Articles | Volume 16, issue 14
Model description paper
26 Jul 2023
Model description paper |  | 26 Jul 2023

Data space inversion for efficient uncertainty quantification using an integrated surface and sub-surface hydrologic model

Hugo Delottier, John Doherty, and Philip Brunner

Related authors

Advancing measurements and representations of subsurface heterogeneity and dynamic processes: towards 4D hydrogeology
Thomas Hermans, Pascal Goderniaux, Damien Jougnot, Jan H. Fleckenstein, Philip Brunner, Frédéric Nguyen, Niklas Linde, Johan Alexander Huisman, Olivier Bour, Jorge Lopez Alvis, Richard Hoffmann, Andrea Palacios, Anne-Karin Cooke, Álvaro Pardo-Álvarez, Lara Blazevic, Behzad Pouladi, Peleg Haruzi, Alejandro Fernandez Visentini, Guilherme E. H. Nogueira, Joel Tirado-Conde, Majken C. Looms, Meruyert Kenshilikova, Philippe Davy, and Tanguy Le Borgne
Hydrol. Earth Syst. Sci., 27, 255–287,,, 2023
Short summary
Spatiotemporal variations in water sources and mixing spots in a riparian zone
Guilherme E. H. Nogueira, Christian Schmidt, Daniel Partington, Philip Brunner, and Jan H. Fleckenstein
Hydrol. Earth Syst. Sci., 26, 1883–1905,,, 2022
Short summary
K. Koutantou, G. Mazzotti, and P. Brunner
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B3-2021, 477–484,,, 2021
Assessing the perturbations of the hydrogeological regime in sloping fens due to roads
Fabien Cochand, Daniel Käser, Philippe Grosvernier, Daniel Hunkeler, and Philip Brunner
Hydrol. Earth Syst. Sci., 24, 213–226,,, 2020
Short summary
Efficient multi-objective calibration and uncertainty analysis of distributed snow simulations in rugged alpine terrain
James M. Thornton, Gregoire Mariethoz, Tristan J. Brauchli, and Philip Brunner
The Cryosphere Discuss.,,, 2019
Manuscript not accepted for further review
Short summary

Related subject area

Simulation of crop yield using the global hydrological model H08 (crp.v1)
Zhipin Ai and Naota Hanasaki
Geosci. Model Dev., 16, 3275–3290,,, 2023
Short summary
How is a global sensitivity analysis of a catchment-scale, distributed pesticide transfer model performed? Application to the PESHMELBA model
Emilie Rouzies, Claire Lauvernet, Bruno Sudret, and Arthur Vidard
Geosci. Model Dev., 16, 3137–3163,,, 2023
Short summary
iHydroSlide3D v1.0: an advanced hydrological–geotechnical model for hydrological simulation and three-dimensional landslide prediction
Guoding Chen, Ke Zhang, Sheng Wang, Yi Xia, and Lijun Chao
Geosci. Model Dev., 16, 2915–2937,,, 2023
Short summary
GEB v0.1: a large-scale agent-based socio-hydrological model – simulating 10 million individual farming households in a fully distributed hydrological model
Jens A. de Bruijn, Mikhail Smilovic, Peter Burek, Luca Guillaumot, Yoshihide Wada, and Jeroen C. J. H. Aerts
Geosci. Model Dev., 16, 2437–2454,,, 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,,, 2023
Short summary

Cited articles

Anderson, M. P., Woessner, W. W., and Hunt, R. J.: Applied groundwater modeling: simulation of flow and advective transport, Academic, Cambridge, MA, ISBN: 978-0-12-058103-0, 2015. 
Aquanty Inc.: HydroGeoSphere Theory Manual, Waterloo, ON, p. 101, (last access: 14 July 2023), 2022. 
Brunner, P. and Simmons, C. T.: HydroGeoSphere: a fully integrated, physically based hydrological model, Groundwater 50, 170–176,, 2012. 
Brunner, P., Doherty, J., and Simmons, C. T.: Uncertainty assessment and implications for data acquisition in support of integrated hydrologic models, Water Resour. Res., 48, W07513,, 2012. 
Brunner, P., Therrien, R., Renard, P., Simmons, C. T., and Hendricks Franssen, H. J.: Advances in understanding river-groundwater interactions, Rev. Geophys., 55, 818–854,, 2017. 
Short summary
Long run times are usually a barrier to the quantification and reduction of predictive uncertainty with complex hydrological models. Data space inversion (DSI) provides an alternative and highly model-run-efficient method for uncertainty quantification. This paper demonstrates DSI's ability to robustly quantify predictive uncertainty and extend the methodology to provide practical metrics that can guide data acquisition and analysis to achieve goals of decision-support modelling.