California Reservoir Drought Sensitivity and Exhaustion Risk Using Statistical Graphical Models

TL;DR

This paper models the complex interactions of California reservoirs during droughts using a novel statistical graphical model, revealing key factors influencing reservoir exhaustion and drought sensitivity.

Contribution

It introduces a latent-variable graphical model for reservoir interactions, enabling reliable analysis of drought impacts and system health in California's water network.

Findings

High correlation between latent factors and PDSI and hydroelectric output

Reservoir exhaustion probability exceeds 50% near PDSI of -6

Model allows assessment of drought sensitivity at individual reservoirs

Abstract

The ongoing California drought has highlighted the potential vulnerability of state water management infrastructure to multi-year dry intervals. Due to the high complexity of the network, dynamic storage changes across the California reservoir system have been difficult to model using either conventional statistical or physical approaches. Here, we analyze the interactions of monthly volumes in a network of 55 large California reservoirs, over a period of 136 months from 2004 to 2015, and we develop a latent-variable graphical model of their joint fluctuations. We achieve reliable and tractable modeling of the system because the model structure allows unique recovery of the best-in-class model via convex optimization with control of the number of free parameters. We extract a statewide `latent' influencing factor which turns out to 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 as exhaustion probability and per-reservoir drought sensitivity. We find that as PDSI approaches -6, there is a probability greater than 50\% of simultaneous exhaustion of multiple large reservoirs.