Estimating total nitrogen and phosphorus losses in a data-poor Ethiopian catchment

Eskinder Zinabu*, Johannes van der Kwast, Peter Kelderman, Kenneth Irvine

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

10 Citations (Scopus)


Selecting a suitable model for a water quality study depends on the objectives, the characteristics of the study area, and the availability, appropriateness, and quality of data. In areas where in-stream chemical and hydrological data are limited but where estimates of nutrient loads are needed to guide management, it is necessary to apply more generalized models that make few assumptions about underlying processes. This paper presents the selection and application of a model to estimate total nitrogen (TN) and total phosphorus (TP) loads in two semiarid and adjacent catchments exposed to pollution risk in north-central Ethiopia. Using specific criteria to assess model suitability resulted in the use of the Pollution Load (PLOAD) model. The model relies on estimates of nutrient loads from point sources such as industries and export coefficients of land use, and it is calibrated using measured TN and TP loads from the catchments. The performance of the calibrated PLOAD model was increased, reducing the sum of errors by 89 and 5% for the TN and TP loads, respectively. The results were validated using independent field data. Next, two scenarios were evaluated: (i) use of riparian buffer strips, and (ii) enhanced treatment of industrial effluents. The model estimated that combined use of the two scenarios could reduce TN and TP loads by nearly 50%. Our modeling is particularly useful for initial characterization of nutrient pollution in catchments. With careful calibration and validation, PLOAD model can serve an important role in planning industrial and agricultural development in data-poor areas.

Original languageEnglish
Pages (from-to)1519-1527
JournalJournal of Environmental Quality
Issue number6
Publication statusPublished - Nov 2017


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