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The Application of State-of-the-Art Statistical Tools in Limnology, towards a Sustainable Future

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: closed (31 December 2021) | Viewed by 9335

Special Issue Editors

Dr. István Gábor Hatvani

E-Mail Website
Guest Editor
Institute for Geological and Geochemical Research, Research Centre for Astronomy and Earth Sciences
Centre for Environmental Sciences, Eötvös Loránd University
Interests: water quality, paleoclimate, geostatistics, data analysis, R
Dr. Norbert Magyar

E-Mail Website
Guest Editor
Budapest Business School – University of Applied Sciences
Interests: water quality, time series analysis,geostatistics, multivariate statistical methods, R
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

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
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 submissions that pass pre-check are 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. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). 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

  • water quality
  • hydrology
  • modeling
  • eutrophication
  • Shallow groundwater
  • nutrients
  • Stochastic analyses

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Published Papers (3 papers)

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Research

15 pages, 6736 KiB  
Article
Joint Spatial Modeling of Nutrients and Their Ratio in the Sediments of Lake Balaton (Hungary): A Multivariate Geostatistical Approach
by Gábor Szatmári, Mihály Kocsis, András Makó, László Pásztor and Zsófia Bakacsi
Water 2022, 14(3), 361; https://doi.org/10.3390/w14030361 - 26 Jan 2022
Cited by 4 | Viewed by 2764
Abstract
Eutrophication, water quality, and environmental status of lakes is a global issue that depends not only on external loadings from industrial, agricultural, and municipal sources but often also on internal loadings from lake sediments. In the latter case, in addition to the quality [...] Read more.
Eutrophication, water quality, and environmental status of lakes is a global issue that depends not only on external loadings from industrial, agricultural, and municipal sources but often also on internal loadings from lake sediments. In the latter case, in addition to the quality and quantity of nutrients stored in sediments, their relative content may be an important factor. In the example of Lake Balaton, we jointly modeled the spatial distribution of the nutrients nitrogen (N) and phosphorus (P) and their ratio (i.e., N:P) in the sediments of the lake and then provided spatial predictions at different scales (i.e., point, basin, and entire lake) with the associated uncertainty. Our aim was to illustrate the merits of applying multivariate geostatistics when spatial modeling of more than one variable is targeted at various scales in water ecosystems. Variography confirmed that there is a spatial interdependence between the nutrients. The results revealed that multivariate geostatistics allows this interdependence to be taken into account and exploited to provide coherent and accurate spatial models. Additionally, stochastic realizations, reproducing the joint spatial variability, can be generated that allow providing spatially aggregated predictions with the associated uncertainty at various scales. Our study highlighted that it is worthy of applying multivariate geostatistics in case spatial modeling of two or more variables, which jointly vary in space, is targeted in water ecosystems. Full article
(This article belongs to the Special Issue The Application of State-of-the-Art Statistical Tools in Limnology, towards a Sustainable Future)
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26 pages, 6137 KiB  
Article
Comparison of Various Growth Curve Models in Characterizing and Predicting Water Table Change after Intensive Mine Dewatering Is Discontinued in an East Central European Karstic Area
by Kamilla Modrovits, András Csepregi, Ilona Kovácsné Székely, István Gábor Hatvani and József Kovács
Water 2021, 13(8), 1047; https://doi.org/10.3390/w13081047 - 10 Apr 2021
Cited by 1 | Viewed by 2909
Abstract
The modeling of karst water level fluctuations is a crucial task in the water resource management of vulnerable karstic areas. In the Transdanubian Range (East Central Europe, Hungary), from 1950 to 1990, coal and bauxite mining were carried out, with large amounts of [...] Read more.
The modeling of karst water level fluctuations is a crucial task in the water resource management of vulnerable karstic areas. In the Transdanubian Range (East Central Europe, Hungary), from 1950 to 1990, coal and bauxite mining were carried out, with large amounts of karst water being extracted, thus lowering the water table by amounts ranging between 10 and 100 m. Since the cessation of mining activities in the early 1990s, the volume of natural recharge has exceeded the amount of dewatering, and the system has begun to return to its original undisturbed state. This apparently welcome development does, however, bring economic and technical engineering problems. The estimation and prediction of such water level changes is often tackled via the use of deterministic approaches, however, in the present case, it is also addressed with an alternative approach using trend estimation to monthly water level data from 107 karst water wells over the period 1990–2017. To approximate the change in karst water levels, (i) growth curve models were fitted to the monthly data, allowing the estimation of karst water levels, at least as far as 2030. Similarly, this was also done with (ii) deterministic modelling in order to describe the recovery process up to 2030. Specifically, measured and predicted values for karst water level were used to derive interpolated (kriged) maps to compare the forecasting power of the two approaches. Comparing the results of the trend analysis with those of the traditional deterministic modelling results, it is apparent that the two approaches predict similar spatial distribution of water levels, but slightly different future water level values. Full article
(This article belongs to the Special Issue The Application of State-of-the-Art Statistical Tools in Limnology, towards a Sustainable Future)
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17 pages, 3413 KiB  
Article
A New Approach in Determining the Decadal Common Trends in the Groundwater Table of the Watershed of Lake “Neusiedlersee”
by Norbert Magyar, István Gábor Hatvani, Miklós Arató, Balázs Trásy, Alfred Paul Blaschke and József Kovács
Water 2021, 13(3), 290; https://doi.org/10.3390/w13030290 - 25 Jan 2021
Cited by 6 | Viewed by 2842
Abstract
Shallow groundwater is one of the primary sources of fresh water, providing river base-flow and root-zone soil water between precipitation events. However, with urbanization and the increase in demand for water for irrigation, shallow groundwater bodies are being endangered. In the present study, [...] Read more.
Shallow groundwater is one of the primary sources of fresh water, providing river base-flow and root-zone soil water between precipitation events. However, with urbanization and the increase in demand for water for irrigation, shallow groundwater bodies are being endangered. In the present study, 101 hydrographs of shallow groundwater monitoring wells from the watershed of the westernmost brackish lake in Europe were examined for the years 1997–2012 using a combination of dynamic factor and cluster analyses. The aims were (i) the determination of the main driving factors of the water table, (ii) the determination of the spatial distribution and importance of these factors, and (iii) the estimation of shallow groundwater levels using the obtained model. Results indicate that the dynamic factor models were capable of accurately estimating the hydrographs (avg. mean squared error = 0.29 for standardized water levels), meaning that the two driving factors identified (evapotranspiration and precipitation) describe most of the variances of the fluctuations in water level. Both meteorological parameters correlated with an obtained dynamic factor (r = −0.41 for evapotranspiration & r = 0.76 for precipitation). The strength of these effects displayed a spatial pattern, as did the factor loadings. On this basis, the monitoring wells could be objectively distinguished into two groups using hierarchical cluster analysis and verified by linear discriminant analysis in 98% of the cases. This grouping in turn was determined to be primarily related to the elevation and the geology of the area. It can be concluded that the application of the data analysis toolset suggested herein permits a more efficient, objective, and reproducible delineation of the primary driving factors of the shallow groundwater table in the area. Additionally, it represents an effective toolset for the forecasting of water table variations, a quality which, in the view of the likelihood of further climate change to come, is a distinctive advantage. The knowledge of these factors is crucial to a better understanding of the hydrogeological processes that characterize the water table and, thus, to developing a proper water resource management strategy for the area. Full article
(This article belongs to the Special Issue The Application of State-of-the-Art Statistical Tools in Limnology, towards a Sustainable Future)
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