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Modelling Drought In South Africa: Meteorological Insights And Predictive Parameters.

Home
Modelling Drought In South Africa: Meteorological Insights And Predictive Parameters.

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Modelling drought in South Africa: meteorological insights and predictive parameters.

Nnaemeka Onyeuwaoma¹, Venkataraman Sivakumar^2,3, Mahesh Bade²

¹Discipline of Physics, School of Chemistry and Physics, College of Agriculture, Engineering and Science, University of KwaZulu Natal, Westville Campus, Durban, 4001, South Africa. onyeuwaoman@ukzn.ac.za.

Environmental Monitoring and Assessment

|September 20, 2024

View abstract on PubMed

Summary

This summary is machine-generated.

South Africa

Keywords:

Drought Precipitation SPI Simulations South Africa

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Area of Science:

Environmental Science
Meteorology
Artificial Intelligence

Background:

South Africa faces persistent drought challenges, causing significant economic impact.
The Western Cape experienced a severe drought from 2015-2018, leading to national disaster status and strict water rationing in Cape Town.
Recurring droughts necessitate improved prediction models for water resource management.

Purpose of the Study:

To develop and evaluate Artificial Neural Network (ANN) models for drought prediction in South Africa.
To assess the efficacy of various meteorological variables and aerosol parameters in drought simulation.
To identify key parameters and location-specific factors influencing drought prediction accuracy.

Main Methods:

Utilized Artificial Neural Network (ANN) for drought prediction simulations.

Employed the Standard Precipitation Index (SPI) as the primary drought indicator, following WMO guidelines.

Incorporated meteorological data (relative humidity, temperature, soil wetness, evapotranspiration, evaporation, sea surface temperature) and aerosol optical depth across four South African locations over 21 years.

Main Results:

ANN models demonstrated significant predictive capabilities, with R² values varying by location and SPI timescale (SPI3 and SPI6).
Evapotranspiration (et) was identified as a crucial parameter for accurate drought simulation.
Predictive accuracy varied considerably across different locations, indicating no universal drought prediction scheme.

Conclusions:

Artificial Neural Networks (ANN) are effective tools for drought studies and simulations in South Africa.
Location-specific factors significantly influence drought prediction models.
The study provides valuable insights for simulating future drought scenarios across South Africa, aiding water resource planning.