Next Article in Journal
Comparing Machine Learning and Decision Making Approaches to Forecast Long Lead Monthly Rainfall: The City of Vancouver, Canada
Next Article in Special Issue
Floods and Countermeasures Impact Assessment for the Metro Colombo Canal System, Sri Lanka
Previous Article in Journal
Characterizing Total Phosphorus in Current and Geologic Utah Lake Sediments: Implications for Water Quality Management Issues
Previous Article in Special Issue
RCP8.5-Based Future Flood Hazard Analysis for the Lower Mekong River Basin
Open AccessTechnical Note

Merging Real-Time Channel Sensor Networks with Continental-Scale Hydrologic Models: A Data Assimilation Approach for Improving Accuracy in Flood Depth Predictions

1
Department of Civil and Construction Engineering, Oregon State University, Corvallis, OR 97331, USA
2
Department of Civil Engineering, University of Texas at Arlington, Arlington, TX 76019, USA
3
Department of Civil & Environmental Engineering, University of Texas at San Antonio, San Antonio, TX 78256, USA
*
Author to whom correspondence should be addressed.
Hydrology 2018, 5(1), 9; https://doi.org/10.3390/hydrology5010009
Received: 27 October 2017 / Revised: 15 January 2018 / Accepted: 18 January 2018 / Published: 21 January 2018
(This article belongs to the Special Issue Advances in Large Scale Flood Monitoring and Detection)
This study proposes a framework that (i) uses data assimilation as a post processing technique to increase the accuracy of water depth prediction, (ii) updates streamflow generated by the National Water Model (NWM), and (iii) proposes a scope for updating the initial condition of continental-scale hydrologic models. Predicted flows by the NWM for each stream were converted to the water depth using the Height Above Nearest Drainage (HAND) method. The water level measurements from the Iowa Flood Inundation System (a test bed sensor network in this study) were converted to water depths and then assimilated into the HAND model using the ensemble Kalman filter (EnKF). The results showed that after assimilating the water depth using the EnKF, for a flood event during 2015, the normalized root mean square error was reduced by 0.50 m (51%) for training tributaries. Comparison of the updated modeled water stage values with observations at testing locations showed that the proposed methodology was also effective on the tributaries with no observations. The overall error reduced from 0.89 m to 0.44 m for testing tributaries. The updated depths were then converted to streamflow using rating curves generated by the HAND model. The error between updated flows and observations at United States Geological Survey (USGS) station at Squaw Creek decreased by 35%. For future work, updated streamflows could also be used to dynamically update initial conditions in the continental-scale National Water Model. View Full-Text
Keywords: data assimilation; ensemble Kalman filter; flood inundation maps; National Water Model (NWM) data assimilation; ensemble Kalman filter; flood inundation maps; National Water Model (NWM)
Show Figures

Figure 1

MDPI and ACS Style

Javaheri, A.; Nabatian, M.; Omranian, E.; Babbar-Sebens, M.; Noh, S.J. Merging Real-Time Channel Sensor Networks with Continental-Scale Hydrologic Models: A Data Assimilation Approach for Improving Accuracy in Flood Depth Predictions. Hydrology 2018, 5, 9.

Show more citation formats Show less citations formats
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop