Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/29908
Title: | Application of geostatistical methods to groundwater salinization problems: A review | Authors: | Panagiotou, Constantinos F. Kyriakidis, Phaedon Tziritis, Evangelos |
Major Field of Science: | Engineering and Technology | Field Category: | Civil Engineering | Keywords: | Geostatistical simulations;Groundwater salinization;Kriging;Multivariate statistics;Surrogate models | Issue Date: | 1-Dec-2022 | Source: | Journal of Hydrology, 2022, vol. 615 | Volume: | 615 | Abstract: | Groundwater salinization is considered to be one of the most severe and complex phenomena affecting coastal regions worldwide, occurring when high concentrations of water-soluble salts are present in groundwater systems. Geostatistics is a branch of statistics used to analyze and predict the spatio-temporal variability of such complex phenomena. In particular, numerous geostatistical approaches and technologies are currently used to identify and map salinity-affected regions, investigate how salinity indicators influence groundwater mechanisms, and eventually design optimal groundwater management policies. This article reviews recent key applications of geostatistical methods to address challenges relevant to groundwater salinization. The basic principles of geostatistics are briefly described, and numerous studies are discussed that employ geostatistical and multivariate tools for identifying the origin of salinity sources, clarifying the relationship among salinity indicators and groundwater processes, and propagating the uncertainty of the inputs to the outputs of either physically-based or surrogate models of relevant geological systems. Finally, several recommendations and future directions are identified with regards to the most popular methods and with regards to key geostatistical methods whose application in this thematic area is still very limited. | URI: | https://hdl.handle.net/20.500.14279/29908 | ISSN: | 00221694 | DOI: | 10.1016/j.jhydrol.2022.128566 | Rights: | © Elsevier B.V. | Type: | Article | Affiliation : | Cyprus University of Technology ERATOSTHENES Centre of Excellence Soil and Water Resources Institute Geospatial Analytics Lab |
Publication Type: | Peer Reviewed |
Appears in Collections: | Άρθρα/Articles |
CORE Recommender
SCOPUSTM
Citations
7
checked on Mar 14, 2024
WEB OF SCIENCETM
Citations
4
Last Week
0
0
Last month
0
0
checked on Nov 1, 2023
Page view(s)
147
Last Week
0
0
Last month
2
2
checked on Nov 6, 2024
Google ScholarTM
Check
Altmetric
This item is licensed under a Creative Commons License