Kim, Ray, & Choi, 2017


Simulations of energy balance components at snow-dominated montane watershed by land surface models.

Kim, D.; Ray, R.L.; Choi, M. (2017)
Earth Environmental Sciences. 76 337.  


The quantification of energy interactions among land surface, atmosphere, and surface vegetation is significant to comprehend the hydrological cycle in montane watersheds. Moreover, elevation change is an essential in causing variations in energy fluxes. Thus, estimating the major components of energy interactions is essential for better understanding of the hydrological process. The advanced land surface models (LSMs); the common land model (CLM) and variables infiltration capacity (VIC) are used to estimate accurate hydrometeorological variables. These hydrometeorological variables such as net radiation and sensible, latent, and ground heat fluxes were estimated using CLM and VIC at upper and lower meteorological stations in Sierra Nevada Mountain, California, USA. The estimated fluxes were compared with observations at each site. The estimated daily and monthly net radiation and sensible heat flux from both models showed good agreement with the observations (R ≥ 0.84). The CLM-modeled estimates showed lower trends during the rainfall periods, which occurred mainly during winter at both sites. In comparison, the estimated daily and monthly latent heat flux from CLM at both sites showed better results with lower RMSE and bias than that from VIC, which underestimated latent heat flux. Both models overestimated ground heat flux, and the variation trend was similar to observation. For sensitivity analysis, according to elevation change, all the estimated energy fluxes had slightly different values at the upper and lower met stations. In future studies, parameterization for the LSMs will be conducted for more robust estimations of hydrometeorological variables in montane watersheds.


Kim, D.; Ray, R.L.; Choi, M. (2017): Simulations of energy balance components at snow-dominated montane watershed by land surface models. Earth Environmental Sciences. 76 337.. DOI: 10.1007/s12665-017-6655-0

This Paper/Book acknowledges NSF CZO grant support.