Spatial agglomeration of drought-affected area detected in northern China

With rapid economic development and population growth, the impacts of drought continue to affect China and the situation will probably be worsened further under projected climate warming. Therefore, understanding the characteristics of drought conditions and its actual impacts is especially important for disaster prevention and mitigations, under climatic warming. Here, we investigated the spatiotemporal variations of drought, using the self-calibrating Palmer Drought Severity Index (scPDSI) based on two evaluation methods, i.e., scPDSI Th (Thornthwaite method for calculating evapotranspiration) and scPDSI PM (Penman–Monteith method for calculating evapotranspiration). Results show the drought severities characterized by these two methods are highly consistent, except that the scPDSI Th shows more drought severity than the scPDSI PM in some regions, e.g., Xinjiang, Tibet, Qinghai, and Inner Mongolia. Based on the scPDSI PM and drought disaster statistics, we adopted a downscaling approach to obtain the county level dataset of drought-affected area from the provincial level. Using this finer-scale dataset, we observed spatial agglomeration characteristics of drought-affected area during the study period, denoting by the Moran’s I. Moreover, results show that the high-high clusters (high values surrounded by high values) of the drought-affected area are mainly distributed in northern China, and the moving direction of high-high clusters presents the East–West pattern. Combining the meteorological data and disaster statistics, this study can improve the accuracy of drought detection, which is important for disaster prevention and mitigations in China.