Study of teleconnection between hydrological variables and climatological variables in a headwater basin of the Maipo River for forecast model application

Abstract

This study conducts a teleconnection analysis of the seasonal streamflow during the dry season (winter and summer) at the Olivares River basin, a headwater of the Maipo River basin, with traditional climate indices (Antarctic Oscillation, Niño1+2, and Niño3.4), new indices obtained from sea surface temperature (SST) anomaly spatial fields, and in situ hydrometeorological variables from the previous season to identify potential predictors for implementing seasonal streamflow forecast models in the study area. To illustrate the potential of the predictors identified, we fit multiple linear regression models (MLRM) for seasonal streamflow forecast for 0- and 3-month lead times. The forecasts are validated using the leave-1-year-out cross-validation (LOOCV) approach and performance metrics such as the Pearson correlation coefficient (R), BIAS, Nash-Sutcliffe efficiency (NSE), and continuous rank probability skill score (CRPSS). Results show a good performance of the forecast model for cross-validation with R and NSE values ranging from 0.55 to 0.95 and from 0.28 to 0.88 for 0- and 3-month lead times during the dry season. This early implementation provides good perspectives for implementing probabilistic seasonal streamflow forecasting models, which can provide a powerful output to develop robust water management strategies to tackle water scarcity in the study area.