Divergent responses of streamflow reanalysis errors to precipitation reanalysis errors modulated by catchment heterogeneity
- 1Sun Yat-sen University (Guangzhou, China)
Abstract
Streamflow reanalysis is vital for water resources management and climate impact assessment; however, the extent to which it is affected by precipitation forcing errors remains poorly understood. Focusing on the Global Flood Awareness System driven by the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (GloFAS-ERA5), this paper details how streamflow reanalysis errors respond to precipitation errors. Specifically, the root mean square errors (RMSEs) are calculated by hydrological year for reanalysis products across 671 catchments in the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset; and by combining catchment-specific linear regression with global panel regression, the effects of precipitation errors on streamflow errors are quantified. The results demonstrate an improved performance from GloFAS-ERA5 v2.1 to v4.0, with the median RMSE decreasing from 2.16 to 1.81 mm. For GloFAS-ERA5 v4.0, the panel regression estimates an average increase of 0.51 mm in streamflow RMSE for each 1 mm increase in precipitation RMSE across the 671 catchments. In the meantime, the corresponding catchment-specific increase of streamflow RMSE reaches up to 2.5 mm in humid catchments but remains below 0.7 mm in arid catchments. These divergent responses suggest that streamflow errors may be more directly associated with precipitation errors under the saturation-excess conditions while soil moisture deficits can dampen their effects. Furthermore, incorporating interaction terms into panel regression increases the coefficient of determination ( R ² ) from 0.16 to 0.36, indicating these responses are modulated by catchment heterogeneity. This modulation is further presented by targeted case studies, indicating that temperature controls the storage and release of snow water, thereby dampening and delaying the responses of streamflow errors to precipitation errors in snow-dominated catchments. These findings provide a valuable diagnostic method and practical guidance for applications of global streamflow reanalysis to complex, heterogeneous catchments.
Versions
Related
- Satellites and Reanalysis Reveal the Dominance of Dynamics over Precipitation in Marine Cold-Air Outbreak Cloud Break-Up
- Environmental heterogeneity shapes divergent assembly processes and network stability of plankton communities along a tropical island coast: Evidence from Hainan Island
- Assessment of Cloud Responses and Effects of Artificial Precipitation Enhancement Using Doppler Dual-Polarization Radar Observations
- Growth responses of Chihuahua pine (Pinus leiophylla) over 128 years of warming and precipitation regime shifts
- Spatiotemporal Heterogeneity in Cascading Snow-Hydrological Responses to Warming and Wetting on the Tibetan Plateau
BibTeX
@article{li2026divergent,
title = {{Divergent responses of streamflow reanalysis errors to precipitation reanalysis errors modulated by catchment heterogeneity}},
author = {Qiang Li and Tongtiegang Zhao and Zexin Chen and Zeqing Huang},
journal = {Hydrology and Earth System Sciences},
year = {2026},
month = sep,
doi = {10.5194/hess-30-6115-2026},
url = {https://hess.copernicus.org/articles/30/6115/2026/},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - Divergent responses of streamflow reanalysis errors to precipitation reanalysis errors modulated by catchment heterogeneity
AU - Qiang Li
AU - Tongtiegang Zhao
AU - Zexin Chen
AU - Zeqing Huang
PY - 2026
DA - 2026/09/30
JO - Hydrology and Earth System Sciences
DO - 10.5194/hess-30-6115-2026
UR - https://hess.copernicus.org/articles/30/6115/2026/
AB - Streamflow reanalysis is vital for water resources management and climate impact assessment; however, the extent to which it is affected by precipitation forcing errors remains poorly understood. Focusing on the Global Flood Awareness System driven by the European Centre for Medium-Range Weather Forecasts Reanalysis v5 (GloFAS-ERA5), this paper details how streamflow reanalysis errors respond to precipitation errors. Specifically, the root mean square errors (RMSEs) are calculated by hydrological year for reanalysis products across 671 catchments in the Catchment Attributes and Meteorology for Large-sample Studies (CAMELS) dataset; and by combining catchment-specific linear regression with global panel regression, the effects of precipitation errors on streamflow errors are quantified. The results demonstrate an improved performance from GloFAS-ERA5 v2.1 to v4.0, with the median RMSE decreasing from 2.16 to 1.81 mm. For GloFAS-ERA5 v4.0, the panel regression estimates an average increase of 0.51 mm in streamflow RMSE for each 1 mm increase in precipitation RMSE across the 671 catchments. In the meantime, the corresponding catchment-specific increase of streamflow RMSE reaches up to 2.5 mm in humid catchments but remains below 0.7 mm in arid catchments. These divergent responses suggest that streamflow errors may be more directly associated with precipitation errors under the saturation-excess conditions while soil moisture deficits can dampen their effects. Furthermore, incorporating interaction terms into panel regression increases the coefficient of determination ( R ² ) from 0.16 to 0.36, indicating these responses are modulated by catchment heterogeneity. This modulation is further presented by targeted case studies, indicating that temperature controls the storage and release of snow water, thereby dampening and delaying the responses of streamflow errors to precipitation errors in snow-dominated catchments. These findings provide a valuable diagnostic method and practical guidance for applications of global streamflow reanalysis to complex, heterogeneous catchments.
SN - 1607-7938
ER -