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...
Published in: | Remote Sensing |
---|---|
Main Authors: | , , , , , |
Format: | Article in Journal/Newspaper |
Language: | Spanish |
Published: |
2022
|
Subjects: | |
Online Access: | http://dspace.ucuenca.edu.ec/handle/123456789/40640 https://www.scopus.com/record/display.uri?eid=2-s2.0-85144814083&origin=resultslist&sort=cp-f&src=s&st1=Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection&sid=42708a3b44795ac4c02f52b41c391b55&sot=b&sdt=b&sl=167&s=TITLE-ABS-KEY%28Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection%29&relpos=0&citeCnt=0&searchTerm= https://doi.org/10.3390/rs14246397 |
id |
ftunivcuenca:oai:dspace.ucuenca.edu.ec:123456789/40640 |
---|---|
record_format |
openpolar |
spelling |
ftunivcuenca:oai:dspace.ucuenca.edu.ec:123456789/40640 2023-05-15T17:35:00+02:00 Development and assessment of seasonal rainfall forecasting models for the bani and the Senegal basins by identifying the best predictive teleconnection Khaliduo Mamadou, Baá Minga León, Saula Verónica Gaona, Gabriel V. Gómez Albores, Miguel Angel Díaz Delgado, Carlos Balcazar, Luis 2022 application/pdf http://dspace.ucuenca.edu.ec/handle/123456789/40640 https://www.scopus.com/record/display.uri?eid=2-s2.0-85144814083&origin=resultslist&sort=cp-f&src=s&st1=Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection&sid=42708a3b44795ac4c02f52b41c391b55&sot=b&sdt=b&sl=167&s=TITLE-ABS-KEY%28Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection%29&relpos=0&citeCnt=0&searchTerm= https://doi.org/10.3390/rs14246397 es_ES spa 2072-4292 http://dspace.ucuenca.edu.ec/handle/123456789/40640 https://www.scopus.com/record/display.uri?eid=2-s2.0-85144814083&origin=resultslist&sort=cp-f&src=s&st1=Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection&sid=42708a3b44795ac4c02f52b41c391b55&sot=b&sdt=b&sl=167&s=TITLE-ABS-KEY%28Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection%29&relpos=0&citeCnt=0&searchTerm= doi:10.3390/rs14246397 Remote Sensing SST Sahel RHUM Persiann-CDR Model MSLP ARTÍCULO 2022 ftunivcuenca https://doi.org/10.3390/rs14246397 2023-01-12T00:56:22Z 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 Repositorio de la Universidad de Cuenca 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 |
Repositorio de la Universidad de Cuenca |
op_collection_id |
ftunivcuenca |
language |
Spanish |
topic |
SST Sahel RHUM Persiann-CDR Model MSLP |
spellingShingle |
SST Sahel RHUM Persiann-CDR Model MSLP Khaliduo Mamadou, Baá Minga León, Saula Verónica Gaona, Gabriel V. Gómez Albores, Miguel Angel Díaz Delgado, Carlos Balcazar, Luis Development and assessment of seasonal rainfall forecasting models for the bani and the Senegal basins by identifying the best predictive teleconnection |
topic_facet |
SST Sahel RHUM Persiann-CDR Model 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 |
Article in Journal/Newspaper |
author |
Khaliduo Mamadou, Baá Minga León, Saula Verónica Gaona, Gabriel V. Gómez Albores, Miguel Angel Díaz Delgado, Carlos Balcazar, Luis |
author_facet |
Khaliduo Mamadou, Baá Minga León, Saula Verónica Gaona, Gabriel V. Gómez Albores, Miguel Angel Díaz Delgado, Carlos Balcazar, Luis |
author_sort |
Khaliduo Mamadou, Baá |
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 |
publishDate |
2022 |
url |
http://dspace.ucuenca.edu.ec/handle/123456789/40640 https://www.scopus.com/record/display.uri?eid=2-s2.0-85144814083&origin=resultslist&sort=cp-f&src=s&st1=Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection&sid=42708a3b44795ac4c02f52b41c391b55&sot=b&sdt=b&sl=167&s=TITLE-ABS-KEY%28Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection%29&relpos=0&citeCnt=0&searchTerm= https://doi.org/10.3390/rs14246397 |
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 |
op_relation |
2072-4292 http://dspace.ucuenca.edu.ec/handle/123456789/40640 https://www.scopus.com/record/display.uri?eid=2-s2.0-85144814083&origin=resultslist&sort=cp-f&src=s&st1=Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection&sid=42708a3b44795ac4c02f52b41c391b55&sot=b&sdt=b&sl=167&s=TITLE-ABS-KEY%28Development+and+Assessment+of+Seasonal+Rainfall+Forecasting+Models+for+the+Bani+and+the+Senegal+Basins+by+Identifying+the+Best+Predictive+Teleconnection%29&relpos=0&citeCnt=0&searchTerm= doi:10.3390/rs14246397 |
op_doi |
https://doi.org/10.3390/rs14246397 |
container_title |
Remote Sensing |
container_volume |
14 |
container_issue |
24 |
container_start_page |
6397 |
_version_ |
1766134016000393216 |