Abstract
Abstract. The Huaihe River Basin having China's highest population density (662 persons per km2) lies in a transition zone between the climates of North and South China, and is thus prone to drought. Therefore, the paper aims to develop an appropriate drought assessment approach for drought assessment in the Huaihe River basin, China. Based on the Principal Component Analysis of precipitation, evapotranspiration, soil moisture and runoff, the three latter variables of which were obtained by use of the Xin'anjiang model, a new multivariate drought index (MDI) was formulated, and its thresholds were determined by use of cumulative distribution function. The MDI, the Standardized Precipitation Index (SPI) and the self-calibrating Palmer Drought Severity Index (sc-PDSI) time series on a monthly scale were computed and compared during 1988, 1999/2000 and 2001 drought events. The results show that the MDI exhibited certain advantages over the sc-PDSI and the SPI in monitoring drought evolution. The MDI formulated by this paper could provide a scientific basis for drought mitigation and management, and references for drought assessment elsewhere in China.
Highlights
Droughts are the costliest one of all natural disasters over the world, and often lead to significant societal, economic, and ecologic impacts (Farahmand and Aghakouchak, 2014)
The multivariate drought index (MDI) thresholds were determined by cumulative distribution function and its applicability was examined by comparison with the Standardized Precipitation Index (SPI) (McKee et al, 1993) and the sc-PDSI (Wells et al, 2004) in assessing 1988, 1999/2000 and 2001 drought events
According to the hydrological forecasting standards issued by the Ministry of Water Resources, China, it can be concluded that the simulated streamflow is of sufficient accuracy and the Xin’anjiang model is applicable to the Huaihe River basin
Summary
Droughts are the costliest one of all natural disasters over the world, and often lead to significant societal, economic, and ecologic impacts (Farahmand and Aghakouchak, 2014). Most of drought indices based on one or two specific hydro-meteorological variables can only monitor one specific physical form of drought: hydrological, meteorological, or agricultural (Rajsekhar et al, 2014; Heim, 2002). Some multivariate drought indices that consider a wide range of hydrological, agricultural, meteorological variables has been proposed and applied (Li et al, 2015; Rajsekhar et al, 2014; Svoboda, 2002; Brown et al, 2008; Hao and AghaKouchak, 2013; Zhang and Jia, 2013; Mu et al, 2013; Keyantash and Dracup, 2004). The aim of this paper is to develop a multivariate drought index
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