Markov-Based Modelling for Reservoir Management: Assessing Reliability and Resilience

TL;DR

This paper introduces a Markov-based framework for modeling reservoir behavior, assessing reliability and resilience, and applies it to a real-world dam case study to support sustainable management under uncertainty.

Contribution

It develops a novel Markovian modeling approach for reservoir performance, including finite and infinite capacity cases, with applications to real-world dam management.

Findings

Derived stationary water balance relations and asymptotic results.

Established normal limit distributions for reservoir storage levels.

Demonstrated model applicability through a case study of Quiebrajano dam.

Abstract

This paper develops a comprehensive Markov-based framework for modelling reservoir behaviour and assessing key performance measures such as reliability and resilience. We first formulate a stochastic model for a finite-capacity dam, analysing its long-term storage dynamics under both independent and identically distributed inflows, following the Moran model, and correlated inflows represented by an ergodic Markov chain in the Lloyd formulation. For this finite case, we establish stationary water balance relations and derive asymptotic results, including a central limit theorem for storage levels. The analysis is then extended to an infinite-capacity reservoir, for which normal limit distributions and analogous long-term properties are obtained. A continuous-state formulation is also introduced to represent reservoirs with continuous inflow processes, generalizing the discrete-state framework. On this basis, we define and evaluate reliability and resilience metrics within the proposed Markovian context. The applicability of the methodology is demonstrated through a real-world case study of the Quiebrajano dam, illustrating how the developed models can support efficient and sustainable reservoir management under hydrological uncertainty.