Abstract
In this study, the influence of subsurface water on the energy budget components of three locations with heterogeneous land surfaces in the Nebraska Sand Hills are examined through observations and use of the Noah land surface model (LSM). Observations of the four primary components of the surface energy budget are compared for a wet interdunal meadow valley, a dry interdunal valley, and a dunal upland location. With similar atmospheric forcing at each site, it was determined that differences in the partitioning of the mean diurnal net radiation (Rnet) existed among the three locations due to the influence of varied soil moisture and vegetation through the year. At the wet valley, observations indicated that almost 65% of the mean daily peak Rnet was used for latent heating, due to the relatively higher soil moisture content resulting from an annual upward gradient of subsurface water and denser vegetation. In sharp contrast, the dunal upland site yielded only 21% of the mean daily peak Rnet going to latent heating, and a greater mean diurnal soil heat flux with typically drier soils and sparser vegetation than at the wet valley. The dry valley partition of the peak Rnet fell between the wet valley and dunal upland site, with approximately 50% going to sensible heating and 50% toward latent heating. In addition to the observational analysis, an uncoupled land surface model was forced with the observations from each site to simulate the energy budgets, with no tuning of the model’s fundamental equations and with little adjustment of the model parameters to improve results. While the model was able to reasonably simulate the mean diurnal and annual energy budget components at all locations, in most instances with root-mean-square errors within 20%–25% of the observed values, the lack of explicit treatment of subsurface water within the model limited predictability, particularly at the wet valley site. For instance, only 25% of the peak mean diurnal Rnet went toward latent heating in the model simulation of the wet valley, compared to 65% as estimated by observations. Model evaluation statistics are presented to document the land surface model’s ability to capture the annual and mean diurnal variations in the surface energy budget terms at the dry valley and dunal upland sites, but the absence of subsurface water results in large errors in the wet valley simulation. From these results, a case is made for the future inclusion of the explicit treatment of subsurface water within the Noah LSM to better approximate the prediction of the surface energy budget in such environments.
Auto-calibration System Developed to Assimilate AMSR-E Data into a Land Surface Model for Estimating Soil Moisture and the Surface Energy Budget
