Interannual and seasonal relationships between photosynthesis and summer soil moisture in the Ili River basin, Xinjiang, 2000-2018.

Soil moisture (SM) is essential for controlling terrestrial carbon uptake, as it directly provides moisture for photosynthesis, especially in arid and semiarid regions. We selected the arid and semiarid Ili River basin (IRB) of Xinjiang as the study area, and investigated the spatial and temporal characteristics and interrelationships with SM and photosynthesis from 2000 to 2018 using the ERA5 products and solar-induced chlorophyll fluorescence (SIF). SM and photosynthesis showed a decreasing trend during the study period. Compared with those in spring and autumn, the variation of summer SM and SIF was more consistent with the interannual variation. Anomaly analysis showed that negative SM anomalies were most profound in 2012-2015, 2008, and 2014. Additionally, we quantified the effect of seasonal SM deficits on photosynthesis by performing model-based experiments. The results indicated that the gross primary productivity (GPP) simulated by the P-model could capture the characteristics of photosynthesis in the IRB, which had a high correlation with SIF (R2 = 0.82, p < 0.001). In 2012-2015, 2008, and 2014, SM deficits caused more GPP reduction in the summers than in the springs or the autumns. The trends were mainly visible in the northern IRB, where GPP was below 40 % of the multi-year mean, and SM was below 23 %. GPP decreased more significantly in grassland than in the forest under the influence of SM deficit. This study reveals seasonal differences in the effects of SM deficit on photosynthesis and emphasizes that the summer SM deficit was the main factor responsible for decreases in GPP in the IRB during the study period. These findings contribute to a better understanding of the relationships between photosynthesis and environmental factors, and provide a reference for an accurate assessment of the regional carbon cycle.

Phenology-based seasonal terrestrial vegetation growth response to climate variability with consideration of cumulative effect and biological carryover.

Vegetation growth is influenced not only by climate variability but also by its past states. However, the differences in the degree of the climate variability and past states affecting vegetation growth over seasons are still poorly understood, particularly given the cumulative climate effects. Relying on the Normalized Difference Vegetation Index (NDVI) data from 1982 to 2014, the vegetation growing season was decomposed into three periods (sub-seasons) - green-up (GSgp), maturity (GSmp), and senescence (GSsp) - following a phenology-based definition. A distributed lag model was then utilized to analyze the time-lag effect of vegetation growth response to climatic factors including precipitation, temperature, and solar radiation during each sub-season. On this basis, the relative importance of climatic factors and vegetation growth carryover (VGC) effect on vegetation growth was quantified at the phenology-based seasonal scale. Results showed that the longest peak lag of precipitation, temperature, and solar radiation occurred in the GSmp, GSsp, and GSgp, with 1.27 (1.13 SD), 0.89 (1.02 SD), and 0.80 (1.04 SD) months, respectively. The influence of climate variability was strongest in the GSgp, and diminished over the season, while the opposite for the VGC effect. The relative influence of each climatic factor also varied between sub-seasons. Vegetation in more than 58% of areas was more affected by temperature in the GSgp, and the proportion decreased to 34.00% and 31.78% in the GSmp and GSsp, respectively. Precipitation and solar radiation acted as the dominant climatic factors in only 28.80% and 20.88% of vegetation areas in the GSgp, but they increased to 35.21%, 32.61% in the GSmp, and 38.20%, 30.02% in the GSsp, respectively. The increased regions influenced by precipitation were mainly in dry areas especially for the boreal and cool temperate climate zones, while increased regions influenced by solar radiation were primarily located in moist areas of mid-high latitudes of the Northern Hemisphere. By introducing the cumulative climate effect, our findings highlight seasonal patterns of vegetation growth affected by climate variability and the VGC effect. The results provide a more comprehensive perspective on climate-vegetation interactions, which may help us to accurately forecast future vegetation growth under accelerating global warming.

Monitoring land degradation and assessing its drivers to support sustainable development goal 15.3 in Central Asia.

Land degradation has become one of the most critical environmental and socioeconomic issues in the world, particularly in Central Asia. Moreover, the realization of Land Degradation Neutrality (LDN) in Central Asia faces enormous challenges in achieving the global Sustainable Development Goal 15.3 (SDG 15.3). It is critical to monitor land degradation and assess its drivers in Central Asia. In this study, an Optimal Land Degradation Index (OLDI) was established as a new index for monitoring land degradation using a constrained optimization algorithm. The spatiotemporal characteristics of LDN were monitored in Central Asia. Further analysis explored the driving force of land degradation in different areas. The results showed that 7.22% and 15.33% of the total land area exhibited land improvement and land degradation, respectively. According to abrupt change analysis, mutation changes in the OLDI were observed in 2005, 2012 and 2015. At the subnational scale, most regions in Central Asia have not achieved the goal of LDN. The residual analysis highlighted the drivers of spatial differences in land degradation performance in Central Asia. Drought was the main driving force affecting land degradation by the compound effect of decreased precipitation and increased temperature on the Ustyurt Plateau, while 24.01% of the land degradation areas resulted from anthropogenic disturbances and were mainly distributed in the areas surrounding the Aral Sea. The results also indicated that 72.56% of the land improvement areas resulted from human activities and were mainly concentrated in the Balkhash Lake Delta and the Amudarya Delta. In Central Asia, the realization of SDG 15.3 by 2030 remains a severe challenge. Restoration measures should be prioritized in land degradation areas in Central Asia to implement the LDN initiative, especially around the Aral Sea.

Disentangling the relative impacts of climate change and human activities on arid and semiarid grasslands in Central Asia during 1982-2015.

