Articles | Volume 16, issue 9
https://doi.org/10.5194/gmd-16-2415-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-2415-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
08 May 2023
Development and technical paper |
| 08 May 2023
| 08 May 2023
Tracing and visualisation of contributing water sources in the LISFLOOD-FP model of flood inundation (within CAESAR-Lisflood version 1.9j-WS)
Matthew D. Wilson
CORRESPONDING AUTHOR
Geospatial Research Institute, Toi Hangarau, University of Canterbury, Christchurch, New Zealand
School of Earth and Environment, Te Kura Aronukurangi, University of Canterbury, Christchurch, New Zealand
Thomas J. Coulthard
Energy and Environment Institute, University of Hull, Hull, United Kingdom
Related authors
Martin Nguyen, Matthew D. Wilson, Emily M. Lane, James Brasington, and Rose A. Pearson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-356, https://doi.org/10.5194/hess-2024-356, 2024
Preprint under review for HESS
Short summary
Short summary
River depth is crucial in flood modelling, yet often unavailable or costly to collect. Estimation methods can fill this gap but have errors affecting flood modelling. Our study quantified flood-prediction uncertainty due to these errors. Among parameters in Conceptual Multivariate Regression (CMR) and Uniform Flow (UF) methods, river width corresponds to the largest uncertainty, followed by flow and slope. Also, the UF-formula depths have higher uncertainty than the CMR-formula ones.
Chayan Banerjee, Kien Nguyen, Clinton Fookes, Gregory Hancock, and Thomas Coulthard
Geosci. Model Dev., 18, 803–818, https://doi.org/10.5194/gmd-18-803-2025, https://doi.org/10.5194/gmd-18-803-2025, 2025
Short summary
Short summary
In geosciences, the reliance on numerical models necessitates the precise calibration of their parameters to effectively translate information from observed to unobserved settings. We introduce a generalizable framework for calibrating numerical models, with a case study of the geomorphological model CAESAR-Lisflood. This approach efficiently identifies the optimal set of parameters for a given numerical model, enabling retrospective and prospective analyses at various temporal resolutions.
Martin Nguyen, Matthew D. Wilson, Emily M. Lane, James Brasington, and Rose A. Pearson
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2024-356, https://doi.org/10.5194/hess-2024-356, 2024
Preprint under review for HESS
Short summary
Short summary
River depth is crucial in flood modelling, yet often unavailable or costly to collect. Estimation methods can fill this gap but have errors affecting flood modelling. Our study quantified flood-prediction uncertainty due to these errors. Among parameters in Conceptual Multivariate Regression (CMR) and Uniform Flow (UF) methods, river width corresponds to the largest uncertainty, followed by flow and slope. Also, the UF-formula depths have higher uncertainty than the CMR-formula ones.
Charlotte Lyddon, Nguyen Chien, Grigorios Vasilopoulos, Michael Ridgill, Sogol Moradian, Agnieszka Olbert, Thomas Coulthard, Andrew Barkwith, and Peter Robins
Nat. Hazards Earth Syst. Sci., 24, 973–997, https://doi.org/10.5194/nhess-24-973-2024, https://doi.org/10.5194/nhess-24-973-2024, 2024
Short summary
Short summary
Recent storms in the UK, like Storm Ciara in 2020, show how vulnerable estuaries are to the combined effect of sea level and river discharge. We show the combinations of sea levels and river discharges that cause flooding in the Conwy estuary, N Wales. The results showed flooding was amplified under moderate conditions in the middle estuary and elsewhere sea state or river flow dominated the hazard. Combined sea and river thresholds can improve prediction and early warning of compound flooding.
Christopher J. Skinner and Thomas J. Coulthard
Earth Surf. Dynam., 11, 695–711, https://doi.org/10.5194/esurf-11-695-2023, https://doi.org/10.5194/esurf-11-695-2023, 2023
Short summary
Short summary
Landscape evolution models allow us to simulate the way the Earth's surface is shaped and help us to understand relevant processes, in turn helping us to manage landscapes better. The models typically represent the land surface using a grid of square cells of equal size, averaging heights in those squares. This study shows that the size chosen by the modeller for these grid cells is important, with larger sizes making sediment output events larger but less frequent.
