The water equivalent of new snow (HNW) plays a crucial role in various fields, including hydrological modeling, avalanche forecasting, and assessing snow loads on structures. However, in contrast to snow depth (HS), obtaining HNW measurements is challenging as well as time-consuming and is hence rarely measured. In this study, we assessed two semi-empirical methods, HS2SWE and ΔSNOW, for estimating HNW. These methods are designed to simulate continuous water equivalent of the snowpack (SWE) from daily HS only, with changes in SWE yielding daily HNW estimates. We compare both parametric methods against HNW predictions from a physics-based snow model (FSM2oshd) that integrates daily HS recordings using data assimilation. For replicating SWE observations, all methods show similar performance, with small relative biases (≤ 3%). At the same time, ΔSNOW tends to underestimate daily HNW by 17%, whereas FSM2oshd combined with a particle filter data assimilation scheme and HS2SWE provide estimates with lower biases (≤ 5%). Thus, our study demonstrates that daily SWE observations or supplementary measurements like HNW are important for validating the day-to-day accuracy of models simulating the daily evaluation of the snowpack. Furthermore, unlike the empirical methods, the physics-based approach can yield information about unobserved variables, such as total solid precipitation amounts, that may differ from HNW due to concurrent melt. Finally, we showcase how the estimated HNW values for HS recordings can be used for improving spatial snowfall obtained from numerical weather prediction models through an optimal interpolation data assimilation scheme.