Late Quaternary Lake-Level Changes of Lake Malawi

Finney, Bruce P.; Scholz, Christopher A.; Johnson, T. C.; Trumbore, S.E.

doi:10.1201/9780203748978-27

Cited by 13 publications

(4 citation statements)

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“…Most of the eastern African lake records suggest dry conditions during much of the last glacial phase [137,138,142] and also proxy records from Tswaing Crater (Pretoria Saltpan) in South Africa and Lake Tritrivakley in Madagascar support dry conditions [143][144][145]. Only Lakes Malawi and Masoko in the southern part of eastern Africa show some evidence of wetter conditions during the last glacial [145][146][147][148]. While the wet conditions in some of these lakes might support a southward shift of the ITCZ due to the disproportionate cooling of the larger landmass in the northern hemisphere during cold phases [149], the complex geographical pattern of wet and dry conditions do not support widespread rainfall in the southern African summer rainfall area at the time.…”

Section: Regional Speleothem Age Distributionsmentioning

confidence: 98%

“…The last glacial phase does however show considerable speleothem formation in the southern African summer rainfall zone (Figure 4). Most of the eastern African lake records suggest dry conditions during much of the last glacial phase [137,138,142] and also proxy records from Tswaing Crater (Pretoria Saltpan) in South Africa and Lake Tritrivakley in Madagascar support dry conditions [143][144][145]. Only Lakes Malawi and Masoko in the southern part of eastern Africa show some evidence of wetter conditions during the last glacial [145][146][147][148].…”

Section: Regional Speleothem Age Distributionsmentioning

confidence: 99%

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A Window into Africa’s Past Hydroclimates: The SISAL_v1 Database Contribution

et al. 2019

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Africa spans the hemispheres from temperate region to temperate region and has a long history of hominin evolution. Although the number of Quaternary palaeoclimatic records from the continent is increasing, much of the history of spatial and temporal climatic variability is still debated. Speleothems, as archives of terrestrial hydroclimate variability, can help reveal this history. Here we review the progress made to date, with a focus on the first version of the Speleothem Isotopes Synthesis and AnaLysis (SISAL) database. The geology of Africa has limited development of large karst regions to four areas: along the northern coast bordering the Mediterranean, eastern Africa and the Horn of Africa, southwestern Africa and southern Africa. Exploitation of the speleothem palaeoclimate archives in these regions is uneven, with long histories of research, e.g., in South Africa, but large areas with no investigations such as West Africa. Consequently, the evidence of past climate change reviewed here is irregularly sampled in both time and space. Nevertheless, we show evidence of migration of the monsoon belt, with enhanced rainfall during interglacials observed in northeast Africa, southern Arabia and the northern part of southern Africa. Evidence from eastern Africa indicates significant decadal and centennial scale rainfall variability. In northwestern and southern Africa, precession and eccentricity influence speleothem growth, largely through changing synoptic storm activity.

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Section: Regional Speleothem Age Distributionsmentioning

confidence: 98%

Section: Regional Speleothem Age Distributionsmentioning

confidence: 99%

A Window into Africa’s Past Hydroclimates: The SISAL_v1 Database Contribution

et al. 2019

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“…Generally, Eastern Africa has shown substantial variability of its climate in the distant to the near past, where distinct wet and dry periods have been witnessed (Conway, 2002;deMenocal and Rind, 2019;Nicholson, 2019;Sutcliffe and Parks, 1999;Wainwright et al, 2020). This led not only to drastic changes in lake levels (Finney et al, 2019;Goman et al, 2017;Grove, 2019;Maina et al, 2018;McCall, 2010;Onywere et al, 2013;Richardson, 1966;Ricketts and Johnson, 2019;Sutcliffe and Parks, 1999;Verschuren, 2001Verschuren, , 2019Whittaker, 2019), but also river flows (e.g. Grove, 2019;Sutcliffe and Parks, 1999).…”

Section: Introductionmentioning

confidence: 99%

Hydroclimatic Analysis of Rising Water Levels in the Great Rift Valley Lakes of Kenya

Herrnegger¹,

Stecher²,

Schwatke³

et al. 2022

Preprint

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Study RegionThe Great Rift Valley lakes of Kenya have recently experienced significant increases in their water levels, negatively impacting the local communities. This has provoked renewed concerns about the causations, with various geological, anthropogenic and hydro-climatic influences hypothesized as potential causes of the water level rises. Study FocusThis study analyses and documents water level fluctuations in Lakes Baringo, Bogoria, Nakuru, Solai, Elementaita and Naivasha. Hydrometeorological analyses are undertaken to understand potential causes and lake volume data is used to derive the "Integrated Catchment Response" (ICR), a magnitude which allows to relate changes in water balance components to signals observed in the lake volume changes. New Hydrological Insights for the RegionCompared to 1984-2009, the recent increases in lake areas range from 21% for Lake Naivasha to an extraordinary 123% for Lake Solai. Mean annual rainfall for 2010-2020 increased by up to 30%. Actual evapotranspiration also increased, but to lesser extent compared to rainfall. The results illustrate that changes in catchment properties due to anthropogenic influences or changes in underground permeability are not necessary to explain the lake level rises. Based on the ICR only surprisingly minor changes in the water balance are necessary to explain the lake level rises, since an increase of only 0.4 -2% of mean annual effective rainfall leads to the observed phenomena.

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“…Lake catchment hydrologic processes can be used to evaluate the causes of lake water level variation. The change in land use, evaporation, precipitation, runoff, and other metrological issues can be the causes of the lake level change (Vuglinskiy et al, 2009, Guo et.al., 2015, Finney et al, 2019. The temporal variation of a lake level depends on the catchment area to lake surface area ratio (Mason et al, 1994, Zhang et al, 2017.…”

Section: Introductionmentioning

confidence: 99%

Lake level rise modeling with Artificial Neural Networks (ANN) and Hydrodynamic model

Bulti

yutura

2022

Preprint

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The water level in a lake increases as a result of changes in the hydrologic processes in the lake catchment that can cause a flood in the lake's surrounding areas. The lake system needs a comprehensive investigation to alleviate an increase in the water level and consequently flooding. Some different methods and approaches can be applied to predict the lake level increase. A water balance study, hydrodynamic model application, and ANN simulation applications are some of the methods. Each method has merit and complexity in terms of data requirement, the reflection of the physical processes, and ease of modeling application. A simple water balance model application is relatively good in considering all the physical processes. However, model outputs are not good during the model performance evaluation. The hydrodynamic models are simulation models which base on the physical processes of the lake system, with the same modification. Somehow it considers all the real processes with simplification. A good understanding of the application provides a favorable condition to provide all the input/factors contributing/ indirectly to a system modeling. The simulation through a hydrodynamic model is within a good range on performance indicators. Artificial Neural networks (ANN) are recent applications that focus on data science rather than physical processes. The ANN provides an opportunity to include all the contributing factors in the modeling processes. The output from ANN modeling processes is in a very good range. The choice of the method of prediction needs to understand the actual event in a lake system and the model's background for good prediction.

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Late Quaternary Lake-Level Changes of Lake Malawi

Cited by 13 publications

References 1 publication

A Window into Africa’s Past Hydroclimates: The SISAL_v1 Database Contribution

A Window into Africa’s Past Hydroclimates: The SISAL_v1 Database Contribution

Hydroclimatic Analysis of Rising Water Levels in the Great Rift Valley Lakes of Kenya

Lake level rise modeling with Artificial Neural Networks (ANN) and Hydrodynamic model

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