Using Machine Learning and the Water Evaluation and Planning model to Evaluate Climate Change Impacts on Surface Water Allocation in the Upper Awash Sub-Basin, Ethiopia
type: CreativeWork
description: Climate change, rapid urbanisation, and population growth are placing escalating pressure on surface water resources in Ethiopia’s Upper Awash Sub-Basin, a region already grappling with rising agricultural demand and limited water availability. This study presents an integrated modelling framework that combines Machine Learning (ML)techniques with the Water Evaluation and Planning (WEAP) system to evaluate andoptimise surface water allocation under future climate and demographic scenarios. Historical climate patterns from 1948 to 2010 were examined using both observed and satellite-based datasets (Princeton datasets). A Random Forest (RF) algorithm trained on these data achieved high predictive accuracy (R² = 0.97 for training; 0.96 for testing; RMSE = 0.12°C). The analysis revealed a historical warming trend of 0.04 to 0.05°C per year and a gradual decline in precipitation, ranging from 0.22 to 0.40 mm per year. Future climate projections based on CMIP6 under the Shared Socioeconomic Pathways (SSP4.5 and SSP8.5) for the period 2025 to 2075 estimate temperature increases between 0.9°C and 1.6°C per year. Projected precipitation changes range from a 15.5% increase (SSP4.5) between 2025 and 2075 to a 6.3% decline (SSP8.5), indicating heightened uncertainty and variability in regional water availability. These climatic changes are expected to intensify hydrological stress across the basin, reducing baseflow by approximately 46.9 mm per year, increasing evapotranspiration to between 890 mm and 1010 mm annually, and causing soil moisture fluctuations from +16.4 mm to −124.9 mm per year. Simultaneously, the population, projected using linearregression and exponential growth models, is expected to grow from 6.3 million in 2025 to nearly 39 million by 2075, substantially increasing water demand across all sectors. The ML-enhanced WEAP model demonstrated improved forecasting capabilities, with the RF model outperforming the Long Short-Term Memory (LSTM) network (MAE: 0.41 vs…
abstract: Climate change, rapid urbanisation, and population growth are placing escalating pressure on surface water resources in Ethiopia’s Upper Awash Sub-Basin, a region already grappling with rising agricultural demand and limited water availability. This study presents an integrated modelling framework that combines Machine Learning (ML)techniques with the Water Evaluation and Planning (WEAP) system to evaluate andoptimise surface water allocation under future climate and demographic scenarios. Historical climate patterns from 1948 to 2010 were examined using both observed and satellite-based datasets (Princeton datasets). A Random Forest (RF) algorithm trained on these data achieved high predictive accuracy (R² = 0.97 for training; 0.96 for testing; RMSE = 0.12°C). The analysis revealed a historical warming trend of 0.04 to 0.05°C per year and a gradual decline in precipitation, ranging from 0.22 to 0.40 mm per year. Future climate projections based on CMIP6 under the Shared Socioeconomic Pathways (SSP4.5 and SSP8.5) for the period 2025 to 2075 estimate temperature increases between 0.9°C and 1.6°C per year. Projected precipitation changes range from a 15.5% increase (SSP4.5) between 2025 and 2075 to a 6.3% decline (SSP8.5), indicating heightened uncertainty and variability in regional water availability. These climatic changes are expected to intensify hydrological stress across the basin, reducing baseflow by approximately 46.9 mm per year, increasing evapotranspiration to between 890 mm and 1010 mm annually, and causing soil moisture fluctuations from +16.4 mm to −124.9 mm per year. Simultaneously, the population, projected using linearregression and exponential growth models, is expected to grow from 6.3 million in 2025 to nearly 39 million by 2075, substantially increasing water demand across all sectors. The ML-enhanced WEAP model demonstrated improved forecasting capabilities, with the RF model outperforming the Long Short-Term Memory (LSTM) network (MAE: 0.41 vs…
author: Hirko, Deme Betele
language: en
date published: 2026-03
date modified: 2026-07-24T04:40:12Z
provider: Stellenbosch University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Stellenbosch : Stellenbosch University