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Predicting median monthly chlorophyll-a concentrations
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: Limnologica, ISSN 0075-9511, E-ISSN 1873-5851, Vol. 43, no 3, 169-176 p.Article in journal (Refereed) Published
Abstract [en]

Chlorophyll-a (Chl-a) is a plant pigment which is used in many environmental monitoring programs as a water quality indicator for lakes. However, monthly Chl-a data are often lacking in many monitored lakes as measurements are concentrated to certain periods of the year. This study investigates two methods of how monthly Chl-a medians can be predicted (i) new monthly regression models from median summer total phosphorus concentrations and latitude, (ii) and with monthly constants added to regression models from the literature. Data from 308 lakes were used and the trophic status of the lakes ranged from oligotrophic to hypertrophic, they were located from northern Sweden (Europe) to southern Florida (North America). These models may be useful for understanding the general Chl-a seasonality in lakes and for managing lakes in which Chl-a measurements are not made over the whole year.

Place, publisher, year, edition, pages
Elsevier, 2013. Vol. 43, no 3, 169-176 p.
Keyword [en]
Chlorophyll-a, Seasonality, Lake, Phosphorus, Regression model, Statistical model
National Category
Environmental Sciences
Research subject
Earth Science with specialization in Environmental Analysis
Identifiers
URN: urn:nbn:se:uu:diva-197880DOI: 10.1016/j.limno.2012.08.011ISI: 000319240700005OAI: oai:DiVA.org:uu-197880DiVA: diva2:614656
Available from: 2013-04-05 Created: 2013-04-05 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.Hytteborn, Julia K.

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