TY - JOUR
T1 - ENSO and Sea Surface Temperature Anomaly Impacts on Streamflow Data in the North Pacific Coast Catchments of Peru
AU - Castillo, Leonardo
AU - Rau, Pedro
AU - Luyo, Jaime
N1 - Publisher Copyright:
© 2022 IAHR.
PY - 2022
Y1 - 2022
N2 - The Pacific coastal sector of Peru is mainly a dry area where precipitation events are episodic, however, on interannual time scales, in the North, there are extraordinary precipitation events associated with El Niño Southern Oscillation (ENSO). Therefore, an evaluation of climatic variables such as the Sea Surface Temperature Anomaly (SSTa) and its correlation with river discharges data is necessary to define prediction models. 19 stations with river discharge data (1965-2015) distributed along the North Pacific coast of Peru are analyzed. To classify El Niño and La Niña events we will use the Oceanic Niño Index (ONI)) where the Niño 3.4 anomalies are represented by the SSTa. A map of lags that improve the correlation in each discharge station and their respective statistical and physic interpretation is proposed. Finally, as results, a predictive model is built for each discharge station based on its preceding flows and the SSTa (corresponding to region 1+2 or region 3.4 associated with a lag that maximizes its correlation). The interpretation of results contains a physical support for the choice of the best significant variables.
AB - The Pacific coastal sector of Peru is mainly a dry area where precipitation events are episodic, however, on interannual time scales, in the North, there are extraordinary precipitation events associated with El Niño Southern Oscillation (ENSO). Therefore, an evaluation of climatic variables such as the Sea Surface Temperature Anomaly (SSTa) and its correlation with river discharges data is necessary to define prediction models. 19 stations with river discharge data (1965-2015) distributed along the North Pacific coast of Peru are analyzed. To classify El Niño and La Niña events we will use the Oceanic Niño Index (ONI)) where the Niño 3.4 anomalies are represented by the SSTa. A map of lags that improve the correlation in each discharge station and their respective statistical and physic interpretation is proposed. Finally, as results, a predictive model is built for each discharge station based on its preceding flows and the SSTa (corresponding to region 1+2 or region 3.4 associated with a lag that maximizes its correlation). The interpretation of results contains a physical support for the choice of the best significant variables.
KW - Correlation Pearson
KW - Discharge data
KW - ENSO
KW - Predictive model
KW - Sea Surface Temperature Anomaly
UR - http://www.scopus.com/inward/record.url?scp=85178386927&partnerID=8YFLogxK
U2 - 10.3850/IAHR-39WC2521716X2022241
DO - 10.3850/IAHR-39WC2521716X2022241
M3 - Conference article
AN - SCOPUS:85178386927
SN - 2521-7119
SP - 7157
EP - 7168
JO - Proceedings of the IAHR World Congress
JF - Proceedings of the IAHR World Congress
T2 - 39th IAHR World Congress, 2022
Y2 - 19 June 2022 through 24 June 2022
ER -