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DroughtCast: A Machine Learning Forecast of the United States Drought Monitor

Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and s...

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Autores principales: Brust, Colin, Kimball, John S., Maneta, Marco P., Jencso, Kelsey, Reichle, Rolf H.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8725730/
https://www.ncbi.nlm.nih.gov/pubmed/34993467
http://dx.doi.org/10.3389/fdata.2021.773478
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author Brust, Colin
Kimball, John S.
Maneta, Marco P.
Jencso, Kelsey
Reichle, Rolf H.
author_facet Brust, Colin
Kimball, John S.
Maneta, Marco P.
Jencso, Kelsey
Reichle, Rolf H.
author_sort Brust, Colin
collection PubMed
description Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought.
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spelling pubmed-87257302022-01-05 DroughtCast: A Machine Learning Forecast of the United States Drought Monitor Brust, Colin Kimball, John S. Maneta, Marco P. Jencso, Kelsey Reichle, Rolf H. Front Big Data Big Data Drought is one of the most ecologically and economically devastating natural phenomena affecting the United States, causing the U.S. economy billions of dollars in damage, and driving widespread degradation of ecosystem health. Many drought indices are implemented to monitor the current extent and status of drought so stakeholders such as farmers and local governments can appropriately respond. Methods to forecast drought conditions weeks to months in advance are less common but would provide a more effective early warning system to enhance drought response, mitigation, and adaptation planning. To resolve this issue, we introduce DroughtCast, a machine learning framework for forecasting the United States Drought Monitor (USDM). DroughtCast operates on the knowledge that recent anomalies in hydrology and meteorology drive future changes in drought conditions. We use simulated meteorology and satellite observed soil moisture as inputs into a recurrent neural network to accurately forecast the USDM between 1 and 12 weeks into the future. Our analysis shows that precipitation, soil moisture, and temperature are the most important input variables when forecasting future drought conditions. Additionally, a case study of the 2017 Northern Plains Flash Drought shows that DroughtCast was able to forecast a very extreme drought event up to 12 weeks before its onset. Given the favorable forecasting skill of the model, DroughtCast may provide a promising tool for land managers and local governments in preparing for and mitigating the effects of drought. Frontiers Media S.A. 2021-12-21 /pmc/articles/PMC8725730/ /pubmed/34993467 http://dx.doi.org/10.3389/fdata.2021.773478 Text en Copyright © 2021 Brust, Kimball, Maneta, Jencso and Reichle. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Big Data
Brust, Colin
Kimball, John S.
Maneta, Marco P.
Jencso, Kelsey
Reichle, Rolf H.
DroughtCast: A Machine Learning Forecast of the United States Drought Monitor
title DroughtCast: A Machine Learning Forecast of the United States Drought Monitor
title_full DroughtCast: A Machine Learning Forecast of the United States Drought Monitor
title_fullStr DroughtCast: A Machine Learning Forecast of the United States Drought Monitor
title_full_unstemmed DroughtCast: A Machine Learning Forecast of the United States Drought Monitor
title_short DroughtCast: A Machine Learning Forecast of the United States Drought Monitor
title_sort droughtcast: a machine learning forecast of the united states drought monitor
topic Big Data
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8725730/
https://www.ncbi.nlm.nih.gov/pubmed/34993467
http://dx.doi.org/10.3389/fdata.2021.773478
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