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Probabilistic Flood Inundation Forecasting Using Rating Curve Libraries
Author(s) -
Buahin Caleb A.,
Sangwan Nikhil,
Fagan Cassandra,
Maidment David R.,
Horsburgh Jeffery S.,
Nelson E. James,
Merwade Venkatesh,
Rae Curtis
Publication year - 2017
Publication title -
jawra journal of the american water resources association
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.957
H-Index - 105
eISSN - 1752-1688
pISSN - 1093-474X
DOI - 10.1111/1752-1688.12500
Subject(s) - rating curve , streamflow , flood forecasting , flood myth , hydrology (agriculture) , ensemble forecasting , probabilistic logic , hydrological modelling , workflow , environmental science , computer science , stage (stratigraphy) , floodplain , flood mitigation , machine learning , artificial intelligence , geology , database , climatology , cartography , geotechnical engineering , geography , drainage basin , paleontology , archaeology , sediment
One approach for performing uncertainty assessment in flood inundation modeling is to use an ensemble of models with different conceptualizations, parameters, and initial and boundary conditions that capture the factors contributing to uncertainty. However, the high computational expense of many hydraulic models renders their use impractical for ensemble forecasting. To address this challenge, we developed a rating curve library method for flood inundation forecasting. This method involves pre‐running a hydraulic model using multiple inflows and extracting rating curves, which prescribe a relation between streamflow and stage at various cross sections along a river reach. For a given streamflow, flood stage at each cross section is interpolated from the pre‐computed rating curve library to delineate flood inundation depths and extents at a lower computational cost. In this article, we describe the workflow for our rating curve library method and the Rating Curve based Automatic Flood Forecasting ( RCAFF ) software that automates this workflow. We also investigate the feasibility of using this method to transform ensemble streamflow forecasts into local, probabilistic flood inundation delineations for the Onion and Shoal Creeks in Austin, Texas. While our results show water surface elevations from RCAFF are comparable to those from the hydraulic models, the ensemble streamflow forecasts used as inputs to RCAFF are the largest source of uncertainty in predicting observed floods.