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Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis

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...

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Autores principales: Ngongondo, Cosmo, Zhou, Yanlai, Xu, Chong-Yu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442765/
https://www.ncbi.nlm.nih.gov/pubmed/32821968
http://dx.doi.org/10.1007/s10661-020-08519-4
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author Ngongondo, Cosmo
Zhou, Yanlai
Xu, Chong-Yu
author_facet Ngongondo, Cosmo
Zhou, Yanlai
Xu, Chong-Yu
author_sort Ngongondo, Cosmo
collection PubMed
description 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.
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spelling pubmed-74427652020-08-27 Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis Ngongondo, Cosmo Zhou, Yanlai Xu, Chong-Yu Environ Monit Assess Article 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. Springer International Publishing 2020-08-21 2020 /pmc/articles/PMC7442765/ /pubmed/32821968 http://dx.doi.org/10.1007/s10661-020-08519-4 Text en © The Author(s) 2020 Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
spellingShingle Article
Ngongondo, Cosmo
Zhou, Yanlai
Xu, Chong-Yu
Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis
title Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis
title_full Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis
title_fullStr Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis
title_full_unstemmed Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis
title_short Multivariate framework for the assessment of key forcing to Lake Malawi level variations in non-stationary frequency analysis
title_sort multivariate framework for the assessment of key forcing to lake malawi level variations in non-stationary frequency analysis
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442765/
https://www.ncbi.nlm.nih.gov/pubmed/32821968
http://dx.doi.org/10.1007/s10661-020-08519-4
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