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dc.date.accessioned2020-12-02T20:53:13Z
dc.date.available2020-12-02T20:53:13Z
dc.date.created2020-09-02T10:25:22Z
dc.date.issued2020
dc.identifier.citationNgongondo, Cosmo Zhou, Yanlai Xu, Chong-Yu . Multivariate framework for the assessment of key forcing to Lake Malawi levels variations in non-stationary frequency analysis. Environmental Monitoring & Assessment. 2020, 192(593)
dc.identifier.urihttp://hdl.handle.net/10852/81375
dc.description.abstractAbstract Lake Malawi in south eastern Africa is a very important freshwater system for the socio-economic development of the riparian countries and communities. The lake has however experienced considerable recession in the levels in recent years. Consequently, frequency analyses of the lake levels premised on time-invariance (or stationarity) in the parameters of the underlying probability distribution functions (pdfs) can no longer be assumed. In this study, the role of hydroclimate forcing factors (rainfall, lake evaporation, and inflowing discharge) and low frequency climate variability indicators (e.g., El Nino Southern Oscillation-ENSO and the Indian Ocean Dipole Mode-IODM) on lake level variations is investigated using a monthly mean lake level dataset from 1899 to 2017. Non-stationarity in the lake levels was tested and confirmed using the Mann-Kendall trend test ( α = 0.05 level) for the first moment and the F test for the second moment ( α = 0.05 level). Change points in the series were identified using the Mann-Whitney-Pettit test. The study also compared stationary and non-stationary lake level frequency during 1961 to 2004, the common period where data were available for all the forcing factors considered. Annual maximum series (AMS) and peak over threshold (POT) analysis were conducted by fitting various candidate extreme value distributions (EVD) and parameter fitting methods. The Akaike information criteria (AIC), Bayesian information criteria (BIC), deviance information criteria (DIC), and likelihood ratios (RL) served as model evaluation criteria. Under stationary conditions, the AMS when fitted to the generalized extreme value (GEV) distribution with maximum likelihood estimation (MLE) was found to be superior to POT analysis. For the non-stationary models, open water evaporation as a covariate of the lake levels with the GEV and MLE was found to have the most influence on the lake level variations as compared with rainfall, discharge, and the low frequency climatic forcing. The results are very critical in flood zoning especially with various planned infrastructural developments around the lakeshore.
dc.languageEN
dc.publisherKluwer Academic/Plenum Publishers
dc.rightsAttribution 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.titleMultivariate framework for the assessment of key forcing to Lake Malawi levels variations in non-stationary frequency analysis
dc.typeJournal article
dc.creator.authorNgongondo, Cosmo
dc.creator.authorZhou, Yanlai
dc.creator.authorXu, Chong-Yu
cristin.unitcode185,15,22,60
cristin.unitnameSeksjon for naturgeografi og hydrologi
cristin.ispublishedtrue
cristin.fulltextoriginal
cristin.qualitycode1
dc.identifier.cristin1826650
dc.identifier.bibliographiccitationinfo:ofi/fmt:kev:mtx:ctx&ctx_ver=Z39.88-2004&rft_val_fmt=info:ofi/fmt:kev:mtx:journal&rft.jtitle=Environmental Monitoring & Assessment&rft.volume=192&rft.spage=&rft.date=2020
dc.identifier.jtitleEnvironmental Monitoring & Assessment
dc.identifier.volume192
dc.identifier.issue9
dc.identifier.doihttps://doi.org/10.1007/s10661-020-08519-4
dc.identifier.urnURN:NBN:no-84453
dc.type.documentTidsskriftartikkel
dc.type.peerreviewedPeer reviewed
dc.source.issn0167-6369
dc.identifier.fulltextFulltext https://www.duo.uio.no/bitstream/handle/10852/81375/2/Ngongondo2020_Article_MultivariateFrameworkForTheAss%2B%25281%2529.pdf
dc.type.versionPublishedVersion
cristin.articleid593
dc.relation.projectNFR/274310


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