Effects of land use land cover change on streamflow of Akaki catchment, Addis Ababa, Ethiopia

Abstract Evaluation of the hydrological impact urbanization-induced land use cover (LULC) changes for medium to large catchments is still an important research topic due lack evidence conclude about how local translate impacts across scales. This study aims provide on effects LULC change streamflow Akaki catchment that hosts Addis Ababa, capital city Ethiopia. Since comparative performance classification algorithms poorly understood, we compared one parametric and five non-parametric machine learning methods mapping using Landsat imageries. To investigate effect streamflow, a semi-distributed HEC-HMS model was calibrated validated daily discharge data at multiple sites. Findings this showed that: (i) accuracy regression tree (CART) superior other classifiers, (ii) from 1990 2020, urban forest increased expense agricultural bare land, (iii) acceptable all stations during both calibration validation periods, (iv) mean annual main rainy seasonal experienced significant increases but simulated highly varied with type classifier. contributes limited catchments, rapidly developing cities are prone regime need be recognized, understood quantified, incorporated into planning development.