Snow’s thermal and radiative properties strongly impact the land surface energy balance and thus the atmosphere above it. Land surface snow information is poorly known in mountainous regions. Few studies have examined the impact of initial land surface snow conditions in high resolution, convection permitting numerical weather prediction models during the mid-latitude cool season. The extent to which land surface snow influences atmospheric energy transport and subsequent surface meteorological states is tested using a high resolution (1km) configuration of the Weather Research and Forecasting (WRF) model, for both calm conditions and weather characteristic of a warm late March Atmospheric River. A set of synthetic but realistic snow states are used as initial conditions for the model runs and the resulting differences are compared. We find that snow reduces/increases two meter air temperatures by as much as 4K during both periods, and that the atmosphere responds to snow perturbations through advection of moist static energy from neighboring regions. Snow mass and snow covered area are both important variables that influence two meter air temperature. Finally, the meteorological states produced from the WRF experiments are used to force an offline hydrologic model, demonstrating that snow melt rates can increase/decrease by factor of two depending on the initial snow conditions used in the parent weather model. We propose that more realistic representations of land surface snow properties in mesoscale models may be a source of hydrometeorological predictability

Joint hydrologic‐atmospheric model frameworks offer novel insights into the terrestrial hydrologic cycle and the potential for improved predictive capabilities for stream discharge and other hydrologic fluxes. In this study, we examine both one‐ and two‐way coupled integrations of the Weather Research and Forecasting (WRF v3.8.1) atmospheric and WRF‐Hydro (v5.0) hydrologic models for four 1000–2000 km2 snow‐dominated mountain watersheds (1500–2100 m mean elevation) in Idaho's Rocky Mountains. In watersheds where anthropogenic withdrawals are minimal (3 of 4 watersheds), we simulate stream discharge with high confidence (KGE > 0.63) for a 20 year period in the uncoupled scenarios, and find that WRF winter precipitation accumulations have less than 15% average error for all but two of the fourteen comparison NRCS Snotel sites. However, annual streamflow biases are highly correlated (r2 > 0.8 in some cases) with the annual errors in WRF cold‐season precipitation, suggesting that process representation of winter orographic precipitation limits hydrologic predictability. In the second part of the study, we evaluate the potential for ‘two‐way’ model coupling to influence hydrologic predictability by examining a 2 month case‐study period with active spring season convective precipitation. We quantify the impacts of resolving hillslope‐scale soil water redistribution on the ABL, and find that while resolving overland and saturated subsurface soil moisture flow influences soil moisture distributions and surface energy fluxes, the impact on precipitation is non‐systematic, as precipitation is generally atmospherically controlled during the study period. Consequently, future efforts should focus on improving winter orographic process representation, as streamflow is highly sensitive to errors in these processes.

The following plots provide additional information about the MCMC posterior parameter sampling methodology used in this study. Traceplots (Figure S2) show the iterations of the MCMC posterior sampling chains. Fourteen independent chains were used. Extending the burn in period for the MCMC sampling may reduce some of the variance in the posterior estimates.

Mountain ranges are vital ”water towers” of the world and are uniquely threatened by anthropogenic climate change. At the same time, the paucity of observing networks limits our understanding of hydrometeorological processes in water-resource critical regions, including the Western United States. In the past decade, non-hydrostatic, convection permitting ( 1-4km horizontal resolution) regional climate models (RCMs) have emerged as a promising tool for both reconstructing regional scale mountain hydroclimates, and for forecasting the impacts of climate perturbations on watersheds and water-resources. Still, challenges remain. To-date, computational and data storage limitations have generally precluded many RCM studies to a handful of individual years, limiting the characterization of model uncertainties/biases and thus the interpretation of model outputs. Moreover, validating spatial precipitation fields from RCMs remains a challenge, as gridded precipitation datasets are highly uncertain in locations far away from observing stations. Consequently, further validations of regional climate models require leveraging diverse or indirect sources of hydrologic information. I develop three studies to meet these challenges in this dissertation. In the first, I examine the fidelity of coupled hydrologic-model/RCM for simulating streamflow in four water resource significant, snow-dominated basins in the Boise River basin. In the second, I develop a long-term RCM simulation (1987- 2020) in the Upper Colorado River basin and evaluate precipitation fields using a novel precipitation-from-streamflow bayesian inference strategy. The third chapter of the dissertation examines orographic precipitation sensitivities to cloud-microphysical parameterizations schemes, and leverages Airborne LIDAR snow-depth datasets to evaluate both spatial patterns of precipitation enhancement and watershed-total precipitation delivery. Together, the results from this dissertation demonstrate the utility of multi-decadal regional climate modeling for interrogating mountain hydro-climates, and demonstrates the opportunities and challenges for leveraging diverse hydrologic data sources (streamflow, airborne LIDAR) and methods (bayesian inference) for evaluating RCMs.

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Atmospheric Rivers (AR) are globally occuring weather features and the primary mechanism through which water vapor moves from the tropics and subtropics towards the mid-latitudes, doing so at rates comparable to the world's largest terrestrial rivers. AR that encounter mountains often cause extreme precipitation in the form of rain and snow, high winds, and flooding in many watersheds. They account for as much as 20-30% of cool season precipitation in the central Idaho Mountains. In the Northern Hemisphere, seasonal snow cover during Winter and Spring months is the most variable land surface component in space and time, and acts on the fluxes of energy and mass into the atmospheric system. To date, there has been little effort to understand how the land surface snow cover states prior to and during the arrival of ARs, acting on the surface mass and energy balance, impact the onset, extent, and evolution of precipitation accumulation during AR events. Using a high resolution coupled land-atmosphere model, I examine the sensitivity of the precipitation regime and atmospheric energy balance to an ensemble of realistic snowcover states during a March 1998 AR case study in central Idaho. The results indicate that snow cover forcing 1) causes reductions of shortwave radiation and sensible heating that are balanced by atmpospheric energy transport, 2) increases atmospheric static stability, and 3) modifies the distributions of total accumulated precipitation by as much as 10mm. v

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