, 15 min, and 1-hour averaged data for PMS7003 sensors (R2≈0.83–0.89), for SDS011 units (R2≈0.79–0.86), and for one unit of ZH03A (R2≈0.74–0.81). R2 values for daily averages were at the level 0.91–0.93 for PMS7003, 0.87–0.90 for SDS011, and 0.89 for ZH03A. OPC-N2 had only a moderate linear relationship with TEOM (R2≈0.53–0.69 for daily data and 0.43–0.61 for shorter time averages). Quite large dispersion of data and high relative errors of PM2.5 estimation were observed for concentration ranges below 20–30 μg/m3. The impact of high relative humidity level was observed for SDS011 and OPC-N2 devices—clear overestimation of outputs was observed above 80% RH.

scholarly journals Coastal wetland resilience to climate change: modelling ecosystem response to rising sea level and salinity in a variable climate

2019 ◽  
Vol 2 (1) ◽  
pp. 1-20
Author(s):  
S.E. Grenfell ◽  
F. Fortune ◽  
M.F. Mamphoka ◽  
N. Sanderson
Keyword(s):  
Climate Change ◽  
Sea Level Rise ◽  
Sea Level ◽  
Coastal Wetland ◽  
Spring Tide ◽  
Water Levels ◽  
Recurrence Interval ◽  
Sediment Accretion ◽  
Tidal Variation ◽  
The Impact

We investigate coastal wetland ecosystem resilience to sea level rise by modelling sea level rise trajectories and the impact on vegetation communities for a coastal wetland in South Africa. The rate of sediment accretion was modelled relative to IPCC sea level rise estimates for multiple RCP scenarios. For each scenario, inundation by neap and spring tide and the 2, 4, and 8 year recurrence interval water level was modelled over a period of 200 years. When tidal variation is considered, the rate of sediment accretion exceeds rising sea levels associated with climate change, resulting in no major changes in terms of inundation. When sea level rise scenarios were modelled in conjunction with recurrence interval water levels, flooding of the coastal wetland was much greater than current levels at 1 in 4 and 1 in 8 year events. In the long term, increases in salinity may cause a reduction in Phragmites australis cover. Very small increases in depth and frequency of inundation are likely to cause an expansion of samphire species at the expense of Juncus spp. The study suggests that for this wetland, variability in flow may be a key factor in balancing wetland resilience.

Climate-forced changes of bio-productivity of Belorussian terrestrial ecosystems

2019 ◽  
pp. 77-88
Author(s):  
S. A. Lysenko
Keyword(s):  
Climate Change ◽  
Carbon Dioxide ◽  
Vegetation Index ◽  
Precipitation Amount ◽  
Terrestrial Ecosystems ◽  
Vegetation Period ◽  
Climatic Trend ◽  
Vegetation Growth ◽  
Current Century ◽  
The Impact

The spatial and temporal particularities of Normalized Differential Vegetation Index (NDVI) changes over territory of Belarus in the current century and their relationship with climate change were investigated. The rise of NDVI is observed at approximately 84% of the Belarus area. The statistically significant growth of NDVI has exhibited at nearly 35% of the studied area (t-test at 95% confidence interval), which are mainly forests and undeveloped areas. Croplands vegetation index is largely descending. The main factor of croplands bio-productivity interannual variability is precipitation amount in vegetation period. This factor determines more than 60% of the croplands NDVI dispersion. The long-term changes of NDVI could be explained by combination of two factors: photosynthesis intensifying action of carbon dioxide and vegetation growth suppressing action of air warming with almost unchanged precipitation amount. If the observed climatic trend continues the croplands bio-productivity in many Belarus regions could be decreased at more than 20% in comparison with 2000 year. The impact of climate change on the bio-productivity of undeveloped lands is only slightly noticed on the background of its growth in conditions of rising level of carbon dioxide in the atmosphere.

scholarly journals Nutrient retention behind a tropical mega-dam: a case study of the Sardar Sarovar Dam, India

