资源环境科技发展态势分析平台

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.