Comparative Analysis of Different Spatial Interpolation Methods Applied to Monthly Rainfall as Support for Landscape Management

Abstract

Landscape management requires spatially interpolated data, whose outcomes are strictly related to models and geostatistical parameters adopted. This paper aimed to implement and compare different spatial interpolation algorithms, both geostatistical and deterministic, of rainfall data in New Zealand. The spatial interpolation techniques used to produce finer-scale monthly rainfall maps were inverse distance weighting (IDW), ordinary kriging (OK), kriging with external drift (KED), and ordinary cokriging (COK). Their performance was assessed by the cross-validation and visual examination of the produced maps. The results of the cross-validation clearly evidenced the usefulness of kriging in the spatial interpolation of rainfall data, with geostatistical methods outperforming IDW. Results from the application of different algorithms provided some insights in terms of strengths and weaknesses and the applicability of the deterministic and geostatistical methods to monthly rainfall. Based on the RMSE values, the KED showed the highest values only in April, whereas COK was the most accurate interpolator for the other 11 months. By contrast, considering the MAE, the KED showed the highest values in April, May, June and July, while the highest values have been detected for the COK in the other months. According to these results, COK has been identified as the best method for interpolating rainfall distribution in New Zealand for almost all months. Moreover, the cross-validation highlights how the COK was the interpolator with the best least bias and scatter in the cross-validation test, with the smallest errors.

Rainfall Potential and Consequences on Structural Soil Degradation of the Most Important Agricultural Region of Mexico

“…Omitting these activities in the future could result in severe damage to the soil structure, rendering it unsuitable for agricultural purposes and diminishing the economic benefits derived from agricultural activities. Therefore, concerted efforts are needed to safeguard Mexico's agricultural resources and promote sustainable land management practices for the benefit of present and future generations [53,54].…”

A Comparison of Spatial Interpolation Methods for Regionalizing Maximum Daily Rainfall Data in South Sulawesi, Indonesia

“…Spatial interpolation assumes that the attributes of the data are continuous in space and exhibit spatial relationships. Several spatial interpolation methods have been widely employed by researchers, including Multiple Linear Regression, Local Polynomial, Inverse Distance Weighted, Ordinary Kriging, Simple Kriging, Universal Kriging, and Empirical Bayesian Kriging (EBK) [1][2][3].…”

“…Meanwhile, the geostatistical interpolation methods employed were Ordinary Kriging, Simple Kriging, Empirical Bayesian Kriging, and Universal Kriging. Caloiero et al [2] employed the Inverse Distance Weighted, Ordinary Kriging, the Kriging with External Drift, and Ordinary Cokriging interpolation methods to interpolate monthly rainfall data in New Zealand. They conducted a comparative analysis of these four interpolation methods.…”

Estimation and Analysis of Seasonal Rainfall Distribution and Potential of Türkiye and Its 25 Main Watersheds

“…Yang and Xing [25] compared kernel interpolation with barrier (KIB), diffusion interpolation with barrier (DIB), IDW, RBF, OK, and EBK methods using precipitation data from different time series in Chongqing (China), and they found that the KIB method provided the highest accuracy. Caloiero et al [26] compared the IDW, OK, KED, and OCoK methods using monthly precipitation data in New Zealand and found OCoK to be the optimal method. Fung et al [27] compared the IDW, OK, multi-scale geographical weighted regression (MGWR), and geographical weighted regression (GWR) methods using precipitation data from different periods in Peninsular Malaysia and found MGWR to be the best-performing model.…”