干旱区生态环境知识资源中心(Arid): An inexact irrigation water allocation optimization model under future climate change

Arid

DOI	10.1007/s00477-018-1597-y
	An inexact irrigation water allocation optimization model under future climate change
	Wang, Youzhi; Liu, Liu; Guo, Ping; Zhang, Chenglong; Zhang, Fan; Guo, Shanshan
通讯作者	Guo, Ping
来源期刊	STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT
ISSN	1436-3240
EISSN	1436-3259
出版年	2019
卷号	33 期号:1 页码:271-285
英文摘要	Due to the widespread uncertainties in agricultural water resources systems and climate change projections, the traditional optimization methods for agricultural water management may have difficulties in generating rational and effective optimal decisions. In order to get optimal future agricultural water allocation schemes for arid areas with consideration of climate change conditions, the model framework established in this paper integrates a statistical downscaling model, back propagation neural networks, and an evapotranspiration model (the Hargreaves model) with inexact irrigation water allocation optimization model under future climate change scenarios. The model framework, which integrates simulation models and optimization models, considers the interactions and uncertainties of parameters, thereby reflecting the realities more accurately. It is applied to the Yingke Irrigation Area in the midstream area of the Heihe River Basin in Zhangye city, Gansu Province, northwest China. Then, water allocation schemes in planning year (2047) under multiple future Representative Concentration Pathways (RCP) scenarios and the status quo (2016) are compared, in order to evaluate the practicability of generated water allocation schemes. The results show that the water shortages of economic crops are improved compared with the status quo under all RCP scenarios while those of the grain crops present opposite results. Meanwhile, the economic benefits decrease from the status quo to planning year under all future scenarios. This phenomenon is directly related to the amount of irrigation water allocation and is indirectly related to the changes of meteorological conditions. The model framework can reveal the regular pattern of hydro-meteorological elements with the impact of climate change. Meanwhile, it can generate irrigation water allocation schemes under various RCPs scenarios which could provide valuable decision support for water resources managers.
英文关键词	Climate change Interval linear programming Statistical downscaling Back propagation neural network Hargreaves model Irrigation water allocation Uncertainty
类型	Article
语种	英语
国家	Peoples R China
收录类别	SCI-E
WOS记录号	WOS:000457465700017
WOS关键词	MANAGEMENT ; RESOURCES ; SIMULATION
WOS类目	Engineering, Environmental ; Engineering, Civil ; Environmental Sciences ; Statistics & Probability ; Water Resources
WOS研究方向	Engineering ; Environmental Sciences & Ecology ; Mathematics ; Water Resources
来源机构	中国农业大学
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/218938
作者单位	China Agr Univ, Coll Water Resources & Civil Engn, Ctr Agr Water Res China, Beijing 100083, Peoples R China
推荐引用方式 GB/T 7714	Wang, Youzhi,Liu, Liu,Guo, Ping,et al. An inexact irrigation water allocation optimization model under future climate change[J]. 中国农业大学,2019,33(1):271-285.
APA	Wang, Youzhi,Liu, Liu,Guo, Ping,Zhang, Chenglong,Zhang, Fan,&Guo, Shanshan.(2019).An inexact irrigation water allocation optimization model under future climate change.STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT,33(1),271-285.
MLA	Wang, Youzhi,et al."An inexact irrigation water allocation optimization model under future climate change".STOCHASTIC ENVIRONMENTAL RESEARCH AND RISK ASSESSMENT 33.1(2019):271-285.