Nationwide temporal variability of droughts in the Kingdom of Eswatini: 1981–2018

Abstract

For adequate mitigation and adaptation measures, it is essential to have detailed analysis of droughts patterns. This study determined the i) occurrence and severity of droughts ii) drought recurrence frequencies and iii) drought trends across different agro-ecological zones in the Kingdom of Eswatini for the period 1981 to 2018. A Standardised Precipitation Index (SPI) computed from long-term precipitation data measured from six meteorological stations was used to determine drought occurrence and severity. Python software (Version 3.6) was applied on the SPI values to predict the recurrence of drought events over time in years. The SPI showed that in the Highveld, 42% of the droughts were moderate, 32% were severe and the remaining 26%, which all occurred post 1980 were extreme (SPI -2.34 to -2.82). The Middleveld had an even proportion of drought categories (29–35%). The Lowveld recorded 62% of moderate, 8% severe and 30% extreme droughts of which 70% occurred post 2000. Moderate droughts were found to recur every 4–5 years while extreme droughts are expected every 13–21 years. These findings are essential for mitigation and adaptation measures geared towards the adverse effects of droughts.

Effects of Rainfall Variability on Maize Production in Eswatini

“…The Lowveld has semi-arid to arid climate with annual range of rainfall between 508 mm and 890 mm and is prone to drought. The Lubombo plateau has a climate almost similar to the Middleveld and has an average altitude of 700 m above mean sea level [23].…”

Temporal dynamics of rainfall using the gamma distribution for the municipality of Chimoio, Mozambique

“…The most commonly used distribution for calculating the probable monthly and annual rainfall is the gamma distribution function with a shape and scale parameter (Mossini Junior et al, 2016;Passos et al, 2017;Shiau, 2020), which, according to Tfwala et al (2020) is defined by its probability density function as shown in Eq. ( 4) below.…”

“…where, 𝑥̅ = arithmetic mean; xg = geometric mean of observations on a monthly scale The gamma distribution is undefined for x = 0, but the rainfall may have a zero value. For a given zero value, the cumulative probability distribution [F(x)] is described according to equation 11 (Tfwala et al, 2020), which takes into account the probability associated with the rainfall value less than or equal to x.…”

Geospatial Tools and Remote Sensing Strategies for Timely Humanitarian Response: A Case Study on Drought Monitoring in Eswatini

“…To address the mitigation of drought risks in Eswatini, there is a need for comprehensive plans and their coordination and implementation in the affected agricultural Despite being the smallest country in the region, the Kingdom of Eswatini, possibly due to its agroecology and larger proportion of population (about 70%) that is still agrarian, has borne the brunt of climate variability, particularly drought. Critical challenges faced by Eswatini include insufficient and/or unreliable rainfall, leading to a decline in food production levels [3,4]. The 2004/2005 crop and food supply assessment by the Food and Agriculture Organization of the United Nations (FAO) and the World Food Programme (WFP) over five years revealed a 70% drop in the production of the country's staple food, maize [5].…”