Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis

An unprecedented soil moisture drought broke out over the Yangtze River basin (YRB) in the summer of 2022 and lasted until the spring of 2023, caused great economic losses and serious environmental issues. With the rapid onset and long-lasting duration, the mega soil drought challenges the current s...

وصف كامل

التفاصيل البيبلوغرافية
الحاوية / القاعدة:Environmental Research Letters
المؤلفون الرئيسيون: Yumiao Wang, Xing Yuan, Yuxiu Liu, Wenyan Wang
التنسيق: مقال
اللغة:الإنجليزية
منشور في: IOP Publishing 2024-01-01
الموضوعات:
الوصول للمادة أونلاين:https://doi.org/10.1088/1748-9326/ad4978
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author Yumiao Wang
Xing Yuan
Yuxiu Liu
Wenyan Wang
author_facet Yumiao Wang
Xing Yuan
Yuxiu Liu
Wenyan Wang
author_sort Yumiao Wang
collection DOAJ
container_title Environmental Research Letters
description An unprecedented soil moisture drought broke out over the Yangtze River basin (YRB) in the summer of 2022 and lasted until the spring of 2023, caused great economic losses and serious environmental issues. With the rapid onset and long-lasting duration, the mega soil drought challenges the current seasonal prediction capacity. Whether the state-of-the-art climate models provide skillful predictions of the onset, persistence and recovery of the 2022–23 mega soil drought needs to be assessed. Identified by the drought area percentage, here we show that the mega soil drought over the YRB started from July, 2022, reached the peak in August, and diminished in April, 2023. Combined with real-time predictions of monthly precipitation released by three climate models participating in the North American multi-model ensemble (NMME) project, we predict the monthly evolution of the 2022–23 soil drought through a joint distribution between precipitation and soil moisture established by the copula method. The results indicate that the NMME/copula prediction well reproduced the spatiotemporal evolution of the mega soil drought at 1 month lead. Using the climatological prediction that relies on the information of initial soil moisture conditions as the reference forecast, the Brier skill score (BSS) values for NMME multi-model ensemble are 0.26, 0.23 and 0.2 for the forecast lead times increased from 1 to 3 months during the entire soil drought period. Specifically, the BSS is 0.14 at 2 months lead during drought onset stage, and 0.26 at 3 months lead during persistence stage, while it is close to zero at all leads during the recovery stage. Our study implies that climate models have great potential in probabilistic seasonal prediction of the onset and persistency of mega soil drought through combining with the copula method.
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spelling doaj-art-04886bda96f441d4a4dde3ca64cdf7542025-08-20T00:56:34ZengIOP PublishingEnvironmental Research Letters1748-93262024-01-0119606401910.1088/1748-9326/ad4978Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysisYumiao Wang0Xing Yuan1https://orcid.org/0000-0001-6983-7368Yuxiu Liu2Wenyan Wang3Key Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology , Nanjing 210044, Jiangsu, People’s Republic of China; School of Hydrology and Water Resources, Nanjing University of Information Science and Technology , Nanjing 210044, Jiangsu, People’s Republic of ChinaKey Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology , Nanjing 210044, Jiangsu, People’s Republic of China; School of Hydrology and Water Resources, Nanjing University of Information Science and Technology , Nanjing 210044, Jiangsu, People’s Republic of China; Institute of Atmospheric Physics, Chinese Academy of Sciences , Beijing 100029, People’s Republic of ChinaKey Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology , Nanjing 210044, Jiangsu, People’s Republic of China; School of Hydrology and Water Resources, Nanjing University of Information Science and Technology , Nanjing 210044, Jiangsu, People’s Republic of ChinaKey Laboratory of Hydrometeorological Disaster Mechanism and Warning of Ministry of Water Resources, Nanjing University of Information Science and Technology , Nanjing 210044, Jiangsu, People’s Republic of China; School of Hydrology and Water Resources, Nanjing University of Information Science and Technology , Nanjing 210044, Jiangsu, People’s Republic of ChinaAn unprecedented soil moisture drought broke out over the Yangtze River basin (YRB) in the summer of 2022 and lasted until the spring of 2023, caused great economic losses and serious environmental issues. With the rapid onset and long-lasting duration, the mega soil drought challenges the current seasonal prediction capacity. Whether the state-of-the-art climate models provide skillful predictions of the onset, persistence and recovery of the 2022–23 mega soil drought needs to be assessed. Identified by the drought area percentage, here we show that the mega soil drought over the YRB started from July, 2022, reached the peak in August, and diminished in April, 2023. Combined with real-time predictions of monthly precipitation released by three climate models participating in the North American multi-model ensemble (NMME) project, we predict the monthly evolution of the 2022–23 soil drought through a joint distribution between precipitation and soil moisture established by the copula method. The results indicate that the NMME/copula prediction well reproduced the spatiotemporal evolution of the mega soil drought at 1 month lead. Using the climatological prediction that relies on the information of initial soil moisture conditions as the reference forecast, the Brier skill score (BSS) values for NMME multi-model ensemble are 0.26, 0.23 and 0.2 for the forecast lead times increased from 1 to 3 months during the entire soil drought period. Specifically, the BSS is 0.14 at 2 months lead during drought onset stage, and 0.26 at 3 months lead during persistence stage, while it is close to zero at all leads during the recovery stage. Our study implies that climate models have great potential in probabilistic seasonal prediction of the onset and persistency of mega soil drought through combining with the copula method.https://doi.org/10.1088/1748-9326/ad4978drought evolutionseasonal predictionNMMEcopulaYangtze River basin
spellingShingle Yumiao Wang
Xing Yuan
Yuxiu Liu
Wenyan Wang
Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis
drought evolution
seasonal prediction
NMME
copula
Yangtze River basin
title Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis
title_full Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis
title_fullStr Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis
title_full_unstemmed Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis
title_short Skillful seasonal prediction of the 2022–23 mega soil drought over the Yangtze River basin by combining dynamical climate prediction and copula analysis
title_sort skillful seasonal prediction of the 2022 23 mega soil drought over the yangtze river basin by combining dynamical climate prediction and copula analysis
topic drought evolution
seasonal prediction
NMME
copula
Yangtze River basin
url https://doi.org/10.1088/1748-9326/ad4978
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AT yuxiuliu skillfulseasonalpredictionofthe202223megasoildroughtovertheyangtzeriverbasinbycombiningdynamicalclimatepredictionandcopulaanalysis
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