Importance of different imperviousness measures for predicting runoff and nutrient emissions from non-urban and urban land-uses at large spatial coverage.

Growing population and urbanization challenge water resources sustainability and require stringent solutions in terms of emission measurements and pollution controls. Advancements in observation techniques have improved the availability of impervious surface data that cover both urban and non-urban areas to assess the impacts of urbanization. However, most models used in macroscale studies continue to derive surface imperviousness based on land-use classes and population data, and the contributions of non-urban impervious surfaces to runoff and nutrient emissions remain largely ignored. Effects of different impervious surface data on the predicted runoff and nutrient emissions is investigated in this study for macroscale urban and non-urban areas in tandem by means of an extended urban module MONERIS - PCRaster to enable scenarios with high-resolution imperviousness data. The results showed that approximately 70% of the total runoff and nutrient emissions nationwide originated from low-to-medium populated impervious surfaces rather than from major urban catchments. Using high-resolution imperviousness data at various aggregation levels resulted in lower biased outputs of predicted runoff and nutrient emissions when compared to results using the estimated impervious data from land-use and population information. The impervious surface shares between urban and non-urban lands revealed the opposite trends of urbanization developments in the less populated areas versus an increasing contribution of emissions from non-urban areas rather than urban centers in densely populated municipalities. Overall, the non-urban impervious surface areas contributed 5-20% of the "hidden" runoff volumes and nutrient emissions from all impervious areas. The results of this study highlight the need of model adaptations regarding the increased availability of high-resolution imperviousness data and the trend of urbanization development beyond urban areas for more accurate quantification of potential flood risks and emission hotspots of macroscale urbanized areas for sustainable water resources management.