Use of nonlinear principal components of CHIRPS precipitation data and ocean–atmospheric variables for streamflow forecasting in an area of scarce data – case study: Tocaría river basin – Orinoquia Colombiana

Accurate streamflow forecasting is essential for mitigating hydrological extremes and supporting sustainable water-resources management, particularly in data-scarce tropical catchments. This study proposes a hybrid forecasting framework that integrates Seasonal Autoregressive Integrated Moving Average models with exogenous inputs (SARIMAX), nonlinear principal components (NLPCs) derived from CHIRPS precipitation data, and large-scale ocean–atmosphere indices (macroclimatic variables, MVs). Four monthly models were developed and evaluated for the Tocaría River basin in the Colombian Orinoquía region: (1) a SARIMA (4,0,4)(0,0,3)12 model; (2) SARIMAX with MVs; (3) SARIMAX with NLPCs; and (4) a hybrid SARIMAX combining both MVs and NLPCs. All models were assessed using both raw and log-transformed streamflow data and benchmarked against simple baseline approaches. The hybrid model fitted to log-transformed streamflow showed consistent skill during both validation and testing (R2 = 0.49 and 0.55, respectively), providing the most accurate reproduction of peak-flow conditions (lowest RMSE = 60.8 m3 s−1). Skill was also maintained over longer lead times: for forecasts up to 24 months ahead, RMSE remained below 50 m3 s−1 and R2 remained above 0.80 across the forecast horizon. These results underscore the effectiveness of integrating nonlinear precipitation patterns, macroclimatic variability, and temporal dependence to improve forecast accuracy under data-scarce conditions, particularly for peak flows and anomalously wet periods that are not adequately captured by the climatological benchmark. The proposed methodology offers a transferable approach for operational forecasting in ungauged or sparsely monitored basins, contributing to early warning systems, drought preparedness, and adaptive water governance in vulnerable tropical regions.