The two time-series are as follows: (i) a synthetic measure of pan evaporation (Epan) from the PenPan formulation, which is based on the Penman equation and the physics of evaporation pans; and (ii) reference crop evapotranspiration (ETrc) from Penman-Monteith. These two measures are driven by North American Land Data Assimilation System (NLDAS) hourly data for temperature, specific humidity, wind speed, and shortwave and longwave downwelling radiation, and are estimated across CONUS at a daily basis over the period 1980 though 2009, at a spatial resolution of 1/8th degree.
The motivations for the current study are two-fold. First, we seek to improve river-forecast skill at the National Weather Service (NWS) Colorado Basin River Forecast Center (CBRFC), both in daily operations and across the multi-month temporal extents of water supply forecast seasons. To this end, the evaporation driver of the Sacramento Soil Moisture Accounting model, which currently consists of climatological monthly pan evaporation values, has been replaced with one that is based on the Epan time-series, and so is temporally dynamic, scientifically sound, and unbiased with respect to pan evaporation observations. Second, we seek to add a climatological context to a new NWS forecast productdaily and weekly ETrcacross the NWS Western Region (roughly the western one-third of CONUS). This will add value to the ETrc forecasts for users who may otherwise be unfamiliar with the concept of reference crop evapotranspiration.
There are many potential uses for the datasets. The time-series will be used to fulfill the two motivations mentioned above: implementing a dynamic evaporation driver (the Epan time-series) in the NWS River Forecast System, we have quantified the errors in current streamflow predictions that are related to the use of static evaporation drivers of the Sac-SMA model. The climatology of the ETrc time-series now adds value to the forecasts at various Weather Forecast Offices across the NWS Western Region. In the future, the time-series may be used to diagnose the effects of climate change and variability on the land surface-atmosphere interface by permitting decomposition of long-term variability and change in evaporative demand.