气候变化领域集成服务门户(CCPortal): Soil Moisture Data Assimilation to Estimate Irrigation Water Use

CCPortal

DOI	10.1029/2019MS001797
	Soil Moisture Data Assimilation to Estimate Irrigation Water Use
	Abolafia-Rosenzweig R.; Livneh B.; Small E.E.; Kumar S.V.
发表日期	2019
ISSN	19422466
起始页码	3670
结束页码	3690
卷号	11 期号:11
英文摘要	Knowledge of irrigation is essential to support food security, manage depleting water resources, and comprehensively understand the global water and energy cycles. Despite the importance of understanding irrigation, little consistent information exists on the amount of water that is applied for irrigation. In this study, we develop and evaluate a new method to predict daily to seasonal irrigation magnitude using a particle batch smoother data assimilation approach, where land surface model soil moisture is applied in different configurations to understand how characteristics of remotely sensed soil moisture may impact the performance of the method. The study employs a suite of synthetic data assimilation experiments, allowing for systematic diagnosis of known error sources. Assimilation of daily synthetic soil moisture observations with zero noise produces irrigation estimates with a seasonal bias of 0.66% and a correlation of 0.95 relative to a known truth irrigation. When synthetic observations were subjected to an irregular overpass interval and random noise similar to the Soil Moisture Active Passive satellite (0.04 cm3 cm−3), irrigation estimates produced a median seasonal bias of <1% and a correlation of 0.69. When systematic biases commensurate with those between NLDAS-2 land surface models and Soil Moisture Active Passive are imposed, irrigation estimates show larger biases. In this application, the particle batch smoother outperformed the particle filter. The presented framework has the potential to provide new information into irrigation magnitude over spatially continuous domains, yet its broad applicability is contingent upon identifying new method(s) of determining irrigation schedule and correcting biases between observed and simulated soil moisture, as these errors markedly degraded performance. ©2019. The Authors.
英文关键词	data assimilation; irrigation; land surface model; particle batch smoother; remote sensing; soil moisture
语种	英语
scopus关键词	Food supply; Irrigation; Remote sensing; Soil surveys; Surface measurement; Water resources; Water supply; Data assimilation; Degraded performance; Irrigation water use; Land surface modeling; Land surface models; Remotely sensed soil moisture; Seasonal irrigation; Systematic diagnosis; Soil moisture; data assimilation; estimation method; experimental study; food security; hydrological cycle; irrigation; irrigation system; land surface; performance assessment; remote sensing; soil moisture; water resource; water use
来源期刊	Journal of Advances in Modeling Earth Systems
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/156828
作者单位	Department of Civil, Environmental, and Architectural Engineering, University of Colorado Boulder, Boulder, CO, United States; Cooperative Institute for Research in Environmental Science (CIRES), University of Colorado Boulder, Boulder, CO, United States; Geological Sciences, University of Colorado Boulder, Boulder, CO, United States; Hydrological Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, United States
推荐引用方式 GB/T 7714	Abolafia-Rosenzweig R.,Livneh B.,Small E.E.,et al. Soil Moisture Data Assimilation to Estimate Irrigation Water Use[J],2019,11(11).
APA	Abolafia-Rosenzweig R.,Livneh B.,Small E.E.,&Kumar S.V..(2019).Soil Moisture Data Assimilation to Estimate Irrigation Water Use.Journal of Advances in Modeling Earth Systems,11(11).
MLA	Abolafia-Rosenzweig R.,et al."Soil Moisture Data Assimilation to Estimate Irrigation Water Use".Journal of Advances in Modeling Earth Systems 11.11(2019).