Impacts of different spatial temperature interpolation methods on snowmelt simulations

Abstract

Spatial interpolation methods can be used to estimate high density air temperature data to drive the temperature index model used to simulate snowmelt processes. Thus, evaluating the impact of different spatial temperature interpolation methods on snowmelt simulations is necessary. This study creates three air temperature datasets based on different methods for a data sparse basin. These datasets include: 1) an inverse distance weighting (IDW) method; 2) an improved IDW method considering the elevation influence on temperature; and 3) combined use of linear regression and MODIS Land Surface Temperature (LST) data. The datasets are verified at observation stations and applied to a snowmelt hydrologic model using the Soil Water Assessment Tool. The simulation results are compared with observed discharge data and uncertainties discussed. Verification at the observation stations indicates that all datasets can reflect station air temperature. Model simulations and uncertainty analysis show that the dataset created by combined use of linear regression and MODIS LST data achieved the best simulation results and smallest uncertainties. The results also indicate that this dataset can accurately and stably reflect the spatial variation of air temperature compared with other data.