Anderson et al. (2026) Reliance on daily mean streamflow data biases inferred flood seasonality

⚠️ Warning: This summary was generated from the abstract only, as the full text was not available.

Identification

• Journal: Environmental Research Letters
• Year: 2026
• Date: 2026-07-22
• Authors: Bailey Anderson, Paul C. Astagneau, Sebastian Gnann, Manuela Brunner
• DOI: 10.1088/1748-9326/ae8e90

Research Groups

Not specified in the provided text.

Short Summary

This study evaluates how the temporal resolution of streamflow data (daily mean versus daily maximum derived from hourly data) affects the characterization of flood seasonality and the identification of extreme flood events.

Objective

• To determine the extent to which data aggregation methods influence the inferred flood seasonality, the modality of flood regimes, and the identification of the largest historical flood events.

Study Configuration

• Spatial Scale: Not explicitly specified, but involves various catchment types (e.g., snow-influenced, summer-storm influenced).
• Temporal Scale: Historical records using hourly, daily mean, and daily maximum resolutions.

Methodology and Data

• Models used: Regime-based framework, truncated Fourier series (to quantify seasonality and modality), and clustering analysis (six groups).
• Data sources: Hourly streamflow data (aggregated into daily mean and daily maximum time series).

Main Results

• Significant differences in flood seasonality between daily mean and daily maximum data were found in bi-modal regimes (influenced by both snowmelt and rainfall) and uni-modal regimes driven by summer storms.
• Minimal differences between resolutions were observed in catchments with strong winter flood seasons or those heavily influenced by snow.
• The identification of the largest magnitude flood events in historical records is highly sensitive to the data aggregation approach.

Contributions

• Highlights the critical importance of sub-daily resolution streamflow data for the accurate characterization of flood seasonality.
• Provides a basis for identifying where high-resolution data are essential for studying flood processes and predicting shifts in hazard types due to climate change.

Funding

Not specified in the provided text.

Citation

@article{Anderson2026Reliance,
  author = {Anderson, Bailey and Astagneau, Paul C. and Gnann, Sebastian and Brunner, Manuela},
  title = {Reliance on daily mean streamflow data biases inferred flood seasonality},
  journal = {Environmental Research Letters},
  year = {2026},
  doi = {10.1088/1748-9326/ae8e90},
  url = {https://doi.org/10.1088/1748-9326/ae8e90}
}