Articles | Volume 11, issue 5
https://doi.org/10.5194/gmd-11-1873-2018
https://doi.org/10.5194/gmd-11-1873-2018
Methods for assessment of models
 | 
15 May 2018
Methods for assessment of models |  | 15 May 2018

The SPAtial EFficiency metric (SPAEF): multiple-component evaluation of spatial patterns for optimization of hydrological models

Julian Koch, Mehmet Cüneyd Demirel, and Simon Stisen

Related authors

Water table driven greenhouse gas emission estimate guides peatland restoration at national scale
Julian Koch, Lars Elsgaard, Mogens H. Greve, Steen Gyldenkærne, Cecilie Hermansen, Gregor Levin, Shubiao Wu, and Simon Stisen
Biogeosciences Discuss., https://doi.org/10.5194/bg-2023-23,https://doi.org/10.5194/bg-2023-23, 2023
Preprint under review for BG
Short summary
Machine-learning-based downscaling of modelled climate change impacts on groundwater table depth
Raphael Schneider, Julian Koch, Lars Troldborg, Hans Jørgen Henriksen, and Simon Stisen
Hydrol. Earth Syst. Sci., 26, 5859–5877, https://doi.org/10.5194/hess-26-5859-2022,https://doi.org/10.5194/hess-26-5859-2022, 2022
Short summary
The precision of satellite-based irrigation quantification in the Indus and Ganges basins
Søren Julsgaard Kragh, Rasmus Fensholt, Simon Stisen, and Julian Koch
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2022-307,https://doi.org/10.5194/hess-2022-307, 2022
Revised manuscript under review for HESS
Short summary
Modelling of the shallow water table at high spatial resolution using random forests
Julian Koch, Helen Berger, Hans Jørgen Henriksen, and Torben Obel Sonnenborg
Hydrol. Earth Syst. Sci., 23, 4603–4619, https://doi.org/10.5194/hess-23-4603-2019,https://doi.org/10.5194/hess-23-4603-2019, 2019
Short summary
Combining satellite data and appropriate objective functions for improved spatial pattern performance of a distributed hydrologic model
Mehmet C. Demirel, Juliane Mai, Gorka Mendiguren, Julian Koch, Luis Samaniego, and Simon Stisen
Hydrol. Earth Syst. Sci., 22, 1299–1315, https://doi.org/10.5194/hess-22-1299-2018,https://doi.org/10.5194/hess-22-1299-2018, 2018
Short summary

Related subject area

Hydrology
Evaluating a global soil moisture dataset from a multitask model (GSM3 v1.0) with potential applications for crop threats
Jiangtao Liu, David Hughes, Farshid Rahmani, Kathryn Lawson, and Chaopeng Shen
Geosci. Model Dev., 16, 1553–1567, https://doi.org/10.5194/gmd-16-1553-2023,https://doi.org/10.5194/gmd-16-1553-2023, 2023
Short summary
SERGHEI (SERGHEI-SWE) v1.0: a performance-portable high-performance parallel-computing shallow-water solver for hydrology and environmental hydraulics
Daniel Caviedes-Voullième, Mario Morales-Hernández, Matthew R. Norman, and Ilhan Özgen-Xian
Geosci. Model Dev., 16, 977–1008, https://doi.org/10.5194/gmd-16-977-2023,https://doi.org/10.5194/gmd-16-977-2023, 2023
Short summary
A simple, efficient, mass-conservative approach to solving Richards' equation (openRE, v1.0)
Andrew M. Ireson, Raymond J. Spiteri, Martyn P. Clark, and Simon A. Mathias
Geosci. Model Dev., 16, 659–677, https://doi.org/10.5194/gmd-16-659-2023,https://doi.org/10.5194/gmd-16-659-2023, 2023
Short summary
Customized deep learning for precipitation bias correction and downscaling
Fang Wang, Di Tian, and Mark Carroll
Geosci. Model Dev., 16, 535–556, https://doi.org/10.5194/gmd-16-535-2023,https://doi.org/10.5194/gmd-16-535-2023, 2023
Short summary
Implementation and sensitivity analysis of the Dam-Reservoir OPeration model (DROP v1.0) over Spain
Malak Sadki, Simon Munier, Aaron Boone, and Sophie Ricci
Geosci. Model Dev., 16, 427–448, https://doi.org/10.5194/gmd-16-427-2023,https://doi.org/10.5194/gmd-16-427-2023, 2023
Short summary

Cited articles

Alexandrov, G. A., Ames, D., Bellocchi, G., Bruen, M., Crout, N., Erechtchoukova, M., Hildebrandt, A., Hoffman, F., Jackisch, C., Khaiter, P., Mannina, G., Matsunaga, T., Purucker, S. T., Rivington, M., and Samaniego, L.: Technical assessment and evaluation of environmental models and software: Letter to the Editor, Environ. Model. Softw., 26, 328–336, https://doi.org/10.1016/j.envsoft.2010.08.004, 2011. 
Brown, B. G., Gotway, J. H., Bullock, R., Gilleland, E., Fowler, T., Ahijevych, D., and Jensen, T.: The Model Evaluation Tools (MET): Community tools for forecast evaluation, in: Preprints, 25th Conf. on International Interactive Information and Processing Systems (IIPS) for Meteorology, Oceanography, and Hydrology, Phoenix, AZ, Amer. Meteor. Soc. A, Vol. 9, 2009. 
Clark, M. P., Kavetski, D., and Fenicia, F.: Pursuing the method of multiple working hypotheses for hydrological modeling, Water Resour. Res., 47, W09301, https://doi.org/10.1029/2010WR009827, 2011. 
Cloke, H. L. and Pappenberger, F.: Evaluating forecasts of extreme events for hydrological applications: An approach for screening unfamiliar performance measures, Meteorol. Appl., 15, 181–197, 2008. 
Download
Short summary
Our work addresses a key challenge in earth system modelling: how to optimally exploit the information contained in satellite remote sensing observations in the calibration of such models. For this we thoroughly test a number of measures that quantify the fit between an observed and a simulated spatial pattern. We acknowledge the difficulties associated with such a comparison and suggest using measures that regard multiple aspects of spatial information, i.e. magnitude and variability.