Abstract:
Rain variability in space and time is one of the most relevant characteristics of tropical rain forest
that is associated with economic, social and ecological implications. Extreme rain events have
significant environmental consequences that cause considerable damages in urban as well as in
rural areas. Temporal variability in precipitation is a key factor influencing the structure and
function of semiarid ecosystems. The climate of the Dire Dawa Administration is being located
in an arid and semi-arid part of the country and dominated by various inter-related factors,
altitude being the most determinant factor and the temporal variability of precipitation affects
the runoff process. This resulted in flash flood disasters and drought during recent years and
requires an urgent course of mitigation measures to alleviate the associated problems. This
requires detail information regarding the temporal distribution of annual and seasonal rainfall
pattern in the region. The present study has been carried out for 20 different probability
distribution {gamma (2P, 3P), generalized gamma (3P, 4P), logistic, log-logistic (2p, 3p), log
gamma, normal, lognormal (2P, 3P), Weibull (2P, 3P), Pearson 5 (2P, 3P), pearson 6 (3P, 4P),
log-pearson 3, generalized extreme value and Gumbel’s maximum distribution } and associated
goodness of fit test using Kolmogorov Smirnov, Anderson Darling and Chi-Squared tests.
Temporal rainfall variability is assessed using statistical trend analysis of the monthly, seasonal
and annual rainfall using the Mann–Kendall test, the Sen’s slope estimator and spearman’s Rho
test. It was observed that according to Kolmogorov Smirnov and Anderson Darling test,
Generalized Extreme value distribution fits well to the rainfall data occupying 45% and 40%
respectively and the log logistic (3p) obtained 32% according to Anderson Darling. Among these
distributions 41% of the rainfall follows general extreme value while 24% and 18% of the DDA
rainfall gamma (2p) and log logistic (3p) distribution respectively. The best distribution for one
day maximum rainfall received during different months in a 39 year was Generalized Extreme
Value and it can be seen that April month received the highest amount of one day maximum
rainfall (25%) followed by January, March and August with the same percent of (13%). The
minimum rainfall of 27.4 mm rainfall can be expected to occur with 97.5 per cent probability and
one year return period and maximum of 125.8 mm rainfall can be received with one per cent
probability and 100 year return period. There are rising rates of precipitation in some months and
decreasing trend in some other months obtained by Mann-Kendall Test together with the Sen’s
Slope and Spearman’s Rho teast even though most of the months was not statistically significant.