Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2019 Oct 24;11(1):712.
doi: 10.4102/jamba.v11i1.712. eCollection 2019.

Monitoring droughts in Eswatini: A spatiotemporal variability analysis using the Standard Precipitation Index

Affiliations

Monitoring droughts in Eswatini: A spatiotemporal variability analysis using the Standard Precipitation Index

Daniel H Mlenga et al. Jamba. .

Abstract

The spatiotemporal analysis of drought is of great importance to Eswatini as the country has been facing recurring droughts with negative impacts on agriculture, the environment and the economy. In 2016, the country experienced the most severe drought in over 35 years, resulting in food shortages, drying up of rivers as well as livestock deaths. The frequent occurrence of extreme drought events makes the use of drought indices essential for drought monitoring, early warning and planning. The aim of this study was to assess the applicability of the Standard Precipitation Index (SPI) for near real-time and retrospective drought monitoring in Eswatini. The 3-, 6- and 12-month SPI were computed to analyse the severity and onset of meteorological drought between 1986 and 2017. The results indicated that the climate of Eswatini exhibits geospatial and temporal variability. Droughts intensified in terms of frequency, severity and geospatial coverage, with the worst drought years being 1985-1986, 2005-2006 and 2015-2016 agricultural seasons. Moderate droughts were the most prevalent, while the frequency of severe and very severe droughts was low. Most parts of the country were vulnerable to mild and moderate agricultural droughts. Spatial analysis showed that the most severe and extreme droughts were mostly experienced in the Lowveld and Middleveld agro-ecological zones. The 3-, 6- and 12-month SPI computations conducted in January detected the onset of early season drought, thereby affirming the applicability of the index for monitoring near real-time and retrospective droughts in Eswatini. Drought monitoring using the SPI provides information for early warning, particularly in drought-prone areas, by depicting a drought before the effects are felt.

Keywords: Eswatini; Standard Precipitation Index; drought; drought monitoring; rainfall; spatial and temporal variability.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no financial or personal relationships that may have inappropriately influenced them in writing this article.

Figures

FIGURE 1
FIGURE 1
Map of Eswatini with agro-ecological zonation and the rainfall stations.
FIGURE 2
FIGURE 2
Mean historical monthly rainfall for Eswatini during the time period 1986–2017.
FIGURE 3
FIGURE 3
Annual rainfall trend for Eswatini (1986–2017).
FIGURE 4
FIGURE 4
Standard Precipitation Index values of Eswatini for three different timescales (3, 6 and 12 months).
FIGURE 5
FIGURE 5
Three-month Standard Precipitation Index values for the Highveld, Middleveld, Lowveld and Lubombo Plateau agro-ecological zones.
FIGURE 6
FIGURE 6
Standard Precipitation Index 3-month timescale during 1985–1986.
FIGURE 7
FIGURE 7
Standard Precipitation Index 3-month timescale during 2004–2005.
FIGURE 8
FIGURE 8
Standard Precipitation Index 3-month timescale during 2005–2006.
FIGURE 9
FIGURE 9
Standard Precipitation Index 3-month timescale during 2015–2016.

References

    1. Alley W.M, 1984, ‘The Palmer drought severity index: Limitations and assumptions’, Journal of Climate and Applied Meteorology 23(7), 1100–1109. 10.1175/1520-0450(1984)023<1100:TPDSIL>2.0.CO;2 - DOI
    1. Alley W.M, 1985, ‘The Palmer drought severity index as a measure of hydrologic drought’, JAWRA Journal of the American Water Resources Association 21(1), 105–114. 10.1111/j.1752-1688.1985.tb05357.x - DOI
    1. Belayneh A. & Adamowski J, 2012, ‘Standard precipitation index drought forecasting using neural networks, wavelet neural networks, and support vector regression’, Applied Computational Intelligence and Soft Computing 2012(2012), 6 10.1155/2012/794061 - DOI
    1. Bokal S, 2006, Standardized precipitation index tool for drought monitoring – Examples from Slovenia, Drought Management Centre for South-Eastern Europe, DMCSEE, viewed 19 June 2017, from http://www.wamis.org/agm/meetings/slovenia10/S3-Bokal-SPI.pdf.
    1. Dai A., Trenberth K.E. & Karl T.R, 1998, ‘Global variations in droughts and wet spells: 1900–1995’, Geophysical Research Letters 25(17), 3367–3370. 10.1029/98GL52511 - DOI

LinkOut - more resources