干旱区生态环境知识资源中心(Arid): Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment

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DOI	10.1016/j.agwat.2020.106334
	Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment
	Elbeltagi, Ahmed; Deng, Jinsong; Wang, Ke; Malik, Anurag; Maroufpoor, Saman
通讯作者	Deng, JS
来源期刊	AGRICULTURAL WATER MANAGEMENT
ISSN	0378-3774
EISSN	1873-2283
出版年	2020
卷号	241
英文摘要	Crop evapotranspiration (ETc) is one of the most basic components of the hydrologic cycle that is effective in irrigation system design and management, water resources planning and scheduling, and hydrologic water balance. Thus, precise estimation of ETc is valuable for various applications of agricultural water engineering, especially in developing countries such as Egypt, which has lack of meteorological data, high cost and time to calculate ETc, and lack of information on future ETc values to consider management scenarios and increase production potential. Also, due to the existence of different climates in Egypt, the estimate of ETc has become a challenge. To this end, the aim of this study was to estimate the ETc to eliminate the limitations mentioned, and analyze the long-term dynamics of ETc based on limited climate data and simple method. Three Egyptian governorates namely Ad Daqahliyah, Ash Sharqiyah, and Kafr ash Shaykh of Nile Delta, were selected as major wheat-producing sites. The required historical required climatic data were collected from open access data library while future data were from two extreme scenarios of the Representative Concentration Pathways (RCP) i.e., RCP 4.5, and RCP 8.5. The available dataset was divided into three parts: (i) calibration from 1970-2000, (ii) validation from 2000-2017, and (iii) prediction from 2022-2035. The deep neural network (DNN) was employed for incorporating historical data and predicting future ETc. For the evaluation of generated DNN models, the research finding indicates that the correlation coefficients between actual versus predicted monthly ETc were found to be 0.95, 0.96, and 0.97 for calibration period, and 0.94, 0.95 and 0.95 for validation at Ad Daqahliyah, Kafr ash Shaykh, and Ash Sharqiyah regions, respectively. For the simulation of future climatic data, maximum temperature (T-max) will increased by 5.19 %, 4.22 %, and 20.82 %, minimum temperature (T-min) will increased by 1.62 %, 36.44 %, and 27.80 %, and solar radiation (SR) will increased by 6.53 %, 18.74 %, and 28.83 % for the study locations, respectively. Moreover, the DNN model exposed that the Kafr ash Shaykh attain the highest values of ETc with an increase of 11.31 %, slightly increased of 1.38 % for Ad Daqahliyah, and decreased by 15.09 % for Ash Sharqiyah in comparison to the historical data. Thus, the proposed model of crop water-use prediction effectively estimated ETc of wheat and make an efficient decision. The developed models produced satisfactory results for water managers to save water and achieve the sustainability of agricultural water.
英文关键词	Crop evapotranspiration DNN model Climate change Future projection
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000566864000005
WOS关键词	DATA-DRIVEN METHODS ; HARGREAVES EQUATION ; WATER PRODUCTIVITY ; CLIMATE VARIABLES ; RIVER-BASIN ; TEMPERATURE ; REGRESSION ; SYSTEM ; SOUTH ; WHEAT
WOS类目	Agronomy ; Water Resources
WOS研究方向	Agriculture ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/326101
作者单位	[Elbeltagi, Ahmed; Deng, Jinsong; Wang, Ke] Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Peoples R China; [Elbeltagi, Ahmed] Mansoura Univ, Agr Engn Dept, Fac Agr, Mansoura 35516, Egypt; [Malik, Anurag] Punjab Agr Univ, Reg Res Stn, Bathinda 151001, Punjab, India; [Maroufpoor, Saman] Univ Tehran, Irrigat & Reclamat Engn Dept, Tehran, Iran
推荐引用方式 GB/T 7714	Elbeltagi, Ahmed,Deng, Jinsong,Wang, Ke,et al. Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment[J],2020,241.
APA	Elbeltagi, Ahmed,Deng, Jinsong,Wang, Ke,Malik, Anurag,&Maroufpoor, Saman.(2020).Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment.AGRICULTURAL WATER MANAGEMENT,241.
MLA	Elbeltagi, Ahmed,et al."Modeling long-term dynamics of crop evapotranspiration using deep learning in a semi-arid environment".AGRICULTURAL WATER MANAGEMENT 241(2020).