1519 Abstract 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 specifc 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 coefcients 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 feld data. Next, two scenarios were evaluated: (i) use of riparian bufer strips, and (ii) enhanced treatment of industrial efuents. 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. Estimating Total Nitrogen and Phosphorus Losses in a Data-Poor Ethiopian Catchment Eskinder Zinabu,* Johannes van der Kwast, Peter Kelderman, and Kenneth Irvine C atchment-based water quality models are essen- tial for management of water quality in industrial- ized and urbanized catchments (Wang et al., 2013; Álvarez-Romero et al., 2014). In the developing world, how- ever, reliable application of water quality models is ofen lack- ing (Singh, 1995; Reggiani and Schellekens, 2003; Wang et al., 2013) for three important reasons. First, limited human capacity to use modeling sofware hampers the use of model- ing in water quality management (Loucks et al., 2005; Rode et al., 2010). Second, the availability and quality of data is ofen inadequate. Lastly, access to proprietary sofware and decision support systems is limited by fnances. Filling these gaps requires both development of local human and institutional capacity, and the design of programs for data collection and monitoring. Although there can be temptation to invest in quite complex modeling, this does not necessarily result in more accurate understanding of the underlying pro- cesses on which such models are based. Such models can also be costly and subject to large errors in predictions from defciencies in the data (Ongley and Booty, 1999). Terefore, starting with a basic model and gradually using more detailed and comprehen- sive models is a sensible approach. Low-cost and less complex models that do not require extensive datasets are useful general approaches for the prediction of (particularly, difuse) pollution from land use (Johnes, 1996; Ding et al., 2010) and industrial development (Mitchell, 2005), and for assessing likely results from diferent management scenarios such as grass bufer strips (Dorioz et al., 2006). A common approach in such situations has been the use of generalized export coefcients that predict an annual load of nutrients from land to water. Although such a “black-box” approach may lack insight into underlying hydro- logical or chemical processes that vary with precipitation and terrain (Noto et al., 2008), they have been useful in estimating overall catchment loads and, at least, relative efects under dif- ferent land uses (Soranno et al., 1996; Ierodiaconou et al., 2005; Shrestha et al., 2008). Using an export coefcients approach can be especially advantageous for data-poor areas and for initial esti- mates relating land use to water quality (Bowes et al., 2008). Abbreviations: BASINS, Better Assessment Science Integrating Point and Nonpoint Sources; BMP, best management practice; PLOAD, Pollution Load; TN, total nitrogen; TP, total phosphorus. E. Zinabu, J. Van der Kwast, P. Kelderman, and K. Irvine, IHE Delft Institute for Water Education, PO Box 3015, DA Delft, the Netherlands; E. Zinabu, Soil and Water Resources Management, Wollo Univ., Dessie, Ethiopia; K. Irvine, Aquatic Ecology and Water Quality Management, Wageningen Univ., PO Box 47, 6700AA Wageningen, the Netherlands. Assigned to Associate Editor Marc Stutter. Copyright © American Society of Agronomy, Crop Science Society of America, and Soil Science Society of America. 5585 Guilford Rd., Madison, WI 53711 USA. All rights reserved. J. Environ. Qual. 46:1519–1527 (2017). doi:10.2134/jeq2017.05.0202 Supplemental material is available online for this article. Received 15 May 2015. Accepted 12 Sept. 2017. *Corresponding author (eyuelesk@gmail.com). Journal of Environmental Quality SHORT COMMUNICATIONS Core Ideas • Export coefcient method is suitable to estimate nutrient loads from data-poor areas. • The PLOAD model is more applicable for small catchments than large catchments. • PLOAD modeling helps identify tradeof between baseline and new industrial development strategy. • Bufer strips and enhancing factory efuent treatment reduce substantial nutrient loads. Published October 19, 2017