Triple Collocation Based Multi-Source Precipitation Merging

Jianzhi Dong,Fangni Lei,Lingna Wei

doi:10.3389/frwa.2020.00001

Abstract

Multi-source precipitation merging has been used for improving global precipitation estimation accuracy. However, current merging techniques heavily rely on gauge-based precipitation and/or streamflow observations, which may contain substantial uncertainties over data-poor regions. This study provides a triple collocation (TC) based framework for merging multi-source precipitation products without the access of high-quality ground observations. In this framework, the error variances of the precipitation products are statistically estimated using TC, which are further employed in parameterizing a least-square-based precipitation merging framework. As validated against high-quality gauge observations collected over Europe, we demonstrate that TC can accurately estimate the relative errors of different precipitation products, which leads to robust multi-source precipitation merging. Results also demonstrate that the TC merged product significantly outperforms the parent products, which is noteworthy - given the strong skills of the reanalyzed (ERA-Interim) precipitation product over Europe. Since TC analysis does not rely on high-quality gauge observations, the proposed TC-based merging framework can be applied globally, and is expected to significantly contribute precipitation data merging over data-poor regions, e.g., Africa and South America.

Highlights

Precipitation is the key driving force of global hydrological cycle (Eltahir and Bras, 1996)
The robustness and the accuracy of triple collocation (TC) in precipitation error analysis has been verified using high-density precipitation networks (e.g., Massari et al, 2017; Li et al, 2018; Dong et al, 2019b). These findings suggest that TC is ideal for precipitation merging— given accurate precipitation errors can be estimated globally without any access of high-quality in-situ observations
Previous studies demonstrate that soil moisture inverted, remotely sensed and model estimated precipitation products contain relatively independent precipitation errors, and can be used for TC analysis (Massari et al, 2017; Dong et al, 2019b)

Summary

Introduction

Precipitation is the key driving force of global hydrological cycle (Eltahir and Bras, 1996). Sensed (RS) and reanalyzed global precipitation products are increasing available (e.g., Huffman et al, 1997; Dee et al, 2011; Ashouri et al, 2015; Funk et al, 2015), which may improve hydrological modeling accuracy over data-poor regions. The optimal merge of different precipitation products requires their error information, e.g., highly accurate products should receive larger weights during merging, and vice versa. The relative weights of different precipitation products are typically calculated using rain gauges and spatially interpolated to unobserved locations (e.g., Shrestha et al, 2011; Funk et al, 2015; Golian et al, 2015; Beck et al, 2019).

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Conclusion