This study introduces a hybrid data assimilation scheme for precise streamflow predictions during intense rainfall and hurricanes. Tested in real events, it outperforms traditional methods by up to 50 %, utilizing ensemble and climatological background covariances. The adaptive algorithm ensures reliability with a small ensemble, offering improved forecasts up to 18 h in advance, marking a significant advancement in flood prediction capabilities.

Hydrologic models developed to assess water availability need to be systematically evaluated. This study evaluates the long-term performance of two high-resolution hydrologic models that simulate streamflow across the contiguous United States. Both models show similar performance overall and regionally, with better performance in minimally disturbed basins than in those impacted by human activity. At about 80 % of the sites, both models outperform the seasonal climatological benchmark.

Measurements of channel characteristics are important for accurate forecasting in the NOAA National Water Model (NWM) but are scarcely available. We seek to improve channel representativeness in the NWM by updating channel geometry and roughness parameters using a large, previously unpublished, dataset of approximately 48 000 gauges. We find that the updated channel parameterization from this new dataset leads to improvements in simulated streamflow performance and channel representation.

The article introduces novel ensemble data assimilation (DA) techniques for streamflow forecasting using WRF-Hydro and DART. Model-related biases are tackled through spatially and temporally varying adaptive prior and posterior inflation. Spurious and physically incorrect correlations, on the other hand, are mitigated using a topologically based along-the-stream localization. Hurricane Florence (2018) in the Carolinas, USA, is used as a test case to investigate the performance of DA techniques.

Drought is a function of both natural and human influences, but fully characterizing the interactions between human and natural influences on drought remains challenging. To better characterize the drought feedback loop, this study combines hydrological and societal perspectives to characterize the potential for drought action. We discuss how the results can be used to reduce potential disagreement among stakeholders and promote sustainable water management.