Articles | Volume 9, issue 1
Geosci. Model Dev., 9, 283–305, 2016
https://doi.org/10.5194/gmd-9-283-2016
Geosci. Model Dev., 9, 283–305, 2016
https://doi.org/10.5194/gmd-9-283-2016

Methods for assessment of models 26 Jan 2016

Methods for assessment of models | 26 Jan 2016

The GEWEX LandFlux project: evaluation of model evaporation using tower-based and globally gridded forcing data

M. F. McCabe et al.

Related authors

Mapping groundwater abstractions from irrigated agriculture: big data, inverse modeling, and a satellite–model fusion approach
Oliver Miguel López Valencia, Kasper Johansen, Bruno José Luis Aragón Solorio, Ting Li, Rasmus Houborg, Yoann Malbeteau, Samer AlMashharawi, Muhammad Umer Altaf, Essam Mohammed Fallatah, Hari Prasad Dasari, Ibrahim Hoteit, and Matthew Francis McCabe
Hydrol. Earth Syst. Sci., 24, 5251–5277, https://doi.org/10.5194/hess-24-5251-2020,https://doi.org/10.5194/hess-24-5251-2020, 2020
Short summary
PREDICTING BIOMASS AND YIELD AT HARVEST OF SALT-STRESSED TOMATO PLANTS USING UAV IMAGERY
K. Johansen, M. J. L. Morton, Y. Malbeteau, B. Aragon, S. Al-Mashharawi, M. Ziliani, Y. Angel, G. Fiene, S. Negrao, M. A. A. Mousa, M. A. Tester, and M. F. McCabe
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W13, 407–411, https://doi.org/10.5194/isprs-archives-XLII-2-W13-407-2019,https://doi.org/10.5194/isprs-archives-XLII-2-W13-407-2019, 2019
Inferring soil salinity in a drip irrigation system from multi-configuration EMI measurements using adaptive Markov chain Monte Carlo
Khan Zaib Jadoon, Muhammad Umer Altaf, Matthew Francis McCabe, Ibrahim Hoteit, Nisar Muhammad, Davood Moghadas, and Lutz Weihermüller
Hydrol. Earth Syst. Sci., 21, 5375–5383, https://doi.org/10.5194/hess-21-5375-2017,https://doi.org/10.5194/hess-21-5375-2017, 2017
Short summary
PUSHBROOM HYPERSPECTRAL IMAGING FROM AN UNMANNED AIRCRAFT SYSTEM (UAS) – GEOMETRIC PROCESSINGWORKFLOW AND ACCURACY ASSESSMENT
D. Turner, A. Lucieer, M. McCabe, S. Parkes, and I. Clarke
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-2-W6, 379–384, https://doi.org/10.5194/isprs-archives-XLII-2-W6-379-2017,https://doi.org/10.5194/isprs-archives-XLII-2-W6-379-2017, 2017
The future of Earth observation in hydrology
Matthew F. McCabe, Matthew Rodell, Douglas E. Alsdorf, Diego G. Miralles, Remko Uijlenhoet, Wolfgang Wagner, Arko Lucieer, Rasmus Houborg, Niko E. C. Verhoest, Trenton E. Franz, Jiancheng Shi, Huilin Gao, and Eric F. Wood
Hydrol. Earth Syst. Sci., 21, 3879–3914, https://doi.org/10.5194/hess-21-3879-2017,https://doi.org/10.5194/hess-21-3879-2017, 2017
Short summary

