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The ongoing California drought has highlighted the potential vulnerability ofstate water management infrastructure to multi-year dry intervals. Due to thehigh complexity of the network, dynamic storage changes across the Californiareservoir system have been difficult to model using either conventionalstatistical or physical approaches. Here, we analyze the interactions ofmonthly volumes in a network of 55 large California reservoirs, over a periodof 136 months from 2004 to 2015, and we develop a latent-variable graphicalmodel of their joint fluctuations. We achieve reliable and tractable modelingof the system because the model structure allows unique recovery of thebest-in-class model via convex optimization with control of the number of freeparameters. We extract a statewide `latent' influencing factor which turns outto be highly correlated with both the Palmer Drought Severity Index (PDSI,$\rho \approx 0.86$) and hydroelectric production ($\rho \approx 0.71$).Further, the model allows us to determine system health measures such asexhaustion probability and per-reservoir drought sensitivity. We find that asPDSI approaches -6, there is a probability greater than 50\% of simultaneousexhaustion of multiple large reservoirs.

California Reservoir Drought Sensitivity and Exhaustion Risk Using Statistical Graphical Models