We present hourly climate data to force land surface process models and assessments over the Merced and Tuolumne watersheds in the Sierra Nevada, California, for the water year 2010-2014 period. Climate data (38 stations) include temperature and humidity (23), precipitation (13), solar radiation (8), and wind speed and direction (8), spanning an elevation range of 333 to 2987 m. Each data set contains raw data as obtained from the source (Level 0), data that are serially continuous with noise and nonphysical points removed (Level 1), and, where possible, data that are gap filled using linear interpolation or regression with a nearby station record (Level 2). All stations chosen for this data set were known or documented to be regularly maintained and components checked and calibrated during the period. Additional time-series data included are available snow water equivalent records from automated stations (8) and manual snow courses (22), as well as distributed snow depth and co-located soil moisture measurements (2-6) from four locations spanning the rain-snow transition zone in the center of the domain. Spatial data layers pertinent to snowpack modeling in this data set are basin polygons and 100 m resolution rasters of elevation, vegetation type, forest canopy cover, tree height, transmissivity, and extinction coefficient. All data are available from online data repositories (https://doi.org/10.

Reading another animals emotional state can enable receivers to anticipate their behavioural motivations, which is important in guiding interactions with that individual. For species living closely alongside humans, the emotional cues that people express can be almost as informative as those of conspecifics. Goats can discriminate differences in emotional valence present in another goats calls, and we investigated whether this ability extends to human speech. We presented goats with a habituation-dishabituation-rehabituation paradigm, where they experienced multiple playbacks of a familiar or unfamiliar human voice conveying a single emotional valence (e.g., angry; habituation phase), before the valence of the voice changed (e.g., happy; dishabituation phase) and then reversed again in-line with the habituation phase (e.g., angry; rehabituation phase). Over the habituation phase, goat behavioural responses decreased, showing evidence of having habituated to the playback stimuli presented. Following a change in emotional valence (dishabituation phase), although goats were overall less likely to respond, those that did looked for longer, suggesting they had perceived the shift in emotional content conveyed in human voice playbacks. We found no changes in physiological arousal (heart rate or heart rate variability) with shifts in playback valence. Goats, as a domesticated species, may have developed a sensitivity to our cues over their long association with humans, but the differences in individual behaviour towards the playback paradigm could highlight a role for learning and individual experience in particular on interspecific emotional communication.

Snow provides fresh meltwater to over a billion people worldwide. Snow dominated watersheds drive western US water supply and are increasingly important as demand depletes reservoir and groundwater recharge capabilities. This motivates our inter- and intra-annual investigation of snow distribution patterns, leveraging the most comprehensive airborne lidar survey (ALS) dataset for snow. Validation results for ALS from both the NASA SnowEx 2017 campaign in Grand Mesa, Colorado and the time series dataset from the Tuolumne River Basin in the Sierra Nevada, in California, are presented. We then assess the consistency in the snow depth patterns for the entire basin (at 20-m resolution) and for subbasin regions (at 3-m resolution) from a collection of 51 ALS that span a six-year period (2013-2018) in the Tuolumne Basin. Strong correlations between ALS from different years near peak SWE confirm that spatial patterns exist between snow seasons. Year-to-year snow depth differs in absolute magnitude, but relative differences are consistent spatially, such that deep and shallow zones occur in the same location. We further show that elevation is the terrain parameter with the largest correlation to snow depth at the basin scale, and we map the expected pattern distribution for periods with similar snow-covered extents. Lastly, we show at a subbasin scale that distribution patterns are more consistent in vegetation-limited areas (bedrock dominated terrain and open meadows) compared to vegetation-rich zones (valley hillslopes and dense canopy cover). The maps of snow patterns and their consistency can be used to determine optimal locations of new long-term monitoring sites, design sampling strategies for future snow surveys, and to improve high resolution snow models.