Evaluation of the Applicability of Three Methods for Climatic Spatial Interpolation in the Hengduan Mountains Region

An ideal spatial interpolation approach is indispensable for obtaining high-quality gridded climatic data in mountainous regions with scarce observations, particularly for the Hengduan Mountains Region (HMR) with dense longitudinal ranges and gorges. However, there is much controversy about the applicability of thin plate smooth spline (TPSS), cokriging, and inverse distance weighting (IDW) in mountainous regions. Here, we use the daily observations of temperature and precipitation at 125 stations in HMR and its surroundings from 1961 to 2018 and adopt three interpolation methods to map the annual average temperature and precipitation at a resolution of 500 m in HMR. Then, we assess the applicability of three interpolation methods in HMR from the perspectives of interpolation accuracy and effects. The evaluation implies a satisfactory interpolation accuracy of TPSS with the highest correlation and lowest error, whether for temperature (R-2 = 0.92, RMSE = 1.2 degrees C) or precipitation (R-2 = 0.54, RMSE = 165.9 mm). In addition, the TPSS could better display the temperature (precipitation) gradient along elevation and depict dry valleys' high-temperature and low-precipitation characteristics. Moreover, the satisfactory interpolation performance of TPSS mainly benefits from the screening of optimal TPSS model that varied primarily with the regional topography feature and meteorological observation density. The uncertainty of gridded climate datasets has become an urgent problem to solve in the complex terrain. This research illustrates the satisfactory applicability of TPSS for climatic spatial interpolation in HMR, providing theoretical support for high-precision interpolation in complex terrain, hopefully improving the regional weather forecasts and disaster warnings.

Automated summary

Based on the research conducted in the Hengduan Mountains Region, it has been found that the thin plate smooth spline (TPSS) is the most suitable method for spatial interpolation of climatic data in this region. TPSS has shown high interpolation accuracy and performance, providing high-precision climatic data that could potentially improve regional weather forecasts and disaster warnings.