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${}^2$).

The assessment of seasonal streamflow forecasting is essential for appropriate water resource management. A suitable seasonal forecasting system requires the evaluation of both numerical weather prediction (NWP) and hydrological models to represent the atmospheric and hydrological processes and conditions in a specific region. In this paper, we evaluated the ECMWF-SEAS5 precipitation product with four hydrological models to represent seasonal streamflow forecasts performed at hydropower plants in the Legal Amazon region. The adopted models included GR4J, HYMOD, HBV, and SMAP, which were calibrated on a daily scale for the period from 2014 to 2019 and validated for the period from 2005 to 2013. The seasonal streamflow forecasts were obtained for the period from 2017 to 2019 by considering a daily scale streamflow simulation comprising an ensemble with 51 members of forecasts, starting on the first day of every month up to 7 months ahead. For each forecast, the corresponding monthly streamflow time series was estimated. A post-processing procedure based on the adjustment of an autoregressive model for the residuals was applied to correct the bias of seasonal streamflow forecasts. Hence, for the calibration and validation period, the results show that the HBV model provides better results to represent the hydrological conditions at each hydropower plant, presenting NSE and NSElog values greater than 0.8 and 0.9, respectively, during the calibration stage. However, the SMAP model achieves a better performance with NSE values of up to 0.5 for the raw forecasts. In addition, the bias correction displayed a significant improvement in the forecasts for all hydrological models, specifically for the representation of streamflow during dry periods, significantly reducing the variability of the residuals.

In rainfall-runoff modeling, the main input variable is precipitation, and the understanding of its temporal and spatial variation is the key for good hydrological simulation results. Conventionally, the precipitated volumes are measured by rain gauges, which are representative of its surroundings and, consequently, it is necessary to apply extrapolation techniques to obtain data in ungauged regions. However, classical techniques are based on mathematical interpolation and do not consider the physical evidence for the occurrence of precipitation. Remote sensing represents a valuable alternative to hydrological modeling due to its wide coverage, and from observations by meteorological satellites and radars, quantitative precipitation estimation is possible. In this sense, the integrated use of data from rain gauges and remote sensing has the potential to improve the accuracy of hydrological simulations. This study aims to evaluate the performance of a hydrological model in the Colider River basin (Brazil), when calibrated with a global product that provides precipitation data based on rain gauges observations, satellite and weather radar. The model used was the MGB-IPH and the data source of precipitation was MSWEP (Multi-Source Weighted-Ensemble Precipitation). Two different calibrations were performed: the first, considering only the precipitation data from rain gauges; the second, considering the precipitation estimated by the product. The comparison between the rain datasets indicates that MSWEP tends to overestimate the precipitation in most cases, except during periods of considerable drought, when it underestimates. Nevertheless, the results in the hydrological simulation were satisfactory, with the model calibrated with MSWEP presenting equivalente or slightly better performance metrics than the one with conventional data. This is an indication that the continuous development of remote sensing products can be the key to increase the reliability of tools that comprise hydrological modeling, such as forecasting hydrological events, climatic hazards and also commercialization of electric energy.Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian National Electricity Regulatory Agency (ANEEL) under its Research and Development Project PD 6491-0503/2018 – “Previsão Hidroclimática com Abrangência no Sistema Interligado Nacional de Energia Elétrica” developed by the Paraná State electric company (COPEL GeT), the Meteorological System of Paraná (SIMEPAR) and the RHAMA Consulting company.

