The research, led by Qiang Li and Tongtiegang Zhao of Sun Yat-Sen University together with colleagues at The Hong Kong University of Science and Technology, examined the Global Flood Awareness System driven by the European Centre for Medium-Range Weather Forecasts Reanalysis v5, known as GloFAS-ERA5. The team evaluated the reanalysis against observations from 671 catchments in the contiguous United States drawn from the Catchment Attributes and Meteorology for Large-sample Studies dataset, a collection of minimally disturbed basins spanning an enormous range of hydroclimatic conditions. For each catchment and each hydrological year, running from 1 October to 30 September, the researchers calculated root mean square errors for streamflow, precipitation and temperature, and then used both catchment-specific linear regression and global panel regression to trace how the errors propagate.

The first headline finding is encouraging: the latest version of the global reanalysis is measurably better than its predecessor. As GloFAS-ERA5 was upgraded from version 2.1 to version 4.0, with the spatial resolution improving from 0.1 to 0.05 degrees and the LISFLOOD model now simulating the full rainfall-runoff process internally rather than relying on pre-computed runoff fields, the median streamflow error across the 671 catchments fell from 2.16 to 1.81 millimetres. The correlation between streamflow and precipitation errors also strengthened, rising from 0.27 to 0.45, underscoring that precipitation accuracy has become an even more decisive factor in the quality of the river flow reconstructions.

That tighter coupling comes with a striking average sensitivity. The panel regression, which pools information across all catchments and years, estimated that for GloFAS-ERA5 v4.0, each 1 millimetre increase in precipitation error is associated with an average increase of 0.51 millimetres in streamflow error. Statistical tests confirmed that this average effect is highly significant, and the random-effects framework the authors selected was validated against alternatives using Breusch-Pagan and Hausman diagnostics. Yet the authors are careful to stress that 0.51 is a statistical average across hundreds of basins, not a universal physical constant, and the catchment-by-catchment analysis reveals just how wide the spread around that average really is.

In humid catchments, defined by an aridity index below 2, the local response of streamflow error to precipitation error climbed as high as 2.5 millimetres for every millimetre of precipitation error, a fivefold amplification of the global mean. In arid catchments, by contrast, the response stayed below 0.7 millimetres. The explanation lies in the physics of runoff generation. When soils are wet and close to saturation, additional rainfall translates almost directly into additional runoff, so any mistake in the precipitation input is faithfully, and often disproportionately, echoed in the simulated river flow. When soils are dry, a large share of the rainfall infiltrates and replenishes storage deficits before runoff thresholds are ever crossed, effectively buffering the streamflow against precipitation errors.

The authors found direct evidence for this buffering mechanism. Across the catchments, the strength of the precipitation error response correlated negatively with the aridity index and precipitation seasonality, and positively with mean precipitation. A supplementary analysis showed that the correlation between streamflow error and a soil wetness index was statistically significant in 473 catchments, with a median coefficient of 0.52, consistent with the idea that antecedent soil moisture governs how much of a precipitation error survives the journey to the river channel. Temperature errors, meanwhile, played almost no consistent direct role in rain-dominated regions, with the pooled correlation between streamflow and temperature errors hovering near zero.

Snow changes the picture entirely. When the researchers added interaction terms between the climate errors and catchment attributes such as precipitation seasonality and the fraction of precipitation falling as snow, the explanatory power of the panel regression jumped, with the coefficient of determination rising from 0.16 to 0.36. The marginal effects revealed a systematic shift: in snow-dominated, high-latitude or mountainous catchments, temperature errors became significant drivers of streamflow errors, because temperature controls when snow accumulates and when it melts.

Two targeted case studies crystallised the contrast. In a rain-dominated catchment, the streamflow error responded almost synchronously to precipitation error, with a significant regression coefficient of 0.69 and no significant temperature effect; the year with the largest precipitation error, at 5.15 millimetres, also produced the largest streamflow error, at 1.41 millimetres. In a snow-dominated catchment, both precipitation and temperature errors mattered, with significant coefficients of 0.43 and 0.37 respectively. Underestimated winter precipitation left the simulated snowpack too thin, while underestimated spring temperatures delayed and reduced the simulated melt, so the resulting streamflow deficits appeared months after the original precipitation errors were made. Storage in the snowpack had effectively dampened and delayed the error propagation.

The practical implications reach well beyond the United States. GloFAS-ERA5 underpins flood awareness, water resource assessment, hydrological model calibration and machine learning applications worldwide, and previous evaluations found it more skilful than a mean-flow benchmark in 86 percent of 1801 global catchments. This study offers those users a diagnostic framework: knowing whether a basin is saturation-excess dominated, moisture-limited or snow-controlled tells you which input errors to worry about most. It also flags where improvements in precipitation reanalysis will pay the biggest dividends, namely in wet regions where errors amplify, and where temperature accuracy matters most, in snowmelt-fed rivers.

The authors also acknowledge the limits of their approach. Annual error metrics integrate storage and lag effects but obscure seasonal and event-scale contrasts, and the reference precipitation from the Daymet dataset carries its own interpolation uncertainties. Even so, by combining large-sample statistics with mechanistic case studies, the work delivers a clear message: there is no single global rule for how rainfall mistakes become river mistakes. The landscape itself decides, and understanding that modulation is essential for anyone trusting global streamflow reanalysis in complex, heterogeneous terrain.

Subject of Research: Propagation of precipitation reanalysis errors into streamflow reanalysis errors across heterogeneous catchments

Article Title: Divergent responses of streamflow reanalysis errors to precipitation reanalysis errors modulated by catchment heterogeneity

Article References: Li, Q., Zhao, T., Chen, Z., & Huang, Z. (2026). Divergent responses of streamflow reanalysis errors to precipitation reanalysis errors modulated by catchment heterogeneity. Hydrology and Earth System Sciences, 30(19), 6115-6129. https://doi.org/10.5194/hess-30-6115-2026

Image Credits: AI Generated

DOI: 10.5194/hess-30-6115-2026

Keywords: streamflow reanalysis, precipitation errors, GloFAS-ERA5, catchment heterogeneity, error propagation, hydrology, snowmelt, soil moisture, panel regression, CAMELS, flood forecasting, ERA5