Primary productivity in a coastal ecosystem: a trophic perspective on a long-term time series

2015 ◽  
Vol 38 (4) ◽  
pp. 1092-1102 ◽  
Author(s):  
Peter Tiselius ◽  
Andrea Belgrano ◽  
Lars Andersson ◽  
Odd Lindahl
Keyword(s):  
Time Series ◽  
Primary Productivity ◽  
Coastal Ecosystem

scholarly journals Multiple Evidence for Climate Patterns Influencing Ecosystem Productivity across Spatial Gradients in the Venice Lagoon

2021 ◽  
Vol 9 (4) ◽  
pp. 363
Author(s):  
Camilla Bertolini ◽  
Edouard Royer ◽  
Roberto Pastres
Keyword(s):  
Time Series ◽  
Primary Productivity ◽  
Clustering Analysis ◽  
Venice Lagoon ◽  
Spatial Effects ◽  
Spatial Gradients ◽  
Climate Forecasting ◽  
Oxygen Production Rate ◽  
Temporal And Spatial

Effects of climatic changes in transitional ecosystems are often not linear, with some areas likely experiencing faster or more intense responses, which something important to consider in the perspective of climate forecasting. In this study of the Venice lagoon, time series of the past decade were used, and primary productivity was estimated from hourly oxygen data using a published model. Temporal and spatial patterns of water temperature, salinity and productivity time series were identified by applying clustering analysis. Phytoplankton and nutrient data from long-term surveys were correlated to primary productivity model outputs. pmax, the maximum oxygen production rate in a given day, was found to positively correlate with plankton variables measured in surveys. Clustering analysis showed the occurrence of summer heatwaves in 2008, 2013, 2015 and 2018 and three warm prolonged summers (2012, 2017, 2019) coincided with lower summer pmax values. Spatial effects in terms of temperature were found with segregation between confined and open areas, although the patterns varied from year to year. Production and respiration differences showed that the lagoon, despite seasonality, was overall heterotrophic, with internal water bodies having greater values of heterotrophy. Warm, dry years with high salinity had lower degrees of summer autotrophy.

scholarly journals Coastal ecosystem monitoring using long-term satellite data records: A case study of Chilika Lake, Odisha

2018 ◽  
Author(s):  
Vivek G. ◽  
Santonu Goswami ◽  
Rabindra N. Samal ◽  
Saroj B. Choudhury
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Coastal Ecosystem ◽  
Data Sets ◽  
Positive Trend ◽  
Dynamic Changes ◽  
Ecosystem Monitoring ◽  
Chilika Lake ◽  
Series Analysis

Changing trend in coastal ecosystem can be quantified by performing time series analysis. Time series analysis performed using long term remote sensing data will help us to identify the dynamic changes happening in the coastal ecosystem and its surrounding regions. In the present study we performed time series analysis on northern sector of Chilika Lake and its nearby regions of Odisha, which is situated in the east coast of India using three decades of freely available Landsat archive data. In order to detect dynamic changes trend parameters were calculated by using available data sets from Landsat Thematic Mapper (TM) and Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) for the observation period from 1988 to 2017. Two multi-spectral indices i.e. NDVI and EVI were generated from the available data sets and the trend analyses were performed using Theil-Sen (T-S) regression method by identifying robust trend parameters (slope and pvalue). The average mean values for NDVI and EVI were recorded at 0.4 and 0.2. Significant positive trend was observed in both vegetation indices (NDVI and EVI) with a mean slope value of 0.004 and 0.003 and pvalue of 0.03 and 0.02 respectively.

scholarly journals Coastal ecosystem monitoring using long-term satellite data records: A case study of Chilika Lake, Odisha

2018 ◽  
Author(s):  
Vivek G. ◽  
Santonu Goswami ◽  
Rabindra N. Samal ◽  
Saroj B. Choudhury
Keyword(s):  
Time Series ◽  
Time Series Analysis ◽  
Coastal Ecosystem ◽  
Data Sets ◽  
Positive Trend ◽  
Dynamic Changes ◽  
Ecosystem Monitoring ◽  
Chilika Lake ◽  
Series Analysis

