Abstract
Under the background of global climate change, accurate monitoring and comprehensive assessment of droughts are of great practical significance to sustain agricultural development. Considering multiple causes and the complexity of the occurrence of drought, this paper employs multiple input variables, i.e., precipitation, temperature, evaporation, and surface water content to construct a modified composite drought index (MCDI) using a series of mathematical calculation methods. The derived MCDI was calculated as a multivariate drought index to measure the drought conditions and verify its accuracy in Hubei Province in China. Compared with the existing multivariate drought index, i.e., meteorological drought composite index (CI), there was a high level of correlation in monitoring drought events in Hubei Province. Moreover, according to the drought historical record, the significant drought processes monitored by the MCDI were consistent with actual drought conditions. Furthermore, temporal and spatial analysis of drought in Hubei Province was performed based on the monitoring results of the MCDI. This paper generalizes the development of the MCDI as a new method for comprehensive assessments of regional drought.
Highlights
Drought is a natural hazard which has a huge impact on the world economy; its primary cause is a persistent lack of precipitation [1]
To investigate the response of modified comprehensive drought index (MCDI) to individual input variables, the mean monthly series of MCDI values for all stations was plotted in Figure 2, together with the plots for percentage of precipitation anomalies (PA), temperature anomalies (TA), evaporation anomalies (ETA), and surface water content index (SWCI)
The proposed MCDI could be generalized by using additional input variables for considering local hydrological conditions, and the drought monitoring based on MCDI in Hubei Province in this study provided a good reference for other regions to conduct comprehensive drought assessment
Summary
Drought is a natural hazard which has a huge impact on the world economy; its primary cause is a persistent lack of precipitation [1]. Timely and precise drought monitoring for occurrence, severity, and spatial extent plays a vital role in drought risk assessment and water resources management. Various drought indices have been developed to measure the drought characteristics, among which most were based on one of two kinds of data source. One is based on traditional meteorological observed data, such as the Standardized Precipitation Index [3], Palmer. Drought Severity Index [4], and Z index [5,6]. The SPI has been extensively applied as a basic index for monitoring drought in many countries, (e.g., United States [7], China [8], and Korea [9] and European countries [10]). The PDSI is a widely used drought index in the United States and Europe, as it considers
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