Evaluation of spatial and temporal characteristics of rainfall in Malawi: a case of data scarce region

Abstract

This paper presents the methods, procedure and results in studying spatial and temporal characteristics of rainfall in Malawi, a data scarce region, between 1960 and 2006. Rainfall variables and indicators from rainfall readings at 42 stations in Malawi, excluding Lake Malawi, were analysed at monthly, seasonal and annual scales. In the study, the data were firstly subjected to quality checks through the cumulative deviations test and the standard normal homogeneity test. Spatial rainfall variability was investigated using the spatial correlation function. Temporal trends were analysed using Mann-Kendall and linear regression methods. Heterogeneity of monthly rainfall was investigated using the precipitation concentration index (PCI). Finally, inter-annual and intra-annual rainfall variability were tested using normalized precipitation anomaly series of annual rainfall series (|AR|) and the PCI (|APCI|), respectively. The results showed that (1) most stations revealed statistically non-significant decreasing rainfall trends for annual, seasonal, monthly and the individual months from March to December at the 5% significance level. The months of January and February (the highest rainfall months), however, had overall positive but statistically non-significant trends countrywide, suggesting more concentration of the seasonal rainfall around these months.(2) Spatial analysis results showed a complex rainfall pattern countrywide with annual mean of 1,095 mm centred to the south of the country and mean inter-annual variability of 26%. (3) Spatial correlation amongst stations was highest only within the first 20 km, typical of areas with strong small-scale climatic influence. (4) The country was further characterised by unstable monthly rainfall regimes, with all PCIs more than 10. (5) An increase in inter-annual rainfall variability was found.

Analysis of mid-twentieth century rainfall trends and variability over southwestern Uganda

“…As described in the literature (Ceballos et al 2004;Nel2009;Ngongondo et al 2011), PCI values below 10 indicate a uniform monthly rainfall distribution in the year, whereas values from 11 to 20 denote seasonality in rainfall distribution. Values above 20 correspond to climates with substantial monthly variability in rainfall amounts (Nel 2009 and references therein).…”

“…A preliminary examination of monthly totals showed that a number of monthly records had missing values. This is a challenge to which a number of studies (Türkeş 1996;Sahin and Cigizoglu 2010;Ngongondo et al 2011) have applied different techniques to make rainfall data set complete. In this study, we applied the linear regression method.…”

“…The most popularly used non-parametric test is the Mann-Kendall (MK) test (Mann 1945;Kendall 1955). It has been widely used for different climatic variables in various trend studies including those of Kampata et al (2008), Kizza et al (2009), Ngongondo et al (2011), Rai et al (2010, and Nsubuga et al (2011). We applied the MK test at a significance level of 5 % to study the temporal trends of the following: (a) total annual rainfall and its coefficient of variability, (b) monthly total rainfall, and (c) seasonal total rainfall.…”

“…This is paramount in water resource planning, agricultural planning, flood frequency monitoring, climate change impacts, environmental assessments, and ecology (Ngongondo et al 2011;Michaelides et al 2009;Ceballos et al 2004). Such information is still scarce because of the few studies that have analyzed the relationship of climate patterns and water management among others especially in the East Africa region.…”

“…Research focusing on national or regional analysis of climatic parameters has not been exhaustive that is why more studies using different methodologies are recommended (Ngongondo et al 2011). The motivation for this study is to investigate further the temporal and spatial rainfall characteristics, including trends for Uganda at high spatial scales, in the Albert-Victoria water management area using old data records.…”