<p>Society&#8217;s increasing demand for water and the need for its long-term management have motivated efforts toward improving seasonal streamflow forecasts. Currently, seasonal climate forecasts are routinely issued in meteorological centers around the world, generating information for decision-making and seasonal streamflow forecasting (SSF) studies that are becoming more frequent. Seasonal streamflow forecast skill derives from land surface initial conditions and atmospheric boundary conditions that mostly depend on large-scale climate phenomena (such as ENSO). Thus, seasonal rainfall predictions produced by dynamic climate models that represent ocean-atmosphere interactions may have a positive impact on streamflow forecasts. In South America, seasonal streamflow forecasts are essential for the hydropower sector, which is responsible for ~65% of the electric energy produced in countries such as Brazil. In this work, we assessed seasonal streamflow forecasts over South America based on a continental-scale application of a hydrologic-hydrodynamic model and precipitation forecasts from the ECMWF's fifth generation seasonal forecast system (SEAS5). Seasonal streamflow forecasts (SEAS5-SF) were evaluated against a reference model run and forecast skill was estimated relative to the Ensemble Streamflow Prediction (ESP) method. The bias correction of SEAS5 predicted precipitation improved the performance of the seasonal streamflow forecasts, frequently turning negative skill results into near null to positive skill. Results indicate that the ESP remains a hard-to-beat method for seasonal streamflow forecasting in South America. SEAS5-SF skill was found to be dependent on initialization month, season, basin and forecast lead time, with greater skill on the initialization month lead time. Rivers where the forecast skill is higher were Amazon, Araguaia, Tocantins and Paran&#225;.</p> <p>&#160;</p> <p>Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian Agency of Electrical Energy (ANEEL) under its Research and Development program 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. The Hydraulic Research Institute (IPH) from the Federal University of Rio Grande do Sul (UFRGS) contribute to part of the project through an agreement with the RHAMA company (IAP-001313).</p>
The design of water distribution systems includes, above all, the determination of the pipe size that meets the requirements of the system and brings reduced annual costs of installation and operation. Among the existing methodologies for the economic pipe diameter determination, Bresse’s equation is still common among designers. This work aims to analyze the efficiency of Bresse’s equation, the MBPW and the MLVPC, comparing them with the MREC. We recommend that the designers do not use the MBPW and the MLVPC. When referring to water distribution systems of small size, it is possible to use the equation of Bresse, as long as it is used with a proper value for its coefficient k. For HDPE, we propose k = 1.17 or k = 1.18. For PVC DEFOFO and GFRP, we suggest the range of 1.19 to 1.23 and 1.29 to 1.32, respectively. Regarding the water distribution systems of bigger dimensions, we recommend the use of MREC as the methodology for the economic pipe diameter determination, due to the impossibility of finding an appropriate value for the Bresse’s equation coefficient.
<p>The flow forecast is used in several sectors of society, bringing benefits in relation to the mitigation of possible impacts in flood events and it is information of great value for the economic sectors associated with agriculture and energy generation. In South America, climate and meteorological variability directly impact these economic sectors. In Brazil, for example, the production of electricity is predominantly hydroelectric generation, which currently represents about 63% of the installed power in the country, in addition to the complementarity between different hydrographic basins and the other sources that make up the Brazilian energy matrix.</p> <p>The Brazilian electricity sector relies on flow forecasts for different time scales, which are used to optimize the available water resources and for the energy commercialization. The National Electric System Operator (ONS) is responsible for coordinating the operation of 153 Hydroelectric Power Plants (HPPs) and uses different hydrological models for flow forecasting. For the 14-day horizon (short term) it&#8217;s used the deterministic rain-flow model called SMAP. For the horizon of 15 to 45 days (sub seasonal) it&#8217;s used the PREVIVAZ, a univariate stochastic model.</p> <p>This work presents the evaluation of the performance of the SMAP model for forecasting in a sub seasonal horizon for 6 reservoirs in the Igua&#231;u River basin, associated with HPPs with a total installed capacity of 7,024 MW, located in the southern region of Brazil. Streamflow forecasts were evaluated using the European Center for Medium-Range Weather Forecasts (ECMWF) sub seasonal forecast, with lead time up to 46 days, from the Subseasonal-to-Seasonal (S2S) project database, and using the Global Ensemble Forecast System (GEFS) sub seasonal forecast, with lead time up to 35 days, from the National Centers for Environmental Prediction (NCEP) of the National Oceanic and Atmospheric Administration (NOAA).</p> <p>The results showed that the flow forecasts for the sub seasonal horizon present good performance for the initial forecast horizon, with degradation in the quality of the results after this horizon. There was also evidence of gain associated with forecasts for the ensemble over the entire horizon. The use of the SMAP model combined with precipitation forecasts in the sub seasonal horizon proved to be superior to the PREVIVAZ model, currently in use at the National Electric System Operator (ONS), with a significant improvement being observed, evidencing the usefulness of flow forecasts based on numerical models of precipitation prediction for the sub seasonal horizon.</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. The Hydraulic Research Institute (IPH) from the Federal University of Rio Grande do Sul (UFRGS) contribute to part of the project through an agreement with the RHAMA company (IAP-001313).</p>
