Preprints
https://doi.org/10.5194/essd-2026-316
https://doi.org/10.5194/essd-2026-316
24 Jul 2026
 | 24 Jul 2026
Status: this preprint is currently under review for the journal ESSD.

GSIM-PLUS: A Gap-Filled Global Monthly Streamflow Dataset for 1995–2015

Mingrui Chen, Bin Yong, Xinzhi Xu, Weihong Liao, Haichen Li, Jinxuan Zhang, jinyu Xiao, Wei Dai, and Jia Wang

Abstract. Global monthly streamflow observations are fundamental for understanding changes in the water cycle, supporting large-sample hydrology, and informing water-resources assessments. However, currently available open station archives still suffer from substantial limitations in temporal continuity and spatial coverage. Here we present GSIM-PLUS, a gap-filled global monthly streamflow dataset for 19952015 designed to improve the completeness and reusability of global runoff records. Using the GSIM monthly archive as the basis, we identified 7,323 high-completeness anchor stations and 8,731 target stations from 30,959 gauges. Basin descriptors from five groups climate, topography, soil, spatial location, and hydrology were used to identify the most similar donor stations for each target site. Donor-Trend Recursive Regression (DTRR) was adopted as the default imputation method, with a guarded fallback to baseline MAML for a limited subset of very-low-flow stations in order to improve production stability under long recursive gaps. Multi-scenario validation shows that DTRR achieved the best overall performance under random 30 % masking (NSE = 0.865; KGE = 0.920) and remained robust for both 12-month continuous gaps (NSE = 0.795) and very long gaps exceeding 25 months (NSE = 0.511). Independent validation using 16 GRDC stations across six regions further confirmed good transferability, while indicating that temporal agreement was generally more robust than exact magnitude reconstruction under donor-limited or long-gap conditions. Under the guarded DTRR production scheme, GSIM-PLUS fills 303,271 missing monthly records for 16,054 stations, increasing the median completeness of target stations from 66.3 % to 81.0 % and that of the full dataset from 86.9 % to 95.2 %. Each released record is accompanied by quality and context metadata, including reconstruction class, gap length, fill method, and basin-context flags. GSIM-PLUS provides a more continuous and traceable global monthly streamflow resource for regional hydrological analysis, large-sample studies, model evaluation, and related monthly-scale applications. The GSIM-PLUS dataset is publicly available through Zenodo at https://doi.org/10.5281/zenodo.21425702.

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Mingrui Chen, Bin Yong, Xinzhi Xu, Weihong Liao, Haichen Li, Jinxuan Zhang, jinyu Xiao, Wei Dai, and Jia Wang

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Mingrui Chen, Bin Yong, Xinzhi Xu, Weihong Liao, Haichen Li, Jinxuan Zhang, jinyu Xiao, Wei Dai, and Jia Wang

Data sets

GSIM-PLUS-v3 Mingrui Chen https://doi.org/10.5281/zenodo.21425702

Model code and software

GSIM-PLUS Mingrui Chen https://github.com/skyhhu2024-create/GSIM-PLUS

Mingrui Chen, Bin Yong, Xinzhi Xu, Weihong Liao, Haichen Li, Jinxuan Zhang, jinyu Xiao, Wei Dai, and Jia Wang

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Short summary
We created a new global record of monthly river flow for 1995 to 2015 by filling gaps in existing station observations. To do this, we matched rivers with similar climate and landscape conditions and used those matches to estimate missing values. Tests showed that the new dataset greatly improves record completeness and makes long-term river data more useful for comparing regions, testing models, and studying water-related change.
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