Abstract:

Spatial interpolation of climate data is frequently required to provide input for regional plant growth models. As no single method among so many available ones to spatial interpolation of climate variables is optimal for all regions and all variables, it is very important to compare the results obtained using alternative methods applied to each set of data. For estimating 30-year of 10-day mean air temperature and monthly photosynthetic active radiation (PAR) fluxes at specific sites in China, we examined ordinary Kriging (OK) and other two relatively simple methods, one is inverse distance squared (IDS) technique and the other is gradient plus inverse-distance-squared (GIDS) technique. Based on the mean absolute errors from cross-validation tests, the methods were ranked as GIDS>OK>IDS for interpolating 10-day mean temperature, and IDS>GIDS>OK for interpolating monthly mean PAR fluxes. GIDS gives the lowest errors which averaged 1.3 oC for 10-day mean temperature. IDS gives the lowest errors averaged 0.46 MJ/(m²·d) for monthly PAR fluxes. Although OK errors were more consistent for temperatures of different seasons, yet GIDS had lower errors during crop growth seasons. Compared with OK, GIDS was simple to apply for interpolating 10-day mean temperature. Since PAR was affected by so many factors, the performance of GIDS and OK were not as good as we expected. It also implied that the complex methods could not ensure the best results. The multiple linear regressions carried out for GIDS revealed strong gradients in temperature and PAR fluxes varied by month in a fairly consistent way. For all 10-days, r was above 0.94 for temperature, and temperatures decreased with increasing latitude, longitude and elevation. The elevation coefficient for temperature during the year ranged from -0.0033~-0.0048 oC/m. For PAR, r was lower than that for temperature. The coefficients of PAR for latitude, longitude and elevation ranged from positive values to negative values during the year. Using a 1 km Digital Elevation Model (DEM) for China, the first 10-day mean temperature and monthly mean PAR flux of July were estimated for each pixel to assess the performance of the three interpolation techniques. From temperature maps, we can see that GIDS gives more reasonable estimates in valleys and mountainous area than the other two techniques. The temperature map generated by GIDS shows the elevation effects of temperature in West China especially in valleys and Tibet Plateau. The strong temperature trends towards the northeast in East China and PAR trends towards the northwest were also shown in the maps.

Key words: spatial interpolation, climate variables inverse distance squared, gradient plus inverse distance squared, ordinary Kriging