Articles | Volume 16, issue 13
https://doi.org/10.5194/gmd-16-3809-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/gmd-16-3809-2023
© Author(s) 2023. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Development and technical paper
| 
11 Jul 2023
Development and technical paper |
| 11 Jul 2023
| 11 Jul 2023
Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress
Zhe Zhang
Global Institute for Water Security, University of Saskatchewan, 11
Innovation Blvd, Saskatoon, SK, S7N 3H5, Canada
Global Institute for Water Security, University of Saskatchewan, 11
Innovation Blvd, Saskatoon, SK, S7N 3H5, Canada
Fei Chen
National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO,
USA
Phillip Harder
Global Institute for Water Security, University of Saskatchewan, 11
Innovation Blvd, Saskatoon, SK, S7N 3H5, Canada
College of Engineering, University of Saskatchewan, 57 Campus Dr,
Saskatoon, SK, S7N 5A9, Canada
Warren Helgason
Global Institute for Water Security, University of Saskatchewan, 11
Innovation Blvd, Saskatoon, SK, S7N 3H5, Canada
College of Engineering, University of Saskatchewan, 57 Campus Dr,
Saskatoon, SK, S7N 5A9, Canada
James Famiglietti
Global Institute for Water Security, University of Saskatchewan, 11
Innovation Blvd, Saskatoon, SK, S7N 3H5, Canada
School of Sustainability, Arizona State University, Tempe, AZ, USA
Prasanth Valayamkunnath
National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO,
USA
School of Earth Environment and Sustainability Sciences, Indian
Institute of Science Education and Research, Thiruvananthapuram, 695551, India
Cenlin He
National Center for Atmospheric Research, P.O. Box 3000, Boulder, CO,
USA
Zhenhua Li
Global Institute for Water Security, University of Saskatchewan, 11
Innovation Blvd, Saskatoon, SK, S7N 3H5, Canada
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Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
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model evaluation).
Quang-Van Doan, Hiroyuki Kusaka, Takuto Sato, and Fei Chen
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Chris M. DeBeer, Howard S. Wheater, John W. Pomeroy, Alan G. Barr, Jennifer L. Baltzer, Jill F. Johnstone, Merritt R. Turetsky, Ronald E. Stewart, Masaki Hayashi, Garth van der Kamp, Shawn Marshall, Elizabeth Campbell, Philip Marsh, Sean K. Carey, William L. Quinton, Yanping Li, Saman Razavi, Aaron Berg, Jeffrey J. McDonnell, Christopher Spence, Warren D. Helgason, Andrew M. Ireson, T. Andrew Black, Mohamed Elshamy, Fuad Yassin, Bruce Davison, Allan Howard, Julie M. Thériault, Kevin Shook, Michael N. Demuth, and Alain Pietroniro
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Julián Gelman Constantin, Lucas Ruiz, Gustavo Villarosa, Valeria Outes, Facundo N. Bajano, Cenlin He, Hector Bajano, and Laura Dawidowski
The Cryosphere, 14, 4581–4601, https://doi.org/10.5194/tc-14-4581-2020, https://doi.org/10.5194/tc-14-4581-2020, 2020
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We present the results of two field campaigns and modeling activities on the impact of atmospheric particles on Alerce Glacier (Argentinean Andes). We found that volcanic ash remains at different snow layers several years after eruption, increasing light absorption on the glacier surface (with a minor contribution of soot). This leads to 36 % higher annual glacier melting. We find remarkably that volcano eruptions in 2011 and 2015 have a relevant effect on the glacier even in 2016 and 2017.
Wenfu Tang, Benjamin Gaubert, Louisa Emmons, Yonghoon Choi, Joshua P. DiGangi, Glenn S. Diskin, Xiaomei Xu, Cenlin He, Helen Worden, Simone Tilmes, Rebecca Buchholz, Hannah S. Halliday, and Avelino F. Arellano
Atmos. Chem. Phys. Discuss., https://doi.org/10.5194/acp-2020-864, https://doi.org/10.5194/acp-2020-864, 2020
Revised manuscript not accepted
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A specific demonstration of the potential use of correlative information from carbon monoxide to refine estimates of regional carbon dioxide emissions from fossil fuel combustion.