The Electric Energy Company of Parana (COPEL GeT), the Meteorological System of Parana (SIMEPAR) and RHAMA Consulting company are undertaking the research project PD-6491- 0503/2018 for the development of a hydrometeorological seasonal forecasting for Brazilian reservoirs. The project, sponsored by the Brazilian Electricity Regulatory Agency (ANEEL) under its research and development program, aims the forecasting of streamflow, at temporal scales ranging from 1 to 270 days, at hydro power enterprises, which are integrated by the National Power System Operator (ONS) through its Interconnected System (SIN). With the aim of implement a seasonal forecasting system using different hydrological modeling approaches, it is necessary first to validate the use of different hydrological models during the calibration and validation stages. This work evaluates the performance of four conceptual hydrological models to represent daily streamflow regimes at four hydropower plants located in the Teles Pires river basin (Brazil). The adopted models included the GR4J, HYMOD, HBV, and the SMAP. The calibration of the parameters for each hydrological model was performed using the SCE-UA method and a triangular weighting function was adopted for routing the hydrograph between sub-watersheds. The evaluation of each model was elaborated by the comparison of the observed and simulated streamflow time series during the calibration (2010-2016) and the validation period (2016-2019) using deterministic metrics and statistical tests. A post-processing procedure based on the quantile-quantile method was applied in order to correct the simulated data and reduce the bias with respect the observed data. In general, the results show that the SMAP model present a better performance to simulate the daily streamflow regimes at the simulated hydropower plants, with Nash-Sutcliffe coefficient (NSE) greater than 0.65, and NSElog values greater than 0.8. In addition, the bias correct procedure shows a significant improvement in the adjust of the simulated data to represent the periodic streamflow regimes in the selected river basin.

The Electric Energy Company of Parana (COPEL GeT), the Meteorological System of Parana (SIMEPAR) and RHAMA Consulting company are undertaking the research project PD-6491-0503/2018 for the development of a hydrometeorological seasonal forecasting for Brazilian reservoirs. The project, sponsored by the Brazilian Electricity Regulatory Agency (ANEEL) under its research and development programme, aims the forecasting of streamflow, at temporal scales ranging from 1 to 270 days, at hydro power enterprises, which are integrated by the National Power System Operator (ONS) through its Interconnected System (SIN). We present in this work the framework built up as interface to the results of this project, which integrate shapefiles of main river basins in Brazil, hydro meteorological information, forecasts of precipitation from seasonal models (e.g., ECMWF’s SEAS5) and derived streamflow from hydrological model used in the project (MGB-SA mainly) for the entire electric energy network of the country. The platform encompasses layers of maps and graphics synchronized by date, respectively to locations of hydro power plants in Brazil, which allows users to perform multiple analysis for either energy planning or routine hydraulic operations. We shall demonstrate examples of applications, such analysis of a flood event happened during the 2014/2015 El Niño episode, which caused heavy precipitation, increased river level and flow into reservoirs in the Iguaçu River basin, disruption of services and economic losses in the South of Brazil. Given the limitations of seasonal precipitation forecasting, the model was successful in predicting the heavy accumulated rainfall in the analyzed period. In parallel, the hydrological model was able to simulate flow peaks well in advance. In addition, the platform allows an overview of the SIN subsystems and respective stored energy, which allows intercomparison and pragmatic analysis of the country's electric energy capacity.

The Electric Energy Company of Parana (COPEL GeT), the Meteorological System of Parana (SIMEPAR) and RHAMA Consulting company are undertaking the research project PD-6491-0503/2018 for the development of a hydrometeorological seasonal forecasting for Brazilian reservoirs. The project, sponsored by the Brazilian Electricity Regulatory Agency (ANEEL) under its research and development programme, aims the forecasting of streamflow, at temporal scales ranging from 1 to 270 days, at hydro power enterprises, which are integrated by the National Power System Operator (ONS) through its Interconnected System (SIN). In the present work, we verify the precipitation seasonal product from SEAS5 from ECMWF against three references, namely model climatology, ERA5 reanalysis and in-situ observations. In order to achieve the results, we extract the values from the model, respectively to the closest location of observations within Brazilian rain gauge network, corresponding to hydro power plants, and compare them to the observed values and ERA5 results, for the period from 2000 to 2020. The accuracy measurement was performed by settling a contingency matrix to estimate the probability of detection (POD), probability of false detection (POFD), the ROC curve, the area under the ROC (AUC) and other related metrics. The statistics are gathered by monthly and by season and by considering three quantile thresholds of rainfall distribution for forecasting, computed for 153 reservoirs of the SIN. The results describe a good performance of SEAS5 for either monthly or seasonal forecast if compared to climatology or ERA5, but less accuracy if compared to the rain gauges, mainly for low quantiles. Despite this, by considering the large extension of the country and its climate diversity, we noticed the SEAS5 is quite promising for using on hydrological forecasting at seasonal scale.