Dynamic regression model for hourly river level forecasting under risk situations: An application to the Ebro River

Cebrián, A.C. (Universidad de Zaragoza) ; Abaurrea, J. (Universidad de Zaragoza) ; Asín, J. (Universidad de Zaragoza) ; Segarra, E.

Resumen: This work proposes a new statistical modelling approach to forecast the hourly river level at a gauging station, under potential flood risk situations and over a medium-term prediction horizon (around three days). For that aim we introduce a new model, the switching regression model with ARMA errors, which takes into account the serial correlation structure of the hourly level series, and the changing time delay between them. A whole modelling approach is developed, including a two-step estimation, which improves the medium-term prediction performance of the model, and uncertainty measures of the predictions. The proposed model not only provides predictions for longer periods than other statistical models, but also helps to understand the physics of the river, by characterizing the relationship between the river level in a gauging station and its influential factors. This approach is applied to forecast the Ebro River level at Zaragoza (Spain), using as input the series at Tudela. The approach has shown to be useful and the resulting model provides satisfactory hourly predictions, which can be fast and easily updated, together with their confidence intervals. The fitted model outperforms the predictions from other statistical and numerical models, specially in long prediction horizons.

Idioma: Inglés

DOI: 10.1007/s11269-018-2114-2

Año: 2018

Publicado en: WATER RESOURCES MANAGEMENT 33, 2 (2018), 523–537
ISSN: 0920-4741

Factor impacto JCR: 2.987 (2018)

Categ. JCR: WATER RESOURCES rank: 18 / 91 = 0.198 (2018) - Q1 - T1
Categ. JCR: ENGINEERING, CIVIL rank: 24 / 132 = 0.182 (2018) - Q1 - T1

Factor impacto SCIMAGO: 1.097 - Water Science and Technology (Q1) - Civil and Structural Engineering (Q1)

Financiación: info:eu-repo/grantAgreement/ES/DGA/E22
Financiación: info:eu-repo/grantAgreement/ES/MICINN/MTM2017-83812-P

Tipo y forma: Article (PostPrint)

Área (Departamento): Área Estadís. Investig. Opera. (Dpto. Métodos Estadísticos)