Quantitative analysis and modeling of minimum flow patterns in Temsa River, Abbay Basin, Ethiopia

The extent and occurrence of extremely low-flow events are necessary to determine the minimum river flow. Since the true probability distribution is usually not known, the best fitting distribution function describing the low flow in the catchment is important for reliable estimation of low flow and its frequency. The Temsa River is one of the most important tributaries of the Abay River Basin in Ethiopia and has a high ecological value for the country that can be affected by land cover changes. Climate influences watershed development, while landscape features control the accumulation and release of water over time, influencing stream flow, such as low flow. Therefore, analyzing the state of river discharge is important for the economic management of water resources. Rapid population growth has raised serious concerns about the adequacy of the Temsa River’s future water intake in terms of quantity and quality. However, future water resources planning requires information on water flow variability and trends. The aim of this study is to identify and analyze the existing Temsa watersheds and their current status based on river water data collected by the Ethiopian Ministry of Water and Energy from 1997 to 2021 GC. Analysis focused on daily flow, mean annual flow, mean monthly flow, and consecutive 7-day mean minimum flow were included in the model. Methods for trend detection and quantification were the Mann–Kendall test (MK) and Sen’s slope estimator (SS). The results of the MK and SS tests indicate the existence of a trend of statistical significance. The study shows a positive trend for two models and a negative trend for the other two models. The daily discharge analysis and the annual average flow analysis show a decreasing trend and the second model shows an increasing trend. BFI results show that the proportion of groundwater in the watershed is moderate, 73.6%, and the lognormal distribution fits the frequency analysis data. © 2023, Faculty of Engineering, Cairo University.