What question did this study set out to answer?

This investigation assesses how land use and land cover (LULC) changes affect the water quality of the Ashebeka River.

June 11, 2026Open Access

Spatial dynamics of water quality and land-use interactions in the Ashebeka River, Ethiopia

GMGetachew MekaEthiopian Civil Service University BMBezatu MengisteEthiopian Civil Service University TATena AlamirewWater and Land Resource Centre

Key Points

This investigation assesses how land use and land cover (LULC) changes affect the water quality of the Ashebeka River.
Utilized Landsat data from 2004, 2014, and 2023 for LULC changes assessment.
Collected water samples from 10 monitoring sites between June and October 2023 for analysis.
Employed mixed linear regression and correlation analyses to determine impacts of LULC on water quality.
LULC patterns explained approximately 72% of the variability in water quality.
Cropland and residential area expansion correlated with increased microbial contamination and elevated nutrient levels.
Forest and shrubland presence were associated with improved water quality indicators.

Abstract

ABSTRACT Graphical abstract illustrating the influence of LULC changes on the water quality (WQ) of the Ashebeka River. It presents LULC dynamics using Landsat data from 2004, 2014, and 2023, highlighting the spatial distribution of these changes and water quality sampling sites. The figure emphasizes the link between land conversion to cropland and residential areas and the decline in WQ indicators, while areas of forest and shrubland are linked to improved WQ conditions. Land use and land cover (LULC) change represents a critical driver of surface water quality deterioration. However, the implications of these changes for water quality and source water protection (SWP) remain inadequately quantified. This investigation seeks to assess the effect of LULC changes on the spatial and temporal characteristics of Ashebeka River water quality, thereby informing catchment-based management strategies for Assela City, Ethiopia. Supervised maximum likelihood classification was employed on Landsat imagery from 2004, 2014, and 2023, yielding an overall accuracy of 86.67% and a Kappa coefficient of 81.46%. Water samples, collected between June and October 2023 from 10 monitoring sites, were analyzed for a range of physicochemical and microbiological indicators, encompassing temperature, pH, total suspended solids (TSS), nitrite, nitrate, ammonia, iron, manganese, fecal coliform (FC), and total coliform (TC). Mixed linear regression and correlation analyses revealed that LULC patterns accounted for approximately 72% of the observed variability in water quality. Specifically, the expansion of cropland and residential areas correlated with increased microbial contamination and nutrient levels, while forest and shrubland cover were linked to enhanced water quality. Given the relatively short monitoring period and limited dataset warrant cautions interpretation and emphasize the need for long-term monitoring of the drinking water supply for Assela City.

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