A simple framework for calibrating hydraulic flood inundation models using Crowd-sourced water levels

DC FieldValueLanguage
dc.contributor.authorDasgupta, Antara
dc.contributor.authorGrimaldi, Stefania
dc.contributor.authorRamsankaran, Raaj
dc.contributor.authorPauwels, Valentijn R. N.
dc.contributor.authorWalker, Jeffrey P.
dc.date.accessioned2023-02-17T11:35:54Z-
dc.date.available2023-02-17T11:35:54Z-
dc.date.issued2022
dc.identifier.issn0022-1694
dc.identifier.urihttp://osnascholar.ub.uni-osnabrueck.de/handle/unios/65543-
dc.description.abstractFloods are the most commonly occurring natural disaster, with the Centre for Research on the Epidemiology of Disasters 2021 report on ``The Non-COVID Year in Disasters'' estimating economic losses worth over USD 51 million and more than 6000 fatalities in 2020. The hydrodynamic models which are used for flood forecasting need to be evaluated and constrained using observations of water depth and extent. While remotely sensed estimates of these variables have already facilitated model evaluation, citizen sensing is emerging as a popular technique to complement real-time flood observations. However, its value for hydraulic model evaluation has not yet been demonstrated. This paper tests the use of crowd-sourced flood observations to quantitatively assess model performance for the first time. The observation set used for performance assessment consists of 32 distributed high water marks and wrack marks provided by the Clarence Valley Council for the 2013 flood event, whose timings of acquisition were unknown. Assuming that these provide information on the peak flow, maximum simulated water levels were compared at observation locations, to calibrate the channel roughness for the hydraulic model LISFLOOD-FP. For each realization of the model, absolute and relative simulation errors were quantified through the root mean squared error (RMSE) and the mean percentage difference (MPD), respectively. Similar information was extracted from 11 hydrometric gauges along the Clarence River and used to constrain the roughness parameter. The calibrated parameter values were identical for both data types and a mean RMSE value of similar to 50 cm for peak flow simulation was obtained across all gauges. Results indicate that integrating uncertain flood observations from crowd-sourcing can indeed generate a useful dataset for hydraulic model calibration in ungauged catchments, despite the lack of associated timing information.
dc.description.sponsorshipBushfire and Natural Hazards CRC of Australia; IITB-Monash Research Academy; University of Osnabrueck; This study was conducted within the framework of the project ``Improving flood forecast skill using remote sensing data,'' funded by the Bushfire and Natural Hazards CRC of Australia. Additionally, gratitude is extended to the Australian Bureau of Meteorology (http://www. bom.gov.au/waterdata/) and New South Wales Manly Hydraulics Laboratory (http://new.mhl.nsw.gov.au/) for the gauge data, in addition to Geoscience Australia and the Clarence Valley Council for sharing field and ancillary data. Antara Dasgupta was funded by a PhD scholarship from the IITB-Monash Research Academy and a Postdoctoral Scholarship from the University of Osnabrueck.
dc.language.isoen
dc.publisherELSEVIER
dc.relation.ispartofJOURNAL OF HYDROLOGY
dc.subjectASSIMILATION
dc.subjectCrowd -sourcing
dc.subjectDELINEATION
dc.subjectEngineering
dc.subjectEngineering, Civil
dc.subjectGeology
dc.subjectGeosciences, Multidisciplinary
dc.subjectHydrodynamic modelling
dc.subjectIDENTIFIABILITY
dc.subjectINDEX NDWI
dc.subjectLISFLOOD-FP
dc.subjectModel evaluation
dc.subjectOPPORTUNITIES
dc.subjectRESOLUTION
dc.subjectROUGHNESS VALUES
dc.subjectSAR
dc.subjectSensitivity analysis
dc.subjectUNCERTAINTY
dc.subjectVALIDATION
dc.subjectWater Resources
dc.titleA simple framework for calibrating hydraulic flood inundation models using Crowd-sourced water levels
dc.typejournal article
dc.identifier.doi10.1016/j.jhydrol.2022.128467
dc.identifier.isiISI:000872395100003
dc.description.volume614
dc.description.issueA
dc.identifier.eissn1879-2707
dc.publisher.placeRADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS
dcterms.isPartOf.abbreviationJ. Hydrol.
dcterms.oaStatusGreen Submitted
local.import.remainsaffiliations : Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Indian Institute of Technology System (IIT System); Indian Institute of Technology (IIT) - Bombay; Monash University; University Osnabruck
local.import.remainsweb-of-science-index : Science Citation Index Expanded (SCI-EXPANDED)
Show simple item record

Page view(s)

3
Last Week
0
Last month
1
checked on Jul 16, 2024

Google ScholarTM

Check

Altmetric