Tom Gleeson, Thorsten Wagener, Petra Döll, Samuel C. Zipper, Charles West, Yoshihide Wada, Richard Taylor, Bridget Scanlon, Rafael Rosolem, Shams Rahman, Nurudeen Oshinlaja, Reed Maxwell, Min-Hui Lo, Hyungjun Kim, Mary Hill, Andreas Hartmann, Graham Fogg, James S. Famiglietti, Agnès Ducharne, Inge de Graaf, Mark Cuthbert, Laura Condon, Etienne Bresciani, and Marc F. P. Bierkens
Hydrol. Earth Syst. Sci. Discuss., https://doi.org/10.5194/hess-2020-378, https://doi.org/10.5194/hess-2020-378, 2020
Revised manuscript not accepted
Cited articles
Agyeman, R. Y. K., Huo, F., Li, Z., and Li, Y.: Modelled changes in selected
agroclimatic indices over the croplands of western Canada under the RCP8.5
scenario, Q. J. Roy. Meteor. Soc., 147, 4454–4467,
https://doi.org/10.1002/qj.4188, 2021.
Annual Crop Inventory: Agriculture and Agri-Food Canada, https://www.agr.gc.ca/atlas/apps/metrics/index-en.html?appid=aci-iac; last access: October 2022.
Bernacchi, C. J., Bagley, J. E., Serbin, S. P., Ruiz-Vera, U. M.,
Rosenthal, D. M., and Vanloocke, A.: Modelling C3 photosynthesis from the
chloroplast to the ecosystem, Plant. Cell Environ., 36, 1641–1657,
https://doi.org/10.1111/pce.12118, 2013.
Carew, R., Meng, T., Florkowski, W. J., Smith, R., and Blair, D.: Climate change impacts on hard red spring wheat yield and production risk: evidence from Manitoba, Canada, Can. J. Plant Sci., 98, 782–795, https://doi.org/10.1139/cjps-2017-0135, 2017.
Cenlin_He, Barlage, M., xutr-bnu, Zhang, Z., Mocko, D., and Chen, F.: CharlesZheZhang/hrldas: HRLDAS driver for NoahMP LSM v4.4 with spring wheat (v4.4), Zenodo [code], https://doi.org/10.5281/zenodo.7556048, 2023a.
Cenlin_He, Barlage, M., Valayamkunnath, P., Gill, D., Mocko, D., and Chen, F.: CharlesZheZhang/noahmp: NoahMP LSM v4.4 with spring wheat (v4.4), Zenodo [code], https://doi.org/10.5281/zenodo.7556046, 2023b.
Chen, F., Manning, K. W., Lemone, M. A., Trier, S. B., Alfieri, J. G.,
Roberts, R., Tewari, M., Niyogi, D., Horst, T. W., Oncley, S. P., Basara, J.
B., and Blanken, P. D.: Description and evaluation of the characteristics of
the NCAR high-resolution land data assimilation system, J. Appl. Meteorol.
Clim., 46, 694–713, https://doi.org/10.1175/JAM2463.1, 2007.
Collatz, G. J., Ball, J. T., Grivet, C., and Berry, J. A.: Physiological and
environmental regulation of stomatal conductance, photosynthesis and
transpiration: a model that includes a laminar boundary layer, Agr. Forest
Meteorol., 54, 107–136, https://doi.org/10.1016/0168-1923(91)90002-8, 1991.
CropScape, USDA NASS and GMU: https://nassgeodata.gmu.edu/CropScape/, last access: October 2022.
