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Spatiotemporal Variations and Climatological Trends in Precipitation Indices in Shaanxi Province, China. ATMOSPHERE 2022. [DOI: 10.3390/atmos13050744] [Citation(s) in RCA: 16] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
Precipitation, as an important part of the hydrological cycle, is often related to flood and drought. In this study, we collected daily rainfall data from 21 rainfall stations in Shaanxi Province from 1961 to 2017, and calculated eight extreme climate indices. Annual and seasonal concentration indices (CI) were also calculated. The trends in the changes in precipitation were calculated using the M–K test and Sen’s slope. The results show that the precipitation correlation index and CI (concentration index) in Shaanxi Province are higher in the south and lower in the north. For the annual scale, the CI value ranges from 0.6369 to 0.6820, indicating that Shaanxi Province has a high precipitation concentration and an uneven distribution of annual precipitation. The eight extreme precipitation indices of most rainfall stations showed a downward trend during the study period, and more than half of the stations passed the 0.05 confidence interval test. Among them, the Z value of PRCPTOT (annual total precipitation in wet days) at Huashan station reached −6.5270. The lowest slope of PRCPTOT reached −14.3395. This shows that annual rainfall in Shaanxi Province has been decreasing in recent decades. These findings could be used to make decisions about water resources and drought risk management in Shaanxi Province, China.
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Miao C, Gou J, Fu B, Tang Q, Duan Q, Chen Z, Lei H, Chen J, Guo J, Borthwick AGL, Ding W, Duan X, Li Y, Kong D, Guo X, Wu J. High-quality reconstruction of China's natural streamflow. Sci Bull (Beijing) 2022; 67:547-556. [PMID: 36546176 DOI: 10.1016/j.scib.2021.09.022] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2021] [Revised: 09/07/2021] [Accepted: 09/08/2021] [Indexed: 01/06/2023]
Abstract
Reconstruction of natural streamflow is fundamental to the sustainable management of water resources. In China, previous reconstructions from sparse and poor-quality gauge measurements have led to large biases in simulation of the interannual and seasonal variability of natural flows. Here we use a well-trained and tested land surface model coupled to a routing model with flow direction correction to reconstruct the first high-quality gauge-based natural streamflow dataset for China, covering all its 330 catchments during the period from 1961 to 2018. A stronger positive linear relationship holds between upstream routing cells and drainage areas, after flow direction correction to 330 catchments. We also introduce a parameter-uncertainty analysis framework including sensitivity analysis, optimization, and regionalization, which further minimizes biases between modeled and inferred natural streamflow from natural or near-natural gauges. The resulting behavior of the natural hydrological system is represented properly by the model which achieves high skill metric values of the monthly streamflow, with about 83% of the 330 catchments having Nash-Sutcliffe efficiency coefficient (NSE) > 0.7, and about 56% of the 330 catchments having Kling-Gupta efficiency coefficient (KGE) > 0.7. The proposed construction scheme has important implications for similar simulation studies in other regions, and the developed low bias long-term national datasets by statistical postprocessing should be useful in supporting river management activities in China.
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Affiliation(s)
- Chiyuan Miao
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China.
| | - Jiaojiao Gou
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Bojie Fu
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China; State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing 100085, China
| | - Qiuhong Tang
- Key Laboratory of Water Cycle and Related Land Surface Processes, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
| | - Qingyun Duan
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Zhongsheng Chen
- College of Land and Resources, China West Normal University, Nanchong 637009, China
| | - Huimin Lei
- State Key Laboratory of Hydroscience and Engineering, Department of Hydraulic Engineering, Tsinghua University, Beijing 100084, China
| | - Jie Chen
- State Key Laboratory of Water Resources and Hydropower Engineering Science, Wuhan University, Wuhan 430072, China
| | - Jiali Guo
- College of Hydraulic and Environmental Engineering, China Three Gorges University, Yichang 443002, China; Engineering Research Center of Eco-environment in Three Gorges Reservoir Region, Ministry of Education, China Three Gorges University, Yichang 443002, China
| | - Alistair G L Borthwick
- School of Engineering, the University of Edinburgh, the King's Buildings, Edinburgh EH9 3JL, UK
| | - Wenfeng Ding
- Changjiang River Scientific Research Institute, Wuhan 430010, China
| | - Xingwu Duan
- Institute of International Rivers and Eco-security, Yunnan University, Kunming 650091, China
| | - Yungang Li
- Institute of International Rivers and Eco-security, Yunnan University, Kunming 650091, China
| | - Dongxian Kong
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Xiaoying Guo
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
| | - Jingwen Wu
- State Key Laboratory of Earth Surface Processes and Resource Ecology, Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
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