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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Published in:Remote Sensing
Main Authors: Luis Balcázar, Khalidou M. Bâ, Carlos Díaz-Delgado, Miguel A. Gómez-Albores, Gabriel Gaona, Saula Minga-León
Format: Text
Language:English
Published: Multidisciplinary Digital Publishing Institute 2022
Subjects:
SST
Online Access:https://doi.org/10.3390/rs14246397
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spelling ftmdpi:oai:mdpi.com:/2072-4292/14/24/6397/ 2023-08-20T04:08:28+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 agris 2022-12-19 application/pdf https://doi.org/10.3390/rs14246397 EN eng Multidisciplinary Digital Publishing Institute https://dx.doi.org/10.3390/rs14246397 https://creativecommons.org/licenses/by/4.0/ Remote Sensing; Volume 14; Issue 24; Pages: 6397 model Sahel SST PERSIANN-CDR RHUM MSLP Text 2022 ftmdpi https://doi.org/10.3390/rs14246397 2023-08-01T07:51:47Z 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. Text North Atlantic MDPI Open Access Publishing Nash ENVELOPE(-62.350,-62.350,-74.233,-74.233) Sutcliffe ENVELOPE(-81.383,-81.383,50.683,50.683) Bani ENVELOPE(-21.506,-21.506,64.898,64.898) Remote Sensing 14 24 6397
institution Open Polar
collection MDPI Open Access Publishing
op_collection_id ftmdpi
language English
topic model
Sahel
SST
PERSIANN-CDR
RHUM
MSLP
spellingShingle model
Sahel
SST
PERSIANN-CDR
RHUM
MSLP
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
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 Text
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 Multidisciplinary Digital Publishing Institute
publishDate 2022
url https://doi.org/10.3390/rs14246397
op_coverage agris
long_lat ENVELOPE(-62.350,-62.350,-74.233,-74.233)
ENVELOPE(-81.383,-81.383,50.683,50.683)
ENVELOPE(-21.506,-21.506,64.898,64.898)
geographic Nash
Sutcliffe
Bani
geographic_facet Nash
Sutcliffe
Bani
genre North Atlantic
genre_facet North Atlantic
op_source Remote Sensing; Volume 14; Issue 24; Pages: 6397
op_relation https://dx.doi.org/10.3390/rs14246397
op_rights https://creativecommons.org/licenses/by/4.0/
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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