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Owing to the effects of camera, illumination, extraction algorithm defect, and other reasons, vector data for reservoir waterbodies extracted from remote sensing data may have quality issues, impacting the efficiency of data utilization in areas such as water resource management and reservoir monitoring. To efficiently detect abnormal data from massive vector products of reservoir waterbodies, a semi-automatic detection method for reservoir waterbody vector data is presented. The method has three phases. First, the original reservoir vector data are preprocessed to obtain the time series of the area of reservoir waterbodies. Second, data modeling with time series of reservoir waterbodies area data is done using the extensible generic anomaly detection system (EGADS) plug-in framework and time series modeling is conducted using the Olympic model. Third, data that have quality problems are identified with KσK\sigma model was used to determine the outliers; thereby, the date of the outliers is detected. Results of accuracy verification show that the sensitivity and specificity of the method were 94.44 and 83.87%, respectively, showing its feasibility for use in anomaly detection in polygonal reservoir waterbody vector data with far greater efficiency than traditional manual inspection.

Drought as a complex natural hazard encompassing comprehensive information on the occurrence and development of drought using a single variable alone is difficult. In the present study, a composite multivariate drought index (CMDI) was developed based on a copula combining three univariate drought indices (standardized precipitation index [SPI], standardized runoff index [SRI], and water storage deficit index [WSDI]) and four timescales (monthly, quarterly, semi-annually, and annually). The new index combines information on terrestrial water storage, precipitation, and runoff to capture drought comprehensively. Based on the CMDI, drought trends and characteristics were assessed for 10 major basins in China from 2003 to 2020 using the seasonal Mann–Kendall test and the run theory. The results showed that the developed CMDI 1) integrated meteorological and hydrological drought information using the SPI, SRI, and WSDI; 2) captured overall drought development more completely and frequently than traditional drought indices; and 3) monitored an increasing trend of drought in the Qinghai-Tibet Plateau and North China Plain, in addition to a more substantial drought in northern China than in southern China. [Display omitted] • We developed Combined Multivariate Drought Index (CMDI). • CMDI monitors drought from multiple time scales and factors. • CMDI allows robust and reliable drought characterization. [ABSTRACT FROM AUTHOR]