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Probabilities of monthly median chlorophyll-a concentrations in subarctic, temperate and subtropical lakes
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. (Miljöanalys)
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. (Miljöanalys)
Uppsala University, Disciplinary Domain of Science and Technology, Earth Sciences, Department of Earth Sciences, LUVAL. (Miljöanalys)
2013 (English)In: Environmental Modelling & Software, ISSN 1364-8152, E-ISSN 1873-6726, Vol. 41, 199-209 p.Article in journal (Refereed) Published
Abstract [en]

High concentrations of chlorophyll-a (chl-a) during summer are by definition a common problem in eutrophicated lakes. Several models have been designed to predict chl-a concentrations but are unable to estimate the probability of predicted concentrations or concentration spans during subsequent months. Two different methods were developed to compute the probabilities of obtaining a certain chl-a concentration. One method is built on discrete Markov chains and the other method on a direct relationship between median chl-a concentrations from two months. Lake managers may use these methods to detect and counteract the risk of high chl-a concentrations and algal blooms during coming months. Both methods were evaluated and applied along different scenarios to detect the probability to exceed chl-a concentration in different coming months. The procedure of computing probabilities is strictly based on general statistics which means that neither method is constrained for chl-a but can also be used for other variables. A user-friendly software application was developed to facilitate and extend the use of these two methods.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 41, 199-209 p.
Keyword [en]
chlorophyll-a, predicting probability, markov chain, lake
Keyword [sv]
chlorophyll-a, prediktion av sannolikheter, markovkedjor, sjöar
National Category
Environmental Sciences
Research subject
Earth Science with specialization in Environmental Analysis
Identifiers
URN: urn:nbn:se:uu:diva-194311DOI: 10.1016/j.envsoft.2012.12.002ISI: 000315974500019OAI: oai:DiVA.org:uu-194311DiVA: diva2:604947
Available from: 2013-02-12 Created: 2013-02-12 Last updated: 2017-12-06Bibliographically 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.
Series
Digital Comprehensive Summaries of Uppsala Dissertations from the Faculty of Science and Technology, ISSN 1651-6214 ; 1300
Keyword
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
Identifiers
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)
Opponent
Supervisors
Available from: 2015-11-05 Created: 2015-09-30 Last updated: 2015-11-10

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Dimberg, Peter H.Bryhn, Andreas C.Hytteborn, Julia K.

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