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.

Irrigation is the largest human intervention in the water cycle that can modulate climate extremes, yet global irrigation water use (IWU) remains largely unknown. Microwave remote sensing offers a practical way to quantify IWU by monitoring changes in soil moisture caused by irrigation. This study evaluates the ability to quantify IWU by assimilating high-resolution (1km) SMAP-Sentinel 1 (SMAP-S1) remotely sensed soil moisture with a physically-based land surface model (LSM) using a particle batch smoother (PBS). A suite of synthetic experiments is devised to evaluate different error sources. Results from the synthetic experimentation show that unbiased simulations with known irrigation timing can produce an accurate irrigation estimate with a mean annual bias of 0.45% and the mean R2 of 96.5%, relative to observed IWU. Unknown irrigation timing can significantly deteriorate the model performance by increasing the mean annual bias to 23% and decreasing the mean R2 to 36%. In real-world experiments, the PBS data assimilation approach provides a mean bias of -18.6% when the timing of irrigation water use is known. This underestimation is possibly attributable to missing part of the irrigation signal. Yet, significantly higher irrigation was estimated over the irrigated pixels compared to the non-irrigated pixels, indicating that data assimilation can skillfully convey irrigation signals to LSMs. LSM calibration provides a 10% improvement to soil moistrue RMSE relative to the open-loop simulation. PBS data assimilation provides an additional 50% improvement to simulated soil moisture RMSE by correcting the model state and superimposing the optimal (unmodeled) irrigation on precipitation forcing.