Proceedings of the Estonian Academy of Sciences, 2015, 64, 3, 1–9 Proceedings of the Estonian Academy of Sciences, 2017, 66, 3, 225–242 https://doi.org/10.3176/proc.2017.3.02 Available online at www.eap.ee/proceedings Modelling stormwater runoff, quality, and pollutant loads in a large urban catchment Bharat Maharjan*, Karin Pachel, and Enn Loigu * Department of Civil Engineering and Architecture, School of Engineering, Tallinn University of Technology, Ehitajate tee 5, 19086 Tallinn, Estonia Received 28 September 2016, accepted 29 October 2016, available online 29 May 2017 © 2017 Authors. This is an Open Access article distributed under the terms and conditions of the Creative Commons Attribution- NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/). Abstract. Identification of stormwater runoff, its pollution load, and their implications for land use is essential in implementing stormwater management strategies. Hydrologic modelling provides an opportunity to assess them at limited data resources. In this study, the stormwater management model SWMM5 is applied for model development for a large basin in Tallinn. A geographic information system tool is used for subcatchment delineation, identification of directly connected impervious areas (DCIAs), and preparation of catchment input parameters. The model is calibrated and verified using sampled storm events to estimate event mean concentrations and annual loads. The predictive capability of the model for quantity is good and for quality moderate. The findings from the model show the percentage of the impervious area in the large catchment to be low at 19.7%. Although DCIAs, in particular roads and roofs, have relatively smaller areas they significantly impact runoff production (up to 75%) and loads (up to 66% total phosphorus and 71% total suspended solids). The first flush at the beginning of runoff is less important in case of a low intensity of rainfall, but heavy rain and snowmelt generate substantial runoff and pollution loads. When grab sampling is applied, it should focus on the medium and large events within 6 hours of storm commencement in order to achieve better mass estimations. Key words: hydrologic modelling, impervious area, event mean concentrations, mass loads, first flush. Abbreviations and symbols ADD – antecedent dry days BOD – biological oxygen demand CC – correlation coefficient Cr – commercial roofs DCIA – directly connected impervious area DEE – Department of Environmental Engineering of Tallinn University of Technology DNCIA – directly not connected impervious area EIA – effective impervious area ELLE – Estonian, Latvian, and Lithuanian Environment group EMC – event mean concentration GIS – geographic information system NOF – normalized objective function NSC – Nash–Sutcliffe coefficient R – residential area Rd – roads RMSD – root mean square deviation RE – relative error Rr – residential roofs SA1 – time weighted sampling SA2 – random grab sampling SA3 – grab sampling within 6 h irrespective of storm size SA4 – grab sampling within 6 h of medium and large storms SWMM – stormwater management model TIA – total impervious area TN – total nitrogen TP – total phosphorus TSS – total suspended solids A – catchment area D imp – impervious depression storage D per – pervious depression storage N imp – impervious surface roughness N per – pervious surface roughness % imp – percentage of impervious area S o – average slope Sc – sensitivity coefficient W – catchment width * Corresponding author, bmaharjan302@gmail.com