The widely-used Noah-MP land surface model (LSM) currently adopts snow albedo parameterizations that are semi-physical in nature with nontrivial uncertainties. To improve physical representations of snow albedo processes, a state-of-the-art snowpack radiative transfer model, the latest version of Snow, Ice, and Aerosol Radiative (SNICAR) model, is integrated into Noah-MP in this study. The coupled Noah-MP/SNICAR represents snow grain properties (e.g., shape and size), snow aging, and physics-based snow-aerosol-radiation interaction processes. We compare Noah-MP simulations employing the SNICAR scheme and the default semi-physical Biosphere-Atmosphere Transfer Scheme (BATS) against in-situ snow albedo observations at three Rocky Mountain field stations. The agreement between simulated and in-situ observed ground snow albedo in the broadband, visible, and near-infrared spectra is enhanced in Noah-MP/SNICAR simulations relative to Noah-MP/BATS simulations. The SNICAR scheme improves the temporal variability of modeled broadband snow albedo, with a nearly twofold higher correlation with observations (r=0.66) than the default BATS snow albedo scheme (r=0.37). The underestimated variability in Noah-MP/BATS is a result of inadequate physical linkage between snow albedo and environmental/snowpack conditions, which is substantially improved by the SNICAR scheme. Importantly, the Noah-MP/SNICAR model, with constraints of snow grain size from the MODIS snow covered area and grain size (MODSCAG) satellite data, physically represents and quantifies the snow albedo and absorption of shortwave radiation in response to snow grain size, non-spherical snow shapes, and light-absorbing particles (LAPs). The coupling framework of the Noah-MP/SNICAR model provides a means to reduce the bias in simulating snow albedo.

Subsurface tile drainage (TD) is a dominant agriculture water management practice in the United States (US) to enhance crop production in poorly-drained soils. Assessments of field- or watershed-level (<50 km2) hydrologic impacts of tile drainage are becoming common; however, a major gap exists in our understanding of regional (>105 km2) impacts of tile drainage on hydrology. The National Water Model (NWM) is a distributed 1-km resolution hydrological model designed to provide accurate streamflow forecasts at 2.7 million reaches across the US. The current NWM lacks tile drainage representation which adds considerable uncertainty to streamflow forecasts in tile-drained areas. In this study, we quantify the performance of the NWM with a newly incorporated tile drainage scheme over the heavily tile-drained Midwestern US. Implementing a tile drainage scheme enhanced the uncalibrated model performance by about 20% to 50% of the calibrated NWM (Calib). The calibrated NWM with tile drainage (CalibTD) showed enhanced accuracy with higher event hit rates and lower false alarm rates than Calib. CalibTD showed better performance in high-flow estimations as tile drainage increased streamflow peaks (14%), volume (2.3%), and baseflow (11%). Regional water balance analysis indicated that tile drainage significantly reduced surface runoff (-7% to -29%), groundwater recharge (-43% to -50%), evapotranspiration (-7% to -13%), and soil moisture content (-2% to -3%). However, infiltration and soil water storage potential significantly increased with tile drainage. Overall, our findings highlight the importance of incorporating the tile drainage process into the operational configuration of the NWM.

The North American Monsoon occurs July-September bringing significant rainfall to Colorado River headwater basins. This rain may buffer streamflow deficiencies caused by reductions in snow accumulation. Using a data-modeling framework, we explore the importance of monsoon rain in streamflow generation over historic conditions in an alpine basin. Annually, monsoon rain contributes 18{plus minus}7% water inputs, generates 10{plus minus}6% streamflow and increases water yield 3{plus minus}2% the following year. The bulk of rain supports evapotranspiration in lower subalpine forests. However, rains have the potential to produce appreciable streamflow at higher elevations where soil storage, forest cover and aridity are low; and rebounds late season streamflow 64{plus minus}13% from simulated reductions in snowpack as a function of monsoon strength. Interannual variability in monsoon efficiency to generate streamflow declines with low snowpack and high aridity, implying the ability of monsoons to replenish streamflow in a warmer future with less snow accumulation will diminish.