Status: this preprint was under review for the journal NHESS but the revision was not accepted.
Exploration of diffusion kernel density estimation in agricultural drought risk analysis: a case study in Shandong, China
W. Chen,Z. Shao,and L. K. Tiong
Abstract. Drought caused the most widespread damage in China, making up over 50 % of the total affected area nationwide in recent decades. In the paper, a Standardized Precipitation Index-based (SPI-based) drought risk study is conducted using historical rainfall data of 19 weather stations in Shandong province, China. Kernel density based method is adopted to carry out the risk analysis. Comparison between the bivariate Gaussian kernel density estimation (GKDE) and diffusion kernel density estimation (DKDE) are carried out to analyze the effect of drought intensity and drought duration. The results show that DKDE is relatively more accurate without boundary-leakage. Combined with the GIS technique, the drought risk is presented which reveals the spatial and temporal variation of agricultural droughts for corn in Shandong. The estimation provides a different way to study the occurrence frequency and severity of drought risk from multiple perspectives.
Received: 10 Sep 2015 – Discussion started: 04 Nov 2015
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In the paper, a Standardized Precipitation Index-based drought risk study is conducted. Kernel density based method is adopted to carry out the risk analysis. The results show that using the Diffusion Kernel function to analyze agricultural drought will eventually help the financial institutes determine compensation by providing a reference for identifying regional drought risk vulnerability, and offer important technological support for drought management for government.
In the paper, a Standardized Precipitation Index-based drought risk study is conducted. Kernel...
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