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The Assessment of Rainfall Prediction Using Climate Models Results and Projections under Future Scenarios: the Machángara Tropical Andean Basin Case
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@article{IJASEIT14686, author = {Angel Vázquez-Patiño and Mario Peña and Alex Avilés}, title = {The Assessment of Rainfall Prediction Using Climate Models Results and Projections under Future Scenarios: the Machángara Tropical Andean Basin Case}, journal = {International Journal on Advanced Science, Engineering and Information Technology}, volume = {11}, number = {5}, year = {2021}, pages = {1903--1911}, keywords = {Machángara basin; rainfall prediction; random forest; climate models; projection; future scenarios; RCP.}, abstract = {Rainfall is vital in the biosphere and predicting it is essential under the possible adverse effects of climate change. Rainfall behavior is linked to the availability of fresh water and the development of almost all the activities necessary for human subsistence. Therefore, knowing their patterns under future scenarios could help decision-makers to plan water use policies. This study used the random forest algorithm to predict rainfall in Chanlud and El Labrado stations, located in the tropical Machángara high mountain basin in Ecuador. Data from the Ecuador project's third national communication (TNC) were used to train the prediction models. First, those models' performance was analyzed to know which climate model results of the TNC provide more information to learn observed rainfall patterns. Then, the rainfall signal was projected under the RCP 4.5 and 8.5 scenarios. Among the most important results obtained, it stands out that the assembly results of the TNC provided the best information to learn rainfall patterns in the present. The performance is the best from January to July, but from August to December it is lower. Rainfall projections under RCP 8.5 are, in general, lower than under RCP 4.5. No significant trends were found in the future. However, a very slight increase (decrease) of rainfall was observed for the driest (wettest) months in both stations, although slightly more accentuated in El Labrado.}, issn = {2088-5334}, publisher = {INSIGHT - Indonesian Society for Knowledge and Human Development}, url = {http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14686}, doi = {10.18517/ijaseit.11.5.14686} }
EndNote
%A Vázquez-Patiño, Angel %A Peña, Mario %A Avilés, Alex %D 2021 %T The Assessment of Rainfall Prediction Using Climate Models Results and Projections under Future Scenarios: the Machángara Tropical Andean Basin Case %B 2021 %9 Machángara basin; rainfall prediction; random forest; climate models; projection; future scenarios; RCP. %! The Assessment of Rainfall Prediction Using Climate Models Results and Projections under Future Scenarios: the Machángara Tropical Andean Basin Case %K Machángara basin; rainfall prediction; random forest; climate models; projection; future scenarios; RCP. %X Rainfall is vital in the biosphere and predicting it is essential under the possible adverse effects of climate change. Rainfall behavior is linked to the availability of fresh water and the development of almost all the activities necessary for human subsistence. Therefore, knowing their patterns under future scenarios could help decision-makers to plan water use policies. This study used the random forest algorithm to predict rainfall in Chanlud and El Labrado stations, located in the tropical Machángara high mountain basin in Ecuador. Data from the Ecuador project's third national communication (TNC) were used to train the prediction models. First, those models' performance was analyzed to know which climate model results of the TNC provide more information to learn observed rainfall patterns. Then, the rainfall signal was projected under the RCP 4.5 and 8.5 scenarios. Among the most important results obtained, it stands out that the assembly results of the TNC provided the best information to learn rainfall patterns in the present. The performance is the best from January to July, but from August to December it is lower. Rainfall projections under RCP 8.5 are, in general, lower than under RCP 4.5. No significant trends were found in the future. However, a very slight increase (decrease) of rainfall was observed for the driest (wettest) months in both stations, although slightly more accentuated in El Labrado. %U http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14686 %R doi:10.18517/ijaseit.11.5.14686 %J International Journal on Advanced Science, Engineering and Information Technology %V 11 %N 5 %@ 2088-5334
IEEE
Angel Vázquez-Patiño,Mario Peña and Alex Avilés,"The Assessment of Rainfall Prediction Using Climate Models Results and Projections under Future Scenarios: the Machángara Tropical Andean Basin Case," International Journal on Advanced Science, Engineering and Information Technology, vol. 11, no. 5, pp. 1903-1911, 2021. [Online]. Available: http://dx.doi.org/10.18517/ijaseit.11.5.14686.