In recent decades, climate change and human activities have severely affected grasslands in Central Asia. Grassland regulation and sustainability in this region require an accurate assessment of the effects of these two factors on grasslands. Based on the abrupt change analysis, linear regression analysis and net primary productivity (NPP), the spatiotemporal patterns of grassland ecosystems in Central Asia during 1982-2015 were studied. Further, the potential NPP (NPPP) was estimated using the Thornthwaite Memorial model and the human-induced NPP (NPPH), which was the difference between NPPP and actual NPP, were used to differentiate the effects of climate change and human activities on the grassland ecosystems, respectively. The grassland NPP showed a slight upward trend during 1982-2015, while two obvious decreasing periods were found before and after the mutation year 1999. Additionally, the main driving forces of the grassland NPP variation for the two periods were different. During 1982-1999, climate change was the main factor controlling grassland NPP increase or decrease, and 84.7% of grasslands experienced NPP reduction, while the regions experiencing an increase represented only 15.3% of the total area. During 1999-2015, the areas of increasing and decreasing grassland NPP represented 41.6% and 58.4% of the total area, respectively. After 1999, human activities became the main driving force of the NPP reduction, whereas climate change facilitated grassland restoration. The five Central Asian countries showed widely divergent relative impacts of climate change and human activities on NPP changes. In Uzbekistan and Turkmenistan, anthropogenic decreases in grassland NPP intensified during 1982-2015, while the negative anthropogenic effects on grassland NPP in Kyrgyzstan and Tajikistan moderated. Further analysis identified precipitation as the major climatic factor affecting grassland variation in most areas of Central Asia and overgrazing as the main form of human activity accelerating grassland degradation. This study improves the understanding of the relative impacts of climate change and human activities on grasslands in Central Asia.

Monitoring land sensitivity to desertification in Central Asia: Convergence or divergence?

In Central Asia, desertification risk is one of the main environmental and socioeconomic issues; thus, monitoring land sensitivity to desertification is an extremely urgent issue. In this study, the combination of convergence patterns and desertification risk is advanced from a technical perspective. Furthermore, the environmentally sensitive area index (ESAI) method was first utilized to monitor the risk of desertification in Central Asia. In the study, the spatial and temporal patterns of desertification risk were illustrated from 1992 to 2015 using fourteen indicators, including vegetation, climate, soil and land management quality. The ESAI spatial convergence across administrative subdivisions was explored for three time intervals: 1992-2000, 2000-2008 and 2008-2015. The results indicated that nearly 13.66% of the study area fell into the critical risk of desertification from 1992 to 2008. However, the risk of desertification has improved since 2008, with critical classifications decreasing by 19.70% in 2015. According to the mutation year detection in the ESAI, 25.89% of the pixels with mutation years from 1992 to 2000 were identified, and this value was higher than that during the other time periods. The convergence analysis revealed that the desertification risk for 1992-2000 tended to diverge with a positive convergence coefficient of 0.13 and converge over the 2000-2008 and 2008-2015 time periods with negative convergence coefficients of -0.534 and -0.268, respectively. According to the spatial convergence analysis, we found that the divergence patterns in northern Central Asia from 1992 to 2000 resulted from the effects of the Soviet Union collapse: cropland abandonment in northern Kazakhstan and rangeland abandonment in Tajikistan, Kyrgyzstan and eastern Kazakhstan. In contrast, most areas from 2000 to 2008 experienced increased sensitivity to desertification with the convergence pattern caused by decreased precipitation, especially in northern Central Asia. However, convergence patterns were found in most regions for 2008-2015 with regard to augmented precipitation, which resulted in decreased sensitivity to desertification. Moreover, the low sensitivity areas were more likely to converge under increased precipitation. In this region, the findings of our study suggested that spatial convergence and divergence acted as related predictors of climate change and human activities, respectively. Thus, the ESAI convergence analysis was considered to provide an early warning of potential desertification.

Spatial and temporal characteristics of droughts in Central Asia during 1966-2015.

In drought-prone regions like Central Asia, drought monitoring studies are paramount to provide valuable information for drought risk mitigation. In this paper, the spatiotemporal drought characteristics in Central Asia are analyzed from 1966 to 2015 using the Climatic Research Unit (CRU) dataset. Drought events, as well as their frequency, duration, severity, intensity and preferred season, are studied by using the Run theory and the Standardized Precipitation Evapotranspiration Index (SPEI) at 3-month, 6-month, and 12-month timescales. The Principle Components Analysis (PCA) and the Varimax rotation method, the Sen's slope and the Modified Mann-Kendall method (MMK), as well as the wavelet analysis are adopted to identify the sub-regional drought patterns and to study the drought trend, periodicity and the possible links between drought variation and large-scale climate patterns, respectively. Results show that the drought characteristics in Central Asia vary considerably. The Hexi Corridor region and the southeastern part suffered from more short-term drought occurrences which mostly occurred in summer while the northeastern part experienced fewer droughts with longer duration and higher severity. Central Asia showed an overall wetting trend with a switch to drying trend since 2003. Regionally, the continuous wetting trend is found in north Kazakhstan while a consistent drying in the Aral Sea and Hexi Corridor region is observed in the last half-century. For 2003-2015, a significant drying pattern is detected in most Central Asia, except the northern Kazakhstan. A common significant 16-64-month periodical oscillation can be detected over the six sub-regions. The drought changes in Central Asia are highly associated with ENSO but less related to the Tibetan Plateau pressure. The North Atlantic Oscillation has an influence on drought change in most Central Asia but less for the Hexi Corridor and the drought variation in eastern Central Asia is affected by the strength of the Siberian High.