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Open AccessArticle

Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method

1
IGA Research Group, Higher Polytechnic School of Ávila, University of Salamanca, Av. de los Hornos Caleros, 50, 05003 Ávila, Spain
2
Department of Industrial Engineering and Civil Engineering, University of Cadiz, Campus Bay of Algeciras, Avda. Ramon Puyol s/n, 11202 Algeciras, Spain
3
Department of Earth Sciences, University of Cadiz, Campus Rio San Pedro, s/n, 11510 Puerto Real, Spain
*
Author to whom correspondence should be addressed.
Water 2020, 12(11), 3137; https://doi.org/10.3390/w12113137
Received: 17 September 2020 / Revised: 4 November 2020 / Accepted: 6 November 2020 / Published: 9 November 2020
(This article belongs to the Special Issue Stochastic Modeling in Hydrology)
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 Témez 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. View Full-Text
Keywords: Bayesian causal modeling; HCH method; hydrological modeling; deterministic and stochastic modeling; rainfall–runoff modeling; temporal dependence; basin memory Bayesian causal modeling; HCH method; hydrological modeling; deterministic and stochastic modeling; rainfall–runoff modeling; temporal dependence; basin memory
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MDPI and ACS Style

Zazo, S.; Molina, J.-L.; Ruiz-Ortiz, V.; Vélez-Nicolás, M.; García-López, S. Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method. Water 2020, 12, 3137. https://doi.org/10.3390/w12113137

AMA Style

Zazo S, Molina J-L, Ruiz-Ortiz V, Vélez-Nicolás M, García-López S. Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method. Water. 2020; 12(11):3137. https://doi.org/10.3390/w12113137

Chicago/Turabian Style

Zazo, Santiago; Molina, José-Luis; Ruiz-Ortiz, Verónica; Vélez-Nicolás, Mercedes; García-López, Santiago. 2020. "Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method" Water 12, no. 11: 3137. https://doi.org/10.3390/w12113137

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