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Variance reduction for trend analysis of hydrochemical data from brackish waters
Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
Linköping University, Department of Mathematics, Statistics. Linköping University, The Institute of Technology.
2003 (English)Report (Other academic)
Abstract [en]

We propose one parametric and one non-parametric method for detection of monotone trends in nutrient concentrations in brackish waters. Both methods take into account that temporal variation in the quality of such waters can be strongly influenced by mixing of salt and fresh water, thus salinity is used as a classification variable in the trend analysis. With the non-parametric approach, Mann-Kendall statistics are calculated for each salinity level, and the parametric method involves the use of bootstrap estimates of the trend slope in a time series regression model. In both cases, tests for each salinity level are combined in an overall trend test.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet , 2003. , 11 p.
Series
LiU-MAT-R, ISSN 0349-246X ; 2
National Category
Mathematics
Identifiers
URN: urn:nbn:se:liu:diva-22757Local ID: 2076OAI: oai:DiVA.org:liu-22757DiVA: diva2:243070
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2012-12-14
In thesis
1. Considering meteorological variation in assessments of environmental quality trends
Open this publication in new window or tab >>Considering meteorological variation in assessments of environmental quality trends
2003 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Time series of environmental data are collected to monitor the effectiveness of new emission reduction policies or to determine the general state of the environment. However, small gradual changes in such variables can easily be concealed by large fluctuations caused by prevailing weather conditions. Hence, there is a real need for procedures that facilitate separation of such natural variation from anthropogenic effects.

Taking meteorological or hydrological variables into consideration in a trend analysis can be done in several ways. The technique chosen to accomplish this objective depends on characteristics of the data set, for example the length of the time series and sampling frequencies, and the kind of relationships that exist between the response variable and the covariates. Two different approaches were examined in the studies underlying this thesis: multivariate non-parametric tests and parametric normalisation procedures. The non-parametric trend test proposed here was newly desinged, thus it was also necessary to conduct simulation studies to examine the performance of this method. By comparison, normalisation techniques have been used over the past few decades mainly to adjust for the impact of meteorological effects on air quality data. The choice of explanatory variables for such procedures was studied: first by examining variable selection procedures based on cross-validation, paying special attention to serially correlated response data; and secondly by considering variables derived from complex physics-based models as alternatives to measured variables. A number of other aspects that might influence the ability to detect trends were also explored, including level shifts due to instrument malfunctions.

Place, publisher, year, edition, pages
Linköping: Linköpings universitet, 2003. 41 p.
Series
Linköping Studies in Statistics, ISSN 1651-1700 ; 3
National Category
Mathematics
Identifiers
urn:nbn:se:liu:diva-22755 (URN)2073 (Local ID)91-7373-615-5 (ISBN)2073 (Archive number)2073 (OAI)
Public defence
2003-04-25, Sal Key 1, Keyhuset, Linköpings universitet, Linköping, 10:15 (Swedish)
Available from: 2009-10-07 Created: 2009-10-07 Last updated: 2012-12-14

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Libiseller, ClaudiaNordgaard, Anders

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