scholarly journals Time Series Data for Dissolved Organic Matter Study in the Northwest Field Canal in Logan, UT

2017 ◽  
Keyword(s):  
Time Series ◽  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Time Series Data ◽  
Series Data

scholarly journals Temporal responses of coastal hypoxia to nutrient loading and physical controls

2009 ◽  
Vol 6 (12) ◽  
pp. 2985-3008 ◽  
Author(s):  
W. M. Kemp ◽  
J. M. Testa ◽  
D. J. Conley ◽  
D. Gilbert ◽  
J. D. Hagy
Keyword(s):  
Time Series ◽  
Organic Matter ◽  
Nutrient Loading ◽  
Time Series Data ◽  
Sewage Treatment ◽  
Series Data ◽  
Time Lags ◽  
Nutrient Loads ◽  
Labile Organic Matter ◽  
Non Linear

Abstract. The incidence and intensity of hypoxic waters in coastal aquatic ecosystems has been expanding in recent decades coincident with eutrophication of the coastal zone. Worldwide, there is strong interest in reducing the size and duration of hypoxia in coastal waters, because hypoxia causes negative effects for many organisms and ecosystem processes. Although strategies to reduce hypoxia by decreasing nutrient loading are predicated on the assumption that this action would reverse eutrophication, recent analyses of historical data from European and North American coastal systems suggest little evidence for simple linear response trajectories. We review published parallel time-series data on hypoxia and loading rates for inorganic nutrients and labile organic matter to analyze trajectories of oxygen (O2) response to nutrient loading. We also assess existing knowledge of physical and ecological factors regulating O2 in coastal marine waters to facilitate analysis of hypoxia responses to reductions in nutrient (and/or organic matter) inputs. Of the 24 systems identified where concurrent time series of loading and O2 were available, half displayed relatively clear and direct recoveries following remediation. We explored in detail 5 well-studied systems that have exhibited complex, non-linear responses to variations in loading, including apparent "regime shifts". A summary of these analyses suggests that O2 conditions improved rapidly and linearly in systems where remediation focused on organic inputs from sewage treatment plants, which were the primary drivers of hypoxia. In larger more open systems where diffuse nutrient loads are more important in fueling O2 depletion and where climatic influences are pronounced, responses to remediation tended to follow non-linear trends that may include hysteresis and time-lags. Improved understanding of hypoxia remediation requires that future studies use comparative approaches and consider multiple regulating factors. These analyses should consider: (1) the dominant temporal scales of the hypoxia, (2) the relative contributions of inorganic and organic nutrients, (3) the influence of shifts in climatic and oceanographic processes, and (4) the roles of feedback interactions whereby O2-sensitive biogeochemistry, trophic interactions, and habitat conditions influence the nutrient and algal dynamics that regulate O2 levels.

scholarly journals Decomposability of soil organic matter over time: The Soil Incubation Database (SIDb, version 1.0) and guidance for incubation procedures

2019 ◽  
Author(s):  
Christina Schädel ◽  
Jeffrey Beem-Miller ◽  
Mina Aziz Rad ◽  
Susan E. Crow ◽  
Caitlin Hicks Pries ◽  
...  
Keyword(s):  
Time Series ◽  
Organic Matter ◽  
Time Series Data ◽  
Model Development ◽  
Organic Matter Content ◽  
Matter Content ◽  
Series Data ◽  
Soil Incubation ◽  
Microbial Decomposition ◽  
Soil C

Abstract. The magnitude of carbon (C) loss to the atmosphere via microbial decomposition is a function of the amount of C stored in soils, the quality of the organic matter, and physical, chemical and biological factors that comprise the environment for decomposition. The decomposability of C is commonly assessed by laboratory soil incubation studies that measure greenhouse gases mineralized from soils under controlled conditions. Here, we introduce the Soil Incubation Database (SIDb) version 1.0, a compilation of time series data from incubations, structured into a new, publicly available database of C flux (carbon dioxide, CO2, or methane, CH4). In addition to open access, the SIDb project also provides a platform for the development of tools for reading and analysis of incubation data as well as documentation for future use and development. In addition to introducing SIDb, we provide reporting guidance for database entry and the required variables that incubation studies need at minimum to be included in SIDb. A key application of this synthesis effort is to better characterize soil C processes in Earth system models, which will in turn reduce our uncertainty in predicting the response of soil C decomposition to a changing climate. We demonstrate a framework to fit curves to a number of incubation studies from diverse ecosystems, depths, and organic matter content using a built-in model development module that integrates SIDb with the existing SoilR package to estimate soil C pools from time series data. The database will help bridge the gap between site-level measurements, which are commonly used in incubation studies, and global remote-sensed data or data products derived from models aimed at assessing global-scale rates of decomposition and C turnover. The SIDb, version 1.0, is archived and publicly available at DOI: https://doi.org/10.5281/zenodo.3470459 (Sierra et al., 2019) and the database is managed under a version-controlled system and centrally stored in GitHub (https://github.com/SoilBGC-Datashare/sidb).

