Abstract

Drought monitoring is useful to minimize the impact of drought on human production and the natural environment. Gravity Recovery and Climate Experiment (GRACE) satellites can directly capture terrestrial water storage anomalies (TWSA) in the large basin, which represents a new source of hydrological information. In this study, the GRACE-based total storage deficit index (TSDI) is employed to investigate the temporal evolution and spatial distribution of drought in Southwest China from 2003 to 2016. The comparison results of TSDI with the standardized precipitation index (SPI), the standardized precipitation evapotranspiration index (SPEI), and the self-calibrating Palmer drought severity index (SC-PDSI) show that TSDI has significant consistency with them, which verifies the reliability of TSDI. The spatial distribution of TSDI was more consistent with the governmental drought reports than SC-PDSI in the most severe drought event from September 2009 to April 2010. Finally, the links between drought and climate indicators are investigated using the partial least square regression (PLSR) model. The results show that insufficient precipitation has the most significant impact on drought in Southwest China, followed by excessive evaporation. Although Southwest China is selected as a case study in this paper, the method can be applied in other regions as well.

Highlights

  • Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.license.Drought is one of the most devastating natural disasters in the world, which will bring damage to agricultural production, social economy, and ecological environment [1,2]

  • The area most affected by drought is the drought event from September 2009 to April 2010, and 33% of Southwest China area suffer from extreme drought (D4), 13%

  • Gravity Recovery and Climate Experiment (GRACE) gravity satellites have proven to be an alternative method of drought monitoring, especially in areas where meteorological and hydrological data from station-observed are insufficient

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Summary

Introduction

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.license (https://creativecommons.org/licenses/by/4.0/).Drought is one of the most devastating natural disasters in the world, which will bring damage to agricultural production, social economy, and ecological environment [1,2]. The. Intergovernmental Panel on Climate Change 2018 report warned that human activities have caused global temperatures to rise by ~1 ◦ C since industrialization, and global warming could reach 1.5 ◦ C between 2030 and 2050 [3]. Due to global climate change, drought events in many areas are expected to intensify in the 21st century [4,5]. It is urgent to carry out effective drought monitoring for the sake of reducing the damage of drought. Traditional drought monitoring approaches rely on meteorological and hydrological data observed by stations, and the spatial distribution of drought is realized through interpolation [6]. The drought monitoring result obtained by interpolation is inaccurate in areas with complex terrain or scarcity of stations [7,8]

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