Incorporating elevation in rainfall interpolation in Tunisia using geostatistical methods

Abstract

This paper compares the performance of three geostatistical algorithms, which integrate elevation as an auxiliary variable: kriging with external drift (KED); kriging combined with regression, called regression kriging (RK) or kriging after detrending; and co-kriging (CK). These three methods differ by the way by in which the secondary information is introduced into the prediction procedure. They are applied to improve the prediction of the monthly average rainfall observations measured at 106 climatic stations in Tunisia over an area of 164 150 km² using the elevation as the auxiliary variable. The experimental sample semivariograms, residual semivariograms and cross-variograms are constructed and fitted to estimate the rainfall levels and the estimation variance at the nodes of a square grid of 20 km?×?20 km resolution and to develop corresponding contour maps. Contour diagrams for KED and RK were similar and exhibited a pattern corresponding more closely to local topographic features when (a) the network is sparse and (b) the rainfall–elevation correlation is poor, while CK showed a smooth zonal pattern. Smaller prediction variances are obtained for the RK algorithm. The cross-validation showed that the RMSE obtained for CK gave better results than for KED or RK.

Editor D. Koutsoyiannis; Associate editor C. Onof

Citation Feki, H., Slimani, M., and Cudennec, C., 2012. Incorporating elevation in rainfall interpolation in Tunisia using geostatistical methods. Hydrological Sciences Journal, 57 (7), 1294–1314.     

利用不同方法估测土壤有机质及其对采样数的敏感性分析

用随机方法从262个采样点中抽取200个点作为已知有机质含量的数据集,将所有采样点的碱解氮作为辅助数据预测有机质的空间分布。利用有机质信息的普通克立格法的方差解释量和预测精度最低,而回归克立格法因在预测过程中加入了回归残差而使方差解释量最大、预测精度最高。为了分析采样数对不同方法预测精度的影响,从上述已知有机质含量的200个点中分别随机抽取40、80、120、160个点构成4个数据集,分别利用它们的有机质信息和不同方法预测了有机质的空间分布,结果表明:对于每个数据集,4种方法的预测精度顺序均为RGK>COK>RG>OK,线性回归法的预测精度随采样点的增加基本不变,而其它三种方法的预测精度却逐渐提高。     

Predictive land value modelling in Guatemala City using a geostatistical approach and Space Syntax

ABSTRACT

Spatial information of land values is fundamental for planners and policy makers. Individual appraisals are costly, explaining the need for predictive modelling. Recent work has investigated using Space Syntax to analyse urban access and explain land values. However, the spatial dependence of urban land markets has not been addressed in such studies. Further, the selection of meaningful variables is commonly conducted under non-spatialized modelling conditions. The objective of this paper is to construct a land value map using a geostatistical approach using Space Syntax and a spatialized variable selection. The methodology is applied in Guatemala City. We used an existing dataset of residential land value appraisals and accessibility metrics. Regression-kriging was used to conduct variable selection and derive a model for spatial prediction. The prediction accuracy is compared with a multivariate regression. The results show that a spatialized variable selection yields a more parsimonious model with higher prediction accuracy. New insights were found on how Space Syntax explains land value variability when also modelling the spatial dependence. Space Syntax can contribute with relevant spatialized information for predictive land value modelling purposes. Finally, the spatial modelling framework facilitates the production of spatial information of land values that is relevant for planning practice.