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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