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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...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Frontiers Media S.A.
2021
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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. |
format | Online Article Text |
id | pubmed-8725730 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | Frontiers Media S.A. |
record_format | MEDLINE/PubMed |
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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