scholarly journals Decomposability of soil organic matter over time: the Soil Incubation Database (SIDb, version 1.0) and guidance for incubation procedures

2020 ◽  
Vol 12 (3) ◽  
pp. 1511-1524 ◽  
Author(s):  
Christina Schädel ◽  
Jeffrey Beem-Miller ◽  
Mina Aziz Rad ◽  
Susan E. Crow ◽  
Caitlin E. Hicks Pries ◽  
...  
Keyword(s):  
Time Series ◽  
Organic Matter ◽  
Time Series Data ◽  
Model Development ◽  
Organic Matter Content ◽  
Matter Content ◽  
Series Data ◽  
Soil Incubation ◽  
Microbial Decomposition ◽  
Soil C

Abstract. The magnitude of carbon (C) loss to the atmosphere via microbial decomposition is a function of the amount of C stored in soils, the quality of the organic matter, and physical, chemical, and biological factors that comprise the environment for decomposition. The decomposability of C is commonly assessed by laboratory soil incubation studies that measure greenhouse gases mineralized from soils under controlled conditions. Here, we introduce the Soil Incubation Database (SIDb) version 1.0, a compilation of time series data from incubations, structured into a new, publicly available, open-access database of C flux (carbon dioxide, CO2, or methane, CH4). In addition, the SIDb project also provides a platform for the development of tools for reading and analysis of incubation data as well as documentation for future use and development. In addition to introducing SIDb, we provide reporting guidance for database entry and the required variables that incubation studies need at minimum to be included in SIDb. A key application of this synthesis effort is to better characterize soil C processes in Earth system models, which will in turn reduce our uncertainty in predicting the response of soil C decomposition to a changing climate. We demonstrate a framework to fit curves to a number of incubation studies from diverse ecosystems, depths, and organic matter content using a built-in model development module that integrates SIDb with the existing SoilR package to estimate soil C pools from time series data. The database will help bridge the gap between point location measurements, which are commonly used in incubation studies, and global remote-sensed data or data products derived from models aimed at assessing global-scale rates of decomposition and C turnover. The SIDb version 1.0 is archived and publicly available at https://doi.org/10.5281/zenodo.3871263 (Sierra et al., 2020), and the database is managed under a version-controlled system and centrally stored in GitHub (https://github.com/SoilBGC-Datashare/sidb, last access: 26 June 2020).

scholarly journals Dissolved organic matter from tropical peatlands impacts shelf sea light availability on coral reefs in the Singapore Strait, Southeast Asia

2021 ◽  
Author(s):  
Patrick Martin ◽  
Nivedita Sanwlani ◽  
Tiffany Wan Qi Lee ◽  
Joel Meng Cheng Wong ◽  
Kristy Chang ◽  
...  
Keyword(s):  
Organic Matter ◽  
Dissolved Organic Matter ◽  
Southeast Asia ◽  
Light Absorption ◽  
Time Series Data ◽  
Light Availability ◽  
Series Data ◽  
Underwater Light ◽  
Tropical Peatlands ◽  
Shelf Seas

AbstractShelf seas provide valuable ecosystem services, but their productivity and ecological functioning depend critically on sunlight transmitted through the water column. Anthropogenic reductions in underwater light availability are thus recognized as a serious threat to coastal habitats. The flux of strongly light-absorbing coloured dissolved organic matter (CDOM) from land to sea may have increased world-wide, but how this has altered the availability and spectral quality of light in shelf seas remains poorly known. Here, we present time-series data from the Sunda Shelf in Southeast Asia, where the monsoon-driven reversal in ocean currents supplies water enriched in CDOM from tropical peatlands for part of the year, resulting in 5–10-fold seasonal variation in light absorption by CDOM. We show that this terrigenous CDOM can dominate underwater light absorption at wavelengths up to 500 nm, and shift in the underwater irradiance spectrum towards longer wavelengths. The seasonal presence of terrigenous CDOM also causes the depth of 10% light penetration to shoal by 1–5 m, or 10–45%. We further estimate that on average 0.6 m, or 25%, of this terrigenous CDOM-mediated shoaling might be attributable to the enhanced loss of dissolved organic matter caused by peatland disturbance. We show that the seasonal change in the light environment is correlated with photo-acclimation by phytoplankton, and infer that terrigenous CDOM likely contributes to limiting the depth distribution of photosynthetic corals. Our results thus reveal an ecologically important but largely overlooked impact of human modifications to carbon fluxes that is likely becoming increasingly important in coastal seas.

