Application of several spatial interpolation techniques to monthly rainfall data in the Calabria region (southern Italy)

2018 ◽  
Vol 38 (9) ◽  
pp. 3651-3666 ◽  
Author(s):  
G. Pellicone ◽  
T. Caloiero ◽  
G. Modica ◽  
I. Guagliardi
Keyword(s):  
Spatial Interpolation ◽  
Southern Italy ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Calabria Region ◽  
Interpolation Techniques

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

2021 ◽  
Vol 11 (20) ◽  
pp. 9566
Author(s):  
Tommaso Caloiero ◽  
Gaetano Pellicone ◽  
Giuseppe Modica ◽  
Ilaria Guagliardi
Keyword(s):  
New Zealand ◽  
Spatial Interpolation ◽  
Cross Validation ◽  
Landscape Management ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
The Other ◽  
Geostatistical Methods ◽  
The Cross ◽  
Almost All

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.

Evaluation of methods of spatial interpolation for monthly rainfall data over the state of Rio de Janeiro, Brazil

2017 ◽  
Vol 134 (3-4) ◽  
pp. 955-965 ◽  
Author(s):  
Gustavo Bastos Lyra ◽  
Tamíres Partelli Correia ◽  
José Francisco de Oliveira-Júnior ◽  
Marcelo Zeri
Keyword(s):  
Rio De Janeiro ◽  
Spatial Interpolation ◽  
The State ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Evaluation Of Methods

scholarly journals Mapping monthly rainfall data in Galicia (NW Spain) using inverse distances and geostatistical methods

2007 ◽  
Vol 10 ◽  
pp. 51-57 ◽  
Author(s):  
J. M. Mirás-Avalos ◽  
A. Paz-González ◽  
E. Vidal-Vázquez ◽  
P. Sande-Fouz
Keyword(s):  
Spatial Dependence ◽  
Ordinary Kriging ◽  
Conditional Simulation ◽  
Monthly Rainfall ◽  
Rainfall Data ◽  
Nw Spain ◽  
Data Set ◽  
Kriging With External Drift ◽  
Interpolation Techniques ◽  
External Drift

Abstract. In this paper, results from three different interpolation techniques based on Geostatistics (ordinary kriging, kriging with external drift and conditional simulation) and one deterministic method (inverse distances) for mapping total monthly rainfall are compared. The study data set comprised total monthly rainfall from 1998 till 2001 corresponding to a maximum of 121 meteorological stations irregularly distributed in the region of Galicia (NW Spain). Furthermore, a raster Geographic Information System (GIS) was used for spatial interpolation with a 500×500 m grid digital elevation model. Inverse distance technique was appropriate for a rapid estimation of the rainfall at the studied scale. In order to apply geostatistical interpolation techniques, a spatial dependence analysis was performed; rainfall spatial dependence was observed in 33 out of 48 months analysed, the rest of the rainfall data sets presented a random behaviour. Different values of the semivariogram parameters caused the smoothing in the maps obtained by ordinary kriging. Kriging with external drift results were according to former studies which showed the influence of topography. Conditional simulation is considered to give more realistic results; however, this consideration must be confirmed with new data.

scholarly journals Comparison of spatial interpolation methods for estimation of weekly rainfall in West Bengal, India

MAUSAM ◽  
2021 ◽  
Vol 68 (1) ◽  
pp. 41-50
Author(s):  
MADHURIMA DAS ◽  
ARNAB HAZRA ◽  
ADITI SARKAR ◽  
SABYASACHI BHATTACHARYA ◽  
PABITRA BANIK
Keyword(s):  
Spatial Interpolation ◽  
West Bengal ◽  
Agricultural Practices ◽  
Rainfall Data ◽  
Absolute Deviation ◽  
Interpolation Technique ◽  
Distance Weighted ◽  
Interpolation Techniques ◽  
Discrete Values ◽  
Inverse Distance

