@misc{10481/110743,
year = {2025},
url = {https://hdl.handle.net/10481/110743},
abstract = {This paper proposes a new statistical framework for modelling precipitation patterns in a specific geographical region using Hidden Markov Models (HMMs).
Unlike conventional HMMs, where the hidden-state process is Markovian, our
approach introduces non-Markovian behaviour by incorporating phase-type distributions to model state durations. This extension allows for a more realistic
representation of alternating dry and wet periods, providing deeper insight into
the temporal structure of the local climate. Building on this framework, we model
reservoir inflow patterns linked to the hidden states and explain observed water
storage levels through a Moran model. The analysis uses historical rainfall and
inflow records, where inflows are influenced by latent climatic conditions rather
than rainfall alone. By integrating these unobserved dynamics, the proposed
model captures system complexity that would be missed by direct observationbased modelling. To evaluate performance, we compare our model against several
standard ARIMA alternatives. The proposed PH-HMM achieves a substantially
lower AIC (204.38) than the best ARIMA specification (211.91) and reduces
mean squared error from 185.74 to 183.10. These improvements demonstrate
that explicitly modelling regime dynamics yields more accurate and parsimonious
representations of inflow processes. Overall, the methodology enhances characterization of underlying climatic regimes and improves inflow modelling, offering
a practical tool for water-resource management and climate-adaptation planning.},
organization = {Spanish Ministry of Science and Innovation- State Research Agency (PID2024-156234NB-C22)},
organization = {IMAG (CEX2020-001105-M / AEI /10.13039/501100011033)},
keywords = {Environmental modelling},
keywords = {Hidden Markov model},
keywords = {Phase-type distributions},
title = {Modeling stochastic inflow patterns to a reservoir with hidden phase-type Markov model},
author = {Gámiz Pérez, María Luz and Montoro Cazorla, Delia and Segovia García, María del Carmen},
}