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A Conjunctive Surface–Subsurface Flow Representation for Mesoscale Land Surface Models

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  • 1 Department of Civil Engineering, Yeungnam University, Gyeongsan, South Korea
  • | 2 Department of Atmospheric and Oceanic Science, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland
  • | 3 Department of Civil and Environmental Engineering, University of Illinois at Urbana–Champaign, Urbana, Illinois
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Abstract

Most current land surface models used in regional weather and climate studies capture soil-moisture transport in only the vertical direction and are therefore unable to capture the spatial variability of soil moisture and its lateral transport. They also implement simplistic surface runoff estimation from local soil water budget and ignore the role of surface flow depth on the infiltration rate, which may result in significant errors in the terrestrial hydrologic cycle. To address these issues, this study develops and describes a conjunctive surface–subsurface flow (CSSF) model that comprises a 1D diffusion wave model for surface (overland) flow fully interacted with a 3D volume-averaged soil-moisture transport model for subsurface flow. The proposed conjunctive flow model is targeted for mesoscale climate application at relatively large spatial scales and coarse computational grids as compared to the traditional coupled surface–subsurface flow scheme in a typical basin. The CSSF module is substituted for the existing 1D scheme in the common land model (CoLM) and the performance of this hydrologically enhanced version of the CoLM (CoLM+CSSF) is evaluated using a set of offline simulations for catchment-scale basins around the Ohio Valley region. The CoLM+CSSF simulations are explicitly implemented at the same resolution of the 30-km grids as the target regional climate models to avoid downscaling and upscaling exchanges between atmospheric forcings and land responses. The results show that the interaction between surface and subsurface flow significantly improves the stream discharge prediction crucial to the terrestrial water and energy budget.

Corresponding author address: Dr. Xin-Zhong Liang, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, 5825 University Research Court, Suite 4001, College Park, MD 20740-3823. E-mail: xliang@umd.edu

Abstract

Most current land surface models used in regional weather and climate studies capture soil-moisture transport in only the vertical direction and are therefore unable to capture the spatial variability of soil moisture and its lateral transport. They also implement simplistic surface runoff estimation from local soil water budget and ignore the role of surface flow depth on the infiltration rate, which may result in significant errors in the terrestrial hydrologic cycle. To address these issues, this study develops and describes a conjunctive surface–subsurface flow (CSSF) model that comprises a 1D diffusion wave model for surface (overland) flow fully interacted with a 3D volume-averaged soil-moisture transport model for subsurface flow. The proposed conjunctive flow model is targeted for mesoscale climate application at relatively large spatial scales and coarse computational grids as compared to the traditional coupled surface–subsurface flow scheme in a typical basin. The CSSF module is substituted for the existing 1D scheme in the common land model (CoLM) and the performance of this hydrologically enhanced version of the CoLM (CoLM+CSSF) is evaluated using a set of offline simulations for catchment-scale basins around the Ohio Valley region. The CoLM+CSSF simulations are explicitly implemented at the same resolution of the 30-km grids as the target regional climate models to avoid downscaling and upscaling exchanges between atmospheric forcings and land responses. The results show that the interaction between surface and subsurface flow significantly improves the stream discharge prediction crucial to the terrestrial water and energy budget.

Corresponding author address: Dr. Xin-Zhong Liang, Department of Atmospheric and Oceanic Science, University of Maryland, College Park, 5825 University Research Court, Suite 4001, College Park, MD 20740-3823. E-mail: xliang@umd.edu
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