The spatiotemporal variability of a stream flow due to the complex interaction of catchment attributes and rainfall induce complexity in hydrology. Researchers have been trying to address this complexity with a number of approaches; river flow regime is one of them. The flow regime can be quantified by means of hydrological indices characterizing five components: magnitude, frequency, duration, timing, and rate of change of flow. Similarly, this study aimed to understand the flow variability of Ethiopian Rivers using the observed daily flow data from 208 gauging stations in the country. With this process, the Hierarchical Ward Clustering method was implemented to group the streams into three flow regimes (1) ephemeral, (2) intermittent, and (3) perennial. Principal component analysis (PCA) is also applied as the second multivariate analysis tool to identify dominant hydrological indices that cause the variability in the streams. The mean flow per unit catchment area (QmAR) and Base flow index (BFI) show an incremental trend with ephemeral, intermittent and perennial streams. Whereas the number of mean zero flow days ratio (ZFI) and coefficient of variation (CV) show a decreasing trend with ephemeral to perennial flow regimes. Finally, the streams in the three flow regimes were characterized with the mean and standard deviation of the hydrological variables and the shape, slope, and scale of the flow duration curve. Results of this study are the basis for further understanding of the ecohydrological processes of the river basins in Ethiopia.

Ethiopia is endowed with water and has a high runoff generation area compared to many countries, but the total stored water only goes up to approximately 36BCM. The problem of water shortage in Ethiopia emanates from the seasonality of rainfall and the lack of infrastructure for storage to capture excess runoff during flood seasons. Based on this premise, a method for a syndicate use of topography, land use and vegetation was applied to locate potential surface water storing sites. The steady-state Topographic Wetness Index (TWI) was used to represent the spatial distribution of water flow and water stagnating across the study area and the Normalized Difference Vegetation Index (NDVI) was used to detect surface water through multispectral analysis. With this approach, a number of water storing sites were identified in three categories: primary sources (water bodies based), secondary sources (Swampy/wetland based) and tertiary sources (the land based). A sample volume analysis for the 120354 water storing sites in category two, gives a 44.92BCM potential storing capacity with average depth of 4 m that improves the annual storage capacity of the country to 81BCM (8.6 % of annual renewable water sources). Finally, the research confirmed the TWI and NDVI based approach for water storing sites works without huge and complicated earth work; it is cost effective and has the potential of solving complex water resource challenges through spatial representation of water resource systems. Furthermore, the application of remote sensing captures temporal diversity and includes repetitive archives of data, enabling the monitoring of areas, even those that are inaccessible, at regular intervals.

Knowledge of spatiotemporal variability of rainfall magnitude, pattern and trend is fundamental for understanding hydrological systems and runoff prediction for both gauged and ungauged catchments. These variables can be derived from rainfall‐monitoring programmes with adequate spatial distribution and temporal coverage. However, rainfall‐gauging stations in most developing countries are distributed sparsely. Remotely sensed rainfall datasets are becoming alternative rainfall data sources for larger area applications and are proven to have adequate spatiotemporal resolutions. Climate forecast system re‐analysis (CFSR) is one such dataset provided by the National Center for Environmental Prediction (NCEP). This dataset captures the rainfall pattern in Ethiopia but with s magnitude bias of over‐ and underestimations. In this study, magnitude bias correction of the CFSR dataset with a linear scaling technique resulted in a rainfall grid of the country with ∼38 km spatial resolution of a 32 year (1979–2010) daily rainfall dataset. For the bias correction, observed annual rainfall from 930 and daily rainfall from 195 rain gauges were used. The study also attempted to understand the space and time variability of the rainfall through the construction of shape, magnitude and composite rainfall regimes for the entire country. The rainfall regimes of the country were developed using the fuzzy overlay technique with multi‐indices of rainfall. The rainfall regimes address the frequency, duration, timing and magnitude variability of rainfall. The performance of the dataset generation and rainfall regime classification was evaluated using Nash–Sutcliffe Efficiency (NSE) and percent bias (PBIAS) values, which were found to be 0.8 and 1.3, respectively.