Cargando…

Soil Moisture Data Assimilation to Estimate Irrigation Water Use

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 irri...

Descripción completa

Detalles Bibliográficos
Autores principales: Abolafia‐Rosenzweig, R., Livneh, B., Small, E.E., Kumar, S.V.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: John Wiley and Sons Inc. 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6988458/
https://www.ncbi.nlm.nih.gov/pubmed/32025280
http://dx.doi.org/10.1029/2019MS001797
_version_ 1783492266575265792
author Abolafia‐Rosenzweig, R.
Livneh, B.
Small, E.E.
Kumar, S.V.
author_facet Abolafia‐Rosenzweig, R.
Livneh, B.
Small, E.E.
Kumar, S.V.
author_sort Abolafia‐Rosenzweig, R.
collection PubMed
description 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 cm(3) 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.
format Online
Article
Text
id pubmed-6988458
institution National Center for Biotechnology Information
language English
publishDate 2019
publisher John Wiley and Sons Inc.
record_format MEDLINE/PubMed
spelling pubmed-69884582020-02-03 Soil Moisture Data Assimilation to Estimate Irrigation Water Use Abolafia‐Rosenzweig, R. Livneh, B. Small, E.E. Kumar, S.V. J Adv Model Earth Syst Research Articles 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 cm(3) 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. John Wiley and Sons Inc. 2019-11-17 2019-11 /pmc/articles/PMC6988458/ /pubmed/32025280 http://dx.doi.org/10.1029/2019MS001797 Text en ©2019. The Authors. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Articles
Abolafia‐Rosenzweig, R.
Livneh, B.
Small, E.E.
Kumar, S.V.
Soil Moisture Data Assimilation to Estimate Irrigation Water Use
title Soil Moisture Data Assimilation to Estimate Irrigation Water Use
title_full Soil Moisture Data Assimilation to Estimate Irrigation Water Use
title_fullStr Soil Moisture Data Assimilation to Estimate Irrigation Water Use
title_full_unstemmed Soil Moisture Data Assimilation to Estimate Irrigation Water Use
title_short Soil Moisture Data Assimilation to Estimate Irrigation Water Use
title_sort soil moisture data assimilation to estimate irrigation water use
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6988458/
https://www.ncbi.nlm.nih.gov/pubmed/32025280
http://dx.doi.org/10.1029/2019MS001797
work_keys_str_mv AT abolafiarosenzweigr soilmoisturedataassimilationtoestimateirrigationwateruse
AT livnehb soilmoisturedataassimilationtoestimateirrigationwateruse
AT smallee soilmoisturedataassimilationtoestimateirrigationwateruse
AT kumarsv soilmoisturedataassimilationtoestimateirrigationwateruse