Estimation of monthly flow data with artificial intelligence in basins with scarce information in the Colombian Orinoquía

ABSTRACT Completing missing hydroclimatological data is crucial for effective water resource management, especially in remote regions such as Colombia's Orinoquía. This study proposes a method for imputing monthly streamflow data from 1985 to 2020 by applying Nonlinear Principal Component Analysis (NLPCA) combined with CHIRPS precipitation data. Sensitivity analyses revealed that including precipitation as an exogenous variable significantly enhances imputation accuracy. The method was applied to four hydrometric stations in the Cravo Sur River basin, with missing data rates ranging from 5% to 63%. The performance of the imputed data was evaluated using Spearman correlation coefficients (0.95-0.99), BIAS (0.2% to -2.1%), and root mean square error (RMSE) values (0.79 to 1.93 m3/s). These results demonstrate the method's robustness and adaptability across varied hydroclimatic conditions within the Orinoquía region.