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GRAPS: Generalized Multi-Reservoir Analyses using probabilistic streamflow forecasts.
- Source :
-
Environmental Modelling & Software . Nov2020, Vol. 133, pN.PAG-N.PAG. 1p. - Publication Year :
- 2020
-
Abstract
- A multi-reservoir simulation-optimization model GRAPS, Generalized Multi-Reservoir Analyses using Probabilistic Streamflow Forecasts, is developed in which reservoirs and users across the basin are represented using a node-link representation. Unlike existing reservoir modeling software, GRAPS can handle probabilistic streamflow forecasts represented as ensembles for performing multi-reservoir prognostic water allocation and evaluate the reliability of forecast-based allocation with observed streamflow. GRAPS is applied to four linked reservoirs in the Jaguaribe Metropolitan Hydro-System (JMH) in Ceará, North East Brazil. Results from the historical simulation and the zero-inflow policy over the JMH system demonstrate the model's capability to support monthly water allocation and reproduce the observed monthly releases and storages. Additional analyses using streamflow forecast ensembles illustrate GRAP's abilities in developing storage-reliability curves under inflow-forecast uncertainty. Our analyses show that GRAPS is versatile and can be applied for 1) short-term operating policy studies, 2) long-term basin-wide planning evaluations, and 3) climate-information based application studies. • GRAPS is a generalized multi-reservoir simulation-optimization model for water allocation and reservoir management. • GRAPS is intended to assist engineers and stakeholders as a decision-supporting tool to support seasonal water allocation. • GRAPS can handle streamflow forecasts as ensembles to quantify the reliability of meeting target storage and demand. [ABSTRACT FROM AUTHOR]
- Subjects :
- *RESERVOIRS
*WATER rights
*FORECASTING
*STREAMFLOW
*WATER supply
Subjects
Details
- Language :
- English
- ISSN :
- 13648152
- Volume :
- 133
- Database :
- Academic Search Index
- Journal :
- Environmental Modelling & Software
- Publication Type :
- Academic Journal
- Accession number :
- 146324812
- Full Text :
- https://doi.org/10.1016/j.envsoft.2020.104802