A century and a half precipitation oxygen isoscape for China generated using data fusion and bias correction

The precipitation oxygen isotopic composition is a useful environmental tracer for climatic and hydrological studies. However, accurate and high-resolution precipitation oxygen isoscapes are currently lacking in China. In this study, a precipitation oxygen isoscape in China for a period of 148 years is built by integrating observed and iGCMs-simulated isotope compositions using an optimal hybrid approach of three data fusion and two bias correction methods. The temporal and spatial resolutions of the isoscape are monthly and 50-60 km, respectively. Results show that the Convolutional Neural Networks (CNN) fusion method performs the best (correlation coefficient larger than 0.95 and root mean square error smaller than 1 parts per thousand), and the other two data fusion methods perform slightly better than the bias correction methods. Thus, the isoscape is generated by using the CNN fusion method for the common 1969-2007 period and by using the bias correction methods for remaining years. The generated isoscape, which shows similar spatio-temporal distributions to observations, is reliable and useful for providing strong support for tracking atmospheric and hydrological processes.

Automated summary

In this study, a 148-year precipitation oxygen isoscape in China was built by integrating observed and iGCMs-simulated isotope compositions. The Convolutional Neural Networks (CNN) fusion method performed the best among the three data fusion methods, and the generated isoscape showed reliable spatio-temporal distributions similar to observations. The isoscape can provide strong support for tracking atmospheric and hydrological processes.