Special Issue "Overcoming Data Scarcity in Earth Science"

A special issue of Data (ISSN 2306-5729).

Deadline for manuscript submissions: 31 August 2019

Special Issue Editors

Guest Editor
Dr. Angela Gorgoglione

Institute of Fluid Mechanics and Environmental Engineering (IMFIA), Engineering College, Universidad de la República, Uruguay
Website | E-Mail
Interests: urban hydrology; water-quality modeling; hydrologic modeling
Guest Editor
Dr. Alberto Castro Casales

Computer Science Department, Engineering College, Universidad de la República, Uruguay
Website | E-Mail
Interests: optical network; optimization; machine learning
Guest Editor
Dr. Christian Chreties Ceriani

Institute of Fluid Mechanics and Environmental Engineering (IMFIA), Engineering College, Universidad de la República, Uruguay
Website | E-Mail
Interests: water resources management; surface hydrology; flood modeling
Guest Editor
Dr. Lorena Etcheverry Venturini

Computer Science Department, Engineering College, Universidad de la República, Uruguay
Website | E-Mail
Interests: data management; open data; data quality

Special Issue Information

Dear Colleagues,

Environmental mathematical models represent one of the key aids for scientists to forecast, create, and evaluate complex scenarios. These models heavily rely on the data collected by direct field observations. However, a functional and comprehensive dataset of any environmental variable is hard to collect, mainly because of: i) the high cost of the monitoring campaigns; and ii) the low reliability in the measurements (e.g., due to occurrences of equipment malfunctions and/or issues related to the equipment location). The lack of a sufficient amount of Earth science data may induce an inadequate representation of the response’s complexity in any environmental system to any type of input/change, both natural and human-induced. In such a case, before undertaking expensive studies to gather and analyze additional data, it is reasonable to first understand what enhancement in estimates of system performance would result if all the available data could be well exploited.

Missing data imputation is an important task in cases where it is crucial to use all available data and not discard records with missing values. Different approaches are available to deal with missing data. Traditional statistical data completion methods are used in different domains to deal with single and multiple imputation problem. More recently, machine learning techniques as clustering and classification, have been proposed to complete missing data.

This Special Issue on “Overcoming Data Scarcity in Earth Science” of the Journal Data is designed to draw attention to the body of knowledge that aims at improving the capacity of exploiting the available data to better represent, understand, predict, and manage the behavior of environmental systems at all practical scales.

Authors are encouraged to submit research articles, reviews, and short communications addressing this theme in this Special Issue.

Dr. Angela Gorgoglione
Dr. Alberto Castro Casales
Dr. Christian Chreties Ceriani
Dr. Lorena Etcheverry Venturini
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Data is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) is waived for well-prepared manuscripts submitted to this issue. Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Earth science data
  • Data scarcity
  • Missing data
  • Data quality
  • Data Imputation
  • Statistical Methods
  • Machine learning
  • Environmental modeling
  • Environmental observations

Published Papers (2 papers)

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Open AccessArticle
Application of Rough Set Theory to Water Quality Analysis: A Case Study
Received: 27 September 2018 / Revised: 29 October 2018 / Accepted: 3 November 2018 / Published: 7 November 2018
Cited by 1 | PDF Full-text (1469 KB) | HTML Full-text | XML Full-text
Abstract
This work proposes an approach to analyze water quality data that is based on rough set theory. Six major water quality indicators (temperature, pH, dissolved oxygen, turbidity, specific conductivity, and nitrate concentration) were collected at the outlet of the watershed that contains the [...] Read more.
This work proposes an approach to analyze water quality data that is based on rough set theory. Six major water quality indicators (temperature, pH, dissolved oxygen, turbidity, specific conductivity, and nitrate concentration) were collected at the outlet of the watershed that contains the George Mason University campus in Fairfax, VA during three years (October 2015–December 2017). Rough set theory is applied to monthly averages of the collected data to estimate one indicator (decision attribute) based on the remainder indicators and to determine what indicators (conditional attributes) are essential (core) to predict the missing indicator. The redundant attributes are identified, the importance degree of each attribute is quantified, and the certainty and coverage of any detected rule(s) is evaluated. Possible decision making rules are also assessed and the certainty coverage factor is calculated. Results show that the core water quality indicators for the Mason watershed during the study period are turbidity and specific conductivity. Particularly, if pH is chosen as a decision attribute, the importance degree of turbidity is higher than the one of conductivity. If the decision attribute is turbidity, the only indispensable attribute is specific conductivity and if specific conductivity is the decision attribute, the indispensable attribute beside turbidity is temperature. Full article
(This article belongs to the Special Issue Overcoming Data Scarcity in Earth Science)
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A High-Resolution Global Gridded Historical Dataset of Climate Extreme Indices
Received: 18 February 2019 / Revised: 8 March 2019 / Accepted: 9 March 2019 / Published: 13 March 2019
PDF Full-text (1338 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
Climate extreme indices (CEIs) are important metrics that not only assist in the analysis of regional and global extremes in meteorological events, but also aid climate modellers and policymakers in the assessment of sectoral impacts. Global high-spatial-resolution CEI datasets derived from quality-controlled historical [...] Read more.
Climate extreme indices (CEIs) are important metrics that not only assist in the analysis of regional and global extremes in meteorological events, but also aid climate modellers and policymakers in the assessment of sectoral impacts. Global high-spatial-resolution CEI datasets derived from quality-controlled historical observations, or reanalysis data products are scarce. This study introduces a new high-resolution global gridded dataset of CEIs based on sub-daily temperature and precipitation data from the Global Land Data Assimilation System (GLDAS). The dataset called “CEI_0p25_1970_2016” includes 71 annual (and in some cases monthly) CEIs at 0.25 × 0.25 gridded resolution, covering 47 years over the period 1970–2016. The data of individual indices are publicly available for download in the commonly used Network Common Data Form 4 (NetCDF4) format. Potential applications of CEI_0p25_1970_2016 presented here include the assessment of sectoral impacts (e.g., Agriculture, Health, Energy, and Hydrology), as well as the identification of hot spots (clusters) showing similar historical spatial patterns of high/low temperature and precipitation extremes. CEI_0p25_1970_2016 fills gaps in existing CEI datasets by encompassing not only more indices, but also by being the only comprehensive global gridded CEI data available at high spatial resolution. Full article
(This article belongs to the Special Issue Overcoming Data Scarcity in Earth Science)
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