Chloe Leach, Tom Coulthard, Andrew Barkwith, Daniel R. Parsons, and Susan Manson
Geosci. Model Dev., 14, 5507–5523, https://doi.org/10.5194/gmd-14-5507-2021, https://doi.org/10.5194/gmd-14-5507-2021, 2021
Short summary
Short summary
Numerical models can be used to understand how coastal systems evolve over time, including likely responses to climate change. However, many existing models are aimed at simulating 10- to 100-year time periods do not represent a vertical dimension and are thus unable to include the effect of sea-level rise. The Coastline Evolution Model 2D (CEM2D) presented in this paper is an advance in this field, with the inclusion of the vertical coastal profile against which the water level can be altered.
Cited articles
Adams, J. M., Gasparini, N. M., Hobley, D. E. J., Tucker, G. E., Hutton, E. W. H., Nudurupati, S. S., and Istanbulluoglu, E.: The Landlab v1.0 OverlandFlow component: a Python tool for computing shallow-water flow across watersheds, Geosci. Model Dev., 10, 1645–1663, https://doi.org/10.5194/gmd-10-1645-2017, 2017. a
Agência Nacional de Águas e Saneamento Básico: Hidroweb v3.2.7, Agência Nacional de Águas e Saneamento Básico [data set], https://www.snirh.gov.br/hidroweb/ (last access: 5 May 2023), 2023. a
Baronas, J. J., Torres, M. A., Clark, K. E., and West, A. J.: Mixing as a
driver of temporal variations in river hydrochemistry: 2. Major and trace
element concentration dynamics in the Andes‐Amazon transition, Water
Resour. Res., 53, 3120–3145, https://doi.org/10.1002/2016WR019729, 2017. a
Bates, P. and De Roo, A.: A simple raster-based model for flood inundation
simulation, J. Hydrol., 236, 54–77, https://doi.org/10.1016/S0022-1694(00)00278-X,
2000. a, b
Beven, K. J. and Kirkby, M. J.: A physically based, variable contributing area
model of basin hydrology/Un modèle à base physique de zone d'appel
variable de l'hydrologie du bassin versant, Hydrol. Sci. B., 24, 43–69,
https://doi.org/10.1080/02626667909491834, 1979. a
Christchurch City Council: Christchurch District Plan, Site of Ecological
Significance: Site Significance Statement – Avon Heathcote Estuary/Ihutai &
Environs, Tech. rep., Christchurch City Council,
https://districtplan.ccc.govt.nz/Images/DistrictPlanImages/Site of Ecological Significance/SES LP 14.pdf (last access: 5 May 2023),
2016. a
Coulthard, T. J.: CAESAR-Lisflood landscape evolution model version 1.9j,
https://sourceforge.net/projects/caesar-lisflood/ (last
access: 8 April 2023), 2019. a
Coulthard, T. J. and Macklin, M. G.: Modeling long-term contamination in river
systems from historical metal mining, Geology, 31, 451,
https://doi.org/10.1130/0091-7613(2003)031<0451:MLCIRS>2.0.CO;2,
2003. a
Coulthard, T. J. and Skinner, C. J.: The sensitivity of landscape evolution models to spatial and temporal rainfall resolution, Earth Surf. Dynam., 4, 757–771, https://doi.org/10.5194/esurf-4-757-2016, 2016. a
Coulthard, T. J. and Van De Wiel, M. J.: Modelling long term basin scale
sediment connectivity, driven by spatial land use changes, Geomorphology,
277, 265–281, https://doi.org/10.1016/j.geomorph.2016.05.027, 2017. a
Coulthard, T. J., Macklin, M. G., and Kirkby, M. J.: A cellular model of
Holocene upland river basin and alluvial fan evolution, Earth Surf. Proc.
Land., 27, 269–288, https://doi.org/10.1002/esp.318, 2002. a
Coulthard, T. J., Neal, J. C., Bates, P. D., Ramirez, J., de Almeida, G. A. M.,
and Hancock, G. R.: Integrating the LISFLOOD-FP 2D hydrodynamic model
with the CAESAR model: implications for modelling landscape evolution,
Earth Surf. Proc. Land., 38, 1897–1906, https://doi.org/10.1002/esp.3478, 2013. a, b, c
de Almeida, G. A. M. and Bates, P.: Applicability of the local inertial
approximation of the shallow water equations to flood modeling, Water Resour.
