Variation of Aridity Index and the Role of Climate Variables in the Southwest China

Abstract

Aridity index (AI), defined as the ratio of annual potential evapotranspiration to annual precipitation, has been widely applied in dividing climate regimes and monitoring drought events. Investigating variation of AI and the role of climate variables are thus of great significant for managing agricultural water resource and maintaining regional ecosystem stability. In this study, with the well-corrected precipitation records and the optimized parameters in estimating solar radiation, we investigated the variation of AI and its climatic attribution in the Southwest China using the observed climate records from 135 meteorological stations during the period of 1993-2015. The results showed that the AI increased significantly (0.0053 year −1 , p < 0.05) from 1993 to 2015 and abruptly increasing occurred around 2002. Approximately 85% of stations showed an increasing trend, while 24% of stations reached up to a significant increasing level, which demonstrated that the Southwest China was getting drier for the recent 20 years. Overall, the AI was most sensitive to maximum air temperature and precipitation with values of 1.11 and −1.00, respectively. The following factors are vapor pressure, solar radiation, minimum temperature, and wind speed. The declining precipitation and ascending maximum air temperature were the key variables in dominating the increase in aridity index during the period of 1993-2015, which contribute 38.98% and 36.26% of AI variation, respectively. Our results highlighted the influence of climate variability on aridity in the Southwest China.

Potential evapotranspiration trends in West Africa from 1906 to 2015

“…PET is influenced by several factors amongst which include net (solar) radiation, relative humidity, air temperatures, wind speed, atmospheric aerosol (including dust particles), type and size of vegetative cover, availability of soil moisture, reflective land surface, and change in land use/land cover [9][10][11]. Thus, any changes in these variables due to climate change are likely to change the value of PET that have a direct impact on precipitation and hydrological regimes as well as crop production through changes in the agro-ecological water balance [12][13][14][15].…”

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

“…Thus, the increasing AI in China will inevitably exert a significant effect on regional drought (Zhang et al, ; Zhang et al, ; Zhang et al, ). For example, in southwestern China, Li et al () found that the AI increased significantly during 1993–2015, and the drying atmosphere caused a strong autumn drought in 2009 (Zhang et al, ). This serious drought significantly reduced crop production and water supply and resulted in a lack of drinking water for more than 10 million people and economic losses of nearly $2.3 billion (Zhang et al, ; Sun et al, ).…”

“…However, biases in precipitation data have been widely recognized due to station distribution, observational techniques and gauge measurement error (Goodison et al, ). Thus, applying corrections to remedy the measurement error in precipitation data is of great necessity before calculating the AI or other similar applications (Li et al, ). In China, Ye et al () studied the precipitation bias correction and found that wind‐induced gauge undercatch is the greatest measurement error in precipitation data in most regions, followed by wetting loss.…”

“…Previous studies have reported the impact of changes in the climate on AI variation (Liu et al, ; Roderick et al, ; Li et al, ; Liu et al, ). For example, in the continental Mediterranean region, the AI values showed an increasing trend and a drying climate from the 1960s (Türkeş, ).…”

“…Meanwhile, some similar work has also been carried out in China. In the humid region of southwestern China, Li et al () found that the AI decreased significantly for the period of 1993–2015 and that temperature was the dominant factor. In the semi‐humid region, Liu et al () explored the variation in the AI and its impact on streamflow in the Han River basin during 1960–2005 and found that the increasing AI dominated the decrease in streamflow with a relative contribution rate of 68.8%.…”