<p>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&#8217;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 Ni&#241;o episode, which caused heavy precipitation, increased river level and flow into reservoirs in the Igua&#231;u 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.</p>
<p>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.</p><p>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 &#8211; &#8220;Previs&#227;o Hidroclim&#225;tica com Abrang&#234;ncia no Sistema Interligado Nacional de Energia El&#233;trica&#8221; developed by the Paran&#225; State electric company (COPEL GeT), the Meteorological System of Paran&#225; (SIMEPAR) and the RHAMA Consulting company.</p>
<p><span>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 </span>Brazilian Electricity Regulatory Agency <span>(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.</span></p>
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