Graphical Exploratory Data Analysis for Categorical Longitudinal and Time Series Data

2013 ◽  
Author(s):  
Stephen J. Tueller ◽  
Richard A. Van Dorn ◽  
Georgiy Bobashev ◽  
Barry Eggleston
Keyword(s):  
Time Series ◽  
Data Analysis ◽  
Exploratory Data Analysis ◽  
Time Series Data ◽  
Series Data ◽  
Exploratory Data

Faktor-Faktor Yang Mempengaruhi Nilai Tukar Dollar Amerika Serikat Terhadap Rupiah Tahun 2000–2013

2017 ◽  
Vol 1 (2) ◽  
pp. 177-191
Author(s):  
Rizki Rahma Kusumadewi ◽  
Wahyu Widayat
Keyword(s):  
Time Series ◽  
Exchange Rate ◽  
Money Supply ◽  
Time Series Data ◽  
The United States ◽  
Economic Conditions ◽  
Series Data ◽  
Arch Model ◽  
United States Dollar ◽  
The Exchange Rate

Exchange rate is one tool to measure a country’s economic conditions. The growth of a stable currency value indicates that the country has a relatively good economic conditions or stable. This study has the purpose to analyze the factors that affect the exchange rate of the Indonesian Rupiah against the United States Dollar in the period of 2000-2013. The data used in this study is a secondary data which are time series data, made up of exports, imports, inflation, the BI rate, Gross Domestic Product (GDP), and the money supply (M1) in the quarter base, from first quarter on 2000 to fourth quarter on 2013. Regression model time series data used the ARCH-GARCH with ARCH model selection indicates that the variables that significantly influence the exchange rate are exports, inflation, the central bank rate and the money supply (M1). Whereas import and GDP did not give any influence.

An Anomaly Detection Method with Exemplar Subsequence for Time Series Data

2016 ◽  
Vol 136 (3) ◽  
pp. 363-372
Author(s):  
Takaaki Nakamura ◽  
Makoto Imamura ◽  
Masashi Tatedoko ◽  
Norio Hirai
Keyword(s):  
Time Series ◽  
Anomaly Detection ◽  
Time Series Data ◽  
Detection Method ◽  
Series Data

The Comparison of Land Subsidence between East and West Side of Bangkok, Thailand

2020 ◽  
Vol 17 (3) ◽  
pp. 1
Author(s):  
Angkana Pumpuang ◽  
Anuphao Aobpaet
Keyword(s):  
Time Series ◽  
Land Subsidence ◽  
Time Series Data ◽  
Series Data ◽  
Industrial Estates ◽  
Vertical Displacements ◽  
Land Deformation ◽  
Extensive Farming ◽  
Chao Phraya River ◽  
Set Up

The land deformation in line of sight (LOS) direction can be measured using time series InSAR. InSAR can successfully measure land subsidence based on LOS in many big cities, including the eastern and western regions of Bangkok which is separated by Chao Phraya River. There are differences in prosperity between both sides due to human activities, land use, and land cover. This study focuses on the land subsidence difference between the western and eastern regions of Bangkok and the most possible cause affecting the land subsidence rates. The Radarsat-2 single look complex (SLC) was used to set up the time series data for long term monitoring. To generate interferograms, StaMPS for Time Series InSAR processing was applied by using the PSI algorithm in DORIS software. It was found that the subsidence was more to the eastern regions of Bangkok where the vertical displacements were +0.461 millimetres and -0.919 millimetres on the western and the eastern side respectively. The districts of Nong Chok, Lat Krabang, and Khlong Samwa have the most extensive farming area in eastern Bangkok. Besides, there were also three major industrial estates located in eastern Bangkok like Lat Krabang, Anya Thani and Bang Chan Industrial Estate. By the assumption of water demand, there were forty-eight wells and three wells found in the eastern and western part respectively. The number of groundwater wells shows that eastern Bangkok has the demand for water over the west, and the pumping of groundwater is a significant factor that causes land subsidence in the area.Keywords: Subsidence, InSAR, Radarsat-2, Bangkok

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