| description abstract | o understand the sources of temporal and spatial variability of atmospheric evaporative demand across the conterminous United States (CONUS), a mean-value, second-moment uncertainty analysis is applied to a spatially distributed dataset of daily synthetic pan evaporation for 1980?2009. This evaporative demand measure is from the ?PenPan? model, which is a combination equation calibrated to mimic observations from U.S. class-A evaporation pans and here driven by six North American Land Data Assimilation System variables: temperature, specific humidity, station pressure, wind speed, and downwelling shortwave and longwave radiation. The variability of evaporative demand is decomposed across various time scales into contributions from these drivers. Contrary to popular expectation and much hydrologic practice, temperature is not always the most significant driver of temporal variability in evaporative demand, particularly at subannual time scales. Instead, depending on the season, one of four drivers (temperature, specific humidity, downwelling shortwave radiation, and wind speed) dominates across different regions of CONUS. Temperature generally dominates in the northern continental interior. This analysis assists land surface modelers in balancing parameter parsimony and physical representativeness. Patterns of dominant drivers are shown to cycle seasonally, with clear implications for modeling evaporative demand in operational hydrology or as a metric of climate change and variability. Depending on the region and season, temperature, specific humidity, downwelling shortwave radiation, and wind speed must together be examined, with downwelling longwave radiation as a secondary input. If any variable may be ignored, it is atmospheric pressure. Parameterizations of evaporative demand based solely on temperature should be avoided at all time scales. | |
