Abstract
From the aspect of global drought monitoring, improving the regional drought monitoring method is becoming increasingly important for the sustainable development of regional agriculture and the economy. The standardized precipitation conversion index (SPCI) calculated by the Global Navigation Satellite System (GNSS) observation is a new means for drought monitoring that has the advantages of simple calculation and real-time monitoring. However, only SPCI with a 12-month scale has been verified on a global scale, while its capability and applicability for monitoring drought at a short time scale in regional areas have never been investigated. Therefore, this study aims to evaluate the performance of SPCI at other time scales in Yunnan, China, and propose an improved method for SPCI. The data of six GNSS stations were selected to calculate SPCI; the standardized precipitation evapotranspiration index (SPEI) and composite meteorological drought index (CI) are introduced to evaluate the SPCI at a short time scale in Yunnan Province. In addition, a modified CI (MCI) was proposed to calibrate the SPCI because of its large bias in Yunnan. Experimental results show that (1) SPCI exhibits better agreement with CI in Yunnan Province when compared to SPEI; (2) the capability of SPCI for drought monitoring is superior to that of SPEI in Yunnan; and (3) the improved SPCI is more suitable for drought monitoring in Yunnan, with a relative bias of 5.43% when compared to the MCI. These results provide a new means for regional drought monitoring in Yunnan, which is significant for dealing with drought disasters and formulating related disaster prevention and mitigation policies.
Highlights
Drought has a significant scope of impact on agriculture, society, economy, and ecosystem health [1,2]; drought forecasting is important for early drought monitoring and warning
Global Navigation Satellite System (GNSS)-derived standardized precipitation conversion index (SPCI) provides a new method for global drought monitoring
GNSS-derived SPCI has the characteristics of a multi-time scale, which can identify the beginning and end of drought events and measure the severity of drought according to the intensity and duration
Summary
Drought has a significant scope of impact on agriculture, society, economy, and ecosystem health [1,2]; drought forecasting is important for early drought monitoring and warning. From 1960 to 2016, the annual economic losses caused by drought were estimated to reach $221 billion [3]. China is affected by long-term drought disasters and has suffered huge social and economic losses in recent decades [4,5]. Many studies have developed drought monitoring models based on hydrology [6], meteorology [7,8], economy [9], agriculture [10] and other aspects [11,12] to quantitatively describe the impact of drought occurrence frequency, duration, and drought intensity [13]. In the past few decades, a series of meteorological drought indexes have been developed, including the Palmer drought severity index (PDSI) [7], self-calibrating PDSI (ScPDSI) [15], standardized
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