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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...
Autores principales: | , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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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 |
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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 |
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