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Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress

dc.contributor.authorZhang, Zhe
dc.contributor.authorLi, Yanping
dc.contributor.authorChen, Fei
dc.contributor.authorHarder, Philip
dc.contributor.authorHelgason, Warren D.
dc.contributor.authorFamiglietti, James
dc.contributor.authorValayamkunnath, Prasanth
dc.contributor.authorHe, Cenlin
dc.contributor.authorLi, Zhenhua
dc.date.accessioned2023-10-06T05:11:04Z
dc.date.available2023-10-06T05:11:04Z
dc.date.issued2023
dc.description© Author(s) 2023. This work is distributed under the Creative Commons Attribution 4.0 License. Published by Copernicus Publications on behalf of the European Geosciences Union.en_US
dc.description.abstractThe US Northern Great Plains and the Canadian Prairies are known as the world’s breadbaskets for their large spring wheat production and exports to the world. It is essential to accurately represent spring wheat growing dynamics and final yield and improve our ability to predict food production under climate change. This study attempts to incorporate spring wheat growth dynamics into the Noah-MP crop model for a long time period (13 years) and fine spatial scale (4 km). The study focuses on three aspects: (1) developing and calibrating the spring wheat model at a point scale, (2) applying a dynamic planting and harvest date to facilitate large-scale simulations, and (3) applying a temperature stress function to assess crop responses to heat stress amid extreme heat. Model results are evaluated using field observations, satellite leaf area index (LAI), and census data from Statistics Canada and the United States Department of Agriculture (USDA). Results suggest that incorporating a dynamic planting and harvest threshold can better constrain the growing season, especially the peak timing and magnitude of wheat LAI, as well as obtain realistic yield compared to prescribing a static province/state-level map. Results also demonstrate an evident control of heat stress upon wheat yield in three Canadian Prairies Provinces, which are reasonably captured in the new temperature stress function. This study has important implications in terms of estimating crop yields, modeling the land–atmosphere interactions in agricultural areas, and predicting crop growth responses to increasing temperatures amidst climate change.en_US
dc.description.sponsorshipGlobal Institute for Water Security, University of Saskatchewan (Agricultural Water Futures grant); the Global Water Futures (Agricultural Water Futures grant); the National Institute of Food and Agriculture (grant no. 2015-67003-23460); the National Science Foundation (grant no. 1739705); and the National Oceanic and Atmospheric Administration (grant no. NA18OAR4590381).en_US
dc.description.versionPeer Revieweden_US
dc.identifier.citationZhang, Z., Li, Y., Chen, F., Harder, P., Helgason, W., Famiglietti, J., Valayamkunnath, P., He, C., and Li, Z.: Developing spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stress , Geosci. Model Dev., 16, 3809–3825, https://doi.org/10.5194/gmd-16-3809-2023, 2023.en_US
dc.identifier.doi10.5194/gmd-16-3809-2023, 2023
dc.identifier.urihttps://hdl.handle.net/10388/15123
dc.language.isoenen_US
dc.publisherEuropean Geosciences Union [Society Publisher], Copernicus Publications [Commercial Publisher]en_US
dc.rightsAttribution 2.5 Canada*
dc.rights.urihttp://creativecommons.org/licenses/by/2.5/ca/*
dc.subjectspring wheat growth dynamicsen_US
dc.subjectNoah-MP crop modelen_US
dc.subjecttemperature stressen_US
dc.subjectsatellite leaf area indexen_US
dc.titleDeveloping spring wheat in the Noah-MP land surface model (v4.4) for growing season dynamics and responses to temperature stressen_US
dc.typeArticleen_US

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