Literature DB >> 24417289

Channel representation in physically based models coupling groundwater and surface water: pitfalls and how to avoid them.

Daniel Käser1, Tobias Graf, Fabien Cochand, Rob McLaren, René Therrien, Philip Brunner.   

Abstract

Recent models that couple three-dimensional subsurface flow with two-dimensional overland flow are valuable tools for quantifying complex groundwater/stream interactions and for evaluating their influence on watershed processes. For the modeler who is used to defining streams as a boundary condition, the representation of channels in integrated models raises a number of conceptual and technical issues. These models are far more sensitive to channel topography than conventional groundwater models. On all spatial scales, both the topography of a channel and its connection with the floodplain are important. For example, the geometry of river banks influences bank storage and overbank flooding; the slope of the river is a primary control on the behavior of a catchment; and at the finer scale bedform characteristics affect hyporheic exchange. Accurate data on streambed topography, however, are seldom available, and the spatial resolution of digital elevation models is typically too coarse in river environments, resulting in unrealistic or undulating streambeds. Modelers therefore perform some kind of manual yet often cumbersome correction to the available topography. In this context, the paper identifies some common pitfalls, and provides guidance to overcome these. Both aspects of topographic representation and mesh discretization are addressed. Additionally, two tutorials are provided to illustrate: (1) the interpolation of channel cross-sectional data and (2) the refinement of a mesh along a stream in areas of high topographic variability.
© 2014, National Ground Water Association.

Entities:  

Mesh:

Year:  2014        PMID: 24417289     DOI: 10.1111/gwat.12143

Source DB:  PubMed          Journal:  Ground Water        ISSN: 0017-467X            Impact factor:   2.671


  1 in total

1.  Machine Learning Analysis of Hydrologic Exchange Flows and Transit Time Distributions in a Large Regulated River.

Authors:  Huiying Ren; Xuehang Song; Yilin Fang; Z Jason Hou; Timothy D Scheibe
Journal:  Front Artif Intell       Date:  2021-04-15
  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.