Changing trend in coastal ecosystem can be quantified by performing time series analysis. Time series analysis performed using long term remote sensing data will help us to identify the dynamic changes happening in the coastal ecosystem and its surrounding regions. In the present study we performed time series analysis on northern sector of Chilika Lake and its nearby regions of Odisha, which is situated in the east coast of India using three decades of freely available Landsat archive data. In order to detect dynamic changes trend parameters were calculated by using available data sets from Landsat Thematic Mapper (TM) and Operational Land Imager and Thermal Infrared Sensor (OLI/TIRS) for the observation period from 1988 to 2017. Two multi-spectral indices i.e. NDVI and EVI were generated from the available data sets and the trend analyses were performed using Theil-Sen (T-S) regression method by identifying robust trend parameters (slope and pvalue). The average mean values for NDVI and EVI were recorded at 0.4 and 0.2. Significant positive trend was observed in both vegetation indices (NDVI and EVI) with a mean slope value of 0.004 and 0.003 and pvalue of 0.03 and 0.02 respectively.

scholarly journals Aerosol optical depth retrievals at the Izaña Atmospheric Observatory from 1941 to 2013 by using artificial neural networks

2016 ◽  
Vol 9 (1) ◽  
pp. 53-62 ◽  
Author(s):  
R. D. García ◽  
O. E. García ◽  
E. Cuevas ◽  
V. E. Cachorro ◽  
A. Barreto ◽  
...  
Keyword(s):  
Neural Networks ◽  
Time Series ◽  
Artificial Neural Networks ◽  
Aerosol Optical Depth ◽  
Optical Depth ◽  
Mineral Dust ◽  
Dust Particles ◽  
Filter Radiometer ◽  
Artificial Neural

Abstract. This paper presents the reconstruction of a 73-year time series of the aerosol optical depth (AOD) at 500 nm at the subtropical high-mountain Izaña Atmospheric Observatory (IZO) located in Tenerife (Canary Islands, Spain). For this purpose, we have combined AOD estimates from artificial neural networks (ANNs) from 1941 to 2001 and AOD measurements directly obtained with a Precision Filter Radiometer (PFR) between 2003 and 2013. The analysis is limited to summer months (July–August–September), when the largest aerosol load is observed at IZO (Saharan mineral dust particles). The ANN AOD time series has been comprehensively validated against coincident AOD measurements performed with a solar spectrometer Mark-I (1984–2009) and AERONET (AErosol RObotic NETwork) CIMEL photometers (2004–2009) at IZO, obtaining a rather good agreement on a daily basis: Pearson coefficient, R, of 0.97 between AERONET and ANN AOD, and 0.93 between Mark-I and ANN AOD estimates. In addition, we have analysed the long-term consistency between ANN AOD time series and long-term meteorological records identifying Saharan mineral dust events at IZO (synoptical observations and local wind records). Both analyses provide consistent results, with correlations  >  85 %. Therefore, we can conclude that the reconstructed AOD time series captures well the AOD variations and dust-laden Saharan air mass outbreaks on short-term and long-term timescales and, thus, it is suitable to be used in climate analysis.

scholarly journals Long-Term Data Traffic Forecasting for Network Dimensioning in LTE with Short Time Series

Electronics ◽  
2021 ◽  
Vol 10 (10) ◽  
pp. 1151
Author(s):  
Carolina Gijón ◽  
Matías Toril ◽  
Salvador Luna-Ramírez ◽  
María Luisa Marí-Altozano ◽  
José María Ruiz-Avilés
Keyword(s):  
Time Series ◽  
Deep Learning ◽  
Time Series Analysis ◽  
Data Traffic ◽  
Medium Term ◽  
Short Term ◽  
Traffic Forecasting ◽  
Series Analysis ◽  
Radio Planning

Network dimensioning is a critical task in current mobile networks, as any failure in this process leads to degraded user experience or unnecessary upgrades of network resources. For this purpose, radio planning tools often predict monthly busy-hour data traffic to detect capacity bottlenecks in advance. Supervised Learning (SL) arises as a promising solution to improve predictions obtained with legacy approaches. Previous works have shown that deep learning outperforms classical time series analysis when predicting data traffic in cellular networks in the short term (seconds/minutes) and medium term (hours/days) from long historical data series. However, long-term forecasting (several months horizon) performed in radio planning tools relies on short and noisy time series, thus requiring a separate analysis. In this work, we present the first study comparing SL and time series analysis approaches to predict monthly busy-hour data traffic on a cell basis in a live LTE network. To this end, an extensive dataset is collected, comprising data traffic per cell for a whole country during 30 months. The considered methods include Random Forest, different Neural Networks, Support Vector Regression, Seasonal Auto Regressive Integrated Moving Average and Additive Holt–Winters. Results show that SL models outperform time series approaches, while reducing data storage capacity requirements. More importantly, unlike in short-term and medium-term traffic forecasting, non-deep SL approaches are competitive with deep learning while being more computationally efficient.

