When and Where Are Multiple Snow Layers Important for Simulations of Snow Accumulation and Melt?

Abstract

AbstractMany watershed‐scale and land surface models incorporate snowmelt modules with simplified representations of the snowpack with three or fewer layers. These modeling choices were traditionally made to reduce model complexity and computational demand while still being able to simulate large model domains. However, these simple snow layering schemes may not always simulate snow processes and the effects of climate change across a range of climatic and geographic conditions. Here we evaluate simple snow layering schemes (having two to five layers, commonly found in watershed‐scale and land surface models) against a synthetic benchmark with up to 100 layers at three locations with different climate conditions using the SUMMA modeling framework. We evaluate 10 different layering configurations of two to five layers with variable thicknesses and show that the effect of the layering scheme varies with site conditions. We find that the layer configuration is more important at a cold high elevation site in the Sierra Nevada, California (∼1.4°C annual average temperature), and at a warm site in the French Alps (∼6.5°C), and less important at a site in Idaho (∼5.0°C). The top layer thickness of the simpler snow layering configurations also influences the simulated snow surface and snowpack temperatures and timing of snowmelt. Our tests showed that the five‐layer model with thin layers near the surface was closest to the benchmark (median NSE = 0.99), and therefore we recommend using multiple snow layers for reliable simulations of snow accumulation and melt across a range of climates.