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Climate warming affects snowfall fractions and snowpack storage, displaces the rain-snow transition zone towards higher elevations, and impacts discharge timing and magnitude as well as low- flow patterns. However, it remains unknown how variations in the spatial and temporal distribution of precipitation at the rain-snow transition zone affect discharge. To investigate this, we used observations from eleven weather stations and snow depths measured in one aerial lidar survey to force a spatially distributed snowpack model (iSnobal/Automated Water Supply Model) in a semi-arid, 1.8 km2 headwater catchment at the rain-snow transition zone. We focused on surface water inputs (SWI; the summation of rainfall and snowmelt) for four years with contrasting climatological conditions (wet, dry, rainy and snowy) and compared simulated SWI to measured discharge. We obtained a strong spatial agreement between snow depth from the lidar survey and model (r2: 0.88), and a median Nash-Sutcliffe Efficiency (NSE) of 0.65 for simulated and measured snow depths for all modelled years (0.75 for normalized snow depths). The spatial pattern of SWI was consistent between the four years, with north-facing slopes producing 1.09 to 1.25 times more SWI than south-facing slopes, and snow drifts producing up to six times more SWI than the catchment average. We found that discharge in a snowy year was almost twice as high as in a rainy year, despite similar SWI. However, years with a lower snowfall fraction did not always have lower annual discharge nor earlier stream drying. Instead, we found that the dry-out date at the catchment outlet was positively correlated to the snowpack melt-out date. These results highlight the heterogeneity of SWI at the rain-snow transition zone and emphasize the need for spatially distributed modelling or monitoring of both the snowpack and rainfall. [ABSTRACT FROM AUTHOR]

Accurately simulating the spatiotemporal distribution of mountain snow water equivalent improves estimates of available meltwater and benefits the water resource management community. In this paper we present the first integration of lidar‐derived distributed snow depth data into a physics‐based snow model using direct insertion. Over four winter seasons (2013–2016) the National Aeronautics and Space Administration/Jet Propulsion Laboratory (NASA/JPL) Airborne Snow Observatory (ASO) performed near‐weekly lidar surveys throughout the snowmelt season to measure snow depth at high resolution over the Tuolumne River Basin above Hetch Hetchy Reservoir in the Sierra Nevada Mountains of California. The modeling component of the ASO program implements the iSnobal model to estimate snow density for converting measured depths to snow water equivalent and to provide temporally complete snow cover mass and thermal states between flights. Over the four years considered in this study, snow depths from 36 individual lidar flights were directly inserted into the model to provide updates of snow depth and distribution. Considering all updates to the model, the correlation between ASO depths and modeled depths with and without previous updates was on average r2 = 0.899 (root‐mean‐square error = 12.5 cm) and r2 = 0.162 (root‐mean‐square error = 41.5 cm), respectively. The precise definition of the snow depth distribution integrated with the iSnobal model demonstrates how the ASO program represents a new paradigm for the measurement and modeling of mountain snowpacks and reveals the potential benefits for managing water in the region. Plain Language Summary: In regions that depend primarily on snow to support life, water availability is becoming an increasingly important topic. National Aeronautics and Space Administration (NASA)'s Airborne Snow Observatory (ASO) is a new platform for estimating the amount of water stored in mountain snowpacks. Since 2013, the ASO has combined detailed measurements of snow depth from an aircraft with snowpack density estimates from a physics‐based snow model to provide predictions of total snow water equivalent stored in the Tuolumne River Basin in the California Sierra Nevada. This work describes the process of updating the snow model using the measured ASO snow depths through a direct insertion process. When the distribution of all the snow in the basin is known more accurately, the model results are improved. Key Points: This is the first near‐real time application of a model state variable being periodically updated using lidar‐derived snow depthsThe first update in each year is most crucial since it defines the spatial pattern of snow accumulation prior to peak snow water equivalentEnsuing updates remain important for setting the spatial distribution of spring storms and when the duration between flights is prolonged [ABSTRACT FROM AUTHOR]

Weather, snow, stream, topographic, and vegetation data are presented from the South Mountain Experimental Catchments. This study site was established in 2007 as a collaborative, long-term research laboratory to address the impacts of western juniper encroachment and woodland treatments in the interior Great Basin region of the western USA. The data provide detailed information on the weather and hydrologic response from four highly instrumented catchments in the late stages of woodland encroachment in a sagebrush steppe landscape. Hourly data from six meteorologic stations and four weirs have been carefully processed, quality checked, and are serially complete. These data are ideal for hydrologic, ecosystem, and biogeochemical modeling. Data presented are publicly available from the USDA National Agricultural Library administered by the Agricultural Research Service [ABSTRACT FROM AUTHOR]