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The loss of snow cover and the initiation of streamflow are key triggers for both terrestrial and aquatic biota. Landscape-scale snowmelt and streamflow dynamics are difficult to estimate, however, because they integrate large spatial extents and can vary rapidly in time. Remotely sensed observations are often temporally discontinuous and point observations lack sufficient spatial density (e.g. point measures from data-logging piezometers). In this study, we employ inexpensive temperature/light sensors to monitor the distribution of snowmelt and headwater stream discharge as a proxy for hydrological state of the landscape with high spatial and temporal resolution. This study was conducted at the Redondo Peak, a...