2021 ◽  
Vol 3 (1) ◽  
Author(s):  
Harish Gupta ◽  
S. Kiran Kumar Reddy ◽  
Mounika Chiluka ◽  
Vamshikrishna Gandla
Keyword(s):  
Arabian Sea ◽  
Inorganic Nitrogen ◽  
Nutrient Retention ◽  
Significant Loss ◽  
N Removal ◽  
Narmada River ◽  
Sardar Sarovar ◽  
The Impact

AbstractIn this study, we demonstrate the impact of the construction of a mega-dam on the nutrient export regime of a large tropical river into the Arabian Sea. Long-term (11 years) fortnight nutrient parameters, upstream and downstream to Sardar Sarovar (SS) Dam, were examined to determine the periodical change in nutrient fluxes from the Narmada River, India. During this 11-year period, the average discharge of the Narmada River upstream to Rajghat (35.3 km3 year−1) was higher than that of downstream at Garudeshwar (33.9 km3 year−1). However, during the same period, the suspended sediment load was reduced by 21 million tons (MT) from 37.9 MT at Rajghat to 16.7 MT at Garudeshwar. Similarly, mean concentrations of dissolved silica (DSi) reduced from 470 (upstream) to 214 µM (downstream), dissolved inorganic phosphate (DIP) from 0.84 to 0.38 µM, and dissolved inorganic nitrogen (DIN) from 43 to 1.5 µM. It means that about 54%, 55%, and 96% flux of DSi, DIP, and DIN retained behind the dam, respectively. The estimated denitrification rate (80,000 kg N km−2 year−1) for the reservoir is significantly higher than N removal by lentic systems, globally. We hypothesize that processes such as biological uptake and denitrification under anoxic conditions could be a key reason for the significant loss of nutrients, particularly of DIN. Finally, we anticipated that a decline in DIN fluxes (by 1.13 × 109 mol year−1) from the Narmada River to the Arabian Sea might reduce the atmospheric CO2 fixation by 7.46 × 109 mol year−1.

scholarly journals Thirty-Nine-Year Wave Hindcast, Storm Activity, and Probability Analysis of Storm Waves in the Kara Sea, Russia

Water ◽  
2021 ◽  
Vol 13 (5) ◽  
pp. 648
Author(s):  
Stanislav Myslenkov ◽  
Vladimir Platonov ◽  
Alexander Kislov ◽  
Ksenia Silvestrova ◽  
Igor Medvedev
Keyword(s):  
Climate Change ◽  
Pareto Distribution ◽  
Linear Trend ◽  
Wind Waves ◽  
Kara Sea ◽  
The Arctic ◽  
Storm Events ◽  
Storm Activity ◽  
Significant Linear Trend ◽  
The Impact

The recurrence of extreme wind waves in the Kara Sea strongly influences the Arctic climate change. The period 2000–2010 is characterized by significant climate warming, a reduction of the sea ice in the Arctic. The main motivation of this research to assess the impact of climate change on storm activity over the past 39 years in the Kara Sea. The paper presents the analysis of wave climate and storm activity in the Kara Sea based on the results of numerical modeling. A wave model WAVEWATCH III is used to reconstruct wind wave fields for the period from 1979 to 2017. The maximum significant wave height (SWH) for the whole period amounts to 9.9 m. The average long-term SWH for the ice-free period does not exceed 1.3 m. A significant linear trend shows an increase in the storm wave frequency for the period from 1979 to 2017. It is shown that trends in the storm activity of the Kara Sea are primarily regulated by the ice. Analysis of the extreme storm events showed that the Pareto distribution is in the best agreement with the data. However, the extreme events with an SWH more than 6‒7 m deviate from the Pareto distribution.

scholarly journals Coastal Mangrove Response to Marine Erosion: Evaluating the Impacts of Spatial Distribution and Vegetation Growth in Bangkok Bay from 1987 to 2017

2020 ◽  
Vol 12 (2) ◽  
pp. 220 ◽  
Author(s):  
Han Xiao ◽  
Fenzhen Su ◽  
Dongjie Fu ◽  
Qi Wang ◽  
Chong Huang
Keyword(s):  
Spatial Distribution ◽  
Vegetation Index ◽  
Normalized Difference Vegetation Index ◽  
Mangrove Ecosystem ◽  
Early Growth ◽  
Growth Stages ◽  
Human Disturbances ◽  
Vegetation Growth ◽  
Two Parameters ◽  
The Impact

