Special Issue "The Application of State-of-the-Art Statistical Tools in Limnology, towards a Sustainable Future"
Deadline for manuscript submissions: 7 February 2021.
Centre for Environmental Sciences, Eötvös Loránd University
Interests: water quality, paleoclimate, geostatistics, data analysis, R
Interests: water quality, time series analysis,geostatistics, multivariate statistical methods, R
The term “limnology” comes from the ancient Greek word λίμνη (limne) meaning lake or pond; however, it is recognized as the discipline involving the study of both fresh- and saline inland waters (Wetzel, 2001).
With problems related to water quality becoming more frequent, more sampling sites are being included in national and international monitoring networks, and their sampling frequency is showing a tendency to increase as well. This trend has as its result an ever-growing amount of data, to a point where this amount is greater than is usual within the scope of “simple” statistical analyses. Thus, in the last few decades stochastic modeling, with the use of time series analysis and multivariate statistical techniques, has increased dramatically in surface and groundwater research. The reason is the increase in the amount and time span of the available data. As a consequence, it has become possible to investigate the relationship between various natural parameters (i.e. random variables in the statistical modeling) and the temporal evolution of the natural processes, observed in discrete time periods as time series.
This Special Issue (SI) aims to attract studies tackling emerging statistical approaches and research topics related to water-quality and quantity modeling in the field of limnology, with the ultimate aim of providing new perspectives to carry the field into its next stages of evolution. Along with addressing new focal points and approaches, the SI is designated to present papers dealing with limnology in a multidisciplinary way coupling water-quality/quantity modeling with other areas of the natural aqueous environment. In particular, water quality model frameworks addressing future problems such as climate change would be of particularly welcome.
Dr. István Gábor Hatvani
Dr. Norbert Magyar
Manuscript Submission Information
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- water quality
- Shallow groundwater
- Stochastic analyses