干旱区生态环境知识资源中心(Arid): Post-processing R tool for SWAT efficiently studying climate change impacts on hydrology, water quality, and crop growth

Arid

DOI	10.1016/j.envsoft.2022.105492
	Post-processing R tool for SWAT efficiently studying climate change impacts on hydrology, water quality, and crop growth
	Ding, Beibei; Liu, Haipeng; Li, Yingxuan; Zhang, Xueliang; Feng, Puyu; Liu, De Li; Marek, Gary W.; Ale, Srinivasulu; Brauer, David K.; Srinivasan, Raghavan; Chen, Yong
通讯作者	Chen, Y
来源期刊	ENVIRONMENTAL MODELLING & SOFTWARE
ISSN	1364-8152
EISSN	1873-6726
出版年	2022
卷号	156
英文摘要	Soil and Water Assessment Tool (SWAT) is widely used for watershed-scale assessment of climate change im-pacts, but post-processing of model outputs is a tedious job. An R tool was developed in this study for batch processing of SWAT output results. A case study was then performed in the Double Mountain Fork Brazos watershed in the Texas Panhandle using an improved SWAT model with the R tool to evaluate the simulated future changes in water balance components, total nitrogen (TN) load, and crop growth over the watershed. The results showed that the average annual future surface runoff increased by 8.9-17.9 mm and 11.5-22.6 mm in the irrigated and dryland cotton areas, respectively. Similarly, future TN load in irrigated and dryland cotton areas increased by approximately 0.4-0.9 kg ha(-1) and 1.9-2.4 kg ha(-1). The yields of irrigated and dryland cotton increased by 91.1%-122.1% and 47.5%-84.0% under the future climate scenarios, respectively.
英文关键词	SWAT -MAD CMIP6 Hydrologic cycle Total nitrogen load Cotton Semi -arid region
类型	Article
语种	英语
开放获取类型	Bronze
收录类别	SCI-E
WOS记录号	WOS:000860212300002
WOS关键词	GREENHOUSE-GAS CONCENTRATIONS ; TEXAS HIGH-PLAINS ; MODELS ; COTTON ; EVAPOTRANSPIRATION ; AVAILABILITY ; IRRIGATION ; YIELD ; CMIP5
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Environmental ; Environmental Sciences ; Water Resources
WOS研究方向	Computer Science ; Engineering ; Environmental Sciences & Ecology ; Water Resources
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/392466
推荐引用方式 GB/T 7714	Ding, Beibei,Liu, Haipeng,Li, Yingxuan,et al. Post-processing R tool for SWAT efficiently studying climate change impacts on hydrology, water quality, and crop growth[J],2022,156.
APA	Ding, Beibei.,Liu, Haipeng.,Li, Yingxuan.,Zhang, Xueliang.,Feng, Puyu.,...&Chen, Yong.(2022).Post-processing R tool for SWAT efficiently studying climate change impacts on hydrology, water quality, and crop growth.ENVIRONMENTAL MODELLING & SOFTWARE,156.
MLA	Ding, Beibei,et al."Post-processing R tool for SWAT efficiently studying climate change impacts on hydrology, water quality, and crop growth".ENVIRONMENTAL MODELLING & SOFTWARE 156(2022).