Long time-series monitoring of mangroves to marine erosion in the Bay of Bangkok, using Landsat data from 1987 to 2017, shows responses including landward retreat and seaward extension. Quantitative assessment of these responses with respect to spatial distribution and vegetation growth shows differing relationships depending on mangrove growth stage. Using transects perpendicular to the shoreline, we calculated the cross-shore mangrove extent (width) to represent spatial distribution, and the normalized difference vegetation index (NDVI) was used to represent vegetation growth. Correlations were then compared between mangrove seaside changes and the two parameters—mangrove width and NDVI—at yearly and 10-year scales. Both spatial distribution and vegetation growth display positive impacts on mangrove ecosystem stability: At early growth stages, mangrove stability is positively related to spatial distribution, whereas at mature growth the impact of vegetation growth is greater. Thus, we conclude that at early growth stages, planting width and area are more critical for stability, whereas for mature mangroves, management activities should focus on sustaining vegetation health and density. This study provides new rapid insights into monitoring and managing mangroves, based on analyses of parameters from historical satellite-derived information, which succinctly capture the net effect of complex environmental and human disturbances.

scholarly journals The time-varying effects of oil prices on oil–gas stock returns of the fragile five countries

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Begüm Yurteri Kösedağlı ◽  
Gül Huyugüzel Kışla ◽  
A. Nazif Çatık
Keyword(s):  
Stock Returns ◽  
Positive Impact ◽  
Structural Break ◽  
Oil Prices ◽  
Time Varying ◽  
Asset Pricing Model ◽  
Daily Data ◽  
Structural Break Test ◽  
The Impact ◽  
Oil Gas

AbstractThis study analyzes oil price exposure of the oil–gas sector stock returns for the fragile five countries based on a multi-factor asset pricing model using daily data from 29 May 1996 to 27 January 2020. The endogenous structural break test suggests the presence of serious parameter instabilities due to fluctuations in the oil and stock markets over the period under study. Moreover, the time-varying estimates indicate that the oil–gas sectors of these countries are riskier than the overall stock market. The results further suggest that, except for Indonesia, oil prices have a positive impact on the sectoral returns of all markets, whereas the impact of the exchange rates on the oil–gas sector returns varies across time and countries.

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>

An investigation of time varying market efficiency: evidence from STOXX Europe Christian index

2021 ◽  
Vol 37 (4) ◽  
pp. 631-643
Author(s):  
Tayyaba Yousaf ◽  
Sadia Farooq ◽  
Ahmed Muneeb Mehta
Keyword(s):  
Price Index ◽  
Abnormal Returns ◽  
Weak Form ◽  
Efficient Market Hypothesis ◽  
Time Varying ◽  
Content Type ◽  
Daily Data ◽  
New Information ◽  
Academic Researchers ◽  
The Impact

Purpose The purpose of this study is to investigate whether the STOXX Europe Christian price index (SECI) follows the premise of efficient market hypothesis (EMH). Design/methodology/approach The study used daily data of SECI for the period of 15 years as its launch date i.e. 31 December 2004 to 31 December 2019. Data are analyzed by taking a full-length sample and fixed-length subsample. For subsample, the data are divided into five subsamples of three years each. Subsample analysis is important for analyzing time varying efficiency of the series, as the market is said to follow EMH if it is being efficient throughout the sample. Both type of samples is examined through linear tests including autocorrelations test and variance ratio (VR) test. Findings Tests applied conclude that SECI is weak-form efficient, which means that the prices of the index include all the relevant past information and immediately react to new information. Hence, the investors cannot earn abnormal returns. Originality/value Religion-based indices grasped the attention of investors, policymakers and academic researchers because of increased concern over ethics in business. Though the impact of religion on the economy have been studied in many ways but the efficiency of religion-based indices have been less explored. The current study is primary in its nature as it analysis the efficiency of SECI. This index is important to explore because Christianity is the world’s top religion with 2.3 billion followers around the globe.

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