干旱区生态环境知识资源中心(Arid): Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management

Arid

DOI	10.1088/1748-9326/ac2fde
	Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management
	Chakraborty, Debaditya; Basagaoglu, Hakan; Gutierrez, Lilianna; Mirchi, Ali
通讯作者	Chakraborty, D (corresponding author), Univ Texas San Antonio, Sch Civil & Environm Engn & Construct Management, 501 W Cesar E Chavez Blvd, San Antonio, TX 78207 USA.
来源期刊	ENVIRONMENTAL RESEARCH LETTERS
ISSN	1748-9326
出版年	2021
卷号	16 期号:11
英文摘要	Trustworthy projections of hydrological droughts are pivotal for identifying the key hydroclimatic factors that affect future groundwater level (GWL) fluctuations in drought-prone karstic aquifers that provide water for human consumption and sustainable ecosystems. Herein, we introduce an explainable artificial intelligence (XAI) framework integrated with scenario-based downscaled climate projections from global circulation models. We use the integrated framework to investigate nonlinear hydroclimatic dependencies and interactions behind future hydrological droughts in the Edwards Aquifer Region, an ecologically fragile groundwater-dependent semi-arid region in southern United States. We project GWLs under different future climate scenarios to evaluate the likelihood of severe hydrological droughts under a warm-wet future in terms of mandated groundwater pumping reductions in droughts as part of the habitat conservation plan in effect to protect threatened and endangered endemic aquatic species. The XAI model accounts for the expected non-linear dynamics between GWLs and climatic variables in the complex human-natural system, which is not captured in simple linear models. The XAI-based analysis reveals the critical temperature inflection point beyond which groundwater depletion is triggered despite increased average precipitation. Compound effects of increased evapotranspiration, lower soil moisture, and reduced diffuse recharge due to warmer temperatures could amplify severe hydrological droughts that lower GWLs, potentially exacerbating the groundwater sustainability challenges in the drought-prone karstic aquifer despite an increasing precipitation trend.
英文关键词	forecasting model explainable artificial intelligence climate change groundwater depletion hydrological drought
类型	Article
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000712050400001
WOS关键词	CLIMATE ; RECHARGE ; CYCLE
WOS类目	Environmental Sciences ; Meteorology & Atmospheric Sciences
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/368234
作者单位	[Chakraborty, Debaditya] Univ Texas San Antonio, Sch Civil & Environm Engn & Construct Management, 501 W Cesar E Chavez Blvd, San Antonio, TX 78207 USA; [Basagaoglu, Hakan] Edwards Aquifer Author, 900 E Quincy St, Antonio, TX 78215 USA; [Gutierrez, Lilianna] Univ Texas San Antonio, Dept Chem Engn, One UTSA Circle, San Antonio, TX 78249 USA; [Mirchi, Ali] Oklahoma State Univ, Dept Biosyst & Agr Engn, 111 Agr Hall, Stillwater, OK 74078 USA
推荐引用方式 GB/T 7714	Chakraborty, Debaditya,Basagaoglu, Hakan,Gutierrez, Lilianna,et al. Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management[J],2021,16(11).
APA	Chakraborty, Debaditya,Basagaoglu, Hakan,Gutierrez, Lilianna,&Mirchi, Ali.(2021).Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management.ENVIRONMENTAL RESEARCH LETTERS,16(11).
MLA	Chakraborty, Debaditya,et al."Explainable AI reveals new hydroclimatic insights for ecosystem-centric groundwater management".ENVIRONMENTAL RESEARCH LETTERS 16.11(2021).