Res., 49, 4833–4844, https://doi.org/10.1002/wrcr.20366, 2013. a
de Almeida, G. A. M., Bates, P., Freer, J. E., and Souvignet, M.: Improving the
stability of a simple formulation of the shallow water equations for 2-D
flood modeling, Water Resour. Res., 48, W05528, https://doi.org/10.1029/2011WR011570, 2012. a
Dottori, F., Salamon, P., Bianchi, A., Alfieri, L., Hirpa, F. A., and Feyen,
L.: Development and evaluation of a framework for global flood hazard
mapping, Adv. Water Resour., 94, 87–102,
https://doi.org/10.1016/j.advwatres.2016.05.002, 2016. a
Dottori, F., Alfieri, L., Bianchi, A., Skoien, J., and Salamon, P.: A new dataset of river flood hazard maps for Europe and the Mediterranean Basin, Earth Syst. Sci. Data, 14, 1549–1569, https://doi.org/10.5194/essd-14-1549-2022, 2022. a
Findlay, R. H. and Kirk, R. M.: Post‐1847 changes in the Avon‐Heathcote
Estuary, Christchurch: A study of the effect of urban development around a
tidal estuary, New Zeal. J. Mar. Fresh., 22, 101–127,
https://doi.org/10.1080/00288330.1988.9516283, 1988. a
Galland, J.-C., Goutal, N., and Hervouet, J.-M.: TELEMAC: A new numerical
model for solving shallow water equations, Adv. Water Resour., 14, 138–148,
https://doi.org/10.1016/0309-1708(91)90006-A, 1991. a
Haddadchi, A., Hicks, M., Olley, J. M., Singh, S., and Srinivasan, M.:
Grid‐based sediment tracing approach to determine sediment sources, Land
Degrad. Dev., 30, 2088–2106, https://doi.org/10.1002/ldr.3407, 2019. a
Hansen, D. and Rattray, M.: New dimensions in estuary classification, Limnol.
Oceanogr., 11, 319–326, https://doi.org/10.4319/lo.1966.11.3.0319, 1966. a
Horritt, M. S., Bates, P. D., Fewtrell, T. J., Mason, D. C., and Wilson, M. D.:
Modelling the hydraulics of the Carlisle 2005 flood event, P. I. Civil
Eng.-Wat. M., 163, 273–281, https://doi.org/10.1680/wama.2010.163.6.273, 2010. a, b
Hunter, N. M., Bates, P. D., Horritt, M. S., and Wilson, M. D.: Simple
spatially-distributed models for predicting flood inundation: A review,
Geomorphology, 90, 208–225, https://doi.org/10.1016/j.geomorph.2006.10.021, 2007. a
Kopmann, R. and Markofsky, M.: Three-dimensional water quality modelling with
TELEMAC-3D, Hydrol. Process., 14, 2279–2292,
https://doi.org/10.1002/1099-1085(200009)14:13<2279::AID-HYP28>3.0.CO;2-7,
2000. a
Land Information New Zealand: Christchurch and Selwyn, Canterbury, New Zealand 2015, OpenTopography [data set], https://doi.org/10.5069/G9JQ0XZV (last access: 5 May 2023), 2015. a, b
LAWA: Land, Air, Water Aotearoa, https://www.lawa.org.nz/,
last access: 8 April 2023. a
Maia, P. D., Maurice, L., Tessier, E., Amouroux, D., Cossa, D., Pérez, M.,
Moreira-Turcq, P., and Rhéault, I.: Mercury distribution and exchanges
between the Amazon River and connected floodplain lakes., Sci. Total
Environ., 407, 6073–6084, https://doi.org/10.1016/j.scitotenv.2009.08.015, 2009. a
Maurice-Bourgoin, L., Quemerais, B., Moreira-Turcq, P., and Seyler, P.:
Transport, distribution and speciation of mercury in the Amazon River at the
confluence of black and white waters of the Negro and Solimões Rivers,
Hydrol. Process., 17, 1405–1417, https://doi.org/10.1002/hyp.1292, 2003. a
McMurtrie, S.: Heavy Metals in Christchurch Fish and Shellfish: 2014 Survey,
Tech. Rep. 08002-ENV01-04, EOS Ecology, New Zealand,
https://api.ecan.govt.nz/TrimPublicAPI/documents/download/2270056 (last access: 5 May 2023),
2015. a
Melack, J. M. and Hess, L. L.: Remote sensing of the distribution and extent of
wetlands in the amazon basin, in: Amazonian Floodplain Forests, edited by:
Junk, W. J., Piedade, M. T. F., Wittmann, F., Schöngart, J., and Parolin,
P., vol. 210, Ecological Studies, 43–59, Springer Netherlands,
Dordrecht, https://doi.org/10.1007/978-90-481-8725-6_3, 2011. a
Mitsch, W. J. and Gosselink, J. G.: Wetlands, Wiley, Hoboken, NJ, 5th edn.,
752 pp., ISBN 978-1-118-67682-0,
2015. a
Neal, J., Villanueva, I., Wright, N., Willis, T., Fewtrell, T., and Bates, P.:
How much physical complexity is needed to model flood inundation?, Hydrol.
