Comparison of the aridity index and its drivers in eight climatic regions in China in recent years and in future projections

Abstract

Intense anthropogenic climate changes are expected to increase atmospheric aridity in the 21st century. The aridity index (AI), defined as the ratio of annual precipitation (Pre) to atmospheric evaporation (potential evapotranspiration [PET]), represents an efficient indicator of climatic changes. However, the variations and underlying drivers of AI values have not been comprehensively compared in different climatic regions. Using the AI calculated on the basis of bias‐corrected precipitation and optimized PET over the period of 1999–2017 and two climate model projections for the coming century, we investigated the response of the AI to climate change and quantified the contributions of climatic factors to AI variations in eight climatic regions in China, that is, the northwest (NW), north‐centre (NC), northeast (NE), North China Plain (NCP), east (E), southeast (SE), southwest (SW) and Tibet Plateau (TP). The results indicated that the AI values in seven of the eight climate regions exhibited negative trends from 1999 to 2017, with mean values ranging from −0.0008 in SW to −0.0414 in NC, while the AI values in the TP region showed a significant positive trend, with a value of 0.0124. Pre was the dominant factor for the variations in AI values in all climate regions, with contribution rates from 65 to 308%, followed by decreasing solar radiation in the NW, NC, E, SE and SW regions; deceasing wind speed in NE and NCP; and deceasing actual vapour pressure in the TP. The effect of increasing temperature on the AI trend was offset by other climate factors. By the end of the 21st century, under the Representative Concentration Pathway 8.5 emission scenario, the AI will significantly increase in five of the eight regions to values approximately 16.5% higher than those during 1999–2017, and this increase in the AI will be dominated by increasing PET. Overall, the shift in the dominant AI factor from Pre in recent years to PET in the future indicates that more attention should be given to the response of the AI to global warming. Furthermore, regional differences in climate change and AI values during 2018–2,100 will inevitably influence water availability and urgently require the development of adaptation strategies for different climatic regions.

Satellite-derived aridity index reveals China's drying in recent two decades

“… 29 It is found that the whole China has undergone a significant drying trend 43 and the drying trends have shifted gradually from north to south. 44 The significant drying trends have been found on the Bohai Bay area, the Tibet Plateau, 43 , 45 the Loess Plateau, and the Yunnan-Guizhou Plateau. 46 On the contrary, the northwest of China is becoming wetter.…”

Evaluation of Five Satellite-Based Precipitation Products for Extreme Rainfall Estimations over the Qinghai-Tibet Plateau

“…In this study, despite implementing a comprehensive range of quality control measures aimed at achieving objective and sound assessment conclusions, certain uncertainties unavoidably persist. Firstly, the sparsity of meteorological stations in the QTP region [74] poses a challenge as many remote sensing precipitation products rely on ground-based information as input data sources. Moreover, the calibration process of SPPs involves even fewer meteorological stations, resulting in a lower correlation between the SPPs and the observations of weather stations in the research of inter-annual variability within this region.…”

Climate Change Information for Regional Impact and for Risk Assessment

“…A growing number of studies highlight the potential for more localized aridity trends, including projection ensembles indicating significant increase in aridity and more frequent and intense droughts in most parts of China (Y. Li et al, 2019;Yao et al, 2020) and India under RCP4.5 and RCP8.5 for the 2020-2100 period (Gupta and Jain, 2018;Bisht et al, 2019;Preethi et al, 2019).…”