De Wit, A. and Boogaard, H.: A gentle introduction to WOFOST, in: Wageningen
Environmental Research, November, https://www.wur.nl/en/research-results/research-institutes/environmental-research/facilities-tools/software-models-and-databases/wofost/documentation-wofost.htm (last access: October 2022), 2021.
ERS USDA wheat: https://www.ers.usda.gov/topics/crops/wheat/wheat-sector-at-a-glance/,
last access: October 2022.
Han, W., Yang, Z., Di, L., and Mueller, R.: CropScape: A Web service based application for exploring and disseminating US conterminous geospatial cropland data products for decision support, Comput. Electron. Agr., 84, 111–123, https://doi.org/10.1016/j.compag.2012.03.005, 2012.
Harley, P. C. and Tenhunen, J. D.: Modeling the Photosynthetic Response of
C3 Leaves to Environmental Factors, in Modeling Crop Photosynthesis – from
Biochemistry to Canopy, 17–39, https://doi.org/10.2135/cssaspecpub19.c2, 1991.
Iizumi, T., Kim, W., and Nishimori, M.: Modeling the Global Sowing and
Harvesting Windows of Major Crops Around the Year 2000, J. Adv. Model. Earth
Sy., 11, 99–112, 2018.
IPCC: Climate change 2014: synthesis report Contribution of Working Groups
I, II and III to the Fifth Assessment Report of the Intergovernmental Panel
on Climate Change, edited by: Pachauri, R. K. and Meyer, L. A., Geneva, IPCC, 151 pp., 2014.
Jägermeyr, J., Müller, C., Ruane, A. C., Elliott, J., Balkovic, J.,
Castillo, O., Faye, B., Foster, I., Folberth, C., Franke, J. A., Fuchs, K.,
Guarin, J. R., Heinke, J., Hoogenboom, G., Iizumi, T., Jain, A. K., Kelly,
D., Khabarov, N., Lange, S., Lin, T.-S., Liu, W., Mialyk, O., Minoli, S.,
Moyer, E. J., Okada, M., Phillips, M., Porter, C., Rabin, S. S., Scheer, C.,
Schneider, J. M., Schyns, J. F., Skalsky, R., Smerald, A., Stella, T.,
Stephens, H., Webber, H., Zabel, F., and Rosenzweig, C.: Climate impacts on
global agriculture emerge earlier in new generation of climate and crop
models, Nat. Food, 2, 873–885, https://doi.org/10.1038/s43016-021-00400-y, 2021.
Lesk, C., Anderson, W., Rigden, A., Coast, O., Jägermeyr, J., McDermid,
S., Davis, K. F. and Konar, M.: Compound heat and moisture extreme impacts
on global crop yields under climate change, Nat. Rev. Earth Environ., 3,
872–889, https://doi.org/10.1038/s43017-022-00368-8, 2022.
Levis, S.: Crop heat stress in the context of Earth System modeling,
Environ. Res. Lett., 9, 061002, https://doi.org/10.1088/1748-9326/9/6/061002, 2014.
Li, Y., Li, Z., Zhang, Z., Chen, L., Kurkute, S., Scaff, L., and Pan, X.: High-resolution regional climate modeling and projection over western Canada using a weather research forecasting model with a pseudo-global warming approach, Hydrol. Earth Syst. Sci., 23, 4635–4659, https://doi.org/10.5194/hess-23-4635-2019, 2019.
Liu, C., Ikeda, K., Rasmussen, R., Barlage, M., Newman, A. J., Prein, A.
F., Chen, F., Chen, L., Clark, M., Dai, A., Dudhia, J., Eidhammer, T.,
Gochis, D., Gutmann, E., Kurkute, S., Li, Y., Thompson, G., and Yates, D.:
Continental-scale convection-permitting modeling of the current and future
climate of North America, Clim. Dynam., 49, 71–95,
https://doi.org/10.1007/s00382-016-3327-9, 2017.
Liu, X., Chen, F., Barlage, M., Zhou, G., and Niyogi, D.: Noah-MP-Crop:
Introducing dynamic crop growth in the Noah-MP land surface model, J.
