Water Balance Model of Lake Malawi and its Sensitivity to Climate Change

Kumambala, Patsani G.

doi:10.2174/1874378101004010152

Cited by 27 publications

(29 citation statements)

References 16 publications

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“…Using the most recent observed climatic parameters of the lake, the predicted level by Neuland (1984) remains below 477 m and further indicated a high probability of negative trend of future water levels as reported in the present study. Kumambala and Ervine (2010) further added that it is very unlikely for the water level to increase to a maximum height of 477 m as it was in 1980. Recent prediction by Kaunda (2015) indicated that near future and far future projects show that water yield will decrease by 8.84% and therefore Lake Malawi water level is expected to drop.…”

Section: Forecastingmentioning

confidence: 99%

Application of stochastic models in predicting Lake Malawi water levels

Makwinja¹,

Phiri²,

Kosamu³

et al. 2017

Int. J. Water Res. Environ. Eng.

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Stochastic models have proven to be practically fundamental in fields such as science, economics, and business, among others. In Malawi, stochastic models have been used in fisheries to forecast fish catches. Nevertheless, forecasting water levels in major lakes and rivers in Malawi has been given little attention despite the availability of ample historical data. Although previous multichannel seismic surveys revealed the presence of low stands (sediment bypass zone) in Lake Malawi indicating that since the beginning of its formation, important water level fluctuations have been occurring, these previous surveys failed to predict and highlight much more clearly the status of these levels in the future. Therefore, the main objective of the study was to fill these research gaps. The study used

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Section: Forecastingmentioning

confidence: 99%

Application of stochastic models in predicting Lake Malawi water levels

Makwinja¹,

Phiri²,

Kosamu³

et al. 2017

Int. J. Water Res. Environ. Eng.

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“…Previous hydrological studies have assessed the trends and relationships between the water level in Lake Malawi [44,45], the discharge in the Shire River [46], and the observed and potential impacts of climate change on the local hydrology [47,48] and hydropower generation [49], as well as the perceived risks and potential adaptation options under climatic and socio-economic uncertainty in the Shire River Basin [50]. The literature has also highlighted that the lake level is highly sensitive to climate variability [46], with cyclic fluctuations in levels being largely subject to annual rainfall patterns and seasonal precipitation and temperature variables anticipating lake level changes by approximately two months.…”

Section: Introductionmentioning

confidence: 99%

“…The literature has also highlighted that the lake level is highly sensitive to climate variability [46], with cyclic fluctuations in levels being largely subject to annual rainfall patterns and seasonal precipitation and temperature variables anticipating lake level changes by approximately two months. According to modelling studies based on downscaled climate projections [48], a warmer climate will likely contribute to a further decrease in the water balance. Concerning discharge in the Shire River, local precipitations and temperature have been found to anticipate river flow surges by 2 d [46].…”

Section: Introductionmentioning

confidence: 99%

Monitoring hydropower reliability in Malawi with satellite data and machine learning

Falchetta

Kasamba

Parkinson

2020

Environ. Res. Lett.

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Hydro-climatic extremes can affect the reliability of electricity supply, in particular in countries that depend greatly on hydropower or cooling water and have a limited adaptive capacity. Assessments of the vulnerability of the power sector and of the impact of extreme events are thus crucial for decisionmakers, and yet often they are severely constrained by data scarcity. Here, we introduce and validate an energy-climate-water framework linking remotely-sensed data from multiple satellite missions and instruments (TOPEX/POSEIDON. OSTM/Jason, VIIRS, MODIS, TMPA, AMSR-E) and field observations. The platform exploits random forests regression algorithms to mitigate data scarcity and predict river discharge variability when ungauged. The validated predictions are used to assess the impact of hydroclimatic extremes on hydropower reliability and on the final use of electricity in urban areas proxied by nighttime light radiance variation. We apply the framework to the case of Malawi for the periods 2000-2018 and 2012-2018 for hydrology and power, respectively. Our results highlight the significant impact of hydro-climatic variability and dry extremes on both the supply of electricity and its final use. We thus show that a modelling framework based on open-access data from satellites, machine learning algorithms, and regression analysis can mitigate data scarcity and improve the understanding of vulnerabilities. The proposed approach can support long-term infrastructure development monitoring and identify vulnerable populations, in particular under a changing climate.

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“…S1), is the third largest of the African Great Lakes and ninth in the world, with an area of 29 600 km 2 , a maximum width of 75 km, and a maximum length of 560 km. Lake Malawi is an important water resource for surrounding tropical southeastern African nations such as Malawi, Mozambique, and Tanzania (Kumambala and Ervine, 2010). In particular, a large part of agriculture and energy in Malawi originates from the water resource of Lake Malawi and the Shire River which flows from the lake; all of the national hydropower stations are built on the Shire River (a total installation capacity of 280 MW; Kumambala and Ervine, 2010) and the largest national sugar plantations are supplied with water from the Shire River.…”

Section: Introductionmentioning

confidence: 99%

Influences of Lake Malawi on the spatial and diurnal variability of local precipitation

Koseki

Mooney

2019

Hydrol. Earth Syst. Sci.

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Abstract. We investigate how the intensity and spatial distribution of precipitation vary around Lake Malawi on a diurnal timescale, which can be valuable information for water resource management in tropical south-eastern African nations. Using a state-of-the-art satellite product and regional atmospheric model, the well-defined diurnal cycle is detected around Lake Malawi with harmonic and principle component analyses: the precipitation is intense during midnight to morning over Lake Malawi and the precipitation peaks in the daytime over the surrounding area. This diurnal cycle in the precipitation around the lake is associated with the lake–land breeze circulation. Comparisons between the benchmark simulation and an idealized simulation in which Lake Malawi is removed reveal that the diurnal variations in precipitation are substantially amplified by the presence of Lake Malawi. This is most evident over the lake and surrounding coastal regions. Lake Malawi also enhances the lake–land breeze circulation; the nocturnal lakeward land breeze generates surface convergence effectively and precipitation intensifies over the lake. Conversely, the daytime landward lake breeze generates the intense divergence over the lake and precipitation is strongly depressed over the lake. The lake–land breeze and the background vapour enriched by Lake Malawi drive primarily a diurnal variation in the surface moisture flux divergence/convergence over the lake and surrounding area which contributes to the diurnal cycle of precipitation in this region.

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Water Balance Model of Lake Malawi and its Sensitivity to Climate Change

Cited by 27 publications

References 16 publications

Application of stochastic models in predicting Lake Malawi water levels

Application of stochastic models in predicting Lake Malawi water levels

Monitoring hydropower reliability in Malawi with satellite data and machine learning

Influences of Lake Malawi on the spatial and diurnal variability of local precipitation

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