دانلود رایگان مقاله انگلیسی تحلیل ویژگی جامع خشکسالی بر اساس یک شاخص غیر خطی چند متغیره خشکسالی - الزویر 2018

Abstract:

It is vital to identify drought events and to evaluate multivariate drought characteristics based on a composite drought index for better drought risk assessment and sustainable development of water resources. However, most composite drought indices are constructed by the linear combination, principal component analysis and entropy weight method assuming a linear relationship among different drought indices. In this study, the multidimensional copulas function was applied to construct a nonlinear multivariate drought index (NMDI) to solve the complicated and nonlinear relationship due to its dependence structure and flexibility. The NMDI was constructed by combining meteorological, hydrological, and agricultural variables (precipitation, runoff, and soil moisture) to better reflect the multivariate variables simultaneously. Based on the constructed NMDI and runs theory, drought events for a particular area regarding three drought characteristics: duration, peak, and severity were identified. Finally, multivariate drought risk was analyzed as a tool for providing reliable support in drought decision-making. The results indicate that: (1) multidimensional copulas can effectively solve the complicated and nonlinear relationship among multivariate variables; (2) compared with single and other composite drought indices, the NMDI is slightly more sensitive in capturing recorded drought events; and (3) drought risk shows a spatial variation; out of the five partitions studied, the Jing River Basin as well as the upstream and midstream of the Wei River Basin are characterized by a higher multivariate drought risk. In general, multidimensional copulas provides a reliable way to solve the nonlinear relationship when constructing a comprehensive drought index and evaluating multivariate drought characteristics.

5. Conclusions

Previous research mainly focused on constructing the composite drought index to analyze multivariate drought characteristics based on the linear combination, principal component analysis, and entropy weight method assuming a linear relationship among different drought indices. In this study, a nonlinear multivariate drought index (NMDI) was constructed based on the multidimensional Archimedean copulas. Then, based on the constructed NMDI and runs theory, drought events regarding three drought characteristics: drought duration, peak and severity were identified. Multivariate drought risks were also assessed in the Wei River Basin for better drought early warning and relief. The main conclusions are as follows: (1) The NMDI was constructed to derive the joint distribution of three drought indices: PAP, SDI, and MPDSI based on their optimal marginal distribution functions and the NMDI formula.

The NMDI formula was computed according to the optimal parameters and corresponding joint distribution function of the optimal copulas. Results show that the most appropriate multidimensional copulas functions to construct the nonlinear multivariate drought index (NMDI) in the JRB, BRB, UWRB, MWRB, LWRB, and WRB are Gumbel, Frank, Frank, Gumbel, Gumbel, and Gumbel, respectively.

(2) Correlation coefficients between the NMDI and single drought index are mostly over 0.7 indicating that the NMDI can reflect the comprehensive meteorological, hydrological and agricultural drought properties simultaneously. This suggests that the copulas can solve the complicated and nonlinear relationship among different drought indices. In addition, the margin-free characteristics are completely preserved by the copulas function when constructing the joint distribution function.