SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Martínez-Acosta, Luisa; Medrano-Barboza, Juan Pablo; López, John Freddy Remolina; López-Lambraño, Álvaro Alberto

doi:10.3390/atmos11060602

Cited by 26 publications

(16 citation statements)

References 42 publications

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“…e temperature change of the seawater will not be affected by policies such as limiting the emission of greenhouse gases [28][29][30]. Since the temperature of seawater is affected by not only long-term greenhouse gas emissions but also seasonal changes due to fluctuations in climate and ocean currents; we constructed a 12-month SARIMA model to study future temperature changes in the target sea area and predict the migration position of the fish school [31][32][33][34][35][36][37][38][39][40]. e model building process is as follows:…”

Section: Problem Sources and Model Assumptionsmentioning

confidence: 99%

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

Chen

et al. 2021

Complexity

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Herring and mackerel are two of the most important pillars of Scottish fisheries. In recent years, global warming has caused a gradual rise in ocean temperatures. In order to survive and reproduce, herring and mackerel populations will migrate. This will have a huge impact on Scotland’s fisheries. Therefore, we need to predict the relocation of fish stocks in advance, make timely adjustments to the fishing range, and minimize the loss of the fishing industry. In this article, we subdivide the research target sea area into 39 regions, establish the optimal SARIMA model for each region based on the collected seawater temperature time series data, and take region 13 and region 15 as examples to fit the ARIMA (3, 3, 1) (1, 2, 1) and ARIMA (2, 3, 1) (0, 2, 1) models with a period of 12. The results show that the SARIMA model fits well in all regions and predicts the temperature changes in the studied sea area from 2021 to 2050. Finally, according to the predicted sea temperature in different periods, the migration position of the fish school is predicted.

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Section: Problem Sources and Model Assumptionsmentioning

confidence: 99%

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

Chen

et al. 2021

Complexity

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“…The density of the rain gauge network in this coastal subbasin is around 160 km 2 /station on average, with a standard deviation of 115 km 2 /station, which is adequate based on the WMO recommendations regarding minimum densities [6]. The areal estimation using the Thiessen method with IDEAM records results in average rainfall of 1392 mm/y, with a standard deviation equal to 170 mm/year, as in previous reports based on individual stations and areal estimations [42,43,67].…”

Section: Lower Sinú River Basinmentioning

confidence: 55%

“…As in most of the country, the precipitation regime in the Sinu River basin follows a unimodal distribution, with a dry season between December and March and a rainy season extending from April to November, with the latter accounting for more than 80% of the total annual rainfall [42]. A short dry period called "Veranillo de San Juan" occurs between July and August in the Colombian Caribbean.…”

Section: Study Areamentioning

confidence: 99%

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

et al. 2021

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Global reanalysis dataset estimations of climate variables constitute an alternative for overcoming data scarcity associated with sparsely and unevenly distributed hydrometeorological networks often found in developing countries. However, reanalysis datasets require detailed validation to determine their accuracy and reliability. This paper evaluates the performance of MERRA2 and ERA5 regarding their monthly rainfall products, comparing their areal precipitation averages with estimates based on ground measurement records from 49 rain gauges managed by the Institute of Hydrology, Meteorology, and Environmental Studies (IDEAM) and the Thiessen polygons method in the Sinu River basin, Colombia. The performance metrics employed in this research are the correlation coefficient, the bias, the normalized root mean square error (NRMSE), and the Nash–Sutcliffe efficiency (NSE). The results show that ERA5 generally outperforms MERRA2 in the study area. However, both reanalyses consistently overestimate the monthly averages calculated from IDEAM records at all time and spatial scales. The negative NSE values indicate that historical monthly averages from IDEAM records are better predictors than both MERRA2 and ERA5 rainfall products.

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“…The best model was chosen from those that fit the data well. The Akaike Information Criterion (AIC) was used to select the optimum model (Martínez-Acosta et al 2020). Each model's AIC was computed, and the lowest AIC was chosen for further future morphological changes.…”

Section: Forecast Morphological Changesmentioning

confidence: 99%

Meandering rivers’ morphological changes analysis and prediction – a case study of Barak river, Assam

Nath

Ghosh

2022

H2Open Journal

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Morphological studies are vital for water resources management, riverbank development, and flood mitigation. In this study, the sinuosity index and bank erosion were used to detect and quantify morphological changes using Landsat data (1990–2020) in the Barak river, India. The morphological changes were investigated in protected areas to analyze the effectiveness of existing protective structures on bank migration, which helps formulate better riverbank restoration plans. Using monthly discharge data from two stream gauge stations, the Seasonal Autoregressive Integrated Moving Average (SARIMA) models were developed. The extensive sediment transportation in the region necessitates studying both the river flow and morphological changes. The developed SARIMA model was used to predict river discharges up to 2025, being trained with data from 2006 to 2015. The validation of the model (2016–2018) shows that the mean absolute percentage error for discharge at two gauging stations is 29.78 and 23.52%, respectively. The analysis shows that the sinuosity index and bank erosion were inversely proportional. The SARIMA model showed that the future monthly discharge in the case study could be substantially higher than the observed series and affect river erosion simultaneously. This approach applies to many other meandering river management and identifies future morphological changes.

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SARIMA Approach to Generating Synthetic Monthly Rainfall in the Sinú River Watershed in Colombia

Cited by 26 publications

References 42 publications

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

[Retracted] Prediction of Fish Migration Caused by Ocean Warming Based on SARIMA Model

Evaluation of Areal Monthly Average Precipitation Estimates from MERRA2 and ERA5 Reanalysis in a Colombian Caribbean Basin

Meandering rivers’ morphological changes analysis and prediction – a case study of Barak river, Assam

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