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He N, Yin J, Slater LJ, Liu R, Kang S, Liu P, Liu D, Xiong L. Global terrestrial drought and its projected socioeconomic implications under different warming targets. THE SCIENCE OF THE TOTAL ENVIRONMENT 2024; 946:174292. [PMID: 38960192 DOI: 10.1016/j.scitotenv.2024.174292] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/18/2023] [Revised: 05/10/2024] [Accepted: 06/23/2024] [Indexed: 07/05/2024]
Abstract
Droughts are increasingly frequent as the Earth warms, presenting adaptation challenges for ecosystems and human communities worldwide. A strategic environmental assessment (SEA) and the integration of adaptation strategies into policies, plans, and programs (PPP) are two important approaches for enhancing climate resilience and fostering sustainable development. This study developed an innovative approach to strengthen the SEA of droughts by quantifying the impacts of future temperature increases. A novel method for projecting drought events was integrated into the SEA process by leveraging multiple data sources, including atmospheric reanalysis, reconstructions, satellite-based observations, and model simulations. We identified drought conditions using terrestrial water storage (TWS) anomalies and applied a random forest (RF) model for disentangling the drivers behind drought events. We then set two global warming targets (2.0 °C and 2.5 °C) and analyzed drought changes under three shared socioeconomic pathways (SSP126, SSP370, SSP585). In a 2.0 °C warming world, over 50 % of the global surface will face increased drought risk. With an additional 0.5 °C increase, >60 % of the land will be prone to further drought escalation. We utilized copulas to build the joint distribution for drought duration and severity, estimating the joint return periods (JRP) for bivariate drought hazard. In tropical and subtropical regions, JRP reductions exceeding half are projected for >33 % of the regional land surface under 2.0 °C warming and for >50 % under 2.5 °C warming. Finally, we projected the impacts of drought events on population and gross domestic product (GDP). Among the three SSPs, under SSP370, population exposure is highest and GDP exposure is minimal under 2.0 °C warming. Global GDP and population risks from drought are projected to increase by 37 % and 24 %, respectively, as warming continues. This study enhances the accuracy of SEA in addressing drought risks and vulnerabilities, supporting climate-resilient planning and adaptive strategies.
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Affiliation(s)
- Nan He
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China
| | - Jiabo Yin
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China.
| | - Louise J Slater
- School of Geography and the Environment, University of Oxford, Oxford, UK
| | - Rutong Liu
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China
| | - Shengyu Kang
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China
| | - Pan Liu
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China
| | - Dedi Liu
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China
| | - Lihua Xiong
- State Key Laboratory of Water Resources Engineering and Management, Wuhan University, Wuhan, Hubei 430072, PR China
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Li W, Bao L, Yao G, Wang F, Guo Q, Zhu J, Zhu J, Wang Z, Bi J, Zhu C, Zhong Y, Lu S. The analysis on groundwater storage variations from GRACE/GRACE-FO in recent 20 years driven by influencing factors and prediction in Shandong Province, China. Sci Rep 2024; 14:5819. [PMID: 38461310 PMCID: PMC10925065 DOI: 10.1038/s41598-024-55588-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2023] [Accepted: 02/26/2024] [Indexed: 03/11/2024] Open
Abstract
Monitoring and predicting the regional groundwater storage (GWS) fluctuation is an essential support for effectively managing water resources. Therefore, taking Shandong Province as an example, the data from Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On (GRACE-FO) is used to invert GWS fluctuation from January 2003 to December 2022 together with Watergap Global Hydrological Model (WGHM), in-situ groundwater volume and level data. The spatio-temporal characteristics are decomposed using Independent Components Analysis (ICA), and the impact factors, such as precipitation and human activities, which are also analyzed. To predict the short-time changes of GWS, the Support Vector Machines (SVM) is adopted together with three commonly used methods Long Short-Term Memory (LSTM), Singular Spectrum Analysis (SSA), Auto-Regressive Moving Average Model (ARMA), as the comparison. The results show that: (1) The loss intensity of western GWS is significantly greater than those in coastal areas. From 2003 to 2006, GWS increased sharply; during 2007 to 2014, there exists a loss rate - 5.80 ± 2.28 mm/a of GWS; the linear trend of GWS change is - 5.39 ± 3.65 mm/a from 2015 to 2022, may be mainly due to the effect of South-to-North Water Diversion Project. The correlation coefficient between GRACE and WGHM is 0.67, which is consistent with in-situ groundwater volume and level. (2) The GWS has higher positive correlation with monthly Global Precipitation Climatology Project (GPCP) considering time delay after moving average, which has the similar energy spectrum depending on Continuous Wavelet Transform (CWT) method. In addition, the influencing facotrs on annual GWS fluctuation are analyzed, the correlation coefficient between GWS and in-situ data including the consumption of groundwater mining, farmland irrigation is 0.80, 0.71, respectively. (3) For the GWS prediction, SVM method is adopted to analyze, three training samples with 180, 204 and 228 months are established with the goodness-of-fit all higher than 0.97. The correlation coefficients are 0.56, 0.75, 0.68; RMSE is 5.26, 4.42, 5.65 mm; NSE is 0.28, 0.43, 0.36, respectively. The performance of SVM model is better than the other methods for the short-term prediction.
