Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Extremvärdesanalys av grundvattennivåmätserier.
KTH, School of Architecture and the Built Environment (ABE), Sustainable development, Environmental science and Engineering, Land and Water Resources Engineering.
2014 (Swedish)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesisAlternative title
Extreme value analysis of groundwater level time series. (English)
Abstract [sv]

Syftet med detta examensarbete är att kunna beräkna sannolikheten av extrema grundvattennivåers återkomsttid. Detta är av betydelse för till exempel dimensionering av grundläggning när risken för hydraulisk bottenupptryckning eller skredrisk måste vantifieras. I föreliggande examensarbete valdes 139 långa grundvattennivåmätserier med varierande hydrogeologiskt miljö ut ur SGU:s grundvattennät. Dessa tidsserier anpassas till olika statistiska fördelningsfunktioner för att prognostisera grundvattennivån som uppträder med en visst återkommsttid. Normal-, Weibull- och Gumbelfördelning liksom logpearson typ 3-fördelning (LP3) och Generaliserad Extremvärdesfördelning (GEV) samt Generaliserad Paretofördelning (GPD) testades och jämfördes. Därutöver beräknades huruvida dessa är lämpliga som modeller för predikteringen av återkommstnivåer.

Två olika ansatser diskuteras för att välja ut tidsseriernas extremvärden, årliga maximiserier och överskridelseserier samt deras lämplighet med hänsyn till grundvattennivåns årstidsfluktuation och periodicitet. I undersökningen framgår att den vedertagna normalfördelningen oftast är en lämplig modell men i vissa fall måste förkastas. GEV och LP3 tillåter oftast en bättre anpassning än normalfördelningen men är känsligare mot outliers. GPD visar sig ha god anpassningsgrad till överskridelseserier. Det krävs dock deklustring av mätserier vilket leder till ett minskad antal värden som fördelningen kan anpassas på.

Abstract [en]

The ability to calculate the probability of extreme groundwater levels is fundamental, when estimating the risk of hydraulic heave at the bottom of an excavation or landslides triggered by excess pore water pressure. This can be done by fitting historic groundwater level data to probability density functions and extrapolating to certain return levels.

However, very little research has been done in the field of estimating extreme groundwater level with probability density functions. The design guide (TK-Geo) of the Swedish Transport Administration (Trafikverket) gives a brief description of a method developed in the eighties. It is based on applying well-established hydrological theory to groundwater level time series. In this study, recent research on hydrologic extreme value analysis is applied and used to bring the methods in groundwater up to date. More than 100 long time-series of groundwater data recorded by SGU in the Swedish groundwater network (often used as reference series) are utilized for testing. Established parameter estimation techniques such as Maximum Likelihood Estimation and Probability-Weighted Moments with L-moments are compared and weighed against the traditionally used Method of Moments.

Swedish research with focus on this topic usually takes advantage of the simplicity of the Block Maxima Approach, while evading the more complex Peaks over Threshold method. These methods are also applied and discussed as to how their use influences the inferences made. Traditionally used statistical distributions such as the Normal, Weibull and Gumbel distributions are compared to the more flexible and presently more popular Generalized Extreme Value distribution and Generalized Pareto distribution. In order to estimate model adequacy a number of goodness-of-fit tests are discussed and implemented, such as the Anderson-Darling test and Kolmogorov-Smirnoff test. This results in a general overview of how to compute return levels for high return periods and which models should be preferred. Fitting probability density distributions requires the data to be independent and identically distributed, a condition, which groundwater level measurements are generally not in accordance with. This is a consequence of the groundwater’s inherent seasonality not only within one year, but also over random numbers of years. Using data with seasonality results in underestimation of extremes and should be avoided. Examples of identification and recommendations for handling this sort of phenomena are given.

Place, publisher, year, edition, pages
2014.
Series
TRITA-LWR Degree Project, ISSN 1651-064X ; 2014:12
Keyword [sv]
Grundvattennivå, Extremvärdesteori, Mätintervall, Årstidsfluktuation
National Category
Civil Engineering
Identifiers
URN: urn:nbn:se:kth:diva-170059OAI: oai:DiVA.org:kth-170059DiVA: diva2:827017
Educational program
Degree of Master - Water System Technology
Supervisors
Available from: 2015-08-11 Created: 2015-06-26 Last updated: 2015-08-11Bibliographically approved

Open Access in DiVA

fulltext(4199 kB)98 downloads
File information
File name FULLTEXT01.pdfFile size 4199 kBChecksum SHA-512
831c84e934c6f6c59c7ace59225847a0518b68447228e315e575a814aec46fa01500a75de9f8ecbdf951cedebe04f1e812e4bc7c89a2c1fce40abd4b8791e7d0
Type fulltextMimetype application/pdf

By organisation
Land and Water Resources Engineering
Civil Engineering

Search outside of DiVA

GoogleGoogle Scholar
Total: 98 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 82 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf