Implementation of a physiographic complexity‐based multiresolution snow modeling scheme

Using a uniform model resolution over a domain is not necessarily the optimal approach for simulating hydrologic processes when considering both model error and computational cost. Fine‐resolution simulations at 100 m or less can provide fine‐scale process representation, but can be costly to apply over large domains. On the other hand, coarser spatial resolutions are more computationally inexpensive, but at the expense of fine‐scale model accuracy. Defining a multiresolution (MR) grid spanning from fine resolutions over complex mountainous areas to coarser resolutions over less complex regions can conceivably reduce computational costs, while preserving the accuracy of fine‐resolution simulations on a uniform grid. A MR scheme was developed using a physiographic complexity metric (CM) that combines surface heterogeneity in forested fraction, elevation, slope, and aspect. A data reduction term was defined as a metric (relative to a uniform fine‐resolution grid) related to the available computational resources for a simulation. The focus of the effort was on the snowmelt season where physiographic complexity is known to have a significant signature. MR simulations were run for different data reduction factors to generate melt rate estimates for three representative water years over a test headwater catchment in the Colorado River Basin. The MR approach with data reductions up to 47% led to negligible cumulative snowmelt differences compared to the fine‐resolution baseline case, while tests with data reductions up to 60% showed differences lower than 2%. Large snow‐dominated domains could therefore benefit from a MR approach to be more efficiently simulated while mitigating error. Key Points Aggregating model resolution was found to alter the terrain physiographic complexity, leading to snowmelt errors A novel multiresolution scheme for raster‐based distributed land surface modeling was developed A data reduction larger than 45% was achieved while preserving the accuracy of fine‐resolution melt rate modeling