Can Artificial Intelligence improve seasonal forecasts and inform reservoir operations? a new paper on WRR
Increasingly variable hydrologic regimes combined with more frequent and intense extreme events call for 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 paper, we propose a novel framework called Climate State Intelligence (CSI), which aims to use artificial intelligence for producing seasonal hydrologic forecasts based on multiple global climate signals and assessing their value on operational decisions. The CSI framework provides an emblematic demonstration for the potential of Artificial Intelligence tools in supporting water management during extreme events. The application to the Lake Como system advances the understanding of hydroclimatic variability in the Alpine region by identifying strong influence of divergent SST patterns dependent on ENSO and NAO phases. Moreover, we find that observed preseason SST anomalies appear more valuable than hydrologic-based seasonal forecasts for informing reservoir operations. More HERE.