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Article: Dynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions

TitleDynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions
Authors
KeywordsDynamic
Yield prediction
Drought
Return period
Scale
Issue Date2014
Citation
Environmental Modelling and Software, 2014, v. 62, p. 454-464 How to Cite?
AbstractAgricultural droughts can create serious threats to food security. Tools for dynamic prediction of drought impacts on yields over large geographical regions can provide valuable information for drought management. Based on the DeNitrification-DeComposition (DNDC) model, the current research proposes a Drought Risk Analysis System (DRAS) that allows for the scenario-based analysis of drought-induced yield losses. We assess impacts on corn yields using two case studies, the 2012 U.S.A. drought and the 2000 and 2009 droughts in Liaoning Province, China. The results show that the system is able to perform daily simulations of corn growth and to dynamically evaluate the large-scale grain production in both regions. It is also capable of mapping the up-to-date yield losses on a daily basis, the additional losses under different drought development scenarios, and the yield-based drought return periods at multiple scales of geographic regions. In addition, detailed information about the water-stress process, biomass development, and the uncertainty of drought impacts on crop growth at a specific site can be displayed in the system. Remote sensing data were used to map the areas of drought-affected crops for comparison with the modeling results. Beyond the conventional drought information from meteorological and hydrological data, this system can provide comprehensive and predictive yield information for various end-users, including farmers, decision makers, insurance agencies, and food consumers.
Persistent Identifierhttp://hdl.handle.net/10722/296831
ISSN
2020 Impact Factor: 5.288
2020 SCImago Journal Rankings: 1.828
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorYu, Chaoqing-
dc.contributor.authorLi, Changsheng-
dc.contributor.authorXin, Qinchuan-
dc.contributor.authorChen, Han-
dc.contributor.authorZhang, Jie-
dc.contributor.authorZhang, Feng-
dc.contributor.authorLi, Xuecao-
dc.contributor.authorClinton, Nick-
dc.contributor.authorHuang, Xiao-
dc.contributor.authorYue, Yali-
dc.contributor.authorGong, Peng-
dc.date.accessioned2021-02-25T15:16:46Z-
dc.date.available2021-02-25T15:16:46Z-
dc.date.issued2014-
dc.identifier.citationEnvironmental Modelling and Software, 2014, v. 62, p. 454-464-
dc.identifier.issn1364-8152-
dc.identifier.urihttp://hdl.handle.net/10722/296831-
dc.description.abstractAgricultural droughts can create serious threats to food security. Tools for dynamic prediction of drought impacts on yields over large geographical regions can provide valuable information for drought management. Based on the DeNitrification-DeComposition (DNDC) model, the current research proposes a Drought Risk Analysis System (DRAS) that allows for the scenario-based analysis of drought-induced yield losses. We assess impacts on corn yields using two case studies, the 2012 U.S.A. drought and the 2000 and 2009 droughts in Liaoning Province, China. The results show that the system is able to perform daily simulations of corn growth and to dynamically evaluate the large-scale grain production in both regions. It is also capable of mapping the up-to-date yield losses on a daily basis, the additional losses under different drought development scenarios, and the yield-based drought return periods at multiple scales of geographic regions. In addition, detailed information about the water-stress process, biomass development, and the uncertainty of drought impacts on crop growth at a specific site can be displayed in the system. Remote sensing data were used to map the areas of drought-affected crops for comparison with the modeling results. Beyond the conventional drought information from meteorological and hydrological data, this system can provide comprehensive and predictive yield information for various end-users, including farmers, decision makers, insurance agencies, and food consumers.-
dc.languageeng-
dc.relation.ispartofEnvironmental Modelling and Software-
dc.rightsThis work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.-
dc.subjectDynamic-
dc.subjectYield prediction-
dc.subjectDrought-
dc.subjectReturn period-
dc.subjectScale-
dc.titleDynamic assessment of the impact of drought on agricultural yield and scale-dependent return periods over large geographic regions-
dc.typeArticle-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.1016/j.envsoft.2014.08.004-
dc.identifier.scopuseid_2-s2.0-85027937099-
dc.identifier.volume62-
dc.identifier.spage454-
dc.identifier.epage464-
dc.identifier.isiWOS:000346751800036-
dc.identifier.issnl1364-8152-

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