Related subject area

Hydrology
Understanding each other's models: an introduction and a standard representation of 16 global water models to support intercomparison, improvement, and communication
Camelia-Eliza Telteu, Hannes Müller Schmied, Wim Thiery, Guoyong Leng, Peter Burek, Xingcai Liu, Julien Eric Stanislas Boulange, Lauren Seaby Andersen, Manolis Grillakis, Simon Newland Gosling, Yusuke Satoh, Oldrich Rakovec, Tobias Stacke, Jinfeng Chang, Niko Wanders, Harsh Lovekumar Shah, Tim Trautmann, Ganquan Mao, Naota Hanasaki, Aristeidis Koutroulis, Yadu Pokhrel, Luis Samaniego, Yoshihide Wada, Vimal Mishra, Junguo Liu, Petra Döll, Fang Zhao, Anne Gädeke, Sam S. Rabin, and Florian Herz
Geosci. Model Dev., 14, 3843–3878, https://doi.org/10.5194/gmd-14-3843-2021,https://doi.org/10.5194/gmd-14-3843-2021, 2021
Short summary
LISFLOOD-FP 8.0: the new discontinuous Galerkin shallow-water solver for multi-core CPUs and GPUs
James Shaw, Georges Kesserwani, Jeffrey Neal, Paul Bates, and Mohammad Kazem Sharifian
Geosci. Model Dev., 14, 3577–3602, https://doi.org/10.5194/gmd-14-3577-2021,https://doi.org/10.5194/gmd-14-3577-2021, 2021
Short summary
InundatEd-v1.0: a height above nearest drainage (HAND)-based flood risk modeling system using a discrete global grid system
Chiranjib Chaudhuri, Annie Gray, and Colin Robertson
Geosci. Model Dev., 14, 3295–3315, https://doi.org/10.5194/gmd-14-3295-2021,https://doi.org/10.5194/gmd-14-3295-2021, 2021
Short summary
Fluxes from soil moisture measurements (FluSM v1.0): a data-driven water balance framework for permeable pavements
Axel Schaffitel, Tobias Schuetz, and Markus Weiler
Geosci. Model Dev., 14, 2127–2142, https://doi.org/10.5194/gmd-14-2127-2021,https://doi.org/10.5194/gmd-14-2127-2021, 2021
Short summary
Parametrization of a lake water dynamics model MLake in the ISBA-CTRIP land surface system (SURFEX v8.1)
Thibault Guinaldo, Simon Munier, Patrick Le Moigne, Aaron Boone, Bertrand Decharme, Margarita Choulga, and Delphine J. Leroux
Geosci. Model Dev., 14, 1309–1344, https://doi.org/10.5194/gmd-14-1309-2021,https://doi.org/10.5194/gmd-14-1309-2021, 2021
Short summary

Cited articles

Adler, R. F., Huffman, G. J., Chang, A., Ferraro, R., Xie, P. P., Janowiak, J., Rudolf, B., Schneider, U., Curtis, S., Bolvin, D., Gruber, A., Susskind, J., Arkin, P., and Nelkin, E.: The version-2 global precipitation climatology project (GPCP) monthly precipitation analysis (1979–present), J. Hydrometeorol., 4, 1147–1167, 2003.
Allen, R. G.: Using the FAO-56 dual crop coefficient method over an irrigated region as part of an evapotranspiration intercomparison study, J. Hydrol., 229, 27–41, 2000.
Allen, R. G., Tasumi, M., and Trezza, R.: Satellite-Based Energy Balance for Mapping Evapotranspiration with Internalized Calibration (METRIC)-Model, J. Irrig. Drain. E., 133, 380–394, 2007.
Armstrong, R. L., Brodzik, M. J., Knowles, K., and Savoie, M.: Global monthly EASE-Grid snow water equivalent climatology, National Snow and Ice Data Center, Digital media, Boulder, CO, USA, 2005.
Badgley, G., Fisher, J. B., Jiménez, C., Tu, K. P., and Vinukollu, R.: On uncertainty in global terrestrial evapotranspiration estimates from choice of input forcing datasets, J. Hydrometeorol., 16, 1449–1455, https://doi.org/10.1175/JHM-D-14-0040.1, 2015.
Download
Short summary
In an effort to develop a global terrestrial evaporation product, four models were forced using both a tower and grid-based data set. Comparisons against flux-tower observations from different biome and land cover types show considerable inter-model variability and sensitivity to forcing type. Results suggest that no single model is able to capture expected flux patterns and response. It is suggested that a multi-model ensemble is likely to provide a more stable long-term flux estimate.