A Feasibility Study of Calculating the MCI Drought Index Using Short-term Records of High-Density Meteorological Stations

Precise drought monitoring is necessary for refined assessment of drought disasters. Usually, the Meteorological Drought Composite Index (MCI) is calculated based on long time series data over 30 years so as to fit stable probability distribution of precipitation. In order to fully utilize short-term high-density station data that are usually less than 20 years for drought monitoring, the parameter method is introduced in this study. Parameter method is to calculate the parameters of the probability distribution function of precipitation using data from neighbor long-term stations, and then interpolate them to that of short-term stations, so that MCI of short-term stations can be calculated. In this study, 31 national stations in 9 provinces were selected for cross-checking and analysis of the errors of MCI calculated from the parameter method, interpolation method, and replacement method. Results show that except in the areas of northwest China where meteorological stations are comparatively sparse, the MCI calculated by the parameter method is significantly better than that of the other two methods. Compared with the interpolation method，the accuracies of MCI calculated by the parameter method  are improved in the central and eastern regions of China, with improved accuracies by over 20% in most parts of North China, Northeast China, Yangtze River Basin, Southwest China, and South China. The magnitude of error caused by the parameter method is close to or smaller than that caused by climate change, representing a good stability of the method. During the serious drought in the Yangtze River Basin in 2022, the comparison of the MCI results from the 86 long-term stations and 2688 short-term high-density stations in Jiangxi Province show that the parameter method used for calculating MCI of short-term stations can create high-resolution map for drought monitoring and provide refined services for drought disaster reduction.