Copula-Based Assessment and Regionalization of Drought Risk in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Data Source and Collection
2.2. Methods
2.2.1. Standardized Precipitation Index
2.2.2. Run Theory
2.2.3. Copula Function
2.2.4. Return Period and Joint Probability
2.2.5. Drought Risk Regionalization
- (1)
- Extract the longitude and latitude of each grid point and generate the corresponding raster layer.
- (2)
- Select the dataset of input data for the implementation of hierarchical clustering. The dataset includes latitude, longitude, and grid-based joint probability of drought duration and severity calculated by Equation (11).
- (3)
- Preprocess the input data by removing the effect of magnitude through standard deviation normalization.
- (4)
- Identify adjacent grids by using the latitude and longitude variables.
- (5)
- Calculate the distance between the grids using the Pearson correlation coefficient and the distance between classes using the sum of squares method.
- (6)
- Determine the adjacency and homogeneity of the regions by minimizing the inter-regional correlation coefficient and maximizing the intra-regional correlation coefficient.
- (7)
- Obtain the final group numbers based on the silhouette width criterion.
- (8)
- Group the adjacent grids based on their similarity in terms of joint probability of drought duration and severity.
- (9)
- Evaluate the results of the drought risk regionalization and identify the areas that are susceptible to drought hazards.
3. Results
3.1. Regional Average Drought Attributes
3.2. Spatial Patterns of Drought Severity and Duration with Various Return Periods
3.3. Joint Probability of Drought Duration and Severity
3.4. Drought Risk Regionalization
4. Discussion
4.1. Drought Characteristics and Risk
4.2. Regionalization
4.3. Limitations and Future Work
5. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
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Drought Level | No | Mild | Moderate | Severe | Extreme |
---|---|---|---|---|---|
SPI value | (−0.5,+∞) | (−1.0,−0.5] | (−1.5,−1.0] | (−2.0,−1.5] | (−∞,−2.0] |
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Li, M.; Wang, G.; Zong, S.; Chai, X. Copula-Based Assessment and Regionalization of Drought Risk in China. Int. J. Environ. Res. Public Health 2023, 20, 4074. https://doi.org/10.3390/ijerph20054074
Li M, Wang G, Zong S, Chai X. Copula-Based Assessment and Regionalization of Drought Risk in China. International Journal of Environmental Research and Public Health. 2023; 20(5):4074. https://doi.org/10.3390/ijerph20054074
Chicago/Turabian StyleLi, Ming, Guiwen Wang, Shengwei Zong, and Xurong Chai. 2023. "Copula-Based Assessment and Regionalization of Drought Risk in China" International Journal of Environmental Research and Public Health 20, no. 5: 4074. https://doi.org/10.3390/ijerph20054074