Improving the surface‐ground water interactions in the Community Land Model: Case study in the Blue Nile Basin. Issue 10 (16th October 2014)

• Record Type: Journal Article
• Authors: Wu, Di D.; Anagnostou, Emmanouil N.; Wang, Guiling; Moges, Semu; Zampieri, Matteo
• Is Part Of: Water resources research. Volume 50:Issue 10(2014:Oct.)
• Page Start: 8015
• Page End: 8033
• Publication Date: 2014-10-16
• Subjects: Hydrology -- Periodicals; 333.91
• DOI: 10.1002/2013WR014501
• Languages: English
• ISSNs: 0043-1397

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>Soil moisture is a key water cycle parameter known to interact with atmospheric processes. Arguably, land surface models that simulate land surface processes and surface fluxes to the atmosphere do not capture adequately the spatial variability of soil moisture, particularly over areas with complex topography. In this study, version 3.5 of the Community Land Model (CLM3.5) is applied with a new parameterization in an effort to correct the spatial bias of soil moisture and understand the consequential effects on the simulated water cycle fluxes and states in the Blue Nile Basin. This parameterization accounts for a groundwater recharge term from surface water, a process that is not included in CLM, providing an effective two‐way interaction scheme between rivers and groundwater. Using satellite soil moisture data, this parameterized term is shown to have a positive correlation to contributing area, defined at each model grid cell and representing the number of grid cells that drain to that local grid cell. With the new parameterization applied to CLM, soil moisture, soil temperature, evapotranspiration flux, water table depth, and vegetation water content all showed significant differences from the control CLM run (without the parameterization) at or above the 95% confidence level. The differences in the spatial distribution of these variables are expected to affect precipitation simulations from regional climate modeling. As the Blue Nile is a region that has one of the greatest interannual and seasonal precipitation variability globally, the ability to predict this variability is essential for optimal reservoir operations including buffering of water resources during times of drought.</p> </abstract>