83rd Annual

Tuesday, 11 February 2003: 1:30 PM
An Evaluation of a 30-year Land Surface Model Simulation using Observational Forcing
Hatim Sharif, NCAR, Boulder, CO; and W. Crow, N. L. Miller, K. E. Bashford, and E. F. Wood
Poster PDF (222.1 kB)
Long-term basin-scale hydrologic response is related to physiographic descriptors and climate data. Multi-year land surface and subsurface processes are coupled to topography as well as large-scale atmospheric processes, soil memory, and vegetation change. The Arkansas/Red River basin has been the focus of investigations to study the interaction of atmosphere with the land surface, subsurface, and vegetation. For this reason, this basin was the first large scale areas studied under the GEWEX Continental Scale International Project (GCIP). Portions of the basin were the site of the latest International H2O Project (IHOP-2002) and the Department of Energy IOP 2002 experiments. The basin has a strong east-west gradient of precipitation, geology, vegetation, and population. A 30-year simulation using the fully distributed version of TOPLATS at 1km resolution for the Arkansas/Red River basin is preformed. Forcing data (precipitation, incoming radiation and surface meteorology) interpolated from meteorological and rain gauge observations are used. TOPLATS is particularly well suited for such an analysis since it combines a detailed representation of surface water and energy balance processes while capturing the topographically induced horizontal redistribution of subsurface water. Analysis of mean-monthly, seasonal, and annual changes in soil moisture, latent heat, sensible, as correlated to large-scale patterns was performed. The correlation between mean-monthly, seasonal, and annual variations in surface energy fluxes, soil moisture, and stream flow and large-scale atmospheric patterns was examined. Results help to clarify the source of long-term hydrologic variability within the basin. In addition, the potential of TOPLATS enhancements - like the addition of a dynamic vegetation module to improve the model's representation of this variability were explored. This work is part of the NSF and DOE water cycle studies.

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