Abstract
Droughts are among the more costly natural hazards, and drought risk analysis has become urgent for the proper planning and management of water resources in grassland ecosystems. We chose Songnen grassland as a case study, used a standardized precipitation evapotranspiration index (SPEI) to model drought characteristics, employed run theory to define the drought event, and chose copula functions to construct the joint distribution for drought variables. We applied two kinds of return periods to conduct a drought risk assessment. After evaluating and comparing several distribution functions, drought severity (DS) was best described by the generalized extreme value (GEV) distribution, whereas drought duration (DD) was best fitted by gamma distribution. The root mean square error (RMSE) and Akaike Information Criterion (AIC) goodness-of-fit measures to evaluate their performance, the best-performing copula is Frank copula to model the joint dependence structure for each drought variables. The results of the secondary return periods indicate that a higher risk of droughts occurs in Keshan county, Longjiang county, Qiqiha’er city, Taonan city, and Baicheng city. Furthermore, a relatively lower risk of drought was found in Bei’an city, Mingquan county, Qinggang county, and qian’an county, and also in the Changling county and Shuangliao city. According to the calculation of the secondary return periods, which considered all possible scenarios in our study, we found that the secondary return period may be the best indicator for evaluating grassland ecosystem drought risk management.
Highlights
Droughts are among the most cost hazards of natural disasters due to the fact that their impacts are significant and widespread, affecting many economic sectors and people at any one time
We found that drought severity is best described by the generalized extreme value (GEV) distribution, whereas drought duration is best fitted by gamma distribution
The standardized precipitation evapotranspiration index (SPEI)-6 scales calculated by using the six months temperature and the precipitation sequence data from April to October which covers the entire grass growing season of each meteorological station, and researcher found that the SPEI-6 scale is more suitable for the analysis of drought [48]
Summary
Droughts are among the most cost hazards of natural disasters due to the fact that their impacts are significant and widespread, affecting many economic sectors and people at any one time. Shiau [10] first used six kinds of Archimedean copula function to link up the marginal distribution of drought duration and drought intensity and established joint probability distributions to conduct a frequency analysis of meteorological drought events. The joint multivariate models of droughts are hard to create, due to the fact that the drought duration and severity always comply with different distributions Both drought duration and severity play a significant role to drought frequency analysis and management, and it is necessary to calculate a joint return period for drought characteristics. Due to this reason, copulas were applied to connect fitted univariate distributions and construct a bivariate joint distribution.
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