Publications

Tan, JB; Xie, XY; Zuo, JQ; Xing, XM; Liu, B; Xia, Q; Zhang, YF (2021). Coupling random forest and inverse distance weighting to generate climate surfaces of precipitation and temperature with Multiple-Covariates. JOURNAL OF HYDROLOGY, 598, 126270.

Abstract
Spatially interpolated temperature and precipitation are hydrological variables that are widely applied in models of ecology, hydrology, agronomy, and other environmental sciences. The accuracy of meteorological data directly determine model performance. However, the interpolation of temperature and precipitation over complex areas is affected by the value observed with surrounding meteorological stations and also by aspects of the physical environment, such as terrain and land cover effects. These effects significantly limit the precise interpolation of temperature and precipitation. To normalize these effects, a new method coupling random forest (RF) and inverse distance weighting (IDW) was proposed, RF-IDW. The proposed method was applied to generate the climate surfaces of both precipitation and temperature in China in 2015. During the interpolation with RF-IDW, the temperature and precipitation were decomposed into a basic part (indicating the atmospheric properties) and local parts (representing the effects of the physical environment). The complex correlations were determined between precipitation/temperature and multiple-covariates of landcover, normalized difference vegetation index (NDVI), elevation, slope, longitude, soil moisture, latitude and Land Surface Temperature (LST) derived from Moderate Resolution Imaging Spectroradiometer (MODIS) data. Next, 10-fold and leave-one-region-out cross-validation were applied to verify the accuracy of RF-IDW for the interpolation of meteorological values. We also compared the RF-IDW performance to that of RF, IDW, Anusplin, and MLR to further investigate its feasibility. Based on 10-fold cross-validation, the coefficient of determinations (R-2) between interpolated value and observed values were 0.962 for precipitation and 0.990 for temperature, indicating that the RF-IDW can accurately interpolate climate surfaces. The performance comparison showed that the RF-IDW reduced the mean absolute error by 55 mm to 120 mm for precipitation interpolation and 0.06 degrees C to 0.8 degrees C for temperature interpolation, especially in the complex region. The result of monthly interpolations implied the good robustness of the RF-IDW. Overall, the proposed RF-IDW approach allows improved spatial interpolation of complex region and suggest alternate methods for the spatial interpolation of hydrological variables.

DOI:
10.1016/j.jhydrol.2021.126270

ISSN:
0022-1694