Hydrological modeling is an important tool for water resources management, providing a feasible solution to represent the main hydrological processes and predict future streamflow regimes. The literature presents a set of hydrological models commonly used to represent the rainfall-runoff process in watersheds with different meteorological and geomorphological characteristics. The response of such models could differ significantly for a single precipitation event, given the uncertainties associated with the input data, parameters, and model structure. In this way, a correct hydrological representation of a watershed should include the evaluation of different hydrological models. This study explores the use and performance of five hydrological models to represent daily streamflow regimes at six hydropower plants located in the Tocantins river basin (Brazil). The adopted models include the GR4J, HYMOD, HBV, SMAP, and MGB-IPH. The evaluation of each model was elaborated considering the calibration (2014–2019) and validation period (2005–2010) using observed data of precipitation and climatological variables. Deterministic metrics and statistical tests were used to measure the performance of each model. For the calibration stage, results show that all models achieved a satisfactory performance with NSE values greater than 0.6. For the validation stage, only the MGB-IPH model present a good performance with NSE values greater than 0.7. A bias correction procedure were applied to correct the simulated data of conceptual models. However, the statistical tests exposed that only the MGB-IPH model could preserve the main statistical properties of the observed data. Thus, this study discusses and presents some limitations of the lumped model to represent daily streamflows in large-scale river basins (>50,000 km${}^2$).

This study evaluates the performance of two high-resolution precipitation forecasting systems—the SIMEPAR operational forecast (FCST-SIM, 5 km) and the ECMWF Integrated Forecasting System (FCST-ENS, ~11 km)—over the PCJ River Basin, São Paulo, Brazil. Forecasts were validated against local rain gauge observations and the Brazilian Daily Weather Gridded Data (BR-DWGD) dataset to assess their suitability as inputs for hydrological models. Performance was evaluated at three strategic control stations (Atibaia, Valinhos, and Buenópolis) during the 2019–2024 period using categorical verification metrics. FCST-SIM consistently outperformed FCST-ENS, particularly during the first forecast lead times, exhibiting higher event-detection skill across all sub-basins. To improve forecast performance, two bias-correction approaches were investigated: Quantile Delta Mapping (QDM) and dry/wet occurrence correction based on frequentist and Bayesian logistic regression models. QDM generally reduced forecast errors, especially at longer lead times, although improvements in Kling–Gupta Efficiency were limited and varied among basins, seasons, and observational datasets. The occurrence-correction approach produced the largest gains in forecast skill, increasing the Critical Success Index from 0.637 to 0.767 (approximately 20%), but still shows its sensibility to the main characteristics of the forecasting model. Overall, the results demonstrate that high-resolution precipitation forecasts can provide valuable inputs for hydrological forecasting in the PCJ Basin when combined with appropriate bias-correction techniques, while highlighting opportunities for further methodological improvements.

Precipitation forecasts from numerical weather prediction systems can contain systematic errors in both occurrence and magnitude, limiting their usefulness for hydrological applications. This study evaluated two operational precipitation forecasting systems against two observational reference datasets across three basins in São Paulo State, Brazil, and assessed statistical post-processing for dry/wet occurrence and precipitation magnitude. Four logistic-regression-based methods were evaluated for occurrence correction, while Quantile Delta Mapping (QDM) was applied to precipitation amounts. Occurrence correction was assessed using POD, FAR, CSI, and ACC, with dry days defined as the event. Changes in individual classifications were evaluated using the exact McNemar test, while changes in categorical metrics across seven lead times were assessed using exact paired permutation tests and bootstrap 95% confidence intervals. A descriptive multi-metric ranking was used to compare correction methods. QDM was evaluated using RMSE skill score and KGE. Occurrence correction modified categorical performance, with effects depending on forecast–observation pairing, basin, and lead time. The McNemar test identified significant classification changes after Holm adjustment in some configurations, whereas the permutation tests did not provide evidence of systematic metric improvement. HBLR-AR1 showed the most balanced overall performance, whereas LR-Seasonal was the least consistent method. QDM improved RMSE skill, particularly at longer lead times, but did not consistently improve KGE. Overall, correction effectiveness depended on forecast–observation discrepancies.