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Overcoming Data Scarcity in Earth Science

Author

Listed:
  • Angela Gorgoglione

    (Department of Fluid Mechanics and Environmental Engineering (IMFIA), School of Engineering, Universidad de la República, Montevideo 11300, Uruguay)

  • Alberto Castro

    (Department of Computer Science (InCo), School of Engineering, Universidad de la República, Montevideo 11300, Uruguay)

  • Christian Chreties

    (Department of Fluid Mechanics and Environmental Engineering (IMFIA), School of Engineering, Universidad de la República, Montevideo 11300, Uruguay)

  • Lorena Etcheverry

    (Department of Computer Science (InCo), School of Engineering, Universidad de la República, Montevideo 11300, Uruguay)

Abstract

The Data Scarcity problem is repeatedly encountered in environmental research. This may induce an inadequate representation of the response’s complexity in any environmental system to any input/change (natural and human-induced). In such a case, before getting engaged with new expensive studies to gather and analyze additional data, it is reasonable first to understand what enhancement in estimates of system performance would result if all the available data could be well exploited. The purpose of this Special Issue, “Overcoming Data Scarcity in Earth Science” in the Data journal, is to draw attention to the body of knowledge that leads at improving the capacity of exploiting the available data to better represent, understand, predict, and manage the behavior of environmental systems at meaningful space-time scales. This Special Issue contains six publications (three research articles, one review, and two data descriptors) covering a wide range of environmental fields: geophysics, meteorology/climatology, ecology, water quality, and hydrology.

Suggested Citation

  • Angela Gorgoglione & Alberto Castro & Christian Chreties & Lorena Etcheverry, 2020. "Overcoming Data Scarcity in Earth Science," Data, MDPI, vol. 5(1), pages 1-5, January.
    • Handle: RePEc:gam:jdataj:v:5:y:2020:i:1:p:5-:d:304128
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    References listed on IDEAS

    as
    1. Angela Gorgoglione & Andrea Gioia & Vito Iacobellis, 2019. "A Framework for Assessing Modeling Performance and Effects of Rainfall-Catchment-Drainage Characteristics on Nutrient Urban Runoff in Poorly Gauged Watersheds," Sustainability, MDPI, vol. 11(18), pages 1-16, September.
    2. Tutz, Gerhard & Ramzan, Shahla, 2015. "Improved methods for the imputation of missing data by nearest neighbor methods," Computational Statistics & Data Analysis, Elsevier, vol. 90(C), pages 84-99.
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    Citations

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    Cited by:

    1. Marco Delle Rose & Paolo Martano, 2023. "Datasets of Groundwater Level and Surface Water Budget in a Central Mediterranean Site (21 June 2017–1 October 2022)," Data, MDPI, vol. 8(2), pages 1-12, February.

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