Key points are not available for this paper at this time.
Assessing the spatial and temporal variability of climate data is one of the basic requirements to solve the hydrological and water management problems. In this manuscript, a comprehensive assessment of data quality, precipitation concentration index (PCI), Mann-Kendall (MK) trend test, and extreme climate change indices analyses were performed. The standard normal homogeneity test (SNHT), the Pettitt test, the Buishand range test, and the Von Neumann ratio test were used for homogeneity analysis, and most of the climate stations exhibited homogenous precipitation series. The annual mean and dry season PCI values demonstrated higher spatial and temporal variability or irregular distribution of precipitation, whereas PCI in the wetter seasons indicated low precipitation concentration in the Upper Awash basin. The MK trend test exhibited a significantly increasing trend of maximum temperature; however, the trend of mean annual and extreme precipitation events was insignificant for the majority of stations. Moreover, the magnitude and trend of ten main extreme precipitation indices were constructed. Unlike the mean annual precipitation, larger values of the extreme 1-day (Rx1day) and 5-day (Rx5day) annual precipitation were obtained in the downstream areas of the basin which could signify the prevalence higher flood risk in this portion of the basin. The highest predicted 100-year annual maximum daily (AMD) precipitation was obtained at Zikuala station as 128.5 mm. Statistical analysis of the magnitude and trend of extreme precipitation indices and spatial variability of climate data is imperative for the integrated water resource planning and management decisions.
Shawul et al. (Wed,) studied this question.