Zhijie Ta

In recent decades, vegetation phenology, as one of the most sensitive and easily observed features under climate change, has changed significantly under the influence of the global warming as a result of the green house effect. Vegetation phenological change is not only highly related to temperature change, but also to precipitation, a key factor affecting vegetation phenological change. However, the response of vegetation phenology to climate change is different in different regions, and the current research still does not fully understand the climate drivers that control phenological change. The study focuses on the Loess Plateau, utilizing the GIMMS NDVI3g dataset to extract vegetation phenology parameters from 1982 to 2015 and analyzing their spatial–temporal variations and responses to climate change. Furthermore, by incorporating emission scenarios of RCP4.5 (medium and low emission) and RCP8.5 (high emission), the study predicts and analyzes the changes in vegetation phenology on the Loess Plateau from 2030 to 2100. The long-term dynamic response of vegetation phenology to climate change and extreme climate is explored, so as to provide a scientific basis for the sustainable development of the fragile Loess Plateau. The key findings are as follows: (1) From 1982 to 2015, the start of the growing season (SOS) on the Loess Plateau shows a non-significant delay (0.06 d/year, p > 0.05), while the end of the growing season (EOS) is significantly delayed at a rate of 0.1 d/year (p < 0.05). (2) In the southeastern part of the Loess Plateau, temperature increases led to a significant advancement of SOS. Conversely, in the Maowusu Desert in the northwest, increased autumn precipitation caused a significant delay in EOS. (3) From 2030 to 2100, under the RCP4.5 and RCP8.5 scenarios, temperatures are projected to rise significantly at rates of 0.018 °C/year and 0.06 °C/year, respectively. Meanwhile, precipitation will either decrease insignificantly at −0.009 mm/year under RCP4.5 or increase significantly at 0.799 mm/year under RCP8.5. In this context, SOS is projected to advance by 19 days and 28 days, respectively, under RCP4.5 and RCP8.5, with advancement rates of 0.049 days/year and 0.228 days/year. EOS is projected to be delayed by 14 days and 27 days (p < 0.05), respectively, with delay rates of 0.084 d/year and 0.2 d/year.

Vertical vegetation differentiation is the most important form of spatial pattern in mountainous areas. It is of great significance to accurately divide vegetation into vertical zones for the study of mountain ecosystems and ecological protection. In order to accurately divide the vertical zone of mountain vegetation and determine the spatial distribution of mountain vegetation, the relationship between the vegetation index of various vegetation types and altitude was examined using remote sensing and geographic information technology. Taking Taibai Mountain, the main peak of the Qinling Mountains in China, as the study area, based on the difference in NDVI between summer and autumn (DNSA), this work constructed a DEM-NDVI scatter plot and quantified the boundary of the vertical zone by the half-peak width calculation method. The findings showed that: (1) the vertical distribution pattern of mountain vegetation may very well be reflected in the scatterplot that NDSA and DEM created; (2) Six vertical belts could be accurately identified to the meter level on Taibai Mountain’s south slope. Up to the altitude, the oak forest zone from the bottom of the mountain to the elevation of 1919 m, the pine-oak mixed forest zone is distributed in 1919–2331 m, the birch forest is distributed in 2115–2585 m, the fir forest is distributed in 2516–3150 m, the redwood forest is distributed in 3109–3551 m, and the alpine scrub meadow is distributed in 3551 m to the peak. On the north slope, 1053–2087 m above sea level is oak forest, 2087–2693 is birch forest, 2562–3006 is fir forest, 2987–3513 m is redwood forest, and 3513 to the top of the mountain is alpine scrub meadow; and (3) the distribution pattern of the vegetation vertical belt on the DEM-NDVI scatter plot was essentially compatible with the vegetation classification results derived from remote sensing images. The DEM-NDVI scatter plot can reflect the average distribution of vegetation population and can more accurately express the characteristics of vegetation vertical zone changes with altitude.

To mitigate the adverse effects of climate warming, the Paris Agreement proposed the goal to reduce global warming up to an increase of 1.5 °C above the preindustrial level. Study of the population and GDP exposure to precipitation extreme events under the 1.5 °C warming target is fundamental for disaster risk mitigation and adaptation on the Loess Plateau. This study projected the population and GDP exposure to extreme precipitation events under the 1.5 °C global warming level on the Loess Plateau using daily precipitation data from CMIP6 outputs and population and GDP data under a Shared Socioeconomic Pathway 1(SSP1) 2.6 scenario. The population and GDP exposure were evaluated by combing the frequency and the areal coverage of the extreme precipitation events. Results show that population and GDP exposure to extreme precipitation events on the Loess Plateau will increase under the 1.5 °C global warming level. The population exposure was projected from 1.32 × 10⁶ to 2.68 × 10⁶ person-year. The population exposure of eastern and southern Loess Plateau is significantly higher than that of the northern region. The annual exposure of GDP ranges from USD 2.9 to 12.3 billion, and the regions with the highest GDP exposure are Zhengzhou, Xi’an, Taiyuan, and Lanzhou. Our results reveal that limiting the increase of global mean temperature to 1.5 °C warming level is of great significance to reduce the social and economic exposure to extreme precipitation events on the Loess Plateau.

