Abstract
Snow is an essential source of freshwater, and remotely sensed snow cover can offer daily spatial data critical to manage and model snowmelt runoff. Cloud cover obscures daily optical remotely sensed snow cover, and uncertainty associated with cloud gap filling methods may be exacerbated by drought thereby limiting effective implementation of snow cover data into snowmelt runoff models. The goal of this research is to provide a cloud free, reliable, and dynamic estimate of daily snow cover with a pattern‐based cloud gap filling approach. It is currently unclear whether seasonal snow depletion patterns are altered during drought, and whether cloud gap filling is negatively impacted. We analysed whether years of moderate severe drought alter patterns of snow depletion and reduce cloud gap filling reliability in the Boise River Basin, Idaho for the period of 2000–2024. We demonstrated moderate severe drought was uncorrelated with maximum snow extent, the onset of spring melt, and the rate of depletion. Patterns of snow depletion were similar at the watershed scale and robust to moderate severe drought (98.7% average correlation), and snowline representation is also highly similar (0.995, average R 2 over 68 models). Average cloud gap filling estimated similarity was 96.73% with a slight reduction during severe drought to 94.76%. Over one sixth of the world's population relies on water from snowmelt and real‐time management of snowmelt runoff requires accurate snowline representation, which we accomplish with the dynamic seasonally recurrent pattern of snow depletion.