RefMan/ProCite (RIS)
TY - JOUR AU - Vázquez-Patiño, Angel AU - Peña, Mario AU - Avilés, Alex PY - 2021 TI - The Assessment of Rainfall Prediction Using Climate Models Results and Projections under Future Scenarios: the Machángara Tropical Andean Basin Case JF - International Journal on Advanced Science, Engineering and Information Technology; Vol. 11 (2021) No. 5 Y2 - 2021 SP - 1903 EP - 1911 SN - 2088-5334 PB - INSIGHT - Indonesian Society for Knowledge and Human Development KW - Machángara basin; rainfall prediction; random forest; climate models; projection; future scenarios; RCP. N2 - Rainfall is vital in the biosphere and predicting it is essential under the possible adverse effects of climate change. Rainfall behavior is linked to the availability of fresh water and the development of almost all the activities necessary for human subsistence. Therefore, knowing their patterns under future scenarios could help decision-makers to plan water use policies. This study used the random forest algorithm to predict rainfall in Chanlud and El Labrado stations, located in the tropical Machángara high mountain basin in Ecuador. Data from the Ecuador project's third national communication (TNC) were used to train the prediction models. First, those models' performance was analyzed to know which climate model results of the TNC provide more information to learn observed rainfall patterns. Then, the rainfall signal was projected under the RCP 4.5 and 8.5 scenarios. Among the most important results obtained, it stands out that the assembly results of the TNC provided the best information to learn rainfall patterns in the present. The performance is the best from January to July, but from August to December it is lower. Rainfall projections under RCP 8.5 are, in general, lower than under RCP 4.5. No significant trends were found in the future. However, a very slight increase (decrease) of rainfall was observed for the driest (wettest) months in both stations, although slightly more accentuated in El Labrado. UR - http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14686 DO - 10.18517/ijaseit.11.5.14686
RefWorks
RT Journal Article ID 14686 A1 Vázquez-Patiño, Angel A1 Peña, Mario A1 Avilés, Alex T1 The Assessment of Rainfall Prediction Using Climate Models Results and Projections under Future Scenarios: the Machángara Tropical Andean Basin Case JF International Journal on Advanced Science, Engineering and Information Technology VO 11 IS 5 YR 2021 SP 1903 OP 1911 SN 2088-5334 PB INSIGHT - Indonesian Society for Knowledge and Human Development K1 Machángara basin; rainfall prediction; random forest; climate models; projection; future scenarios; RCP. AB Rainfall is vital in the biosphere and predicting it is essential under the possible adverse effects of climate change. Rainfall behavior is linked to the availability of fresh water and the development of almost all the activities necessary for human subsistence. Therefore, knowing their patterns under future scenarios could help decision-makers to plan water use policies. This study used the random forest algorithm to predict rainfall in Chanlud and El Labrado stations, located in the tropical Machángara high mountain basin in Ecuador. Data from the Ecuador project's third national communication (TNC) were used to train the prediction models. First, those models' performance was analyzed to know which climate model results of the TNC provide more information to learn observed rainfall patterns. Then, the rainfall signal was projected under the RCP 4.5 and 8.5 scenarios. Among the most important results obtained, it stands out that the assembly results of the TNC provided the best information to learn rainfall patterns in the present. The performance is the best from January to July, but from August to December it is lower. Rainfall projections under RCP 8.5 are, in general, lower than under RCP 4.5. No significant trends were found in the future. However, a very slight increase (decrease) of rainfall was observed for the driest (wettest) months in both stations, although slightly more accentuated in El Labrado. LK http://ijaseit.insightsociety.org/index.php?option=com_content&view=article&id=9&Itemid=1&article_id=14686 DO - 10.18517/ijaseit.11.5.14686