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1 May 2018 The Modelling Approach for Predicting Coastal Pollutions using Rainfall Distributions over Different Land Use/Land Cover
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Abstract

Bae, H.-K., 2018. The modelling approach for predicting costal pollutions using rainfall distributions over different land use/land cover. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 11–15. Coconut Creek (Florida), ISSN 0749-0208.

In this study, a modeling approach using rainfall distribution over different land use/land cover was developed and tested to predict coastal pollutions at the Aliso Beach, California, USA. Rainfall distributions over each land use/land cover of the study area, Aliso Creek Watershed, were calculated to use as input variables for the approach. In addition to rainfall distributions, streamflow and previous total coliform concentrations were also used as input variables. Six-hour. averages of rainfall and streamflow data were used since the approach estimated total coliform concentrations for every Six-hour. The estimations from the previous step, previous total coliform concentration, were used for last input variable if observation data were not available since total coliform concentrations were measured once every three days. Six different model scenarios were tested mainly focusing on rainfall events. As the model scenarios became more complicated, better estimations were shown. The approach showed the possibilities for finer time scale prediction, such as 1 hr. or less time scale or even real-time predictions, since precipitation and streamflow can be available for finer time scale and estimated previous concentrations would be used if observation data were not available.

©Coastal Education and Research Foundation, Inc. 2018
Hun-Kyun Bae "The Modelling Approach for Predicting Coastal Pollutions using Rainfall Distributions over Different Land Use/Land Cover," Journal of Coastal Research 85(sp1), 11-15, (1 May 2018). https://doi.org/10.2112/SI85-003.1
Received: 30 November 2017; Accepted: 10 February 2018; Published: 1 May 2018
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