Process., 26, 2264–2282, https://doi.org/10.1002/hyp.8339, 2012. a
Neal, J., Dunne, T., Sampson, C., Smith, A., and Bates, P.: Optimisation of the
two-dimensional hydraulic model LISFLOOD-FP for CPU architecture,
Environ. Modell. Softw., 107, 148–157, https://doi.org/10.1016/j.envsoft.2018.05.011,
2018. a
Neal, J. C., Bates, P. D., Fewtrell, T. J., Hunter, N. M., Wilson, M. D., and
Horritt, M. S.: Distributed whole city water level measurements from the
Carlisle 2005 urban flood event and comparison with hydraulic model
simulations, J. Hydrol., 368, 42–55, https://doi.org/10.1016/j.jhydrol.2009.01.026,
2009. a
O'Loughlin, F., Paiva, R., Durand, M., Alsdorf, D., and Bates, P.: A
multi-sensor approach towards a global vegetation corrected SRTM DEM
product, Remote Sens. Environ., 182, 49–59, https://doi.org/10.1016/j.rse.2016.04.018,
2016. a, b
O'Loughlin, F., Neal, J., Schumann, G., Beighley, E., and Bates, P.: A
LISFLOOD-FP hydraulic model of the middle reach of the Congo, J. Hydrol.,
580, 124203, https://doi.org/10.1016/j.jhydrol.2019.124203, 2020. a
Qi, W., Ma, C., Xu, H., Chen, Z., Zhao, K., and Han, H.: Low Impact Development
Measures Spatial Arrangement for Urban Flood Mitigation: An Exploratory
Optimal Framework based on Source Tracking, Water Resour. Manag., 35,
3755–3770, https://doi.org/10.1007/s11269-021-02915-2, 2021. a, b
Qi, W., Ma, C., Xu, H., and Zhao, K.: Urban flood response analysis for
designed rainstorms with different characteristics based on a tracer-aided
modeling simulation, J. Clean. Prod., 355, 131797,
https://doi.org/10.1016/j.jclepro.2022.131797, 2022. a
Richey, J. E., Melack, J. M., Aufdenkampe, A. K., Ballester, V. M., and Hess,
L. L.: Outgassing from Amazonian rivers and wetlands as a large tropical
source of atmospheric CO2., Nature, 416, 617–620, https://doi.org/10.1038/416617a,
2002. a
Robins, P. E., Lewis, M. J., Simpson, J. H., Howlett, E. R., and Malham, S. K.:
Future variability of solute transport in a macrotidal estuary, Estuar.
Coast. Shelf S., 151, 88–99, https://doi.org/10.1016/j.ecss.2014.09.019, 2014. a
Robins, P. E., Farkas, K., Cooper, D., Malham, S. K., and Jones, D. L.: Viral
dispersal in the coastal zone: A method to quantify water quality risk.,
Environ. Int., 126, 430–442, https://doi.org/10.1016/j.envint.2019.02.042, 2019. a, b
Sampson, C. C., Smith, A. M., Bates, P. D., Neal, J. C., Alfieri, L., and
Freer, J. E.: A high-resolution global flood hazard model., Water Resour.
Res., 51, 7358–7381, https://doi.org/10.1002/2015WR016954, 2015. a
Shaw, J., Kesserwani, G., Neal, J., Bates, P., and Sharifian, M. K.: LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs, Geosci. Model Dev., 14, 3577–3602, https://doi.org/10.5194/gmd-14-3577-2021, 2021. a
Trigg, M. A., Wilson, M. D., Bates, P. D., Horritt, M. S., Alsdorf, D. E.,
Forsberg, B. R., and Vega, M. C.: Amazon flood wave hydraulics, J. Hydrol.,
374, 92–105, https://doi.org/10.1016/j.jhydrol.2009.06.004, 2009. a
Van De Wiel, M., Coulthard, T., Macklin, M., and Lewin, J.: Embedding
reach-scale fluvial dynamics within the CAESAR cellular automaton landscape
evolution model, Geomorphology, 90, 283–301, 2007. a
van der Peet, M. and Measures, R.: Avon-Heathcote Tidal Barrier Pre-Feasibility
Study, Tech. rep., Christchurch City Council, Christchurch, New Zealand,
https://www.ccc.govt.nz/assets/Documents/Environment/Water/Tidal-Barrier/Avon-Heathcote-Estuary-Tidal-Barrier-Pre-Feasibility-Study.pdf (last access: 5 May 2023),
2015. a
Vauchel, P., Santini, W., Guyot, J. L., Moquet, J. S., Martinez, J. M.,
Espinoza, J. C., Baby, P., Fuertes, O., Noriega, L., Puita, O., Sondag, F.,
Fraizy, P., Armijos, E., Cochonneau, G., Timouk, F., de Oliveira, E.,
Filizola, N., Molina, J., and Ronchail, J.: A reassessment of the suspended
sediment load in the Madeira River basin from the Andes of Peru and Bolivia
to the Amazon River in Brazil, based on 10 years of data from the HYBAM
monitoring programme, J. Hydrol., 553, 35–48,
https://doi.org/10.1016/j.jhydrol.2017.07.018, 2017.
