Articles | Volume 18, issue 3
https://doi.org/10.5194/gmd-18-803-2025
https://doi.org/10.5194/gmd-18-803-2025
Model description paper
 | 
12 Feb 2025
Model description paper |  | 12 Feb 2025

Introducing Iterative Model Calibration (IMC) v1.0: a generalizable framework for numerical model calibration with a CAESAR-Lisflood case study

Chayan Banerjee, Kien Nguyen, Clinton Fookes, Gregory Hancock, and Thomas Coulthard

Related authors

Thresholds for estuarine compound flooding using a combined hydrodynamic–statistical modelling approach
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
Testing the sensitivity of the CAESAR-Lisflood landscape evolution model to grid cell size
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
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 and Thomas J. Coulthard
Geosci. Model Dev., 16, 2415–2436, https://doi.org/10.5194/gmd-16-2415-2023,https://doi.org/10.5194/gmd-16-2415-2023, 2023
Short summary
The Coastline Evolution Model 2D (CEM2D) V1.1
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
Catchment-scale drought: capturing the whole drought cycle using multiple indicators
Abraham J. Gibson, Danielle C. Verdon-Kidd, Greg R. Hancock, and Garry Willgoose
Hydrol. Earth Syst. Sci., 24, 1985–2002, https://doi.org/10.5194/hess-24-1985-2020,https://doi.org/10.5194/hess-24-1985-2020, 2020
Short summary

Related subject area

Numerical methods
Advances in land surface forecasting: a comparison of LSTM, gradient boosting, and feed-forward neural networks as prognostic state emulators in a case study with ecLand
Marieke Wesselkamp, Matthew Chantry, Ewan Pinnington, Margarita Choulga, Souhail Boussetta, Maria Kalweit, Joschka Bödecker, Carsten F. Dormann, Florian Pappenberger, and Gianpaolo Balsamo
Geosci. Model Dev., 18, 921–937, https://doi.org/10.5194/gmd-18-921-2025,https://doi.org/10.5194/gmd-18-921-2025, 2025
Short summary
Subgrid corrections for the linear inertial equations of a compound flood model – a case study using SFINCS 2.1.1 Dollerup release
Maarten van Ormondt, Tim Leijnse, Roel de Goede, Kees Nederhoff, and Ap van Dongeren
Geosci. Model Dev., 18, 843–861, https://doi.org/10.5194/gmd-18-843-2025,https://doi.org/10.5194/gmd-18-843-2025, 2025
Short summary
Development of a high-order global dynamical core using the discontinuous Galerkin method for an atmospheric large-eddy simulation (LES) and proposal of test cases: SCALE-DG v0.8.0
Yuta Kawai and Hirofumi Tomita
Geosci. Model Dev., 18, 725–762, https://doi.org/10.5194/gmd-18-725-2025,https://doi.org/10.5194/gmd-18-725-2025, 2025
Short summary
A joint reconstruction and model selection approach for large-scale linear inverse modeling (msHyBR v2)
Malena Sabaté Landman, Julianne Chung, Jiahua Jiang, Scot M. Miller, and Arvind K. Saibaba
Geosci. Model Dev., 17, 8853–8872, https://doi.org/10.5194/gmd-17-8853-2024,https://doi.org/10.5194/gmd-17-8853-2024, 2024
Short summary
Assimilation of snow water equivalent from AMSR2 and IMS satellite data utilizing the local ensemble transform Kalman filter
Joonlee Lee, Myong-In Lee, Sunlae Tak, Eunkyo Seo, and Yong-Keun Lee
Geosci. Model Dev., 17, 8799–8816, https://doi.org/10.5194/gmd-17-8799-2024,https://doi.org/10.5194/gmd-17-8799-2024, 2024
Short summary

Cited articles

Abbaspour, K. C., Johnson, C. A., and van Genuchten, M. T.: Estimating uncertain flow and transport parameters using a sequential uncertainty fitting procedure, Vadose Zone J., 3, 1340–1352, https://doi.org/10.2136/vzj2004.1340, 2004. a
Banerjee, C.: Iterative Model Calibration (IMC), Zenodo [code and data set], https://doi.org/10.5281/zenodo.12747679, 2024. a, b
Barnhart, K. R., Tucker, G. E., Doty, S. G., Shobe, C. M., Glade, R. C., Rossi, M. W., and Hill, M. C.: Inverting topography for landscape evolution model process representation: 1. Conceptualization and sensitivity analysis, J. Geophys. Res.-Earth, 125, e2018JF004961, https://doi.org/10.1029/2018JF004961, 2020. a
Beck, H., Hirpa, F. A., Lorini, V., Lorenzo, A., and Tomer, S. K.: LISFLOOD Hydrological Model Calibration Tool, Joint Research Centre (JRC), Princeton; European Commission, https://ec-jrc.github.io/lisflood-calibration/1_introduction/ (last access: 2 January 2024), 2018. a, b, c, d
Becker, R., Koppa, A., Schulz, S., Usman, M., aus der Beek, T., and Schueth, C.: Spatially distributed model calibration of a highly managed hydrological system using remote sensing-derived ET data, J. Hydrol., 577, 123944, https://doi.org/10.1016/j.jhydrol.2019.123944, 2019. a
Download
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.
Share