A statistical forecast scheme of precipitation in the Upper Bermejo River Basin in Argentina

• Published in: International journal of river basin management Vol. 21; no. 2; pp. 153 - 166
• Main Authors: Ayala, S. N., González, M. H., Rolla, A. L.
• Format: Journal Article
• Language: English
• Published: Abingdon Taylor & Francis 03-04-2023; Taylor & Francis Ltd
• Subjects: Anomalies; circulation patterns; El Nino; El Nino phenomena; Environmental risk; Flood risk; Meridional wind; Modelling; Performance measurement; Precipitation; Precipitation forecasting; Precipitation variability; Rain; Rainfall; Rainfall anomalies; Rainy season; Regression analysis; Regression models; Risk; Risk assessment; River basin development; River basins; Rivers; Sea surface; Sea surface temperature; Seasonal precipitation; Spring; Spring (season); Spring precipitation; Statistical analysis; Stream discharge; Stream flow; streamflow; Surface temperature; Weather forecasting; Wet season; Argentina; Bermejo River
• ISSN: 1571-5124; 1814-2060
• DOI: 10.1080/15715124.2021.1932952

The Bermejo River, located in northern Argentina, has a flow regime controlled by precipitation. In an area characterized by its risk of flooding and land-sliding during the summer, seasonal precipitation forecast becomes a valuable tool for risk assessment and better management of hydric resources. This study focuses on identifying remote forcings of precipitation variability for the upper sub-basin of the Bermejo River Basin, and developing multiple linear regression models of areal spring precipitation (September to November), the beginning of the rainy season, considering predictors monitored on the preceding August. Positive rainfall anomalies in spring relate to higher monthly and maximum daily streamflow in the upper and lower sub-basins. Two forecast models arose as the ones with best performance when using leave-one-out-cross-validation. Predictors involved in these models (four and three predictors, respectively) emphasize the influence of the circulation in middle-low levels over the Pacific Ocean, as well as of the sea surface temperature in the El Niño region and the low-level meridional wind in tropical South America. The two models share similar performance metrics, although the model with less predictors has a better skill for the detection of normal and above-normal rainfall seasons.