RT journal article
T1 Modeling River Runoff Temporal Behavior through a Hybrid Causal-Hydrological (HCH) Method
A1 Zazo, Santiago
A1 Molina, José-Luis
A1 Ruiz Ortiz, Verónica
A1 Vélez Nicolás, María de las Mercedes
A1 García López, Santiago
A2 Ciencias de la Tierra
A2 Ingeniería Industrial e Ingeniería Civil
K1 Bayesian causal modeling
K1 HCH method; hydrological modeling
K1 deterministic and stochastic modeling
K1 modeling; rainfall&#8211
K1 runoff modeling
K1 temporal dependence
K1 basin memory
AB The uncertainty in traditional hydrological modeling is a challenge that has not yet been overcome. This research aimed to provide a new method called the hybrid causal-hydrological (HCH) method, which consists of the combination of traditional rainfall-runoff models with novel hydrological approaches based on artificial intelligence, called Bayesian causal modeling (BCM). This was implemented by building nine causal models for three sub-basins of the Barbate River Basin (SW Spain). The models were populated by gauging (observing) short runoff series and from long and short hydrological runoff series obtained from the Temez rainfall-runoff model (T-RRM). To enrich the data, all series were synthetically replicated using an ARMA model. Regarding the results, on the one hand differences in the dependence intensities between the long and short series were displayed in the dependence mitigation graphs (DMGs), which were attributable to the insufficient amount of data available from the hydrological records and to climate change processes. The similarities in the temporal dependence propagation (basin memory) and in the symmetry of DMGs validate the reliability of the hybrid methodology, as well as the results generated in this study. Consequently, water planning and management can be substantially improved with this approach.
PB MDPI
SN 2073-4441
YR 2020
FD 2020-11
LK http://hdl.handle.net/10498/24182
UL http://hdl.handle.net/10498/24182
LA eng
DS Repositorio Institucional de la Universidad de Cádiz
RD 05-nov-2026