Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection
The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results still need to be improved. In this study, linear, polynomial, and exponential models are developed to for...
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2022
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ftdoajarticles:oai:doaj.org/article:da1f4948698e4c2fb2418940aac7d44f 2023-05-15T17:34:49+02:00 Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection Luis Balcázar Khalidou M. Bâ Carlos Díaz-Delgado Miguel A. Gómez-Albores Gabriel Gaona Saula Minga-León 2022-12-01T00:00:00Z https://doi.org/10.3390/rs14246397 https://doaj.org/article/da1f4948698e4c2fb2418940aac7d44f EN eng MDPI AG https://www.mdpi.com/2072-4292/14/24/6397 https://doaj.org/toc/2072-4292 doi:10.3390/rs14246397 2072-4292 https://doaj.org/article/da1f4948698e4c2fb2418940aac7d44f Remote Sensing, Vol 14, Iss 6397, p 6397 (2022) model Sahel SST PERSIANN-CDR RHUM MSLP Science Q article 2022 ftdoajarticles https://doi.org/10.3390/rs14246397 2022-12-30T19:30:26Z The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results still need to be improved. In this study, linear, polynomial, and exponential models are developed to forecast rainfall in the Bani and Senegal River basins. All three models use Atlantic sea surface temperature (SST). A fourth algorithm using stepwise regression was also developed for the precipitation estimates over these two basins. The stepwise regression algorithm uses SST with covariates, mean sea level pressure (MSLP), relative humidity (RHUM), and five El Niño indices. The explanatory variables SST, RHUM, and MSLP were selected based on principal component analysis (PCA) and cluster analysis to find the homogeneous region of the Atlantic with the greatest predictive ability. PERSIANN-CDR rainfall data were used as the dependent variable. Models were developed for each pixel of 0.25° × 0.25° spatial resolution. The second-order polynomial model with a lag of about 11 months outperforms all other models and explains 87% of the variance in precipitation over the two watersheds. Nash–Sutcliffe efficiency (NSE) values were between 0.751 and 0.926 for the Bani River basin and from 0.175 to 0.915 for the Senegal River basin, for which the lowest values are found in the driest area (Sahara). Results showed that the North Atlantic SST shows a more robust teleconnection with precipitation dynamics in both basins. Article in Journal/Newspaper North Atlantic Directory of Open Access Journals: DOAJ Articles Bani ENVELOPE(-21.506,-21.506,64.898,64.898) Nash ENVELOPE(-62.350,-62.350,-74.233,-74.233) Sutcliffe ENVELOPE(-81.383,-81.383,50.683,50.683) Remote Sensing 14 24 6397 |
institution |
Open Polar |
collection |
Directory of Open Access Journals: DOAJ Articles |
op_collection_id |
ftdoajarticles |
language |
English |
topic |
model Sahel SST PERSIANN-CDR RHUM MSLP Science Q |
spellingShingle |
model Sahel SST PERSIANN-CDR RHUM MSLP Science Q Luis Balcázar Khalidou M. Bâ Carlos Díaz-Delgado Miguel A. Gómez-Albores Gabriel Gaona Saula Minga-León Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection |
topic_facet |
model Sahel SST PERSIANN-CDR RHUM MSLP Science Q |
description |
The high variability of rainfall in the Sahel region causes droughts and floods that affect millions of people every year. Several rainfall forecasting models have been proposed, but the results still need to be improved. In this study, linear, polynomial, and exponential models are developed to forecast rainfall in the Bani and Senegal River basins. All three models use Atlantic sea surface temperature (SST). A fourth algorithm using stepwise regression was also developed for the precipitation estimates over these two basins. The stepwise regression algorithm uses SST with covariates, mean sea level pressure (MSLP), relative humidity (RHUM), and five El Niño indices. The explanatory variables SST, RHUM, and MSLP were selected based on principal component analysis (PCA) and cluster analysis to find the homogeneous region of the Atlantic with the greatest predictive ability. PERSIANN-CDR rainfall data were used as the dependent variable. Models were developed for each pixel of 0.25° × 0.25° spatial resolution. The second-order polynomial model with a lag of about 11 months outperforms all other models and explains 87% of the variance in precipitation over the two watersheds. Nash–Sutcliffe efficiency (NSE) values were between 0.751 and 0.926 for the Bani River basin and from 0.175 to 0.915 for the Senegal River basin, for which the lowest values are found in the driest area (Sahara). Results showed that the North Atlantic SST shows a more robust teleconnection with precipitation dynamics in both basins. |
format |
Article in Journal/Newspaper |
author |
Luis Balcázar Khalidou M. Bâ Carlos Díaz-Delgado Miguel A. Gómez-Albores Gabriel Gaona Saula Minga-León |
author_facet |
Luis Balcázar Khalidou M. Bâ Carlos Díaz-Delgado Miguel A. Gómez-Albores Gabriel Gaona Saula Minga-León |
author_sort |
Luis Balcázar |
title |
Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection |
title_short |
Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection |
title_full |
Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection |
title_fullStr |
Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection |
title_full_unstemmed |
Development and Assessment of Seasonal Rainfall Forecasting Models for the Bani and the Senegal Basins by Identifying the Best Predictive Teleconnection |
title_sort |
development and assessment of seasonal rainfall forecasting models for the bani and the senegal basins by identifying the best predictive teleconnection |
publisher |
MDPI AG |
publishDate |
2022 |
url |
https://doi.org/10.3390/rs14246397 https://doaj.org/article/da1f4948698e4c2fb2418940aac7d44f |
long_lat |
ENVELOPE(-21.506,-21.506,64.898,64.898) ENVELOPE(-62.350,-62.350,-74.233,-74.233) ENVELOPE(-81.383,-81.383,50.683,50.683) |
geographic |
Bani Nash Sutcliffe |
geographic_facet |
Bani Nash Sutcliffe |
genre |
North Atlantic |
genre_facet |
North Atlantic |
op_source |
Remote Sensing, Vol 14, Iss 6397, p 6397 (2022) |
op_relation |
https://www.mdpi.com/2072-4292/14/24/6397 https://doaj.org/toc/2072-4292 doi:10.3390/rs14246397 2072-4292 https://doaj.org/article/da1f4948698e4c2fb2418940aac7d44f |
op_doi |
https://doi.org/10.3390/rs14246397 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
24 |
container_start_page |
6397 |
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1766133764726980608 |