Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method
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 approach...
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MDPI AG
2020-11-01
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| Series: | Water |
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| Online Access: | https://www.mdpi.com/2073-4441/12/11/3137 |
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| author | Santiago Zazo José-Luis Molina Verónica Ruiz-Ortiz Mercedes Vélez-Nicolás Santiago García-López |
| author_facet | Santiago Zazo José-Luis Molina Verónica Ruiz-Ortiz Mercedes Vélez-Nicolás Santiago García-López |
| author_sort | Santiago Zazo |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-10T15:00:17Z |
| format | Article |
| id | doaj.art-e3bd3faf0b4041b3b64925a8379c02ce |
| institution | Directory Open Access Journal |
| issn | 2073-4441 |
| language | English |
| last_indexed | 2024-03-10T15:00:17Z |
| publishDate | 2020-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Water |
| spelling | doaj.art-e3bd3faf0b4041b3b64925a8379c02ce2023-11-20T20:18:44ZengMDPI AGWater2073-44412020-11-011211313710.3390/w12113137Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) MethodSantiago Zazo0José-Luis Molina1Verónica Ruiz-Ortiz2Mercedes Vélez-Nicolás3Santiago García-López4IGA Research Group, Higher Polytechnic School of Ávila, University of Salamanca, Av. de los Hornos Caleros, 50, 05003 Ávila, SpainIGA Research Group, Higher Polytechnic School of Ávila, University of Salamanca, Av. de los Hornos Caleros, 50, 05003 Ávila, SpainDepartment of Industrial Engineering and Civil Engineering, University of Cadiz, Campus Bay of Algeciras, Avda. Ramon Puyol s/n, 11202 Algeciras, SpainDepartment of Earth Sciences, University of Cadiz, Campus Rio San Pedro, s/n, 11510 Puerto Real, SpainDepartment of Earth Sciences, University of Cadiz, Campus Rio San Pedro, s/n, 11510 Puerto Real, SpainThe 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.https://www.mdpi.com/2073-4441/12/11/3137Bayesian causal modelingHCH methodhydrological modelingdeterministic and stochastic modelingrainfall–runoff modelingtemporal dependence |
| spellingShingle | Santiago Zazo José-Luis Molina Verónica Ruiz-Ortiz Mercedes Vélez-Nicolás Santiago García-López Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method Water Bayesian causal modeling HCH method hydrological modeling deterministic and stochastic modeling rainfall–runoff modeling temporal dependence |
| title | Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method |
| title_full | Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method |
| title_fullStr | Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method |
| title_full_unstemmed | Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method |
| title_short | Modeling River Runoff Temporal Behavior through a Hybrid Causal–Hydrological (HCH) Method |
| title_sort | modeling river runoff temporal behavior through a hybrid causal hydrological hch method |
| topic | Bayesian causal modeling HCH method hydrological modeling deterministic and stochastic modeling rainfall–runoff modeling temporal dependence |
| url | https://www.mdpi.com/2073-4441/12/11/3137 |
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