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Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations

(2019) ADVANCES IN WATER RESOURCES. 130. p.229-243
Author
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Abstract
Reliable flood forecasting systems are the prerequisite for proper flood warning systems. Currently, satellite remote sensing (SRS) observations are widely used to improve model forecasts. Although they provide distributed information, they are sometimes unable to satisfy flood modellers' needs due to low overpass frequencies and high measuring uncertainties. This paper assesses the potential of sparsely distributed, in situ floodplain water level sensors to provide accurate, near-real time flood information as a means to enhance flood predictions. A synthetic twin experiment evaluates the assimilation of different sensor network configurations, designed through time series clustering and Voronoi spacing. With spatio-temporal RMSEs reaching up to 1 cm, the study demonstrates great potential. Adequate sensor placement proved crucial for improved performance. In practice, observation locations should be chosen such that they are located rather close to the river, to increase the likelihood of early flooding and thus acquiring valuable information at an early stage of flooding. Furthermore, high measuring frequencies benefit the simulations, though one should be careful not to overcorrect water levels as these may result in inconsistencies. Lastly, a network size of 5 to 7 observations yields good results, while an increasing number of observations generally diminishes the importance of extra observations. Our findings could greatly contribute to future flood observing systems to either compensate for ungauged areas, or complement current SRS practices.
Keywords
Flood monitoring, Data assimilation, Observation network, ENSEMBLE KALMAN FILTER, HYDRAULIC MODELS, TIME-SERIES, SEQUENTIAL ASSIMILATION, SATELLITE-OBSERVATIONS, OBSERVATION IMPACT, RIVER DISCHARGE, UNCERTAINTY, CALIBRATION, STATE

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Citation

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MLA
Van Wesemael, Alexandra, et al. “Improving Flood Inundation Forecasts through the Assimilation of in Situ Floodplain Water Level Measurements Based on Alternative Observation Network Configurations.” ADVANCES IN WATER RESOURCES, vol. 130, 2019, pp. 229–43, doi:10.1016/j.advwatres.2019.05.025.
APA
Van Wesemael, A., Landuyt, L., Lievens, H., & Verhoest, N. (2019). Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations. ADVANCES IN WATER RESOURCES, 130, 229–243. https://doi.org/10.1016/j.advwatres.2019.05.025
Chicago author-date
Van Wesemael, Alexandra, Lisa Landuyt, Hans Lievens, and Niko Verhoest. 2019. “Improving Flood Inundation Forecasts through the Assimilation of in Situ Floodplain Water Level Measurements Based on Alternative Observation Network Configurations.” ADVANCES IN WATER RESOURCES 130: 229–43. https://doi.org/10.1016/j.advwatres.2019.05.025.
Chicago author-date (all authors)
Van Wesemael, Alexandra, Lisa Landuyt, Hans Lievens, and Niko Verhoest. 2019. “Improving Flood Inundation Forecasts through the Assimilation of in Situ Floodplain Water Level Measurements Based on Alternative Observation Network Configurations.” ADVANCES IN WATER RESOURCES 130: 229–243. doi:10.1016/j.advwatres.2019.05.025.
Vancouver
1.
Van Wesemael A, Landuyt L, Lievens H, Verhoest N. Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations. ADVANCES IN WATER RESOURCES. 2019;130:229–43.
IEEE
[1]
A. Van Wesemael, L. Landuyt, H. Lievens, and N. Verhoest, “Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations,” ADVANCES IN WATER RESOURCES, vol. 130, pp. 229–243, 2019.
@article{8634881,
  abstract     = {{Reliable flood forecasting systems are the prerequisite for proper flood warning systems. Currently, satellite remote sensing (SRS) observations are widely used to improve model forecasts. Although they provide distributed information, they are sometimes unable to satisfy flood modellers' needs due to low overpass frequencies and high measuring uncertainties. This paper assesses the potential of sparsely distributed, in situ floodplain water level sensors to provide accurate, near-real time flood information as a means to enhance flood predictions. A synthetic twin experiment evaluates the assimilation of different sensor network configurations, designed through time series clustering and Voronoi spacing. With spatio-temporal RMSEs reaching up to 1 cm, the study demonstrates great potential. Adequate sensor placement proved crucial for improved performance. In practice, observation locations should be chosen such that they are located rather close to the river, to increase the likelihood of early flooding and thus acquiring valuable information at an early stage of flooding. Furthermore, high measuring frequencies benefit the simulations, though one should be careful not to overcorrect water levels as these may result in inconsistencies. Lastly, a network size of 5 to 7 observations yields good results, while an increasing number of observations generally diminishes the importance of extra observations. Our findings could greatly contribute to future flood observing systems to either compensate for ungauged areas, or complement current SRS practices.}},
  author       = {{Van Wesemael, Alexandra and Landuyt, Lisa and Lievens, Hans and Verhoest, Niko}},
  issn         = {{0309-1708}},
  journal      = {{ADVANCES IN WATER RESOURCES}},
  keywords     = {{Flood monitoring,Data assimilation,Observation network,ENSEMBLE KALMAN FILTER,HYDRAULIC MODELS,TIME-SERIES,SEQUENTIAL ASSIMILATION,SATELLITE-OBSERVATIONS,OBSERVATION IMPACT,RIVER DISCHARGE,UNCERTAINTY,CALIBRATION,STATE}},
  language     = {{eng}},
  pages        = {{229--243}},
  title        = {{Improving flood inundation forecasts through the assimilation of in situ floodplain water level measurements based on alternative observation network configurations}},
  url          = {{http://dx.doi.org/10.1016/j.advwatres.2019.05.025}},
  volume       = {{130}},
  year         = {{2019}},
}

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