Rainfall is one of the most eloquently researched contemporary meteorological phenomena affecting the agricultural practices dramatically, particularly along the humid, sub-tropics, where agriculture is predominantly rainfed. It is a key parameter of agricultural production in West Bengal due to lack irrigation facilities in most of the areas. Thus, it is very important to have detailed information of rainfall distribution pattern of West Bengal. In practice rainfall data is collected only at few discrete stations scattered all over the whole state. However, rainfall is a spatially continuous phenomenon rather than discrete. Thus it becomes essential to apply a robust spatial interpolation technique to transform the discrete values into a continuous spatial pattern. In the present study, three spatial interpolation techniques namely Kriging, Inverse Distance Weighted (IDW) and SPLINE, are used for a comparative analysis to identify the most efficient interpolation technique. Weekly average rainfall data available between 1901 and 1985 for 19 standard meteorological weeks (SMW), Week 22 to Week 40 are used for the analysis. The errors of the three interpolation techniques are analyzed and the best method is chosen based on the minimum mean absolute deviation (MAD) and the minimum mean squared deviation (MSD) criteria. The IDW method is found to be the best spatial interpolation technique.

scholarly journals Spatial Interpolation of Daily Rainfall Data for Local Climate Impact Assessment over Greater Sydney Region

2015 ◽  
Vol 2015 ◽  
pp. 1-12 ◽  
Author(s):  
Xihua Yang ◽  
Xiaojin Xie ◽  
De Li Liu ◽  
Fei Ji ◽  
Lin Wang
Keyword(s):  
Spatial Interpolation ◽  
Mean Squared Error ◽  
Daily Rainfall ◽  
Climate Impact ◽  
Rainfall Data ◽  
Rainfall Erosivity ◽  
Local Climate ◽  
Erosion Risk ◽  
Daily Rainfall Data ◽  
Interpolation Techniques

This paper presents spatial interpolation techniques to produce finer-scale daily rainfall data from regional climate modeling. Four common interpolation techniques (ANUDEM, Spline, IDW, and Kriging) were compared and assessed against station rainfall data and modeled rainfall. The performance was assessed by the mean absolute error (MAE), mean relative error (MRE), root mean squared error (RMSE), and the spatial and temporal distributions. The results indicate that Inverse Distance Weighting (IDW) method is slightly better than the other three methods and it is also easy to implement in a geographic information system (GIS). The IDW method was then used to produce forty-year (1990–2009 and 2040–2059) time series rainfall data at daily, monthly, and annual time scales at a ground resolution of 100 m for the Greater Sydney Region (GSR). The downscaled daily rainfall data have been further utilized to predict rainfall erosivity and soil erosion risk and their future changes in GSR to support assessments and planning of climate change impact and adaptation in local scale.

scholarly journals Spatial Interpolation on Rainfall Data over Peninsular Malaysia Using Ordinary Kriging

2013 ◽  
Vol 63 (2) ◽  
Author(s):  
Suhaila Jamaludin ◽  
Hanisah Suhaimi
Keyword(s):  
Spatial Analysis ◽  
Ordinary Kriging ◽  
Spatial Interpolation ◽  
Rainfall Intensity ◽  
Peninsular Malaysia ◽  
Rainfall Data ◽  
Kriging Method ◽  
Intensity Index ◽  
Dry Conditions ◽  
Two Indices

This study presents the spatial analysis of the rainfall data over Peninsular Malaysia. 70 rainfall stations were utilized in this study. Due to the limited number of rainfall stations, the Ordinary Kriging method which is one of the techniques in Spatial Interpolation was used to estimate the values of the rainfall data and to map their spatial distribution. This spatial analysis was analysed according to the two indices that describe the wet events and another two indices that characterize dry conditions. Large areas at the east experienced high rainfall intensity compared to the areas in the west, northwest and southwest. The small value that has been obtained in Aridity Intensity Index (AII) reflects that the high amount of rainfall in the eastern areas is not contributed by low-intensity events (less than 25th percentile). In terms of number of consecutive dry days, Northwestern areas in Peninsular Malaysia recorded the highest value. This finding explains the occurrence of a large number of floods and soil erosions in the eastern areas.

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