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Affiliation(s)
- Wanqiu Li
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China.
| | - Lifeng Bao
- State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, CAS, Wuhan, 430077, China
| | - Guobiao Yao
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
| | - Fengwei Wang
- College of Surveying and Geo-Informatics, Tongji University, Shanghai, 200092, China
| | - Qiuying Guo
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
| | - Jie Zhu
- China Earthquake Networks Center, Beijing, 100045, China
| | - Jinjie Zhu
- 801 Institute of Hydrogeology and Engineering Geology, Shandong Provincial Bureau of Geology & Mineral Resources, Jinan, 250014, China
| | - Zhiwei Wang
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China.
| | - Jingxue Bi
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
| | - Chengcheng Zhu
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
| | - Yulong Zhong
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan, 430074, China
| | - Shanbo Lu
- School of Surveying and Geo-Informatics, Shandong Jianzhu University, Jinan, 250101, China
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Zhou Z, Ding Y, Fu Q, Wang C, Wang Y, Cai H, Liu S, Huang S, Shi H. Insights from CMIP6 SSP scenarios for future characteristics of propagation from meteorological drought to hydrological drought in the Pearl River Basin. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 899:165618. [PMID: 37474042 DOI: 10.1016/j.scitotenv.2023.165618] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/02/2023] [Revised: 07/15/2023] [Accepted: 07/16/2023] [Indexed: 07/22/2023]
Abstract
Drought is a common and widely distributed natural hazard. Analyzing and predicting drought characteristics and propagation are important for the early warning, prevention, and mitigation of drought disasters. This study used the precipitation and runoff outputs from General Circulation Models (GCMs) of Coupled Model Intercomparison Project Phase 6 (CMIP6) to evaluate the meteorological drought (MD) and hydrological drought (HD) characteristics in the Pearl River Basin (PRB) under two Shared Socioeconomic Pathways (SSPs) (i.e., SSP2-4.5 and SSP5-8.5). The propagation characteristics of external propagation (response between different type of drought) and internal propagation (drought development and recovery stages of a single type of drought) were also comprehensively investigated based on CMIP6. The results revealed that: 1) the percentage of grids within the dry range of MD and HD will decrease from the historical period to the future period under the two scenarios. The PRB is projected to exhibit wetter patterns; 2) Higher emission scenarios (SSP5-8.5) are more likely to weaken dryness conditions; 3) regarding the external propagation, the drought response time from MD to HD would be 2 months, and there would be no significant change under two scenarios; and 4) regarding the internal propagation, during three study periods (1971-2010, 2021-2060 and 2061-2100), the MD (HD) average recovery time changed from 3.90 (3.36) to 3.75 (3.41) and then to 3.95 (3.43) months under the SSP2-4.5 scenario, and changed from 3.93 (3.46) to 3 (3.51) and then to 3.7 (3.25) months under the SSP5-8.5 scenario. These results aid in understanding future drought characteristics and drought propagation under climate change.
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Affiliation(s)
- Zhaoqiang Zhou
- School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, China; Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China; State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yibo Ding
- Yellow River Engineering Consulting Co., Ltd., Zhengzhou 450003, China
| | - Qiang Fu
- School of Water Conservancy and Civil Engineering, Northeast Agricultural University, Harbin, China
| | - Can Wang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China; State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Yao Wang
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China; State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Hejiang Cai
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China; Department of Civil and Environmental Engineering, National University of Singapore, Singapore
| | - Suning Liu
- State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China
| | - Shengzhi Huang
- State Key Laboratory of Eco-Hydraulics in Northwest Arid Region of China, Xi'an University of Technology, Xi'an, China
| | - Haiyun Shi
- Shenzhen Key Laboratory of Precision Measurement and Early Warning Technology for Urban Environmental Health Risks, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China; State Environmental Protection Key Laboratory of Integrated Surface Water-Groundwater Pollution Control, School of Environmental Science and Engineering, Southern University of Science and Technology, Shenzhen, Guangdong, China.
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Zhong Y, Hu E, Wu Y, An Q, Wang C, Bai H, Gao W. Reconstructing a long-term water storage-based drought index in the Yangtze River Basin. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 883:163403. [PMID: 37059147 DOI: 10.1016/j.scitotenv.2023.163403] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/30/2022] [Revised: 03/30/2023] [Accepted: 04/05/2023] [Indexed: 06/03/2023]
Abstract
Drought is a prolonged dry period in the natural climate cycle, and is one of the most costly weather events. The Gravity Recovery and Climate Experiment (GRACE) derived terrestrial water storage anomalies (TWSA) have been widely used to assess drought severity. However, the relatively short cover period of GRACE and GRACE Follow-On limit our knowledge about the characterization and evolution of drought over decades time scale. This study proposes a standardized GRACE reconstructed TWSA index (SGRTI) to assess the drought severity based on a statistical reconstruction method calibrated by GRACE observations. Results show that the SGRTI correlates well with 6-month scale SPI and SPEI, with correlation coefficients reaching 0.79 and 0.81 in the YRB from 1981 to 2019. Soil moisture can capture drought condition like the SGRTI, while cannot further reflect deeper water storage depletion. The SGRTI is also comparable to the SRI and in-situ water level. As a case study for the Yangtze River Basin, its three sub-basins experience more frequent droughts, shorter drought duration, and lower severity drought, as identified by SGRTI during 1992-2019 relative to 1963-1991. The presented SGRTI in this study can provide a valuable supplement to the drought index before the GRACE era.
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Affiliation(s)
- Yulong Zhong
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China; Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China
| | - E Hu
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
| | - Yunlong Wu
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
| | - Qing An
- Artificial Intelligence School, Wuchang University of Technology, Wuhan 430223, China
| | - Changqing Wang
- State Key Laboratory of Geodesy and Earth's Dynamics, Innovation Academy for Precision Measurement Science and Technology, Chinese Academy of Sciences, Wuhan 430077, China.
| | - Hongbing Bai
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
| | - Wei Gao
- School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430078, China
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