Evaluation of Spatial Interpolation Techniques for Operational Climate Monitoring in the Philippines

Abstract

To overcome the limitation of low network density and sparse distribution of meteorological stations, spatial interpolation is being performed for estimating meteorological variables that are not geographically covered by existing observation network. While there are several readily available spatial interpolation techniques, it is still difficult to determine which one best estimates actual observation. Considering the stimulus for disaster risk reduction, hydrological, agricultural, and other applications of interpolated data, this study compared six interpolation techniques (Inverse Distance Weighted (IDW), Completely Regularized Spline (CRS), Tension Spline (TS), Ordinary Kriging (OK), Universal Kriging (UK), and ANUSPLIN) that have been recommended in tropical maritime region. Validation results comparing historical monthly and interpolated rainfall data from 1981−2010 in 65 stations in the Philippines show that OK has the best performance among the aforementioned techniques followed by ANUSPLIN and TS. Ultimately, this study is a contribution to the existing inadequate literatures that have documented and evaluated interpolation techniques that can be used in archipelagic regions with prominent climate variability.(Citation: Basconcillo, J. Q., G. A.

Impacts of global warming on the cropping systems of China under technical improvements from 1961 to 2016

“…This is mainly because new indices considered crop variety replacements. Moreover, the Anusplin meteorological interpolation method applied in the study considered the effects of the terrain factor, which also led to differences from previous research that used IDW or Kriging interpolation methods (Yang et al., 2015; Basconcillo et al., 2017). Although the cumulative temperature during the crop‐growing period in the DCS region increased by almost 400 °C, the northern limit line of the DCS did not change dramatically.…”

“…Anusplin was used in the study to determine the limitation lines by using the observed temperature datasets in China, and it is a professional interpolation software package developed by the Australian National University based on the thin plate smooth spline technique (Hutchinson & Xu, 2013). Thin plate spline can determine the degree of smoothness of an output surface from the sampling sites through generalized cross validation (Basconcillo et al., 2017). The Anusplin model is particularly suitable for processing a time series of meteorological data under a complex mountain environment and performs better than other spatial interpolation methods (e.g., IDW and Kriging) (Liu, Shanguan, Liu, & Ding, 2018a; Zhu, Zhang, Zhang, & Zhu, 2019).…”

Spatiotemporal Variation and Climate Influence Factors of Vegetation Ecological Quality in the Sanjiangyuan National Park

“…The spatial locations of the meteorological stations are shown in Figure 1. The inverse distance weighted technique was used to interpolate the data into raster layers [30]. For further analysis, the spatial time series of the yearly NDVI, NPP, temperature, and precipitation from the year 2000 to 2016 were generated.…”

Comparison of spatial interpolation methods for annual and seasonal rainfall in two hotspots of biodiversity in South America

“…To overcome the low-density network constraint and sparse distribution of the meteorological stations, the spatial interpolation becomes an important tool to estimate climatological variables not geographically covered by the existing observation network (Andrade & Moreano 2013, Basconcillo et al 2017, Berndt & Haberlandt 2018. However, there is little evidence that a single interpolation method is ideal for several conditions.…”

“…Essentially, this happens because each technique depends on the dataset characteristics. So, a technique may be suitable for some variables or regions but may not work for others (Basconcillo et al 2017). The generation of continuous surfaces can be performed by a variety of methods, but the difficulty is to choose the one that best reproduces the real surface (Caruso & Quarta 1998, Ly et al 2011.…”