Seawater intrusion process-based models (2010-2021)

Published: 28 February 2024| Version 2 | DOI: 10.17632/xz5wkk5jst.2
Gustavo Panique Casso


This database provides information over seawater intrusion modeling in coastal systems over the period 2010-2021. This includes characteristics of models, such as model purposes, model scales, data attributes, modeled factors and uncertainty analysis. This information results from a bibliometric analysis applied to investigate systematically related publications. Bibliometrics was first presented by Pritchard (1969), in which quantitative analyses and statistical measurements were applied on publications in order to gain a systematic, transparent, and reproducible review on the existing knowledge base, from that, allowed advancing research lines


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Bibliographic data was completed on January, 2024, on the Scopus website ( ). Scopus database contains the largest international abstract and citation collection of peer-reviewed scientific literature. Scopus currently indexes 24,600 titles (journals, magazines, reports) from more than 5,000 international publishers. To investigate studies on seawater intrusion modeling, we launched a detailed review of the studies published in peer-reviewed journals on the Scopus platform for the timeframe 2010-2021 by applying the query TITLE-ABS-KEY ({seawater intrusion} OR {saltwater intrusion} OR {sea water intrusion} OR {salt water intrusion} AND (groundwater OR aquifer*) AND (model* OR simulation* OR assess*)) AND PUBYEAR > 2009 AND PUBYEAR < 2022). In total, 535 publications were collected. We scrutinized these studies by eliminating publications on unrelated topics and hypothetical case studies or laboratory experiments, ultimately obtaining 193 modeling studies. The studies collected were used to investigate factors of interest, including model types, model purposes, model complexity, model temporal and spatial scales, salinity data attributes, and uncertainty analysis. Based on the solution adopted for solving the mixing zone, two types of process-based SWI models were identified: numerical and analytical, which, according to their purpose, were subcategorized into explicative and predictive models. Predictive models were further linked with the sustainable development goals (SDGs) of the 2030 agenda of the United Nations. Additionally, we evaluated model complexity according to the number of processes integrated in each process-based model. Given their spatial scale discretization, we identified 2D or 3D models, and local- or regional-scale models. Futher, we investigated the relationship between spatial model attributes, such as model grid size and number of model layers, with model complexity. Similarly, given their temporal resolution, we identified steady-state and transient models, evaluating their calculation time in accordance to model complexity. We evaluated the data attributes that are used for SWI model development by identifying data collection methods, sampling frequency, and sampling duration. Finally, we evaluated the use of uncertainty analysis and model validation techniques in SWI process-based models. Note that, for this review of process-based models, we excluded other SWI assessment methods, such as: risk and vulnerability approaches, data-driven and probabilistic models , or lumped methods. The classification of factors is described in Panique-Casso et al. Seawater intrusion process-based modeling: a review of the last years of assessment and future directions (submitted)


Universiteit Gent


Seawater Salinity Adaptation, Salinity Studies, Seawater