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Abstract
This study investigates the performance of TRMM and PERSIANN satellite rainfall data as input in a reliable rainfall-runoff model in order to provide information to the flood early warning for the Voshmgir Dam, Iran. Calibration of both continuous daily rainfall-runoff and an event-based flood was done using HEC-HMS Nelder-Mead (NM) method. Furthermore, simulations based on daily versus 3-hourly TRMM were compared to evaluate the effect of input time-step of rainfall-runoff model. Results show that the deficit and constant loss method is able to successfully predict the observed runoff. In addition, the Green-Ampt method using 3-hourly TRMM data showed a good capability to simulate daily peak discharges. The current study demonstrates the suitability of HEC-HMS for continuous and event-based runoff simulation in a complex watershed. Therefore, this research will have a significant contribution to the future development of water resources planning in this catchment in particular and in other data-scarce catchments.
Acknowledgements
We appreciate NASA for providing the TRMM dataset. Also, we would like to express our appreciation to the Iran Water Resources Company for providing observed data of the study area.
Additional information
Funding
Notes on contributors
Peiman Parisouj
Peiman Parisouj M.Sc. student in the Department of Civil Engineering, Gyeongsang National University, South Korea. He got a B.Sc. degree from Azad University with civil engineering. He specializes in GIS, remote sensing, machine learning and climate change in hydrology. He has published 2 articles.
Taesam Lee
Taesam Lee PhD is an associate professor in the Department of Civil Engineering at Gyeongsang National University. He got a PhD degree from Colorado State University with stochastic simulation of streamflow. He specializes in surface-water hy.drology, meteorology, and climatic changes in hydrological extremes publishing more than 40 technical papers.
Hamid Mohebzadeh
Hamid Mohebzadeh PhD candidate in the Department of Civil Engineering, Gyeongsang National University, South Korea. He got a M.Sc. degree from Buali Sina University with Irrigation and Drainage. He specializes in GIS, remote sensing, machine learning, geostatistics and climate change in hydrology. He has published more than 10 articles.
Hadi Mohammadzadeh Khani
Hadi Mohammadzadeh Khani PhD candidate in the Department of Environmental Science, University of Quebec at Trois-Rivieres, Quebec, Canada. He specializes in surface-water hydrology, climate change and remote sensing.