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Science questions
Manabendra Saharia edited this page Jun 1, 2018
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Flood frequency estimates maybe more uncertain than appreciated.
Drive a stochastic (ensemble) event to reveal true FF uncertainty and understanding sensitivities across case study basins in the Western US
- Hydrologic Model Structure - Expanding from one model to multi model ensemble
- Model Parameters - Vary model parameters
- Initial Conditions - Develop many model Initial Conditions that are internally consistent for each model structure
- Stochastic simulations of historical events with randomly perturbed initial conditions (ICs) and thousands of precipitation input scenarios from defined precipitation frequency curves using one model and model parameter set.
- Current FF estimates may be overconfident or, alternatively, require more calibration and parameter development than is necessary for some studies.
- Framework for Understanding Structural Errors (FUSE) that allows users to systematically change hydrologic model components (structures) to create a rigorous multi-model ensemble.
- FUSE uses a common numerical solver across all model structures, so that changes in hydrologic model simulations are due to only the changes in model structure.
- FUSE will mimic operational models such as HEC-HMS
- Newman (2015) hydromet dataset with 100 realizations
- Multi-Objective COMplex evolution, MOCOM method etc.
- DELSA etc.
- Multi-model ensemble and MOCOM calibrations for the pilot basin.
- ICs generated quickly using the Newman dataset to drive each model and parameter set in a 30+ year continuous simulation.
- Event based simulations in the historical simulation period
- Multiple calibrated model parameters for each model ensemble member via MOCOM
- DELSA
- Apply the end-to-end FF and DELSA systems to the remaining basins