Increasingly variable hydrologic regimes combined with more frequent and intense extreme events are challenging water systems management worldwide. These trends emphasize the need of accurate medium‐ to long‐term predictions to timely prompt anticipatory operations. Despite in some locations global climate oscillations and particularly the El Niño Southern Oscillation (ENSO) may contribute to extending forecast lead times, in other regions there is no consensus on how ENSO can be detected, and used as local conditions are also influenced by other concurrent climate signals. In this work, we introduce the Climate State Intelligence framework to capture the state of multiple global climate signals via artificial intelligence and improve seasonal forecasts. These forecasts are used as additional inputs for informing water system operations and their value is quantified as the corresponding gain in system performance.

In the Report "Self-organization of river channels as a critical filter on climate signals," Figs. 2 and 4 were affected by an error in the treatment of the equation within the Python programming environment; specifically, the exponent 3/2 was mistakenly computed as integer division (3/2 1) instead of float division (3.0/2.0 1.5). Because the integral is mostly dependent on time and not shear velocity, the differences in the corrected figures are slight, and the conclusions of the paper are not affected. The HTML and PDF versions of the paper have been corrected, as have the supplementary materials (figs.

Spatial and temporal variations in rainfall are hypothesized to influence landscape evolution through erosion and sediment transport by rivers. However, determining the relation between rainfall and river dynamics requires a greater understanding of the feedbacks between flooding and a river's capacity to transport sediment. We analyzed channel geometry and stream-flow records from 186 coarse-grained rivers across the United States. We found that channels adjust their shape so that floods slightly exceed the critical shear velocity needed to transport bed sediment, independently of climatic, tectonic, and bedrock controls. The distribution of fluid shear velocity associated with floods is universal, indicating that self-organization of near-critical channels filters the climate signal evident in discharge.