Integrated river basin assessment framework combining probabilistic streamflow reconstruction, Bayesian bias correction, and drought storyline analysis

Summary. Assessing drought risk in institutionally complex river basins requires both a realistic representation of water management operations and streamflow records that capture the most severe historical droughts. In many basins, the drought of record pre-dates the hydrologic model datasets and much of the gauge network, and headwater catchments are ungauged.

This paper presents an integrated assessment framework that addresses these gaps by combining:

1. Probabilistic streamflow reconstruction — a flow-duration-curve based prediction in ungauged basins (PUB) method that leverages the USGS National Hydrologic Model and the available gauge record to produce an ensemble of 1,000 daily streamflow realizations at all model nodes for 1945–2023;
2. Bayesian bias correction of the reconstructed flows; and
3. Drought storyline analysis using the Pywr-DRB water management model to stress-test the modern Delaware River Basin’s operating policies against a reconstruction of the 1960s drought of record.

Results reveal significant vulnerabilities in New York City’s drinking water supply under 1960s-like conditions, and tensions between supporting downstream flow targets and controlling saltwater intrusion risks to Philadelphia’s water supply.

• Paper: https://doi.org/10.1016/j.envsoft.2025.106756 (published online October 2025)
• Data release (reconstruction ensemble and simulation outputs): Zenodo, doi:10.5281/zenodo.15101164
• Tutorial for running the ensemble with Pywr-DRB: Pywr-DRB-Reconstruction-Ensemble-Tutorial and the accompanying blog post

Recommended citation: Amestoy, T. J., Hamilton, A. L., & Reed, P. M. (2026). Integrated river basin assessment framework combining probabilistic streamflow reconstruction, Bayesian bias correction, and drought storyline analysis. Environmental Modelling & Software, 195, 106756. https://doi.org/10.1016/j.envsoft.2025.106756