Food-water-land-ecosystem nexus in typical Chinese dryland under different future scenarios.

Healthy coupling of the food-water-land-ecosystem (FWLE) nexus is the basis for achieving sustainable development (SD), and FWLE in drylands is frontier scientific issues in the study of coupled human land systems. To comprehensively safeguard the future food, water, and ecological security of drylands, this study examined the implications for FWLE linkages in a typical Chinese dryland from the perspective of future land-use change. First, four different land-use scenarios were proposed using a land-use simulation model with a gray multi-objective algorithm, including an SD scenario. Then, the variation of three ecosystem services was explored: water yield, food production, and habitat quality. Finally, redundancy analysis was used to derive the future drivers of FWLE and explore their causes. The following results were obtained. In the future in Xinjiang, under the business as usual scenario, urbanization will continue, forest area will decrease, and water production will decline by 371 million m3. In contrast, in the SD scenario, this negative impact will be substantially offset, water scarcity will be alleviated, and food production will increase by 1.05 million tons. In terms of drivers, the anthropogenic drivers will moderate the future urbanization of Xinjiang to some extent, with natural drivers dominating the sustainable development scenario by 2030 and a potential 22 % increase in the drivers of precipitation. This study shows how spatial optimization can help protect the sustainability of the FWLE nexus in drylands and simultaneously provides clear policy recommendations for regional development.

Atmospheric dryness thresholds of grassland productivity decline in China.

Atmospheric dryness events are bound to have a broad and profound impact on the functions and structures of grassland ecosystems. Current research has confirmed that atmospheric dryness is a key moisture constraint that inhibits grassland productivity, yet the risk threshold for atmospheric dryness to initiate ecosystem productivity loss has not been explored. Based on this, we used four terrestrial ecosystem models to simulate gross primary productivity (GPP) data, analyzed the role of vapor pressure deficit (VPD) in regulating interannual variability in Chinese grasslands by focusing on the dependence structure of VPD and GPP, and then constructed a bivariate linkage function to calculate the conditional probability of ecosystem GPP loss under atmospheric dryness, and further analyzed the risk threshold of ecosystem GPP loss triggered by atmospheric dryness. The main results are as follows: we found that (1) the observed and modeled VPD of Chinese grasslands increases rapidly in both historical and future periods. VPD has a strongly negative regulation on ecosystem GPP, and atmospheric dryness is an important moisture constraint that causes deficit and even death to ecosystem GPP. (2) The probability of the enhanced atmospheric dryness that induced GPP decline in Chinese grasslands in the future period increases significantly. (3) When the VPD is higher than 40.07 and 27.65 percentile of the past and future time series, respectively, the risk threshold of slight ecosystem GPP loss can be easily initiated by atmospheric dryness. (4) When the VPD is higher than 82.57 and 65.09 percentile, respectively, the threshold of moderate ecosystem GPP loss can be exceeded by the benchmark probability. (5) The risk threshold of severe ecosystem GPP loss is not initiated by atmospheric dryness in the historical period, and the threshold of severe ecosystem GPP loss can be initiated when the future VPD is higher than 91.92 percentile. In total, a slight atmospheric dryness event is required to initiate a slight ecosystem GPP loss threshold, and a stronger atmospheric dryness event is required to initiate a severe ecosystem GPP loss. Our study enhances the understandings of past and future atmospheric dryness on grassland ecosystems, and strongly suggests that more attention be invested in improving next-generation models of vegetation dynamics processes with respect to the response of mechanisms of ecosystem to atmospheric dryness.

Spatiotemporal variability characteristics of extreme climate events in Xinjiang during 1960-2019.

Under the global warming, it is particularly important to explore the response of extreme climate to global climate change over the arid regions. Based on daily temperature (maximum, minimum, and average) and precipitation data from meteorological stations in Xinjiang, China, we analyzed the spatiotemporal characteristics of extreme temperature and extreme precipitation events via combining thin plate smoothing spline function interpolation, Sen's slope, and Mann-Kendall test. Our results showed that during 1960-2019, the extreme low temperature index of frost days (FD), icing days (ID), cold days (TX10p), cold nights (TN10p), and cold speel duration index (CSDI) all showed the downward trend to varying degrees, and the extreme high temperature index of summer days (SD25), warm days (TX90p), warm night (TN90p), and warm speel duration index (WSDI) all showed an upward trend to varying degrees, and the extreme low temperature index of high altitude mountains decreases more than that of the basin and plains. In addition, all the extreme temperature indices are closely related to the annual average temperature in Xinjiang (R > 0.6). Among the extreme precipitation indices, except for the consecutive dry days (CDD), the other extreme precipitation indices showed increasing trends to different degrees, but the changes in extreme precipitation in Xinjiang were mainly manifested by the increase of heavy precipitation in a short period (the increase of heavy precipitation and extreme heavy precipitation was the largest, 44.8 mm/10a and 17.6 mm/10a, respectively) and spatially concentrated in the Ili River and Altai Mountains in northern Xinjiang. Meanwhile, annual precipitation was positively correlated with the extreme precipitation index (R > 0.4), except for the CDD. This study provides theoretical support for the prevention and control of natural disasters in the dry zone.

