Abstract:[Objective] This study aims to explore the regional water stress state and their causes and provides a theoretical basis for the development and allocation of rational water resources.[Methods] A three-dimensional water footprint model was built to analyze the sustainability of water resources by using the sustainability index of water resource. The logarithmic mean divisia index (LMDI) factor decomposition model was also applied to quantitatively analyze the structural, technological, economic, and population effects on water resources in Anhui Province from 2007 to 2016.[Results] ① From 2007 to 2016, the water footprint of Anhui Province, in which the internal water footprints accounted for 97.88% to 98.73%, at first increased and then decreased. The consumption of water by agriculture among the internal water footprints was the highest and showed the same variation as the overall water footprint. In 2016, the per capita agricultural water use in Anhui Province decreased from north to south, meanwhile, the Yangtze River basin accounted for the highest proportion in the other types of water footprint. ② During 2007-2016, the footprint depth of the Anhui Province fluctuated within 2~4 and eventually declined from north to south. The results indicated that the overall sustainable use of water resources in Anhui Province increased from 2007 to 2016. ③ The driving factors of the water footprint in Anhui Province showed that the technical effect was a reverse driving, and the economic effect contributed the most to the positive driving.[Conclusion] The three-dimensional water footprint model used in this study can be a helpful tool to better reflect the actual situation of the water footprint in Anhui Province. Although of the use of water resources generally increased, optimizing the structure of the water resources utilization and accelerating improvements in the efficiency of water resource usage is necessary to alleviate the pressure on water resources resulting from economic development and population growth.