Tao Wang

The temperature in arid region of China has been volatile risen over the past 60 years, with the warming range as high as 0.34℃/10a, nearly three times that of the global average. There were seasonal and spatial differences in the amplitude of temperature variation. The warming is the most obvious at 0.50℃/10a in winter and the smallest at 0.27℃/ 10a in spring. During the last 60 years, the annual precipitation has shown a weak upward trend, which the increase in summer was the largest at 2.50 mm/10a and in winter was the smallest at 1.20 mm/10a. Inland river flow is a barometer of climate change, which accounting for more than 85% of the total water resources in arid region of China. The changes of climate warming, glacier retreat and melting of ice and snow have an important impact on the spatial and temporal changes of river flow, brought about the mountainous runoff raised. Along with the better water supply conditions, the positive result is the expansion of artificial oasis (oasification) and the negative result is the aeolian desertification development.Oasification is the transition process from desert to oasis in arid region resulted from human and natural factors. Aeolian desertification is land degradation through wind erosion mainly resulted from the human impacts in arid, semiarid and sub-humid regions of China. The expansion of aosification is always along with the menace of aeolian desertification. There are 65% of the aosification transformed from no-aosis area and 35% came from natural oasis by cultivation, which means that the aosification is the process of reclaim desert land and natural oasis changed to artificial oasis. The area of aeolian desertification mainly distribute over the periphery of oasis-desert and the middle and lower reaches of endorheic river. It can be said that the aosification is the development process from natural ecosystems to artificial ecosystems which extended the living space to human being. However, the low level or disorder aosification management can decrease the stability of the oasis and cause oasis degradation with the reduction or loss of land productivity, often can trigger a series of ecological problems such as river break, lakes shrinking, vegetation degradation, soil erosion and aeolian desertification. Oasification enlarged and intensively managed areas of farming land mostly, which needs more and more water resources, so that some areas of transitional zone of oasis-desert and of lower reaches of endorheic river evolved into aeolian desertification because of the water shortage. Finally, we propose a suitable dimensions of oases with different water consumption characteristics based on the construction of multi-objective balance methods and mathematical models for healthy development of aosification and prevention of aeolian desertification in arid region of China.Keywords: Water Manage, Oasification, Aeolian Desertification, Arid region of China

The impacts of drought on Gross Primary Productivity (GPP) and Evapotranspiration (ET) play an important role in understanding the carbon–water process of dryland ecosystems. However, just via correlation analysis, the response mechanism of vegetation production and ET to droughts is not well understood. Based on a modified Vegetation Photosynthesis Model (VPM) and a revised Penman–Monteith (PM) model, GPP and ET were simulated to examine their sensitivity to drought and quantitative dynamics among biomes with the drought index in NADRs. The diverse response of GPP and ET to drought depending on biomes, grassland, barren/sparse vegetation and shrub showed a positive response to summer drought, while cropland and forest showed a negative response to summer drought. From the normal summers to extreme drought summers, GPP and ET reduced by 0.36 g C m⁻² day⁻¹ and 0.18 mm day⁻¹, nearly 10.54% and 12.77%, respectively. Some compensation mechanisms (i.e., physiological changes of vegetation species to resistant drought) or drought timescale weaken the drought impacts in insignificant correlated regions (GPP or ET and SPEI) with lower reduction rates. Compared with persistent or multiple droughts, the impacts of abrupt wet–dry shifts on GPP and ET were weak with lower rates (4.44% for GPP, 0.92% for ET). Notably, the wet winter and warm spring weakens the summer drought impacts on GPP in some parts of grasslands. These observations would be useful to understand the ecosystem process and to account for the dynamics of ecosystem water use efficiency during drought disturbance in depth.

The yardangs surrounding the Suoyang City ruins are proven to be wind-eroded landforms developed in an oasis which was used for agriculture in history. According to OSL and ¹⁴C dating, as well as historical records of local human activities, we suggest that the formation of yardangs in the Suoyang City oasis probably started in the mid-Yuan Dynasty of China (AD 1291). After being abandoned, the Suoyang City oasis quickly evolved into desert land with yardangs and nebkhas under the background of desertification enlargement in a cold, dry climate in the Hexi Corridor. Although human factors are considered to have played an important role in the process of desertification, the effect imposed by climatic changes should not be ignored. Desertification constitutes a serious threat to human survival and development, we should reasonably develop and utilize water and land resources, effectively prevent and control desertification, and promote the harmonious development between man and nature in arid areas.

Mu Us Sandy Land is a typical semi-arid vulnerable ecological zone, characterized by vegetation degradation and severe desertification. Effectively identifying desertification changes has been a topical environmental issue in China. However, most previous studies have used a single method or remote sensing index to monitor desertification, and lacked an efficient and high-precision monitoring system. In this study, an optimal monitoring scheme that considers multiple indicators combination and different machine learning methods (Classification and Regression Tree-Decision Tree, CART-DT; Random Forest, RF; Convolutional Neural Networks, CNN) was developed and used to analyze the spatial–temporal patterns of desertification from 2000 to 2018 in Mu Us Sandy Land. The results showed that: (a) The random forest model performed best for monitoring desertification based on medium and low-resolution remote sensing images, and the four-index combination (Albedo, NDVI, LST and TGSI) obtained the highest classification accuracy (OA = 87.67%) in Mu Us Sandy Land. Surprisingly, the model accuracy of the three-index combination (NDVI, LST and TGSI) (OA = 85.74%) is comparable to the four-index combination. (b) The TGSI index used to characterize soil information performs well, while the LST is not conducive to the extraction of desertified land in several desertification monitoring indicators. (c) Since 2000, the area of extremely severe desertified land has shown a reversal trend; however, there is significant interannual fluctuation in the total and light desertification land area affected by extreme climate. This research provides a novel approach and a valuable reference for monitoring the evolution of desertification in regional studies, and the results improve the research system of desertification and provide a data basis for desertification cause analysis and prevention.