Satellite greenness and solar-induced chlorophyll fluorescence reveal reverse desertification in Gurbantunggut Desert.

The desertification reversal is a process of revegetation and natural restoration in fragile dryland areas due to human activities and climate change mediation. Understanding the impact of desertification reversion on terrestrial ecosystems, including vegetation greenness and photosynthetic capacity, is crucial for land policy-making and carbon-cycle model improvement. However, the phenomenon of desertification reversal is rarely mentioned in previous studies, which dramatically limits the understanding of vegetation dynamics in the arid area. Therefore, it is of great necessity to investigate the status of desertification reversal on the ecosystem in arid areas. In this study, we first reported the phenomenon of desertification reversion over the southern edge of the Gurbantunggut Desert through the Moderate-resolution Imaging Spectroradiometer classification map year by year. We discussed the consequences, ways, and causes of desertification reversion. Our results showed that the desertification reversal significantly increased vegetation greenness and photosynthetic capacity, which largely offset the negative impact of desertification on the ecosystem productivity; cropland expansion and grassland's natural restoration were the two main ways of desertification reversal; the improvement of soil-water condition was an essential environmental factor leading to the phenomenon of reverse desertification. This finding highlights the importance of desertification reversal in the carbon cycle of dryland ecosystems and prove that desertification reversal is an integral part of global and dryland vegetation greening.

Interannual variability in summer climate change controls GPP long-term changes.

Global environmental change is rapidly altering the dynamics of terrestrial vegetation, with implications for the functioning of the Earth system and the provision of ecosystem services. How vegetation responds to a changing environment is an important scientific issue, but there is a lack of coverage of the relative contributions that long-term variation and interannual variability in vegetation across seasons play in ecosystem response to global change. Here, we used four terrestrial ecosystem models provided by MsTMIP to examine four key environmental drivers of gross primary productivity (GPP) change over the period 1901-2010. Our findings showed that (1) for all seasons, interannual variability in climate change are the main environmental factor controlling seasonal GPP variability. (2) Summer is the key season controlling the variation of annual GPP, and its long-term trend and interannual variability can explain 61.50% of the variation of grassland GPP in China. (3) Interannual variability in summer climate change exceeded the CO2 fertilization effect and nitrogen deposition as the controlling component (more than 40%) of long-term variation in Chinese grassland GPP. These studies highlight the important role of interannual variability in climate in reshaping the seasonality of vegetation growth, and will provide a precursor to future environmental drivers that can be precisely attributed to global vegetation change.

Dynamic characteristics and driving factors of vegetation greenness under changing environments in Xinjiang, China.

Global environment changes rapidly alter regional hydrothermal conditions, which undoubtedly affects the spatiotemporal dynamics of vegetation, especially in arid and semi-arid areas. However, identifying and quantifying the dynamic evolution and driving factors of vegetation greenness under the changing environment are still a challenge. In this study, gradual trend analysis was applied to calculate the overall spatiotemporal trend of the normalized difference vegetation index (NDVI) time series of Xinjiang province in China, the abrupt change analysis was used to detect the timing of breakpoint and trend shift, and two machine learning methods (boosted regression tree and random forest) were used to quantify the key factors of vegetation change and their relative contribution rate. The results have shown that vegetation has experienced overall recovery over the past 20 years in Xinjiang, and greenness increased at a rate of 17.83 10-4 year-1. Cropland, grassland, and sparse vegetation were the main biome types where vegetation restoration is happening. Nearly 10% of the pixels (about 166000 km2) were detected to have breakpoints from 2004 to 2016 of the monthly NDVI, and most of the breakpoints were concentrated in the ecotone of various biomes. CO2 concentration was the most prevalent environmental factor to increase vegetation greenness, because continuous emission of CO2 greatly enhanced the fertilization effect, further promoted vegetation growth. Besides, cropland expansion and desertification control were the vital anthropogenic factors to vegetation turning "green" in Xinjiang, and most areas under anthropogenic were mainly in oasis areas. These findings provide new insights and measures for the regional response strategies and terrestrial ecosystem protection.