Open AccessDeveloping a decision support framework for real‐time flood management using integrated models
Author(s) -
Şensoy A.,
Uysal G.,
Şorman A.A.
Publication year - 2018
Publication title -
journal of flood risk management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.049
H-Index - 36
ISSN - 1753-318X
DOI - 10.1111/jfr3.12280
Subject(s) - snowmelt , flood myth , flood forecasting , environmental science , flood control , hydrology (agriculture) , flood warning , warning system , channel (broadcasting) , hec hms , scada , hydrological modelling , surface runoff , decision support system , computer science , geology , engineering , data mining , geography , geotechnical engineering , archaeology , climatology , electrical engineering , biology , ecology , telecommunications , computer network
Flood risk mitigation during real‐time flood events comprising snowmelt under hydrological uncertainty is particularly challenging. Giving this due consideration, the aim is to develop a Decision Support System (DSS) for the real‐time operation of a reservoir and the channel downstream. This was accomplished with continuous catchment monitoring, runoff forecasting, optimal reservoir decisions, river, and flood inundation analysis. The proposed DSS consists of four major components: (1) Supervisory Control and Data Acquisition ( SCADA ) system for real‐time monitoring; (2) Hydrologic Engineering Center ( HEC ) – Hydrological Modelling System ( HMS ) to simulate rainfall/snowmelt runoff process; (3) HEC – Reservoir Simulation System ( ResSim ) for operation of the controlled reservoir; and (4) HEC – River Analysis System ( RAS ) for two‐dimensional unsteady river analysis and flood inundation mapping. After the DSS is set up, a real‐time operation for an extreme case scenario was simulated to test the performance, and flood maps were generated. Yuvacık Dam Basin in Turkey has a robust gauge network but is rather limited in reservoir storage and downstream channel capacity. This is the basis for selection of the area in the study. Results show that the developed DSS is useful for forecasting and reservoir operations, in addition to serving as an early warning system.