Evaluating Input Data and Rain Snow Separation Improvements to the
National Water Model Simulation of Snow Water Equivalent
Abstract
We compared snowfall, and snow water equivalent (SWE) accumulation and
ablation simulations from the WRF-Hydro model with the U.S. National
Water Model (NWM) configuration against observations at a set of
representative point locations from Snow Telemetry (SNOTEL) sites across
the western U.S. We focused on the model’s partitioning of precipitation
between rain and snow and selected sites that span the variability of
the percentage of rain on snow precipitation events. Our results show
that the NWM generally under-estimates SWE and tends to melt snow
earlier than observations in part due to errors in the precipitation and
air temperature inputs. We reduced some of the observed and modeled
discrepancies by using SNOTEL snow-adjusted precipitation and removing
air temperature biases, based on observations. These input changes
produced an average 59% improvement in the peak SWE. Modeled peak SWE
was further improved using humidity-dependent rain-snow-separation. Both
dew point and wet-bulb parameterizations were evaluated, with the
dew-point parameterization giving better overall improvement, reducing
the bias in SWE by 18% compared to the NWM air temperature-based
scheme. This modification also improved melt timing with the number of
site years having difference between modeled and observed date of half
melt from peak SWE six or more days reduced by 6%. These SWE magnitude
and timing improvements varied when analyzed for each rain-on-snow
percentage class, with generally better results at sites where most
precipitation events fall either as snow or as rain, and less
improvement when there is a mix of snow and rain-on-snow events.