scholarly journals Assessing Stream-Aquifer Connectivity in a Coastal California Watershed

Water ◽  
2021 ◽  
Vol 13 (4) ◽  
pp. 416
Author(s):  
Bwalya Malama ◽  
Devin Pritchard-Peterson ◽  
John J. Jasbinsek ◽  
Christopher Surfleet
Keyword(s):  
Time Series ◽  
Spectral Analysis ◽  
Water Level ◽  
Time Series Data ◽  
Series Data ◽  
Low Permeability ◽  
Pumping Tests ◽  
Stream Flows ◽  
The Impact

We report the results of field and laboratory investigations of stream-aquifer interactions in a watershed along the California coast to assess the impact of groundwater pumping for irrigation on stream flows. The methods used include subsurface sediment sampling using direct-push drilling, laboratory permeability and particle size analyses of sediment, piezometer installation and instrumentation, stream discharge and stage monitoring, pumping tests for aquifer characterization, resistivity surveys, and long-term passive monitoring of stream stage and groundwater levels. Spectral analysis of long-term water level data was used to assess correlation between stream and groundwater level time series data. The investigations revealed the presence of a thin low permeability silt-clay aquitard unit between the main aquifer and the stream. This suggested a three layer conceptual model of the subsurface comprising unconfined and confined aquifers separated by an aquitard layer. This was broadly confirmed by resistivity surveys and pumping tests, the latter of which indicated the occurrence of leakage across the aquitard. The aquitard was determined to be 2–3 orders of magnitude less permeable than the aquifer, which is indicative of weak stream-aquifer connectivity and was confirmed by spectral analysis of stream-aquifer water level time series. The results illustrate the importance of site-specific investigations and suggest that even in systems where the stream is not in direct hydraulic contact with the producing aquifer, long-term stream depletion can occur due to leakage across low permeability units. This has implications for management of stream flows, groundwater abstraction, and water resources management during prolonged periods of drought.

scholarly journals Maps, trends, and temperature sensitivities—phenological information from and for decreasing numbers of volunteer observers

2021 ◽  
Author(s):  
Ye Yuan ◽  
Stefan Härer ◽  
Tobias Ottenheym ◽  
Gourav Misra ◽  
Alissa Lüpke ◽  
...  
Keyword(s):  
Climate Change ◽  
Time Series ◽  
Strong Decrease ◽  
Temperature Changes ◽  
Interpolation Accuracy ◽  
Observation Network ◽  
Shiny App ◽  
Long Term Trends ◽  
Autumn And Winter

AbstractPhenology serves as a major indicator of ongoing climate change. Long-term phenological observations are critically important for tracking and communicating these changes. The phenological observation network across Germany is operated by the National Meteorological Service with a major contribution from volunteering activities. However, the number of observers has strongly decreased for the last decades, possibly resulting in increasing uncertainties when extracting reliable phenological information from map interpolation. We studied uncertainties in interpolated maps from decreasing phenological records, by comparing long-term trends based on grid-based interpolated and station-wise observed time series, as well as their correlations with temperature. Interpolated maps in spring were characterized by the largest spatial variabilities across Bavaria, Germany, with respective lowest interpolated uncertainties. Long-term phenological trends for both interpolations and observations exhibited mean advances of −0.2 to −0.3 days year−1 for spring and summer, while late autumn and winter showed a delay of around 0.1 days year−1. Throughout the year, temperature sensitivities were consistently stronger for interpolated time series than observations. Such a better representation of regional phenology by interpolation was equally supported by satellite-derived phenological indices. Nevertheless, simulation of observer numbers indicated that a decline to less than 40% leads to a strong decrease in interpolation accuracy. To better understand the risk of declining phenological observations and to motivate volunteer observers, a Shiny app is proposed to visualize spatial and temporal phenological patterns across Bavaria and their links to climate change–induced temperature changes.

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