Potter et al. (2026) Including rainfall sequencing and autocorrelation adjustment as a component of bias correction for hydroclimate projections

Identification

• Journal: Environmental Modelling & Software
• Year: 2026
• Date: 2026-07-23
• Authors: Nicholas J. Potter, D.E. Robertson, Anjana Devanand, Francis H. S. Chiew
• DOI: 10.1016/j.envsoft.2026.107121

Research Groups

• CSIRO Environment (Canberra, ACT)
• CSIRO Environment (Clayton, VIC)

Short Summary

This paper presents a new bias correction method called RhoQM that corrects rainfall sequencing and autocorrelation in hydroclimate projections. The method is developed to address the limitations of standard quantile-quantile mapping methods, which can underestimate mean streamflow and high streamflow events due to the lack of consideration of rainfall sequencing.

Objective

• To develop a bias correction method for hydroclimate projections that corrects rainfall sequencing and autocorrelation.
• To evaluate the performance of RhoQM on historical and future rainfall data from regional climate models (RCMs) in south-eastern Australia.

Study Configuration

• Spatial Scale: Regional scale, focusing on south-eastern Australia.
• Temporal Scale: Daily to annual time scales.

Methodology and Data

• Models used: RhoQM bias correction method, which combines empirical quantile mapping with autocorrelation correction via Cholesky decomposition of the fitted autocovariance matrix.
• Data sources: CORDEX-CMIP6 downscaled climate projections ensemble, SILO (Scientific Information for Land Owners) rainfall dataset, and reanalysis data.

Main Results

• RhoQM reduces residual bias in rainfall metrics compared to standard quantile-quantile mapping methods.
• The method is particularly effective in correcting biases in mean annual rainfall, 95th and 99th percentile of daily rainfall amounts, and multi-day accumulation maxima.
• The results also show that the direction of bias (under- or overprediction) is primarily determined by the RCM used rather than the host GCM.

Contributions

• This paper contributes to the development of a new bias correction method for hydroclimate projections that corrects rainfall sequencing and autocorrelation.
• The results demonstrate the effectiveness of RhoQM in reducing residual bias in rainfall metrics, which can improve the accuracy of hydroclimate projections.

Funding

• This research was funded by the Australian Government's National Environmental Science Programme (NESP) through the Climate Adaptation Flagship.

Citation

@article{Potter2026Including,
  author = {Potter, Nicholas J. and Robertson, D.E. and Devanand, Anjana and Chiew, Francis H. S.},
  title = {Including rainfall sequencing and autocorrelation adjustment as a component of bias correction for hydroclimate projections},
  journal = {Environmental Modelling & Software},
  year = {2026},
  doi = {10.1016/j.envsoft.2026.107121},
  url = {https://doi.org/10.1016/j.envsoft.2026.107121}
}