Geophys. Res.-Atmos., 121, 13953–13972, https://doi.org/10.1002/2016JD025597,
2016.
Lobell, D. B., Schlenker, W., and Costa-Roberts, J.: Climate Trends and
Global Crop Production Since 1980, Science, 80, 616–620,
https://doi.org/10.1126/science.1204531, 2011a.
Lobell, D. B., Bänziger, M., Magorokosho, C., and Vivek, B.: Nonlinear
heat effects on African maize as evidenced by historical yield trials, Nat.
Clim. Change, 1, 42–45, https://doi.org/10.1038/nclimate1043, 2011b.
McDermid, S. S., Mearns, L. O., and Ruane, A. C.: Representing agriculture
in Earth System Models: Approaches and priorities for development, J. Adv.
Model. Earth Sy., 9, 2230–2265, https://doi.org/10.1002/2016MS000749, 2017.
Mondal, S., Singh, R. P., Mason, E. R., Huerta-Espino, J., Autrique, E., and
Joshi, A. K.: Grain yield, adaptation and progress in breeding for
early-maturing and heat-tolerant wheat lines in South Asia, F. Crop. Res.,
192, 78–85, https://doi.org/10.1016/j.fcr.2016.04.017, 2016.
Myneni, R., Knyazikhin, Y., and Park, T.: MOD15A2H MODIS/Terra Leaf Area
Index/FPAR 8-Day L4 Global 500m SIN Grid V006, NASA EOSDIS
Land Processes DAAC [data set], https://doi.org/10.5067/MODIS/MOD15A2H.006, 2015.
Niu, G.-Y., Yang, Z.-L., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Kumar, A., Manning, K., Niyogi, D., Rosero, E., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 1. Model description and evaluation with local-scale
measurements, J. Geophys. Res., 116, D12109, https://doi.org/10.1029/2010JD015139,
2011.
Perkins, S. E. and Alexander, L. V.: On the measurement of heat waves, J.
Climate, 26, 4500–4517, https://doi.org/10.1175/JCLI-D-12-00383.1, 2013.
Prasad, P. V. V., Pisipati, S. R., Momčilović, I., and Ristic, Z.:
Independent and Combined Effects of High Temperature and Drought Stress
During Grain Filling on Plant Yield and Chloroplast EF-Tu Expression in
Spring Wheat, J. Agron. Crop Sci., 197, 430–441,
https://doi.org/10.1111/j.1439-037X.2011.00477.x, 2011.
Prein, A. F., Rasmussen, R. M., Ikeda, K., Liu, C., Clark, M. P., and
Holland, G. J.: The future intensification of hourly precipitation extremes,
Nat. Clim. Change, 7, 48–52, https://doi.org/10.1038/nclimate3168, 2016.
Qian, B., De Jong, R., Warren, R., Chipanshi, A., and Hill, H.: Statistical
spring wheat yield forecasting for the Canadian prairie provinces, Agr.
Forest Meteorol., 149, 1022–1031, https://doi.org/10.1016/j.agrformet.2008.12.006,
2009.
Qian, B., Zhang, X., Smith, W., Grant, B., Jing, Q., Cannon, A. J.,
Neilsen, D., McConkey, B., Li, G., Bonsal, B., Wan, H., Xue, L. and Zhao,
J.: Climate change impacts on Canadian yields of spring wheat, canola and
maize for global warming levels of 1.5 ∘C, 2.0 ∘C, 2.5 ∘C and 3.0 ∘C, Environ. Res. Lett., 14, 074005,
https://doi.org/10.1088/1748-9326/ab17fb, 2019.
Rasmussen, R. and Liu, C.: High Resolution WRF Simulations of the Current and Future Climate of North America, Research Data Archive at the National Center for Atmospheric Research, Computational and Information Systems Laboratory [data set], https://doi.org/10.5065/D6V40SXP, 2017.
