干旱区生态环境知识资源中心(Arid): Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling

Arid

DOI	10.1016/j.scitotenv.2021.148177
	Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling
	Paul, Manashi; Rajib, Adnan; Negahban-Azar, Masoud; Shirmohammadi, Adel; Srivastava, Puneet
通讯作者	Negahban-Azar, M (corresponding author), Univ Maryland, 1449 Ans Sc Bldg, College Pk, MD 20817 USA.
来源期刊	SCIENCE OF THE TOTAL ENVIRONMENT
ISSN	0048-9697
EISSN	1879-1026
出版年	2021
卷号	791
英文摘要	In watersheds located in semi-arid regions, vegetation dynamics, evapotranspiration (ET), and associated water and energy balances collectively play a major role in controlling hydrological regimes and crop yield. As such, it is challenging to predict the complex hydrological processes and biophysical dynamics. This challenge increases in areas with limited data availability. The key objective of this study was to evaluate the direct integration of re-motely sensed Leaf Area Index (LAI) data into a hydrological model to improve streamflow, ET, and crop yield es-timates. We also demonstrated how an improved model integrated with remotely sensed LAI data can inform water managers by predicting water productivity (WP) under different irrigation schemes. We took agricultural-dominated San Joaquin Watershed in California, United States, as our testbed and integrated the Moderate Resolution Imaging Spectroradiometer (MODIS) 500-m resolution 4-day total LAI data into the SWAT (Soil and Water Assessment Tool) model. Results showed that, compared to conventional SWAT model that relies on semi-empirical equations and user inputs for simulating biophysical processes, direct LAI integra-tion into SWAT model (SWAT-LAI) notably captured the actual vegetation dynamics and improved ET and crop yield estimations. The WP simulated by the improved SWAT-LAI model for almond and grape yields varied within a range from 0.363 to 3.81 kg/m(3) and 0.32 to 4.76 kg/m(3) across different irrigation applications. The out-comes of this study showed that deficit irrigation application could be a viable option in water stressed regions, since it can save a substantial amount of irrigation water and maintain the higher water productivity required for both almond and grape yield production. This study shows an evidence of how remotely sensed data integrated into hydrological models can serve as a decision support tool by providing quantitative information on crop water use and crop production. (C) 2021 Elsevier B.V. All rights reserved.
英文关键词	Leaf Area Index SWAT Water productivity Decision tree Deficit irrigation Semi-arid regions
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000686005100016
WOS关键词	RADIATION USE EFFICIENCY ; REGULATED DEFICIT IRRIGATION ; WHEAT YIELD ESTIMATION ; SWAT MODEL ; SOIL-MOISTURE ; CROP-YIELD ; CLIMATE-CHANGE ; REDUCED IRRIGATION ; RIVER-BASIN ; LAND-USE
WOS类目	Environmental Sciences
WOS研究方向	Environmental Sciences & Ecology
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/364570
作者单位	[Paul, Manashi; Negahban-Azar, Masoud; Shirmohammadi, Adel; Srivastava, Puneet] Univ Maryland, 1449 Ans Sc Bldg, College Pk, MD 20817 USA; [Rajib, Adnan] Texas A&M Univ, Kingsville, TX 78363 USA
推荐引用方式 GB/T 7714	Paul, Manashi,Rajib, Adnan,Negahban-Azar, Masoud,et al. Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling[J],2021,791.
APA	Paul, Manashi,Rajib, Adnan,Negahban-Azar, Masoud,Shirmohammadi, Adel,&Srivastava, Puneet.(2021).Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling.SCIENCE OF THE TOTAL ENVIRONMENT,791.
MLA	Paul, Manashi,et al."Improved agricultural Water management in data-scarce semi-arid watersheds: Value of integrating remotely sensed leaf area index in hydrological modeling".SCIENCE OF THE TOTAL ENVIRONMENT 791(2021).