Near-real-time estimates of mountain snow water equivalent (SWE) are essential for streamflow forecasting and other applications, and snowpack heterogeneity controls summer water supply volumes, peak flow magnitudes, and other snowmelt runoff dynamics. Spatially resolved SWE quantification often depends on extrapolation from sparse regional measurements. When these measurements are not representative, the resulting spatial datasets are misleading. Here, we leverage complete density profiles from 36 snow pits (including eight at least 2 m deep) and repeat airborne lidar surveys spanning the Wind River Range (Wyoming, USA) to quantify missing heterogeneity in widely available snow density and SWE datasets at the point scale, grid scale (3 m to 1 km), and watershed scale. Our field measurements strategically constrain expected endmembers of snowpack variability, including rarely sampled snowpack zones such as deep drifts, steep slopes, avalanche runouts, and high elevations. Complete vertical profiling of a 5.9 m drift in a nivation hollow reveals a bulk density of 585 kg/m³, rivaling the density of avalanche debris and exceeding the predictions of prior empirical snow density models by 14 % to 35 %. In contrast, we contemporaneously observe bulk densities as low as 339 kg/m³ in adjacent forested areas. All 88 of the in-situ daily snow monitoring sites (SNOTEL) in the State of Wyoming are located in forested areas, which cannot account for alpine snowpack heterogeneity. This missing density and depth heterogeneity propagates into watershed-scale assessments. Across three large-domain near-real-time gridded SWE datasets, the standard deviation of SWE at the 500 m grid-scale is underestimated by 33 % to 75 % compared to our lidar-based data aggregated to the same resolution. These extrapolated SWE datasets underestimate the snowpack storage in a heavily drifted 1 km² glacial cirque basin by 65 % to 73 % despite only 2 % to 17 % underestimation of watershed-average SWE, underscoring the problem of missing heterogeneity. Spatially resolved SWE quantification in mountain regions can benefit from lidar and strategic field density sampling to better account for dense wind drifts and other drivers of heterogeneity in near-real-time.