Rosenzweig, C., Jones, J. W., Hatfield, J. L., Ruane, A. C., Boote, K. J.,
Thorburn, P., Antle, J. M., Nelson, G. C., Porter, C., Janssen, S., Asseng,
S., Basso, B., Ewert, F., Wallach, D., Baigorria, G., and Winter, J. M.: The
Agricultural Model Intercomparison and Improvement Project (AgMIP):
Protocols and pilot studies, Agr. Forest Meteorol., 170, 166–182,
https://doi.org/10.1016/j.agrformet.2012.09.011, 2013.
Sacks, W. J., Deryng, D., Foley, J. A., and Ramankutty, N.: Crop planting
dates: an analysis of global patterns, Global Ecol. Biogeogr., 19, 607–620,
https://doi.org/10.1111/j.1466-8238.2010.00551.x, 2010.
Saiyed, I. M., Bullock, P. R., Sapirstein, H. D., Finlay, G. J., and Jarvis,
C. K.: Thermal time models for estimating wheat phenological development and
weather-based relationships to wheat quality, Can. J. Plant Sci., 89,
429–439, https://doi.org/10.4141/CJPS07114, 2009.
Semenov, M. A. and Shewry, P. R.: Modelling predicts that heat stress, not drought, will increase vulnerability of wheat in Europe, Sci. Rep., 1, 66, https://doi.org/10.1038/srep00066, 2011.
Setiyono, T. D., Weiss, A., Specht, J., Bastidas, A. M., Cassman, K. G., and
Dobermann, A.: Understanding and modeling the effect of temperature and
daylength on soybean phenology under high-yield conditions, F. Crop. Res.,
100, 257–271, https://doi.org/10.1016/j.fcr.2006.07.011, 2007.
Siebert, S., Ewert, F., Eyshi Rezaei, E., Kage, H., and Graß, R.: Impact
of heat stress on crop yield – On the importance of considering canopy
temperature, Environ. Res. Lett., 9, 044012, https://doi.org/10.1088/1748-9326/9/4/044012,
2014.
Skamarock, W., Klemp, J., Dudhia, J., Gill, D., Barker, M., Duda, M., Huang, X.-Y., Wang, W., and Powers, J.: A Description of the Advanced Research WRF Version 3, (No. NCAR/TN-475+STR), University Corporation for Atmospheric Research, https://doi.org/10.5065/D68S4MVH, 2008.
Statistics Canada: Table 32-10-0002-01 Estimated areas, yield and production of principal field crops by Small Area Data Regions, in metric and imperial units, https://doi.org/10.25318/3210000201-eng, web archive: https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=3210000201, last access: October 2022.
United States U.S. National Agricultural Statistics Service NASS (US NASS): Web archive, https://quickstats.nass.usda.gov/, last access: October 2022.
USDA NASS: Economics, Statistics and Market Information System, Usual
Planting and Harvesting Dates for US Field Crops, https://usda.library.cornell.edu/concern/publications/vm40xr56k (last access: October 2022), 2010.
Wang, E., Martre, P., Zhao, Z., Ewert, F., Maiorano, A., Rötter, R. P.,
Kimball, B. A., Ottman, M. J., Wall, G. W., White, J. W., Reynolds, M. P.,
Alderman, P. D., Aggarwal, P. K., Anothai, J., Basso, B., Biernath, C.,
Cammarano, D., Challinor, A. J., De Sanctis, G., Doltra, J., Fereres, E.,
Garcia-Vila, M., Gayler, S., Hoogenboom, G., Hunt, L. A., Izaurralde, R. C.,
Jabloun, M., Jones, C. D., Kersebaum, K. C., Koehler, A. K., Liu, L.,
Müller, C., Naresh Kumar, S., Nendel, C., O'Leary, G., Olesen, J. E.,
Palosuo, T., Priesack, E., Eyshi Rezaei, E., Ripoche, D., Ruane, A. C.,
Semenov, M. A., Shcherbak, I., Stöckle, C., Stratonovitch, P., Streck,
T., Supit, I., Tao, F., Thorburn, P., Waha, K., Wallach, D., Wang, Z., Wolf,
J., Zhu, Y., and Asseng, S.: The uncertainty of crop yield projections is
reduced by improved temperature response functions, Nat. Plants, 3, 17102,
https://doi.org/10.1038/nplants.2017.102, 2017.
