Accumulation of snowfall during winter and snowmelt in the subsequent spring or earlier summer provides a dominant water source in alpine regions. Most land surface and hydrological models use near-surface air temperature (T a ) thresholds to partition precipitation into snow and rain, underestimating snowfall over drier regions. We developed a snow-rain partitioning scheme using the wet-bulb temperature (T w ), which is closer to the surface temperature of a falling hydrometeor than T a . T w becomes more depressed in drier environments as derived from T w depression equation using T a and surface air humidity, resulting in a greater fraction of snowfall. We implemented this new T w scheme in the Noah-MP land surface model and evaluated the model against a high-quality ground-based snow product over the contiguous United States. The results suggest that the new T w scheme substantially improves the model skill in simulating snow depth and snow water equivalent over most snow-covered grids, especially the higher and drier continental mountain ranges in the Western United States, while it retains the modeling accuracy over the more humid Eastern United States.Plain Language Summary The partitioning between rainfall and snowfall is important for understanding the impacts of climate change and water resource availability. Most land surface and hydrological models use surface air temperature to partition precipitation into rain and snow and thus underestimate snowfall and snow mass accumulated on the ground in the drier Western United States. A falling hydrometeor evaporates or sublimates at its surface depending on the humidity of the surrounding air and cools off, resulting in a surface temperature that is cooler than the air temperature. The depressed surface temperature is close to the wet-bulb temperature. We developed a scheme using the wet-bulb temperature and tested it with a physically based snow model over the contiguous United States. The testing results strongly support the use of wet-bulb temperature, which enhances snowfall and the snow mass on the ground more significantly over the higher and drier mountains in the Western United States, while it retains the modeling accuracy in the more humid Eastern United States.

Accurate estimation of the spatio‐temporal distribution of snow water equivalent is essential given its global importance for understanding climate dynamics and climate change, and as a source of fresh water. Here, we explore the potential of using the Long Short‐Term Memory (LSTM) network for continental and regional scale modeling of daily snow accumulation and melt dynamics at 4‐km pixel resolution across the conterminous US (CONUS). To reduce training costs (data are available for ∼0.31 million snowy pixels), we combine spatial sampling with stagewise model development, whereby the network is first pretrained across the entire CONUS and then subjected to regional fine‐tuning. Accordingly, model evaluation is focused on out‐of‐sample predictive performance across space (analogous to the prediction in ungauged basins problem). We find that, given identical inputs (precipitation, temperature, and elevation), a single CONUS‐wide LSTM provides significantly better spatio‐temporal generalization than a regionally calibrated version of the physical‐conceptual temperature‐index‐based SNOW17 model. Adding more meteorological information (dew point temperature, vapor pressure deficit, longwave radiation, and shortwave radiation) further improves model performance, while rendering redundant the local information provided by elevation. Overall, the LSTM exhibits better transferability than SNOW17 to locations that were not included in the training data set, reinforcing the advantages of structure learning over parameter learning. Our results suggest that an LSTM‐based approach could be used to develop continental/global‐scale systems for modeling snow dynamics.

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In a real Banach space E with a uniformly differentiable norm, we prove that a new iterative sequence converges strongly to a fixed point of an asymptotically nonexpansive mapping. The results in this paper improve and extend some recent results of other authors.

Introduction The Problem of Continental-Scale Estimation of Snow Water EquivalentAccurate monitoring of the large-scale dynamics of snowpack is essential for understanding the details of climate dynamics and climate change (Robinson et al., 1993). Warming under a changing climate is expected to cause snowpack to melt earlier in the year (Xiao, 2021; and to reduce the amount of water stored as snow (Musselman et al., 2021;Nijssen et al., 2001). This is expected to have broad and potentially severe impacts to ecosystem productivity (Boisvenue & Running, 2006), winter flood risk (Musselman et al., 2018), groundwater recharge (Ford et al., 2020), agriculture and food security (Qin et al., 2020;Shindell et al., 2012), wildfire hazard (Westerling, 2016), and frequency and severity of drought (Arevalo et al., 2021). In western North America, snow is the primary source of water and streamflow (Li et al., 2017), while globally it supports the water supply needs for more than 1 billion people (Barnett et al., 2005). Therefore, having accurate estimates of the quantity of water stored in snowpack, called snow water equivalent (SWE), is critical for the forecasting and management of water supply and hydropower (Bales et al., 2006;Mankin et al., 2015).