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Evaluation of a Conjunctive Surface–Subsurface Process Model (CSSP) over the Contiguous United States at Regional–Local Scales

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  • 1 Division of Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois at Urbana–Champaign, Urbana, Illinois
  • 2 Department of Atmospheric Sciences, and Division of Illinois State Water Survey, Institute of Natural Resource Sustainability, University of Illinois at Urbana–Champaign, Urbana, Illinois
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Abstract

This study presents a comprehensive evaluation on a Conjunctive Surface–Subsurface Process Model (CSSP) in predicting soil temperature–moisture distributions, terrestrial hydrology variations, and land–atmosphere exchanges against various in situ measurements and synthetic observations at regional–local scales over the contiguous United States. The CSSP, rooted in the Common Land Model (CoLM) with a few updates from the Community Land Model version 3.5 (CLM3.5), incorporates significant advances in representing hydrology processes with realistic surface (soil and vegetation) characteristics. These include dynamic surface albedo based on satellite retrievals, subgrid soil moisture variability of topographic controls, surface–subsurface flow interactions, and bedrock constraint on water table depths. As compared with the AmeriFlux tower measurements, the CSSP and CLM3.5 reduce surface sensible and latent heat flux errors from CoLM by 10 W m−2 on average, and have much higher correlations with observations for daily latent heat variations. The CSSP outperforms the CLM3.5 over the crop, grass, and shrub sites in depicting the latent heat annual cycles. While retaining the improvement for soil moisture in deep layers, the CSSP shows further advantage over the CLM3.5 in representing seasonal and interannual variations in root zones. The CSSP reduces soil temperature errors from the CLM3.5 (CoLM) by 0.2 (0.7) K at 0.1 m and 0.3 (0.6) K at 1 m; more realistically captures seasonal–interannual extreme runoff and streamflow over most regions and snow depth anomalies in high latitude (45°–52°N); and alleviates climatological water table depth systematic bias (absolute error) by about 1.2 (0.4) m. Clearly, the CSSP performance is overall superior to both the CoLM and CLM3.5. The remaining CSSP deficiencies and future refinements are also discussed.

Current affiliation: Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey.

Current affiliation: Department of Atmospheric and Oceanic Science, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland.

Corresponding author address: Dr. Xin-Zhong Liang, Department of Atmospheric and Oceanic Science, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, 2419 Computer and Space Science, College Park, MD 20742-2452. E-mail: xliang@umd.edu

Abstract

This study presents a comprehensive evaluation on a Conjunctive Surface–Subsurface Process Model (CSSP) in predicting soil temperature–moisture distributions, terrestrial hydrology variations, and land–atmosphere exchanges against various in situ measurements and synthetic observations at regional–local scales over the contiguous United States. The CSSP, rooted in the Common Land Model (CoLM) with a few updates from the Community Land Model version 3.5 (CLM3.5), incorporates significant advances in representing hydrology processes with realistic surface (soil and vegetation) characteristics. These include dynamic surface albedo based on satellite retrievals, subgrid soil moisture variability of topographic controls, surface–subsurface flow interactions, and bedrock constraint on water table depths. As compared with the AmeriFlux tower measurements, the CSSP and CLM3.5 reduce surface sensible and latent heat flux errors from CoLM by 10 W m−2 on average, and have much higher correlations with observations for daily latent heat variations. The CSSP outperforms the CLM3.5 over the crop, grass, and shrub sites in depicting the latent heat annual cycles. While retaining the improvement for soil moisture in deep layers, the CSSP shows further advantage over the CLM3.5 in representing seasonal and interannual variations in root zones. The CSSP reduces soil temperature errors from the CLM3.5 (CoLM) by 0.2 (0.7) K at 0.1 m and 0.3 (0.6) K at 1 m; more realistically captures seasonal–interannual extreme runoff and streamflow over most regions and snow depth anomalies in high latitude (45°–52°N); and alleviates climatological water table depth systematic bias (absolute error) by about 1.2 (0.4) m. Clearly, the CSSP performance is overall superior to both the CoLM and CLM3.5. The remaining CSSP deficiencies and future refinements are also discussed.

Current affiliation: Department of Civil and Environmental Engineering, Princeton University, Princeton, New Jersey.

Current affiliation: Department of Atmospheric and Oceanic Science, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, College Park, Maryland.

Corresponding author address: Dr. Xin-Zhong Liang, Department of Atmospheric and Oceanic Science, and Earth System Science Interdisciplinary Center, University of Maryland, College Park, 2419 Computer and Space Science, College Park, MD 20742-2452. E-mail: xliang@umd.edu
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