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.

Rainfall extremes often result in the occurrence of flood events with associated loss of life and infrastructure in Malawi. However, an understanding of the frequency of occurrence of such extreme events either for design or disaster planning purposes is often limited by data availability at the desired temporal and spatial scales. Regionalisation, which involves ''trading time for space'' by pooling together observations for stations with similar behavior, is an alternative approach for more accurate determination of extreme events even at ungauged areas or sites with short records. In this study, regional frequency analysis of rainfall extremes in Southern Malawi, large parts of which are flood prone, was undertaken. Observed 1-, 3-, 5-and 7-day annual maximum rainfall series for the period 1978-2007 at 23 selected rainfall stations in Southern Malawi were analysed. Cluster analysis using scaled at-site characteristics was used to determine homogeneous rainfall regions. L-moments were applied to derive regional index rainfall quantiles. The procedure also validated the three rainfall regions identified through homogeneity and heterogeneity tests based on Monte Carlo simulations with regional average L-moment ratios fitted to the Kappa distribution. Based on assessments of the accuracy of the derived index rainfall quantiles, it was concluded that the performance of this regional approach was satisfactory when validated for sites not included in the sample data. The study provides an estimate of the regional characteristics of rainfall extremes that can be useful in among others flood mitigation and engineering design.

This study evaluated the performance of the Food and Agriculture Organization (FAO) PenmanMonteith (PM) reference evapotranspiration (ETo) method for various limited data scenarios in southern Malawi. The study further evaluated the full data PM method against the radiation-based Priestley-Taylor (PT) and the temperature-based Hargreaves (HAG) methods, which are less dataintensive approaches commonly used to estimate ETo in data-scarce situations. A comprehensive daily climate dataset observed at the Nchalo Sugar Estate in southern Malawi for the period 1971-2007 was the basis of the study. The results suggested that lack of data on wind speed and actual vapour pressure did not significantly affect the PM ETo estimates. However, the estimation of radiation using various combinations of observed wind speed and relative humidity all resulted in significant deviations from the PM ETo. Further, the HAG and PT methods significantly underestimated the PM. However, the PM method computed with estimated climate variables instead of observed ciimate variables still outperformed both the PT and HAG methods if their original parameters and estimated radiation were used. Thus, new monthly parameters for the PT and the HAG methods are proposed for more accurate daily ETo estimates.

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