Enabling Advanced Snow Physics Within Land Surface Models Through an Interoperable Model-Physics Coupling Framework

Accurate estimation of snow accumulation and melt is a critical part of decision-making in snow-dominated watersheds. In this study, we demonstrate a flexible methodology to couple a detailed snow model, Crocus, separately to two different land surface models (LSMs), Noah-MP and Noah. The original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) are used to simulate snow depth, snow water equivalent, and other water and energy states and fluxes. The results of simulations are compared against a wide range of independent gridded and point scale reference data sets. Our results show that coupling the detailed snow model, Crocus, with the LSMs improves the snow depth and snow water equivalent relative to independent observations. Overall, larger improvements are obtained with coupling Crocus to the Noah LSM, with the coupled Noah-Crocus configuration reducing the RMSE and bias of snow depth from 2% to 12% and 57% to 75%, respectively, relative to Snow Data Assimilation System (SNODAS) and snow product from the University of Arizona. On the other hand, smaller improvements are obtained by coupling Crocus with Noah-MP. The Coupled Noah-MP-Crocus reduces the snow depth bias but slightly degrades the RMSE of snow depth and snow water equivalent. The corresponding impacts in other water budget terms such as evapotranspiration, soil moisture, and streamflow, however, are mixed, pointing to the significant need to improve the coupling assumptions of these processes within land models. Overall, the interoperable coupling framework demonstrated here offers the opportunity to include more detailed snow physics and processes, and to advance data assimilation systems through improved exploitation of information from snow remote sensing instruments. We introduce a new approach that combines Crocus, a detailed snow model, with land surface models (LSMs) in the NASA Land Information System. As a demonstration, we coupled Crocus with Noah-MP and Noah. In each time step, Crocus uses atmospheric forcing data, snow-ground interface temperature, and soil water content from the LSMs to run snow physics. It then returns total profile snow water equivalent (SWE) and snow depth (SD) states to the LSMs. We compare the SD, SWE, and other water and energy states and fluxes from the original LSMs and the coupled models (Noah-MP-Crocus and Noah-Crocus) against various independent reference data sets. The coupled Noah-Crocus configuration improves the RMSE and bias of SD and SWE. However, the coupled Noah-MP-Crocus configuration improves the bias but slightly degrades the RMSE of SD and SWE. Despite the improvements in SD and SWE, a corresponding enhancement in evapotranspiration and soil moisture is not observed. We attribute these discrepancies to uncertainties in LSM parameterizations and associated biases in these variables. The coupling framework introduced here is modular and can be extended to enable the interaction between a given specialized physics model and the general LSM. This work presents a new framework that couples the Crocus snow model, with Land Surface Models within the NASA Land Information System The coupled systems improve the estimation of snow depth and snow water equivalent relative to the gridded observations The corresponding impacts in other water budget terms, such as evapotranspiration, soil moisture, and streamflow, are mixed