Abstract Until recently, hydrological impacts and prediction caused by climate change have been popular issues. However, a basin-scale hydro-environmental study has begun to attract attention recently due to the difficulties in watershed model calibration and uncertainty propagation in the data relaying procedure from the GCM (General Circulation Model) process to watershed modeling process, which leads to unrealistic projections. In addition, reliable downscaling scheme is essential in utilizing the GCM model output for hydrological applications. The main objective of this study is to suggest a reliable ANN (Artificial Neural Network)-based GCM scenario and its application for hydro-environmental projection using a watershed model. In this report, the Namgang Dam watershed in the Nakdong river basin was selected as the case study. To examine the vulnerability of the Namgang dam watershed caused by climate change, the change in streamflow and pollutant material runoff due to climate change were predicted using the watershed model, SWAT (Soil Water Assessment Tool), based on the IPCC’s A1B GCM scenario, which is downscaled using the ANN and Nonstationary Quantile Mapping. The results of this study will be used for suggesting an effective counterplan from an engineering point of view, and developing an integrated water sources management system.


    Zugriff

    Zugriff prüfen

    Verfügbarkeit in meiner Bibliothek prüfen

    Bestellung bei Subito €


    Exportieren, teilen und zitieren



    Titel :

    Hydro-environmental runoff projection under GCM scenario downscaled by Artificial Neural Network in the Namgang Dam watershed, Korea


    Beteiligte:
    Kang, Boosik (Autor:in) / Kim, Young Do (Autor:in) / Lee, Jong Mun (Autor:in) / Kim, Seong Joon (Autor:in)

    Erschienen in:

    Erscheinungsdatum :

    2015-01-20


    Format / Umfang :

    12 pages




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch





    Prediction of Runoff Using Artificial Neural Networks

    Vivekanandan, N. / Kuratorium für Forschung im Küsteningenieurwesen (KFKI) | HENRY – Bundesanstalt für Wasserbau (BAW) | 2010

    Freier Zugriff

    Development Of Autonomous Downscaled Model Car Using Neural Networks And Machine Learning

    Karni, Uvais / Ramachandran, S. Shreyas / Sivaraman, K. et al. | IEEE | 2019


    Testing and Analysis of Downscaled Composite Wing Box

    Cheol-Won Kong / Jae-Sung Park / Jae-Heon Cho et al. | AIAA | 2002