Error Characteristics and Scale Dependence of Current Satellite Precipitation Estimates Products in Hydrological Modeling

Abstract

Satellite precipitation estimates (SPEs) are promising alternatives to gauge observations for hydrological applications (e.g., streamflow simulation), especially in remote areas with sparse observation networks. However, the existing SPEs products are still biased due to imperfections in retrieval algorithms, data sources and post-processing, which makes the effective use of SPEs a challenge, especially at different spatial and temporal scales. In this study, we used a distributed hydrological model to evaluate the simulated discharge from eight quasi-global SPEs at different spatial scales and explored their potential scale effects of SPEs on a cascade of basins ranging from approximately 100 to 130,000 km2. The results indicate that, regardless of the difference in the accuracy of various SPEs, there is indeed a scale effect in their application in discharge simulation. Specifically, when the catchment area is larger than 20,000 km2, the overall performance of discharge simulation emerges an ascending trend with the increase of catchment area due to the river routing and spatial averaging. Whereas below 20,000 km2, the discharge simulation capability of the SPEs is more randomized and relies heavily on local precipitation accuracy. Our study also highlights the need to evaluate SPEs or other precipitation products (e.g., merge product or reanalysis data) not only at the limited station scale, but also at a finer scale depending on the practical application requirements. Here we have verified that the existing SPEs are scale-dependent in hydrological simulation, and they are not enough to be directly used in very fine scale distributed hydrological simulations (e.g., flash flood). More advanced retrieval algorithms, data sources and bias correction methods are needed to further improve the overall quality of SPEs.

QRF4P‐NRT: Probabilistic Post‐Processing of Near‐Real‐Time Satellite Precipitation Estimates Using Quantile Regression Forests

“…And IMERG‐F is selected as a reference for verifying the model performance of the proposed post‐processing model. The quality of IMERG products has also been confirmed in our previous studies by comparing them with other satellite precipitation estimates products (Y. Zhang et al., 2021a, 2021b). The IMERG‐E and IMERG‐F data selected in this study are the latest IMERG version 6 products.…”

“…The quality of IMERG products has also been confirmed in our previous studies by comparing them with other satellite precipitation estimates products (Y. Zhang et al, 2021aZhang et al, , 2021b. The IMERG-E and IMERG-F data selected in this study are the latest IMERG version 6 products.…”

“…Since the Tropical Rainfall Measuring Mission project, precipitation estimates based on satellite retrieval have been considered as a promising tool for large‐scale precipitation measurements (Hou et al., 2013; Huffman et al., 2007). Multiple satellite precipitation estimates products are available (Sun et al., 2018; Y. Zhang et al., 2021a). Depending on whether they are bias‐corrected and the record length of the data, they can be broadly classified into three categories: real‐time or near‐real‐time products (RTs or NRTs), bias‐corrected products (BCs), and climate data records (Y. Zhang et al., 2021a).…”

“…Multiple satellite precipitation estimates products are available (Sun et al., 2018; Y. Zhang et al., 2021a). Depending on whether they are bias‐corrected and the record length of the data, they can be broadly classified into three categories: real‐time or near‐real‐time products (RTs or NRTs), bias‐corrected products (BCs), and climate data records (Y. Zhang et al., 2021a). Among them, NRTs (or RTs) with very short latency time are the ideal product for operational applications such as flood monitoring and forecasting (Hong et al., 2007; Zhou et al., 2014).…”

“…Since the Tropical Rainfall Measuring Mission project, precipitation estimates based on satellite retrieval have been considered as a promising tool for large-scale precipitation measurements (Hou et al, 2013;Huffman et al, 2007). Multiple satellite precipitation estimates products are available (Sun et al, 2018;Y. Zhang et al, 2021a).…”