Mathematical model for streamflow prediction in an Andean basin by Pearson correlation with ocean temperature
Keywords:
Average Monthly Flows, Ocean temperatures, Predictive Mathematical Models, Water ResourcesAbstract
Streamflow prediction constitutes a fundamental tool in water resources decision-making and risk management actions. The possibility of implementing a mathematical model for the prediction of streamflow in an Andean basin was studied, identifying, through Pearson's correlation coefficient, the best correlations between the time series of mean monthly streamflow (Qm) and the sea surface temperature (SST), considering up to 11 lags. The monthly SST data were obtained from the MODIS-NASA sensor processed on the Ocean Color platform, selecting cells of 2° longitude and 4° latitude to cover the strip +180° lon / -180° lon, from -20 °lat / +20° lat, analyzing a total of 3600 cells. The water resource was characterized by the Qm time series of the La Donjuana station on the Pamplonita river (Norte de Santander, Colombia). The time window studied was from July 2002 to December 2015 (162 months). Linear models were built for each month by selecting the lag that produced the maximum correlation and verifying values of the p statistic, which were much lower than 0.001. The models were evaluated using the mean square error and the Nash-Sutcliffe efficiency, differentiating Normal years, El Niño years, and La Niña years. Satisfactory results were found for the prediction in La Niña years (wet) with lags greater than three months. The investigation is expected to be extended to consider a greater range of latitudes and to consider other Andean basins.
Downloads
References
D.I. Jeong and Y.O. Kim, “Rainfall runoff models using artificial neural networks for ensemble streamflow prediction”, Hydrological Process, vol. 19, no. 19, pp. 3819-3835, December 2005
L.E. Besaw, D.M. Rizzo, P.R. Bierman and W.R. Hackett, “Advances in ungauged streamflow prediction using artificial neural networks”, Journal of Hydrology, vol. 386, no. 1-4, pp. 27-37, May 2010
M. Küçük, and N. Ağirali˙ oğlu, “Wavelet regression technique for streamflow prediction”, Journal of applied statistics, vol. 33, no. 9, pp. 943-960, November 2006
T. Razavi and P. Coulibaly, “Streamflow prediction in ungauged basins: review of regionalization methods”, Journal of hydrologic engineering, vol. 18, no. 8, pp. 958-975, August 2013
P. Pieper, A. Düsterhus and J. Baehr, “Improving seasonal predictions of meteorological drought by conditioning on ENSO states”, Environmental Research Letters, vol. 16, no. 9, pp.1-11, August 2021
W.U., Hassan and M.A. Nayak, “Global teleconnections in droughts caused by oceanic and atmospheric circulation patterns”, Environmental Research Letters, vol. 16, no. 1, pp. 1-12, December 2020
B. T. Jong, M. Ting and R., Seager, “El Niño's impact on California precipitation: Seasonality, regionality, and El Niño intensity”, Environmental Research Letters, vol. 11, no. 5, pp. 1-11, May 2016
M.B. Switanek, P.A. Troch and C.L. Castro, “Improving seasonal predictions of climate variability and water availability at the catchment scale”, Journal of Hydrometeorology, vol 10, no. 6, pp. 1521-1533, December 2009
V.M. Cvetkovic and J. Martinović, “Innovative solutions for flood risk management”, International Journal of Disaster Risk Management, vol. 2, no. 2, pp. 71-100, December 2020
P. Muñoz, J. Orellana-Alvear, P. Willems and R. Célleri, “Flash-flood forecasting in an Andean mountain catchment—Development of a step-wise methodology based on the random forest algorithm”, Water, vol 10, no. 11, pp. 1519, November 2018
P.J. Werdell and C.R. McClain, “Satellite remote sensing: ocean color” (No. GSFC-E-DAA-TN65587). Elsevier, 2019. Available at: https://pace.oceansciences.org/docs/werdell_and_mcclain_2019_eos3.pdf
H.M. Dierssen and K. Randolph, Remote sensing of ocean color. New York: Earth System Monitoring. Springer, 2013
K. Pearson, “Mathematical contributions to the theory of evolution. On a form of spurious correlation which may arise when indices are used in the measurement of organs”, Proceedings of the Royal Society of London, vol. 60, no. 359-367, pp. 489-498, December 1897
J.E. Nash and J.V. Sutcliffe, “River flow forecasting through conceptual models - Part I—A discussion of principles”, Journal of hydrology, vol. 10, no. 3, pp. 282-290, April 1970
K.E. Trenberth, El niño southern oscillation (ENSO). Encyclopedia of Ocean Sciences (Third Edition), Academic Press, 2019
S. Córdoba-Machado, R. Palomino-Lemus, S.R. Gámiz-Fortis, Y. Castro-Díez and M.J. Esteban-Parra, “Influence of tropical Pacific SST on seasonal precipitation in Colombia: prediction using El Niño and El Niño Modoki”, Climate Dynamics, vol. 44, no. 5-6, pp. 1293-1310, March 2015
A.M. Grimm, V.R. Barros and M.E. Doyle, “Climate variability in southern South America associated with El Niño and La Niña events”, Journal of climate, vol. 13, no. 1, pp. 35-58, January 2000
N. Hoyos, J. Escobar, J.C. Restrepo, A.M. Arango and J.C. Ortiz, “Impact of the 2010–2011 La Niña phenomenon in Colombia, South America: the human toll of an extreme weather event”, Applied Geography, vol. 39, pp. 16-25, May 2013
P.N. DiNezio, C. Deser, Y. Okumura and A. Karspeck, “Predictability of 2-year La Niña events in a coupled general circulation model", Climate dynamics, vol. 49, no. 11, pp. 4237-4261, December 2017
J.J. Luo, G. Liu, H. Hendon, O. Alves and T. Yamagata, “Inter-basin sources for two-year predictability of the multi-year La Niña event in 2010–2012”, Scientific reports, vol. 7, no. 1, pp. 1-7, May 2017
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
