Skip to main content

Research Repository

Advanced Search

Predicting spatial distribution of stable isotopes in precipitation by classical geostatistical- and machine learning methods

Erdélyi, Dániel; Hatvani, István Gábor; Jeon, Hyeongseon; Jones, Matthew; Tyler, Jonathan; Kern, Zoltán

Predicting spatial distribution of stable isotopes in precipitation by classical geostatistical- and machine learning methods Thumbnail


Dániel Erdélyi

István Gábor Hatvani

Hyeongseon Jeon

Professor of Quaternary Science

Jonathan Tyler

Zoltán Kern


Stable isotopes of precipitation are important natural tracers in hydrology, ecology, and forensics. The spatially explicit predictions of oxygen and hydrogen isotopes in precipitation are obtained through different interpolation techniques. In the present study we aim to examine the performance of various interpolation techniques when predicting the spatial distribution of precipitation stable isotopes. The efficiency of combined geostatistical tools (i.e. regression kriging; RK) and various machine learning methods (including regression enhanced random forest methods: MRRF, RERF) are compared in interpolating the spatial variability of precipitation stable oxygen isotope values from two different sampling networks in Europe. To assess the performance of the models, mean squared error (MSE), nonparametric Kling Gupta efficiency (KGE), absolute differences and relative mean absolute error metrics were employed. It was found that the combination of the different regression techniques with Random Forest can produce estimations with comparable accuracy in terms of descending order of overall average MSE, MRRF: 2.61, RK: 2.77, RERF: 2.99, RF: 3.08. The best performing combined random forest model variant (MRRF) outperformed regression kriging in terms of a hybrid error metric (KGE) by 7.5%. Sequential random rarefying the station networks showed that machine-learning methods are more capable of maintaining high prediction accuracy even with fewer input data. This can be a great advantage when a suitable method is needed to predict the stable isotope composition of precipitation for large spatial domains where the spatial density of data stations shows large differences.

Journal Article Type Article
Acceptance Date Jan 12, 2023
Online Publication Date Jan 16, 2023
Publication Date Feb 1, 2023
Deposit Date Jan 30, 2023
Publicly Available Date Feb 2, 2023
Journal Journal of Hydrology
Print ISSN 0022-1694
Publisher Elsevier BV
Peer Reviewed Peer Reviewed
Volume 617
Issue Part C
Article Number 129129
Keywords Water Science and Technology
Public URL
Publisher URL


You might also like

Downloadable Citations