MPDI-based comparison on spatiotemporal variation of drought over oasis and desert in Tarim Basin

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The effects of water stress on croplands in the Aral Sea basin

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Vegetation dynamics and responses to climate change and human activities in Central Asia

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Spatial Homogenization Adjustment and Application of Weather Station Networks in Xinjiang, China

In this study, we define the S0 value (buffer zone area centred on a meteorological station) and two inhomogeneity measurement parameters, the station domain area and station network density, for 89 weather stations in the Xinjiang region, and we construct the weight coefficient of the station network according to the station domain area. Applying the weight coefficient, we calculate the mean temperature, maximum temperature, and minimum temperature in January, April, July, October, and annually in the Xinjiang region from 1961 to 2021. The results show that the S0 value of 200,000 km2 is suitable for determining the weight coefficient of the station network in the Xinjiang region. The two measurement parameters can quantitatively reflect the inhomogeneity of the distribution of 89 weather stations in the Xinjiang region. The spatial distribution density of the station network is positively proportional to the station network density and inversely proportional to the station domain area and weight coefficient of the stations. The equal-weighted average is lower than the spatially homogenized revised average, which underestimates the mean temperature in the Xinjiang region, and the spatially homogenized revised average better reflects the real temperature in the Xinjiang region. The annual and monthly mean temperatures, maximum and minimum temperatures calculated by the spatially homogenized revised average, and the equal-weighted average have the same upwards trend, and the mean temperature warming trend calculated by the two methods have differences, but the differences are not significant. The annual, January, April, July, and October minimum temperature warming trends according to the spatial homogenization revised average are greater than the maximum temperature warming trend and the mean temperature warming trend, and the annual minimum temperature warming trend is 3.3 times the annual maximum warming trend and two times the annual mean temperature

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 phenologybased 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

Assimilation of sparse crown by using GO and GORT model with Remotely Sensed in the Tarim River Basin, Xinjiang, China - art. no. 67492G

The sparse crown along both riversides of the Tarim River plays an important role in firming the sand and restraining the desertification. It is very difficult to obtain the spectrum information from the remotely sensed data because of the low percentage of coverage of the sparse vegetation, which affects the classification accuracy of the identification of ground objects and the extraction of vegetation biophysics. It is a key obstruction in developing the quantification of the RS technology. Taking the sparse vegetation at the Tarim River Basin as the research object, this paper predicts the surface bidirectional reflectance of the discontinuous plant canopies in the extremely and based on the observed ground spectrum. Two different approaches are presented for the tree and the shrub. The first is to simulate the spectrum of the tree with the Geometric Optical-Radiative Transfer model based on ground observation. In the second approach,the spectral responses of sparse shrub and bare soil have been simulated using the linear Geometric Optical (GO) model. Comparing the simulated bidirectional reflectance with actual remote sensing data (EO-I), the spectral differences of these data are analyzed

Effects of Land Cover Change on Vegetation Carbon Source/Sink in Arid Terrestrial Ecosystems of Northwest China, 2001–2018

The arid terrestrial ecosystem carbon cycle is one of the most important parts of the global carbon cycle, but it is vulnerable to external disturbances. As the most direct factor affecting the carbon cycle, how land cover change affects vegetation carbon sources/sinks in arid terrestrial ecosystems remains unclear. In this study, we chose the arid region of northwest China (ARNWC) as the study area and used net ecosystem productivity (NEP) as an indicator of vegetation carbon source/sink. Subsequently, we described the spatial distribution and temporal dynamics of vegetation carbon sources/sinks in the ARNWC from 2001–2018 by combining the Carnegie-Ames-Stanford Approach (CASA) and a soil microbial heterotrophic respiration (RH) model and assessed the effects of land cover change on them through modeling scenario design. We found that land cover change had an obvious positive impact on vegetation carbon sinks. Among them, the effect of land cover type conversion contributed to an increase in total NEP of approximately 1.77 Tg C (reaching 15.55% of the original value), and after simultaneously considering the effect of vegetation growth enhancement, it contributed to an increase in total NEP of approximately 14.75 Tg C (reaching 129.61% of the original value). For different land cover types, cropland consistently contributed the most to the increment of NEP, and the regeneration of young and middle-aged forests also led to a significant increase in forest carbon sinks. Thus, our findings provide a reference for assessing the effects of land cover change on vegetation carbon sinks, and they indicated that cropland expansion and anthropogenic management dominated the growth of vegetation carbon sequestration in the ARNWC, that afforestation also benefits the carbon sink capacity of terrestrial ecosystems, and that attention should be paid to restoring and protecting native vegetation in forestland and grassland regions in the future

Monitoring the long-term desertification process and assessing the relative roles of its drivers in Central Asia

Desertification is one of the main ecological environmental problems in Central Asia. To prevent and eradicate this problem, it is extremely urgent to monitor the long-term desertification process and assess the relative roles of its drivers. Based on an analytical hierarchy process, in this research, the spatiotemporal features of the desertification process were surveyed from 1982 to 2012 using four indicators. Further analysis was focused on determining the relative importance of multiple driving factors causing desertification in different ecosystems. The results revealed significant desertification expansion in the western part of Central Asia, with the most severe desertification occurring in eastern Xinjiang Province and the Ustyurt Plateau, with mean desertification indexes (DIs) as high as 0.8. According to a change-year analysis, mutation years of desertification were observed from 1993 to 2002 for most vegetation types. The desertification process for different vegetation types results from different major driving factors. Climatic factors, including decreased precipitation, increased temperature and drought, were the main drivers of desertification, especially for grasslands, forests and sparse vegetation. The desertification process of sparse vegetation and croplands was expanded and triggered by human activities: oil and gas exploration in the southern Ustyurt Plateau and agricultural abandonment in northern Kazakhstan. The results also indicated that after the collapse of the Soviet Union, rangeland abandonment in eastern Kazakhstan triggered desertification reversion in grasslands. Furthermore, due to inefficient irrigation water use, severe salt accumulation in croplands of the Amu Darya River delta resulted in desertification expansion in this region. In Central Asia, the desertification process in forests and areas of sparse vegetation was extremely sensitive to climatic variations, while that in croplands and grasslands was vulnerable to human activities. Therefore, regional governments should strive to reverse desertification to protect and improve this fragile, arid ecological environment