Future changes in drought characteristics in Central Asia are projected at the regional scale using 21 climate models from phase 5 of the Coupled Model Intercomparison Project (CMIP5). Based on the standardized precipitation index (SPI) and standardized precipitation evapotranspiration index (SPEI), drought characteristics were characterized by drought frequency at 1-, 3-, and 12-month timescales. The drought duration was analyzed based on SPI1 and SPEI1. Drought indices were calculated by the multimodel ensemble (MME) from 21 CMIP5 models. The varimax rotation method was used to identify drought conditions for the entire area and seven drought subregions. In general, the projection results of future drought in Central Asia are related to the choice of drought index, and SPI and SPEI show different results. The drought frequency based on SPEI1, SPEI3, and SPEI12 showed an increasing trend in the future periods, that is, the drought frequency based on monthly, seasonal, and annual timescales will show an increase trend in the future periods. However, for SPI1, SPI3, and SPI12, the drought frequency will decrease in the future. SPI projected that the duration of drought will decrease in the future, while SPEI mainly showed an increasing trend. The results of the study should be of sufficient concern to policymakers to avoid land degradation, crop loss, water resource deficit, and economic loss.

Using historical data compiled by the Climate Research Unit, spatial and temporal analysis, trend analysis, empirical orthogonal function (EOF) analysis, and Taylor diagram analysis were applied to test the ability of 24 Coupled Model Intercomparison Project Phase 5 (CMIP5) climate models to accurately simulate the annual mean surface air temperature in central Asia from the perspective of the average climate state and climate variability. Results show that each model can reasonably capture the spatial distribution characteristics of the surface air temperature in central Asia but cannot accurately describe the regional details of climate change impacts. Some of the studied models, including CNRM-CM5, GFDL-CM3, and GISS-E2-H, could better simulate the high- and low-value centers and the contour distribution of the surface air temperature. Taylor diagram analysis showed that the root mean square errors of all models were less than 3, the standard deviations were between 8.36 and 13.45, and the spatial correlation coefficients were greater than 0.96. EOF analysis showed that the multi-model ensemble can accurately reproduce the surface air temperature characteristics in central Asia from 1901 to 2005, including the rising periods and the fluctuations of the north and south inversion phases. Overall, this study provides a valuable reference for future climate prediction studies in central Asia.

The Coupled Model Intercomparison Project Phase 5 (CMIP5) provides data, which is widely used to assess global and regional climate change. In this study, we evaluated the ability of 37 global climate models (GCMs) of CMIP5 to simulate historical precipitation in Central Asia (CA). The relative root mean square error (RRMSE), spatial correlation coefficient, and Kling-Gupta efficiency (KGE) were used as criteria for evaluation. The precipitation simulation results of GCMs were compared with the Climatic Research Unit (CRU) precipitation in 1986–2005. Most models show a variety of precipitation simulation capabilities both spatially and temporally, whereas the top six models were identified as having good performance in CA, including HadCM3, MIROC5, MPI-ESM-LR, MPI-ESM-P, CMCC-CM, and CMCC-CMS. As the GCMs have large uncertainties in the prediction of future precipitation, it is difficult to find the best model to predict future precipitation in CA. Multi-Model Ensemble (MME) results can give a good simulation of precipitation, and are superior to individual models.

Despite the ecological importance of forest tree species, our study was motivated by scarce data on dendrochronology studies limited to the northwestern part of Tajikistan due to lack of access to such a part of the country which is difficult to access. Current studies on Junipers in the northwestern part of Tajikistan identified that tree ring-based climate reconstruction is possible. Cores of Juniperus Turkestanica from Taboshar area, Sogd province were developed into 176-year tree-ring width chronology. In the current study a summer (June-September) minimum temperature reconstruction spanning AD 1840–2016 was developed, which can explain 30.0% of the instrumental variableness for the period of 1940–2015. Five warmest (1856–1866, 1869–1871, 1907–1916, 1923–1935, 1988–2016) and four coldest time periods (1840–1855, 1872–1906, 1917–1922, 1940–1984) were identified. The developed reconstruction coincides with cold and warm periods with prior investigations from tree-rings in China, Mongolia, Kyrgyzstan, Kazakhstan and Tajikistan. Spatial correlation analysis with the gridded minimum temperature data shows that the temperature reconstruction covers geographical representation over High Asia. Significant correlation was found between temperature reconstruction with summer SST, which suggests strong linkages of regional temperature variability with Indian-Ocean climate system. The obtained spectral peaks from the spectral analysis of the summer temperature reconstruction were significantly at 84.7-year (90%), 2.9-year (99%), 2.6-year (99%), 2.5-year (99%), 2.4-year (99%) and 2.2-year (99%).

Drought has become an important threat to industrial and agricultural production and human activities in Central Asia. Using the Standardized Precipitation Evapotranspiration Index (SPEI), we explored the spatial and temporal patterns of dry and wet changes in Central Asia from 1930–2014. The Mann-Kendall trend test and empirical orthogonal function analysis were used to analyze the characteristics of drought in the interannual and seasonal scales. The results showed that the interannual SPEI index in Central Asia presented a distinct phased feature, in the late 1930s, 1970s and 1990s, three consecutive periods of drought occurred. Seasonal SPEI presented different characteristics, with no obvious drying trends in spring and summer. Autumn and winter showed a wet trend. While an obviously wetting tendency has been detected for the winter season, it started in the mid-1950s. Spatially, drought frequency is 42.87% over the entire region of Central Asia from 1930 to 2014. Kyrgyzstan and Tajikistan are wet relative to other countries in Central Asia, with the frequency of drought occurring at 41.6% and 41.9%, respectively; lower than the average frequency of drought in Central Asia. The average frequency of drought in Kazakhstan is 43.1%. Droughts frequently occur in the central and southern parts of Kazakhstan, with the frequency of drought reaching 46.76%. Droughts hardly occur in eastern of Kazakhstan, as well as in the northeastern region. Turkmenistan and Uzbekistan are the two driest countries in Central Asia, especially in the desert region. The frequency of drought was 50.68% and 47.64% respectively.