a
Vopel, K., Wilson, P. S., and Zeldis, J.: Sediment-seawater solute flux in a
polluted New Zealand estuary, Mar. Pollut. Bull., 64, 2885–2891,
https://doi.org/10.1016/j.marpolbul.2012.08.011, 2012. a, b
Vousdoukas, M. I., Bouziotas, D., Giardino, A., Bouwer, L. M., Mentaschi, L., Voukouvalas, E., and Feyen, L.: Understanding epistemic uncertainty in large-scale coastal flood risk assessment for present and future climates, Nat. Hazards Earth Syst. Sci., 18, 2127–2142, https://doi.org/10.5194/nhess-18-2127-2018, 2018. a
Wilson, M. and Coulthard, T.: Tracing and visualisation of contributing water
sources in a model of flood inundation. GeoComputation 2019, The University
of Auckland, https://doi.org/10.17608/k6.auckland.9869972.v2, 2019. a, b
Wilson, M., Bates, P., Alsdorf, D., Forsberg, B., Horritt, M., Melack, J.,
Frappart, F., and Famiglietti, J.: Modeling large-scale inundation of
Amazonian seasonally flooded wetlands, Geophys. Res. Lett., 34, L15404,
https://doi.org/10.1029/2007GL030156, 2007. a, b, c, d
Wilson, M. D. and Coulthard, T. J.: Tracing and visualisation of contributing
water sources in a model of flood inundation: video supplement, Zenodo [video],
https://doi.org/10.5281/zenodo.5548535, 2021. a
Wilson, M. D. and Coulthard, T. J.: CAESAR-Lisflood v1.9j-WS (water source
tracing and visualisation), Zenodo [code and data set], https://doi.org/10.5281/zenodo.7589023, 2023. a
Wing, O. E. J., Bates, P. D., Sampson, C. C., Smith, A. M., Johnson, K. A., and
Erickson, T. A.: Validation of a 30 m resolution flood hazard model of the
conterminous United States, Water Resour. Res., 53, 7968–7986, https://doi.org/10.1002/2017WR020917,
2017. a
Wittmann, F., Junk, W. J., and Piedade, M. T.: The várzea forests in Amazonia:
flooding and the highly dynamic geomorphology interact with natural forest
succession, Forest Ecol. Manag., 196, 199–212,
https://doi.org/10.1016/j.foreco.2004.02.060, 2004. a
Woodley, K.: Avon-Heathcote Estuary/Ihutai New Zealand: Site Information
Sheet on East Asian-Australasian Flyway Network Sites (SIS) – 2017
version, Tech. rep., Partnership for the East Asian – Australasian Flyway
(EAAFP),
https://eaaflyway.net/wp-content/uploads/2018/07/EAAF137_SIS_Avon-Heathcote-Estuary_Ihutai.pdf (last access: 5 May 2023),
2018. a
Yu, D. and Coulthard, T. J.: Evaluating the importance of catchment
hydrological parameters for urban surface water flood modelling using a
simple hydro-inundation model, J. Hydrol., 524, 385–400,
https://doi.org/10.1016/j.jhydrol.2015.02.040, 2015. a
Short summary
During flooding, the sources of water that inundate a location can influence impacts such as pollution. However, methods to trace water sources in flood events are currently only available in complex, computationally expensive hydraulic models. We propose a simplified method which can be added to efficient, reduced-complexity model codes, enabling an improved understanding of flood dynamics and its impacts. We demonstrate its application for three sites at a range of spatial and temporal scales.
During flooding, the sources of water that inundate a location can influence impacts such as...
