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Improving River Flow Simulation Using a Coupled Surface-Groundwater Model for Integrated Water Resources Management

9 pagesPublished: September 20, 2018

Abstract

Accurate simulation of both land surface and groundwater hydrologic processes in river catchments is an important step for integrated water resources management, particularly for catchments where both surface water and groundwater resources are used conjunctively. In this paper, we present a study on a complex river catchment – the Dee River catchment in the United Kingdom using a coupled land surface model (SWAT) and groundwater model (MODFLOW) to improve the performances of both models otherwise used separately, hence serving the IWRM goals of optimizing conjunctive use of surface and groundwater. The model can also be used to evaluate the sensitivity of stream flows to changing climate, groundwater extraction, and land use alternations. Preliminary results show that the coupled model can improve river flow simulation especially baseflow simulation while significantly improving the overall water balance model simulations during periods of low flow.

Keyphrases: coupled models, hydrological processes, IWRM, SWAT-MODFLOW

In: Goffredo La Loggia, Gabriele Freni, Valeria Puleo and Mauro De Marchis (editors). HIC 2018. 13th International Conference on Hydroinformatics, vol 3, pages 1--9

Links:
BibTeX entry
@inproceedings{HIC2018:Improving_River_Flow_Simulation,
  author    = {Salam Abbas and Yunqing Xuan and Ryan Bailey},
  title     = {Improving River Flow Simulation Using a Coupled Surface-Groundwater Model for Integrated Water Resources Management},
  booktitle = {HIC 2018. 13th International Conference on Hydroinformatics},
  editor    = {Goffredo La Loggia and Gabriele Freni and Valeria Puleo and Mauro De Marchis},
  series    = {EPiC Series in Engineering},
  volume    = {3},
  pages     = {1--9},
  year      = {2018},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2516-2330},
  url       = {https://easychair.org/publications/paper/KlRv},
  doi       = {10.29007/6ft7}}
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