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Predicting Total Nitrogen, Total Phosphorus, Total Organic Carbon, Dissolved Oxygen and Iron in Deep Waters of Swedish Lakes
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. (Miljöanalys)
2015 (English)In: Environmental Modelling and Assessment, ISSN 1420-2026, E-ISSN 1573-2967, Vol. 20, no 5, 411-423 p.Article in journal (Refereed) Published
Abstract [en]

In many lakes, the physical and chemical characteristics are monitored for surface waters but not for deep waters. Yet, deep waters may be important for understanding the dynamics of lake water chemistry variables over the year. In this study, multiple regression models have been created for five different variables, total phosphorus, total nitrogen, total organic carbon, dissolved oxygen (DO) and iron, in the deep water for 61 Swedish temperate or subarctic lakes. The investigated season was February to October, depending on the data availability. Regressions used the corresponding variables from the surface water as well as different morphometric parameters as independent variables. It was possible to construct meaningful models (r2 > 0.65; p < 0.05) for most of the variables and months. However, it was not possible to attain this criterion for some months regarding the DO concentration. Surface water concentrations were in general most important for predicting corresponding deep water concentrations. An exception was that during summer, DO differed considerably between surface waters and deep waters and voluminous lakes had particularly high DO concentrations in deep waters. No cross-systems relationship could be found between deepwater hypoxia and total phosphorus in deep waters during summer when phosphorus diffusion from sediments is most likely. A mass-balance modelling example was applied to illustrate the use of the produced models. These findings may provide a better understanding of the dynamics of these five variables in temperate or subarctic lakes.

Place, publisher, year, edition, pages
2015. Vol. 20, no 5, 411-423 p.
Keyword [en]
Lake, Morphometry, Mass-balance, Deep water, Water quality
National Category
Earth and Related Environmental Sciences Environmental Sciences
Research subject
Earth Science with specialization in Environmental Analysis
URN: urn:nbn:se:uu:diva-263166DOI: 10.1007/s10666-015-9456-4ISI: 000360554900001OAI: oai:DiVA.org:uu-263166DiVA: diva2:857102
Available from: 2015-09-28 Created: 2015-09-28 Last updated: 2015-11-10Bibliographically approved
In thesis
1. Predictions Within and Across Aquatic Systems using Statistical Methods and Models
Open this publication in new window or tab >>Predictions Within and Across Aquatic Systems using Statistical Methods and Models
2015 (English)Doctoral thesis, comprehensive summary (Other academic)
Alternative title[sv]
Prediktioner inom och mellan akvatiska system med statistiska metoder och modeller
Abstract [en]

Aquatic ecosystems are an essential source for life and, in many regions, are exploited to a degree which deteriorates their ecological status. Today, more than 50 % of the European lakes suffer from an ecological status which is unsatisfactory. Many of these lakes require abatement actions to improve their status, and mathematical models have a great potential to predict and evaluate different abatement actions and their outcome. Several statistical methods and models exist which can be used for these purposes; however, many of the models are not constructed using a sufficient amount or quality of data, are too complex to be used by most managers, or are too site specific. Therefore, the main aim of this thesis was to present different statistical methods and models which are easy to use by managers, are general, and provide insights for the development of similar methods and models.

To reach the main aim of the thesis several different statistical and modelling procedures were investigated and applied, such as genetic programming (GP), multiple regression, Markov Chains, and finally, well-used criteria for the r2 and p-value for the development of a method to determine temporal-trends. The statistical methods and models were mainly based on the variables chlorophyll-a (chl-a) and total phosphorus (TP) concentrations, but some methods and models can be directly transferred to other variables.

The main findings in this thesis were that multiple regressions overcome the performance of GP to predict summer chl-a concentrations and that multiple regressions can be used to generally describe the chl-a seasonality with TP summer concentrations and the latitude as independent variables. Also, it is possible to calculate probabilities, using Markov Chains, of exceeding certain chl-a concentrations in future months. Results showed that deep water concentrations were in general closely related to the surface water concentrations along with morphometric parameters; these independent variables can therefore be used in mass-balance models to estimate the mass in deep waters. A new statistical method was derived and applied to confirm whether variables have changed over time or not for cases where other traditional methods have failed. Finally, it is concluded that the statistical methods and models developed in this thesis will increase the understanding for predictions within and across aquatic systems.

Place, publisher, year, edition, pages
Uppsala: Acta Universitatis Upsaliensis, 2015. 59 p.
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1300
Lake, Water quality, Chlorophyll-a, Total phosphorus, Seasonality, Morphometry, Regression model, Probability, Markov chain, Genetic programming, Temporal-trend
National Category
Earth and Related Environmental Sciences Environmental Sciences Oceanography, Hydrology, Water Resources Probability Theory and Statistics
Research subject
Earth Science with specialization in Environmental Analysis
urn:nbn:se:uu:diva-263283 (URN)978-91-554-9362-2 (ISBN)
Public defence
2015-11-27, Hambergsalen, Villavägen 16, Uppsala, 10:00 (English)
Available from: 2015-11-05 Created: 2015-09-30 Last updated: 2015-11-10

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