Streamflow forecasts from continental to global scale hydrological models have gained attention, but their performance against operational forecasts at local to regional scales must be evaluated. This study assesses the skill of medium-range, weekly streamflow forecasts for 147 large Brazilian hydropower plants (HPPs) and compares their performance with forecasts issued operationally by the National Electric System Operator (ONS). A continental-scale hydrological model was forced with ECMWF medium-range forecasts, and outputs were corrected using quantile mapping (QM) and autoregressive model approaches. By using both corrections, the percentage of HPPs with skillful forecasts against climatology and persistence for 1–7 days ahead increased substantially for low to moderate (9% to 56%) and high (72% to 94%) flows, while using only the QM correction allowed positive skill mainly for low to moderate flows and for 8–15 days ahead (29% to 64%). Compared with the ONS, the corrected continental-scale forecasts issued for the first week exhibited equal or better performance in 60% of the HPPs, especially for the North and Southeast subsystems, the DJF and MAM months, and for HPPs with less installed capacity. The findings suggest that using simple corrections on streamflow forecasts issued by continental-scale models can result in competitive forecasts even for regional-scale applications.
<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 align="justify"><span lang="en-US">Short-to-medium range streamflow forecasting is essential for planning and operating hydropower plants (HPPs). The Brazilian National Interconnected System (SIN) is composed of more than 150 HPPs that are located over a wide range of climate and hydrological conditions. Forecasts of natural inflow into the SIN reservoirs are important to establish optimal operating rules to reduce costs with other energy sources, therefore influencing the prices in the energy market. The objective of this work is twofold: (i) evaluate the skill of ensemble streamflow forecasts for the SIN hydropower plants based on continental-scale hydrological modeling (MGB-SA) and medium-range ECWMF rainfall forecasts (MGB-ECMWF), and (ii) compare the MGB-ECMWF forecasts to those produced operationally by the Electric System National Operator (ONS). The MGB-ECMWF predictions were additionally bias-corrected and updated using quantile mapping and auto-regressive model approaches, and were assessed in the period from 2015 to 2020 in terms of weekly averages. The forecast skill was estimated relative to both streamflow climatology and persistency using the CRPS metric, while the comparison between MGB-ECMWF and operational forecasts was performed using deterministic metrics typically adopted by ONS. The skill of MGB-ECMWF forecasts was substantially improved (especially in the first week) by the use of output correction methods, which were demonstrated to be essential for quantitative streamflow forecasting using a continental-scale hydrological model. The relative performance between ONS and MGB-ECMWF forecasts was quite variable (exhibiting positive and negative values) over the geographical extent of the SIN, although in several locations the MGB-ECMWF forecasts have performed equal to or even better than those issued by ONS. Finally, the results presented here provide insights for investigations and applications of streamflow forecasts using continental-scale modeling and simple output correction techniques, which can bring benefits, for example, in the optimization of the reservoir operation and electricity generation.</span></p> <p align="justify"><span lang="en-US">Acknowledgments: This work presents part of the results obtained during the project granted by the Brazilian Agency of Electrical Energy (ANEEL) under its Research and Development program 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. The Hydraulic Research Institute (IPH) from the Federal University of Rio Grande do Sul (UFRGS) contribute to part of the project through an agreement with the RHAMA company (IAP-001313).</span></p>
A comparison of methodologies was carried out to develop an operational flow forecasting system for a mountainous basin. The case studied was the Boi river, which is located in a small mountainous watershed in southern Brazil. This watershed is part of a conservation unit well known for the ecotourism activities carried out in that place. Among the activities, the Boi river trail can be highlighted, which is carried out following the riverbed. For this reason, the development of an alert system for this basin can help in determining the trail's closure in situations where the water flow is strong and could present risks to tourists safety. Thereby, two different scenarios were considered for the development of the forecasting system. First, the flow forecasts were performed using SOPREVA and considered a horizon of 1 day (short term forecasts). The second scenario considered medium-term forecasts (1 to 10 days horizon) and utilized the HEC-RTS as a basis for developing the forecast system. In both cases, the forecasts were based on the ensemble precipitation estimates of GEFS. The obtained results showed that the actual alarm rates were 0.77 and 0.86 for SOPREVA and HEC-RTS, respectively, when considering a horizon of one day in advance. The evaluation of the medium-term forecasts presented good results of this system for horizons up to 3 days in advance. Finally, the results of both considered scenarios showed that the systems could be used as a basis for management of the Boi river trail.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.