SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia
Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial aut...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-06-01
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| Series: | Atmosphere |
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| Online Access: | https://www.mdpi.com/2073-4433/11/6/602 |
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| author | Luisa Martínez-Acosta Juan Pablo Medrano-Barboza Álvaro López-Ramos John Freddy Remolina López Álvaro Alberto López-Lambraño |
| author_facet | Luisa Martínez-Acosta Juan Pablo Medrano-Barboza Álvaro López-Ramos John Freddy Remolina López Álvaro Alberto López-Lambraño |
| author_sort | Luisa Martínez-Acosta |
| collection | DOAJ |
| description | Seasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed. |
| first_indexed | 2024-03-10T19:19:09Z |
| format | Article |
| id | doaj.art-ac2cffd240944780adc66c37b899c981 |
| institution | Directory Open Access Journal |
| issn | 2073-4433 |
| language | English |
| last_indexed | 2024-03-10T19:19:09Z |
| publishDate | 2020-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Atmosphere |
| spelling | doaj.art-ac2cffd240944780adc66c37b899c9812023-11-20T03:09:22ZengMDPI AGAtmosphere2073-44332020-06-0111660210.3390/atmos11060602SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in ColombiaLuisa Martínez-Acosta0Juan Pablo Medrano-Barboza1Álvaro López-Ramos2John Freddy Remolina López3Álvaro Alberto López-Lambraño4Faculty of Engineering, Architecture and Design, Universidad Autónoma de Baja California, Baja California, Ensenada 22860, MexicoFaculty of Civil Engineering, GICA Group, Universidad Pontificia Bolivariana Seccional Montería, Cra. 6 # 97ª—99, Montería 230002, Córdoba, ColombiaFaculty of Civil Engineering, GICA Group, Universidad Pontificia Bolivariana Seccional Montería, Cra. 6 # 97ª—99, Montería 230002, Córdoba, ColombiaFaculty of Electronic Engineering, ITEM Group, Universidad Pontificia Bolivariana Seccional Montería, Carrera. 6 # 97ª—99, Montería 230002, Córdoba, ColombiaFaculty of Engineering, Architecture and Design, Universidad Autónoma de Baja California, Baja California, Ensenada 22860, MexicoSeasonal Auto Regressive Integrative Moving Average models (SARIMA) were developed for monthly rainfall time series. Normality of the rainfall time series was achieved by using the Box Cox transformation. The best SARIMA models were selected based on their autocorrelation function (ACF), partial autocorrelation function (PACF), and the minimum values of the Akaike Information Criterion (AIC). The result of the Ljung–Box statistical test shows the randomness and homogeneity of each model residuals. The performance and validation of the SARIMA models were evaluated based on various statistical measures, among these, the Student’s t-test. It is possible to obtain synthetic records that preserve the statistical characteristics of the historical record through the SARIMA models. Finally, the results obtained can be applied to various hydrological and water resources management studies. This will certainly assist policy and decision-makers to establish strategies, priorities, and the proper use of water resources in the Sinú river watershed.https://www.mdpi.com/2073-4433/11/6/602time series modellingtime seriesseasonalitystochastic process |
| spellingShingle | Luisa Martínez-Acosta Juan Pablo Medrano-Barboza Álvaro López-Ramos John Freddy Remolina López Álvaro Alberto López-Lambraño SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia Atmosphere time series modelling time series seasonality stochastic process |
| title | SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia |
| title_full | SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia |
| title_fullStr | SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia |
| title_full_unstemmed | SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia |
| title_short | SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia |
| title_sort | sarima approach to generating synthetic monthly rainfall in the sinu river watershed in colombia |
| topic | time series modelling time series seasonality stochastic process |
| url | https://www.mdpi.com/2073-4433/11/6/602 |
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