Articles | Volume 15, issue 22
https://doi.org/10.5194/gmd-15-8453-2022
https://doi.org/10.5194/gmd-15-8453-2022
Model description paper
 | 
21 Nov 2022
Model description paper |  | 21 Nov 2022

Implementation of a new crop phenology and irrigation scheme in the ISBA land surface model using SURFEX_v8.1

Arsène Druel, Simon Munier, Anthony Mucia, Clément Albergel, and Jean-Christophe Calvet

Related authors

Spatio-temporal variations and uncertainty in land surface modelling for high latitudes: univariate response analysis
Didier G. Leibovici, Shaun Quegan, Edward Comyn-Platt, Garry Hayman, Maria Val Martin, Mathieu Guimberteau, Arsène Druel, Dan Zhu, and Philippe Ciais
Biogeosciences, 17, 1821–1844, https://doi.org/10.5194/bg-17-1821-2020,https://doi.org/10.5194/bg-17-1821-2020, 2020
Short summary
Towards a more detailed representation of high-latitude vegetation in the global land surface model ORCHIDEE (ORC-HL-VEGv1.0)
Arsène Druel, Philippe Peylin, Gerhard Krinner, Philippe Ciais, Nicolas Viovy, Anna Peregon, Vladislav Bastrikov, Natalya Kosykh, and Nina Mironycheva-Tokareva
Geosci. Model Dev., 10, 4693–4722, https://doi.org/10.5194/gmd-10-4693-2017,https://doi.org/10.5194/gmd-10-4693-2017, 2017
Short summary
Improving the dynamics of Northern Hemisphere high-latitude vegetation in the ORCHIDEE ecosystem model
D. Zhu, S. S. Peng, P. Ciais, N. Viovy, A. Druel, M. Kageyama, G. Krinner, P. Peylin, C. Ottlé, S. L. Piao, B. Poulter, D. Schepaschenko, and A. Shvidenko
Geosci. Model Dev., 8, 2263–2283, https://doi.org/10.5194/gmd-8-2263-2015,https://doi.org/10.5194/gmd-8-2263-2015, 2015
Short summary

Related subject area

Biogeosciences
Optimising CH4 simulations from the LPJ-GUESS model v4.1 using an adaptive Markov chain Monte Carlo algorithm
Jalisha T. Kallingal, Johan Lindström, Paul A. Miller, Janne Rinne, Maarit Raivonen, and Marko Scholze
Geosci. Model Dev., 17, 2299–2324, https://doi.org/10.5194/gmd-17-2299-2024,https://doi.org/10.5194/gmd-17-2299-2024, 2024
Short summary
The XSO framework (v0.1) and Phydra library (v0.1) for a flexible, reproducible, and integrated plankton community modeling environment in Python
Benjamin Post, Esteban Acevedo-Trejos, Andrew D. Barton, and Agostino Merico
Geosci. Model Dev., 17, 1175–1195, https://doi.org/10.5194/gmd-17-1175-2024,https://doi.org/10.5194/gmd-17-1175-2024, 2024
Short summary
AgriCarbon-EO v1.0.1: large-scale and high-resolution simulation of carbon fluxes by assimilation of Sentinel-2 and Landsat-8 reflectances using a Bayesian approach
Taeken Wijmer, Ahmad Al Bitar, Ludovic Arnaud, Remy Fieuzal, and Eric Ceschia
Geosci. Model Dev., 17, 997–1021, https://doi.org/10.5194/gmd-17-997-2024,https://doi.org/10.5194/gmd-17-997-2024, 2024
Short summary
SAMM version 1.0: a numerical model for microbial- mediated soil aggregate formation
Moritz Laub, Sergey Blagodatsky, Marijn Van de Broek, Samuel Schlichenmaier, Benjapon Kunlanit, Johan Six, Patma Vityakon, and Georg Cadisch
Geosci. Model Dev., 17, 931–956, https://doi.org/10.5194/gmd-17-931-2024,https://doi.org/10.5194/gmd-17-931-2024, 2024
Short summary
A model of the within-population variability of budburst in forest trees
Jianhong Lin, Daniel Berveiller, Christophe François, Heikki Hänninen, Alexandre Morfin, Gaëlle Vincent, Rui Zhang, Cyrille Rathgeber, and Nicolas Delpierre
Geosci. Model Dev., 17, 865–879, https://doi.org/10.5194/gmd-17-865-2024,https://doi.org/10.5194/gmd-17-865-2024, 2024
Short summary

Cited articles

Adegoke, J. O., Pielke, R. A., Eastman, J., Mahmood, R., and Hubbard, K. G.: Impact of Irrigation on Midsummer Surface Fluxes and Temperature under Dry Synoptic Conditions: A Regional Atmospheric Model Study of the U.S. High Plains, Mon. Weather Rev., 131, 556–564, https://doi.org/10.1175/1520-0493(2003)131<0556:IOIOMS>2.0.CO;2, 2003. 
Albergel, C., Dutra, E., Bonan, B., Zheng, Y., Munier, S., Balsamo, G., de Rosnay, P., Munoz-Sabater, J., and Calvet, J.-C.: Monitoring and forecasting the impact of the 2018 summer heatwave on vegetation, Remote Sens., 11, 520, https://doi.org/10.3390/rs11050520, 2019. 
Al-Yaari, A., Ducharne, A., Tafasca, S., Mizuochi, H., and Cheruy, F.: Influence of irrigation on the bias between ORCHIDEE and FLUXCOM evapotranspiration products, 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS, 6552–6555, https://doi.org/10.1109/IGARSS47720.2021.9554734, 2021. 
AQUASTAT and FAO: Country Fact Sheet, United States of America, http://www.fao.org/nr/water/aquastat/data/cf/readPdf.html?f=USA-CF_eng.pdf (last access: 15 November 2022), 2019. 
Baret, F., Weiss, M., Lacaze, R., Camacho, F., Makhmara, H., Pacholcyzk, P., and Smets, B.: GEOV1: LAI and FAPAR essential climate variables and FCOVER global time series capitalizing over existing products. Part1: Principles of development and production, Remote Sens. Environ., 137, 299–309, https://doi.org/10.1016/j.rse.2012.12.027, 2013. 
Download
Short summary
Crop phenology and irrigation is implemented into a land surface model able to work at a global scale. A case study is presented over Nebraska (USA). Simulations with and without the new scheme are compared to different satellite-based observations. The model is able to produce a realistic yearly irrigation water amount. The irrigation scheme improves the simulated leaf area index, gross primary productivity, evapotransipiration, and land surface temperature.