Wang, E., Brown, H. E., Rebetzke, G. J., Zhao, Z., Zheng, B., and Chapman,
S. C.: Improving process-based crop models to better capture
genotype × environment × management interactions, J. Exp.
Bot., 70, 2389–2401, https://doi.org/10.1093/jxb/erz092, 2019.
Wutzler, T., Lucas-Moffat, A., Migliavacca, M., Knauer, J., Sickel, K., Šigut, L., Menzer, O., and Reichstein, M.: Basic and extensible post-processing of eddy covariance flux data with REddyProc, Biogeosciences, 15, 5015–5030, https://doi.org/10.5194/bg-15-5015-2018, 2018.
Xu, X., Chen, F., Barlage, M., Gochis, D., Miao, S., and Shen, S.: Lessons
Learned From Modeling Irrigation From Field to Regional Scales, J. Adv.
Model. Earth Sy., 11, 2428–2448, https://doi.org/10.1029/2018MS001595, 2019.
Yang, Z. L., Niu, G. Y., Mitchell, K. E., Chen, F., Ek, M. B., Barlage, M.,
Longuevergne, L., Manning, K., Niyogi, D., Tewari, M., and Xia, Y.: The
community Noah land surface model with multiparameterization options
(Noah-MP): 2. Evaluation over global river basins, J. Geophys. Res.-Atmos.,
116, 1–16, https://doi.org/10.1029/2010JD015140, 2011.
Zhang, X., Alexander, L., Hegerl, G. C., Jones, P., Tank, A. K., Peterson,
T. C., Trewin, B., and Zwiers, F. W.: Indices for monitoring changes in
extremes based on daily temperature and precipitation data, WIREs Clim.
Chang., 2, 851–870, https://doi.org/10.1002/wcc.147, 2011.
Zhang, Z.: Noah-MP data for modeling Canadian spring wheat study, Zenodo [data set], https://doi.org/10.5281/zenodo.7023831, 2022.
Zhang, Z., Li, Y., Chen, F., Barlage, M., and Li, Z.: Evaluation of
convection-permitting WRF CONUS simulation on the relationship between soil
moisture and heatwaves, Clim. Dynam., 55, 235–252, https://doi.org/10.1007/s00382-018-4508-5, 2018.
Zhang, Z., Li, Y., Barlage, M., Chen, F., Miguez-Macho, G., Ireson, A., and Li, Z.: Modeling groundwater responses to climate change in the Prairie Pothole Region, Hydrol. Earth Syst. Sci., 24, 655–672, https://doi.org/10.5194/hess-24-655-2020, 2020a.
Zhang, Z., Barlage, M., Chen, F., Li, Y., Helgason, W., Xu, X., Liu, X., and
Li, Z.: Joint Modeling of Crop and Irrigation in the central United States
Using the Noah-MP Land Surface Model, J. Adv. Model. Earth Sy., 12, 7,
https://doi.org/10.1029/2020MS002159, 2020b.
Short summary
Crop models incorporated in Earth system models are essential to accurately simulate crop growth processes on Earth's surface and agricultural production. In this study, we aim to model the spring wheat in the Northern Great Plains, focusing on three aspects: (1) develop the wheat model at a point scale, (2) apply dynamic planting and harvest schedules, and (3) adopt a revised heat stress function. The results show substantial improvements and have great importance for agricultural production.
Crop models incorporated in Earth system models are essential to accurately simulate crop growth...
