Preprints
https://doi.org/10.5194/hess-2024-83
https://doi.org/10.5194/hess-2024-83
20 Mar 2024
 | 20 Mar 2024
Status: this preprint is currently under review for the journal HESS.

A decomposition approach to evaluating the local performance of global streamflow reanalysis

Tongtiegang Zhao, Zexin Chen, Yu Tian, and Xiaohong Chen

Abstract. While global streamflow reanalysis provides valuable information for water resources management, its local performance in the time-frequency domain is yet to be investigated. This paper presents a novel decomposition approach to evaluating streamflow reanalysis by combining wavelet transform with machine learning. Specifically, the time series of streamflow reanalysis and observation are respectively decomposed and then the approximation components of reanalysis are compared to those of observed streamflow. Furthermore, the accumulated local effects are derived to showcase the influences of catchment attributes on the performance of raw reanalysis at different scales. For streamflow reanalysis generated by the Global Flood Awareness System, a case study is devised based on streamflow observations from the Catchment Attributes and Meteorology for Large-sample Studies. The results highlight that the reanalysis tends to be more effective in characterizing seasonal, annual and multi-annual features than daily, weekly and monthly features. The Kling-Gupta Efficiency (KGE) values of raw reanalysis and approximation components are primarily influenced by precipitation seasonality. That is, high values of KGE tend to be observed in catchments where there is more precipitation in winter, which can be due to low evaporation that results in reasonable simulations of soil moisture and baseflow processes. The longitude, mean precipitation and mean slope also influence the local performance of approximation components. On the other hand, attributes on geology, soils and vegetation appear to play a relatively small part in the performance of approximation components. Overall, this paper provides useful information for practical applications of global streamflow reanalysis.

Tongtiegang Zhao, Zexin Chen, Yu Tian, and Xiaohong Chen

Status: open (until 16 May 2024)

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Tongtiegang Zhao, Zexin Chen, Yu Tian, and Xiaohong Chen
Tongtiegang Zhao, Zexin Chen, Yu Tian, and Xiaohong Chen

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Short summary
The local performance plays a critical part in practical applications of global streamflow reanalysis. This paper develops a decomposition approach to facilitating evaluations at different timescales. It is found that the reanalysis tends to be more effective in characterizing seasonal, annual and multi-annual features than daily, weekly and monthly features. The local performance is shown to be primarily influenced by precipitation seasonality, longitude, mean precipitation and mean slope.