The Integrated Precipitation and Hydrology Experiment - Hydrologic Applications for the Southeast US (IPHEx-H4SE) Part II: Atmospheric Forcing and Topographic Corrections

Dataset

In order to prepare atmospheric forcing data sets to drive the hydrologic models at high spatial resolution, it is necessary to apply appropriate downscale methods and bias correction schemes to the coarse reanalysis products. In this manuscript, first we describe the methodology to derive a high-resolution (1×1 km2, hourly) atmospheric forcing data set from 3-hr NARR (North American Regional Reanalysis) products originally at 32×32km resolution, and second we illustrate the value and utility of the downscaled products to drive hydrologic models offline through analysis of a long-term (5-year) continuous simulation of water and energy budgets in the Southern Appalachians against flux tower observations. The IPHEx-H4SE atmospheric forcing data set includes elevation corrected air temperature and lapse rate, specific humidity, friction velocity, surface layer winds, incoming longwave radiation, and topographically and cloudiness corrected incoming shortwave radiation that enable simulating water and energy fluxes from diurnal to annual time-scales, and for extreme events. Although the 5-year simulation presented here was conducted with a randomly selected rainfall product among those recommended in the companion report (EPL-2013-H4SE-3) without re-initialization or data assimilation, and therefore does not represent an optimal simulation with the hydrological model but rather a baseline control simulation that integrates and propagates the uncertainty in all forcing data sets, the results clearly illustrate the benefit of using the bias corrected NARR atmospheric forcing fields made available here. UTM 17N (WGS84), resolution 1Km, the boundary info: Top 4135816.67952; Left 57548.6334476; Right 773548.633448; Bottom 3525816.67952 These data were originally made available at http://hdl.handle.net/10161/8958 and were migrated to the Duke Digital Repository on 01/02/2018. Additional data from 2012-2013 were added to the Duke Digital Repository on 01/02/2018. Minor modifications were made to the README file to reflect the revised arrangement of the files within the DDR.

Duke Authors

Cited Authors

  • Barros, A; Tao, J

Published Date

  • July 22, 2014