Role of temporal resolution of meteorological inputs for process‐based snow modelling. Issue 19 (8th August 2018)

• Record Type: Journal Article
• Authors: Sohrabi, Mohammad M.; Tonina, Daniele; Benjankar, Rohan; Kumar, Mukesh; Kormos, Patrick; Marks, Danny
• Is Part Of: Hydrological processes. Volume 32:Issue 19(2018)
• Page Start: 2976
• Page End: 2989
• Publication Date: 2018-08-08
• Subjects: alpine tree line -- model accuracy -- mountainous regions -- physics‐based snow modelling -- rain‐snow transition -- temporal resolution; Hydrology -- Periodicals; Hydrology -- Research -- Periodicals; Hydrologic models -- Periodicals; Hydrological forecasting -- Periodicals; 631.432
• DOI: 10.1002/hyp.13242
• Languages: English
• ISSNs: 0885-6087

Abstract

Abstract: Accurate snow accumulation and melt simulations are crucial for understanding and predicting hydrological dynamics in mountainous settings. As snow models require temporally varying meteorological inputs, time resolution of these inputs is likely to play an important role on the model accuracy. Because meteorological data at a fine temporal resolution (~1 hr) are generally not available in many snow‐dominated settings, it is important to evaluate the role of meteorological inputs temporal resolution on the performance of process‐based snow models. The objective of this work is to assess the loss in model accuracy with temporal resolution of meteorological inputs, for a range of climatic conditions and topographic elevations. To this end, a process‐based snow model was run using 1‐, 3‐, and 6‐hourly inputs for wet, average, and dry years over Boise River Basin (6, 963 km 2 ), which spans rain dominated (≤1, 400 m), rain–snow transition (>1, 400 and ≤1, 900 m), snow dominated below tree line (>1, 900 and ≤2, 400 m), and above tree line (>2, 400 m) elevations. The results show that sensitivity of the model accuracy to the inputs time step generally decreases with increasing elevation from rain dominated to snow dominated above tree line. Using longer than hourly inputs causes substantial underestimation of snow cover area (SCA) and snow water equivalent (SWE) in rain‐dominated and rain–snow transition elevations, due to the precipitation phase mischaracterization. In snow‐dominated elevations, the melt rate is underestimated due to errors in estimation of net snow cover energy input. In addition, the errors in SCA and SWE estimates generally decrease toward years with low snow mass, that is, dry years. The results indicate significant increases in errors in estimates of SCA and SWE as the temporal resolution of meteorological inputs becomes coarser than an hour. However, use of 3‐hourly inputs can provide accurate estimates at snow‐dominated elevations. The study underscores the need to record meteorological variables at an hourly time step for accurate process‐based snow modelling.